WO2022184059A1

WO2022184059A1 - Detection method and related device

Info

Publication number: WO2022184059A1
Application number: PCT/CN2022/078645
Authority: WO
Inventors: 程浩洋
Original assignee: 华为技术有限公司
Priority date: 2021-03-02
Filing date: 2022-03-01
Publication date: 2022-09-09
Also published as: CN114996890A

Abstract

A detection method and a related device. The detection method is applicable in a service end. The detection method comprises: acquiring a first dataset, which comprises M pieces of data related to an air suspension system of a first vehicle (S701); acquiring N second datasets, each of which comprises one or multiple pieces of data of the M pieces of data, and the N second datasets corresponding to N features (S702), which comprise one or more of an adjustment feature, a service life feature, and a material feature of the air suspension system; and determining, on the basis of the N second datasets and of weights corresponding to the N features, a first detection result of the air suspension system (S703).

Description

A detection method and related equipment

This application claims the priority of the Chinese patent application with the application number 202110228778.8 and the application title "A detection method and related equipment" filed with the China Patent Office on March 2, 2021, the entire contents of which are incorporated into this application by reference .

technical field

The present application relates to the technical field of air suspension, and in particular, to a detection method and related equipment.

Background technique

With the improvement of people's living standards, people have higher and higher requirements for car driving. A high-quality suburban utility vehicle (sport utility vehicle, SUV) should have both the comfort of a sedan and the passing performance of an off-road vehicle. The air suspension systems available on the market are the best option to achieve this. Among them, the air suspension system can determine the height change of the vehicle body through the on-board computer according to the different road conditions and the signal of the distance sensor, and then control the air compressor and exhaust valve to automatically compress or extend the spring, thereby lowering or raising the car chassis The ground clearance can be increased to increase the stability of the high-speed body or the passability of complex road conditions, so as to improve the riding comfort and handling.

However, the gas hit by the air compressor into the air spring usually contains a certain amount of moisture, and the moisture contains various impurities, not pure water. During the use of the air suspension system, impurities in the air spring will continue to accumulate, and the rubber of the air spring will gradually age, eventually leading to cracks. In order to solve the above problems, many automobile manufacturers often choose to use desiccant to dry the gas in the air spring. However, due to the short service life of the desiccant itself, if it is not replenished in time, the later air spring will still have more serious problems. aging and damage. Moreover, due to the working principle of the air suspension system, the gas needs to be compressed and released frequently, thereby further shortening the service life of the rubber material.

Under normal circumstances, the probability of air suspension system failure increases exponentially with its use time, which brings great driving hidden dangers to car owners. However, due to the complex structure of the air suspension system, the inspection and maintenance of the air suspension system are often neglected when car owners maintain and maintain their vehicles. Therefore, most car owners are unaware of the specific status of the air suspension system in their vehicle, and even when the air suspension system is on the verge of life, they are not aware of this hidden danger, which will endanger the driving safety of the car owner and even cause serious damage. traffic accidents, endangering the lives and property of the public.

Therefore, how to realize a more comprehensive and accurate detection of the air suspension system in the car and ensure the driving safety of the car owner is an urgent problem to be solved.

SUMMARY OF THE INVENTION

The embodiments of the present application provide a detection method and related equipment, which can more comprehensively and accurately perform real-time detection on the air suspension system in the vehicle, and ensure driving safety.

In a first aspect, an embodiment of the present application provides a detection method, which is applied to a server, wherein the method may include: acquiring a first data set; the first data set includes a detection method related to an air suspension system of a first vehicle. M pieces of data; M is an integer greater than or equal to 1; N second data sets are obtained; each of the N second data sets includes one or more data among the M pieces of data ; The N second data sets correspond to N types of features, and the N types of features include one or more of the adjustment features, life characteristics and material characteristics of the air suspension system; N is an integer greater than or equal to 1; The first detection result of the air suspension system is determined according to the N second data sets and the weights corresponding to the N types of features.

In a possible implementation manner, the M pieces of data in the first data set are data collected by the first vehicle during driving and/or in a parking state.

With the method provided in the first aspect, when a vehicle (such as the first vehicle) is running or parking, the server can receive a large amount of data uploaded by the vehicle (for example, it can include the air suspension system in the vehicle during the running of the vehicle). Data such as the compressed gas volume, the released gas volume and the rising temperature of the air suspension system collected in real time during each adjustment). Then, the server can classify the received large amount of data based on different characteristics of different data, and obtain data sets corresponding to each type of characteristics. Finally, the server can comprehensively consider the data sets corresponding to the various features and the respective weights of the various features, and calculate the detection result of the air suspension system (for example, calculate the current wear rate of the suspension system, etc.), so as to achieve Multi-dimensional, more comprehensive and more accurate inspection of the air suspension system. However, in the prior art, when testing the air suspension system, only some components in the air suspension system can only be tested for individual items through the corresponding testing equipment at the local end, resulting in incomplete and inaccurate testing results. Seriously endanger the driver's driving safety, and even cause serious traffic accidents, damage to public property and personal safety. In this way, compared with the prior art, the embodiment of the present application can upload a large amount of data for the air suspension system collected in real time during the driving process of the vehicle to the server, and then, under the support of the large amount of data, based on different characteristics of the data (such as adjustment characteristics, life characteristics and material characteristics, etc.) and the respective weights of various characteristics (such as considering the influence of data of different characteristics on the use of the air suspension system), to establish a more accurate and effective multi-dimensional detection system, so as to Realize more comprehensive and accurate real-time detection of air suspension system, effectively avoid traffic accidents caused by sudden failure of air suspension system, and ensure driving safety.

In a possible implementation manner, the above method may further include: determining a second detection result of the air suspension system based on the first detection result of the air suspension system; the first detection result includes the The wear rate of the air suspension system; the second detection result includes the failure probability of the air suspension system and the usable time of the air suspension system.

In this embodiment of the present application, the server may further evaluate the current air suspension system's fault-prone rate and usable time based on the first detection result obtained by calculation (for example, the wear rate of the air suspension system), so as to realize the detection of the current air suspension system. The air suspension system is more comprehensive and multi-level detection, which further allows users to more comprehensively and intuitively grasp the use of the air suspension system in their vehicle (or the health status of the air suspension system), effectively ensuring driving safety.

In a possible implementation manner, the acquiring the first data set includes: receiving a data stream from the first vehicle; the data stream includes K pieces of data related to the air suspension system; based on importance The sampling method samples the K pieces of data included in the data stream to obtain the first data set; the K pieces of data include the M pieces of data; K is an integer greater than or equal to M.

In the embodiment of the present application, the vehicle can upload a large amount of collected data to the server in real time in the form of a data stream. The server can use the importance sampling method to sample a large amount of data in the data stream and obtain some of the data. It should be noted that although a large amount of data in the data stream is sampled, the final data obtained by the server is still Therefore, the operating cost and calculation amount can be further reduced, and the detection efficiency can be guaranteed under the premise of ensuring the accuracy of the detection results.

In a possible implementation manner, the above method may further include: receiving a query request sent by the first vehicle; sending the first detection of the air suspension system to the first vehicle based on the query request result and the second detection result.

In the embodiment of the present application, when the user wants to know the health status of the air suspension system in his vehicle, he can send a corresponding query request to the server through the vehicle, and the server receives the query request accordingly. Then, based on the query request, the server can send the corresponding detection results (such as the first detection result and the second detection result, that is, the wear rate, failure-prone rate, and availability of the above-mentioned air suspension system) to the vehicle. duration, etc.). This allows users to grasp the health status of the air suspension system in their vehicle in time, so that they can be repaired in a timely manner when they are seriously worn or on the verge of service life, so as to avoid sudden failure of the air suspension system during driving, effectively reduce driving hidden dangers, and ensure Drive safely.

In a possible implementation manner, the above method may further include: determining a target terrain corresponding to the driving process of the first vehicle, and sending the target terrain to the first vehicle; the target terrain is used for The first vehicle issues a corresponding regulation strategy to the air suspension system according to the target terrain; the target terrain is one of sand, snow, rocks and ice; the regulation strategy includes The regulation strategy of at least one parameter among the height parameter, vibration parameter and damping parameter corresponding to the air suspension system.

In this embodiment of the present application, the server may also determine its current terrain (such as sand, snow, rocks, or ice, etc.) based on data collected during vehicle driving (such as the power signal of the air suspension system, etc.). Then, the server can send the terrain (for example, a terrain model pre-built for the terrain) to the vehicle. Finally, the vehicle can issue corresponding control strategies for the height parameters, vibration parameters and damping parameters of its air suspension system according to the terrain, which can effectively improve the driving comfort and reduce the wear and tear of the air suspension system caused by extreme terrain. , to ensure driving safety.

In a possible implementation manner, the above method may further include: if the first detection result and/or the second detection result satisfy a preset condition, sending the first detection result to the first vehicle , the second detection result and corresponding warning information; the warning information is used to warn the user to perform maintenance on the air suspension system; wherein the preset condition includes that the wear rate of the air suspension system is greater than the first A threshold value and/or the failure susceptibility rate of the air suspension system is greater than a second threshold value and/or the usable duration of the air suspension system is less than a third threshold value.

In this embodiment of the present application, if the server calculates and obtains any one or more of the wear rate, failure-prone rate, and usable duration of the air suspension system, which has endangered driving safety (for example, the wear rate is greater than the first threshold value) (such as 50%), the fault prone rate is greater than the second threshold (such as 40%), and the usable time is less than the third threshold (such as 30 hours)), that is, the air suspension system has been detected by the server. The damage is more serious , it is easy to endanger driving safety, and when maintenance is required, the server can directly send its detection results and corresponding warning information to the corresponding vehicle. The warning information can be used for manhole cover owners to repair their air suspension system, so as to avoid traffic accidents caused by sudden failure of the air suspension system during driving, and effectively ensure driving safety.

In a possible implementation manner, the above method may further include: if the first detection result and/or the second detection result satisfy the preset condition, acquiring a preset range of the first vehicle information of at least one auto repair shop in the vehicle, and send the information of the at least one auto repair shop to the first vehicle; the information includes the respective addresses of the at least one auto repair shop, the address of the at least one auto repair shop and the At least one of the distance between the two, charging price, user evaluation and driving route planning.

In the embodiment of the present application, as mentioned above, the air suspension system has been detected by the server, and the damage to the air suspension system is relatively serious, which is likely to endanger driving safety. When maintenance is required, the server can further push the nearby vehicle to the vehicle. Information about the auto repair shop (or 4S shop, etc.), such as the address of the auto repair shop, the distance from the current vehicle, the toll price, user evaluation and driving route planning, etc. Therefore, the maintenance convenience is provided for the car owner, so that the car owner can repair the air suspension system in the vehicle in time to ensure driving safety.

In a possible implementation manner, the determining the first detection result of the air suspension system according to the N second data sets and the weights corresponding to the N types of features includes: based on the N second data sets Two data sets and preset scoring criteria, respectively, to calculate the corresponding score values of the N types of features; The first detection result of the air suspension system.

In this embodiment of the present application, the server may first calculate the score values corresponding to the various features based on the acquired data sets corresponding to the various features and the preset scoring criteria. For example, the higher the score, the more serious the damage. . Then, the server can calculate and obtain the first detection result of the air suspension system based on the score values corresponding to the various types of features and the weights of the various types of features. In this way, the embodiments of the present application can comprehensively consider the degree of influence of various data in the air suspension system on its wear rate, so that the calculated wear rate of the air suspension system is more comprehensive, accurate and effective, so that the air suspension system will be more comprehensive. , Accurate detection, effectively avoid traffic accidents caused by sudden failure of the air suspension system, and ensure driving safety.

In a possible implementation manner, the above method may further include: acquiring a third data set, where the third data set includes P pieces of data related to the respective air suspension systems of the plurality of second vehicles; P is greater than 1 Integer; based on the third data set, determining the first detection result of each of the plurality of second vehicles; based on the first detection result of each of the plurality of second vehicles and the first detection result of the first vehicle For the first detection result, the scoring criteria and/or the respective weights of the N-type features are modified.

In this embodiment of the present application, the server can also receive a large amount of data uploaded by multiple vehicles when they are running or parked and collected for the air suspension system in the vehicle, and based on the above method The suspension system is detected, and the detection results of the respective air suspension systems of the multiple vehicles are obtained by calculation. Then, the server can modify the original scoring criteria and/or the respective weights of various features used in the calculation process based on a large number of detection results (for example, a large number of calculated wear rates of the air suspension systems in the vehicle). Thereby, the accuracy of the detection result is further improved, the traffic accident caused by the inaccurate detection result and the failure to maintain the air suspension system in time is avoided, and the driving safety is effectively ensured.

In a possible implementation, the M pieces of data include at least one compressed gas volume, at least one released gas volume, at least one elevated temperature, at least one compressed air density, and at least one compressed air density related to the air suspension system, and the air A plurality of the adjustment frequency, duration of use, product model and product specification of the suspension system; wherein, the second data set corresponding to the adjustment feature includes the at least one compressed gas volume, the at least one released gas volume, the one or more of the at least one rise temperature, the at least one air compression density, and the adjustment frequency; the second data set corresponding to the life characteristics includes the use time; the material characteristics corresponding to The second data set includes one or more of the product model number and the product specification.

In this embodiment of the present application, the vehicle may perform all-round data collection for the air suspension system in the vehicle during driving, for example, it may include the volume of compressed gas related to the adjustment of the air suspension system during the driving process of the first vehicle, Released gas volume, rising temperature, air compression density, and the corresponding adjustment frequency, duration of use, product model and product specifications and other data. The data used to detect the air suspension system is comprehensively enriched. In this way, with the support of a large amount of data in all directions, the detection results obtained by the embodiments of the present application are more comprehensive and accurate, and driving safety is effectively ensured.

In a second aspect, an embodiment of the present application provides a detection method, the method may include: acquiring a data stream, and sending the data stream to a server; the data stream includes K related to the air suspension system of the first vehicle The data stream is used by the server to sample the K data included in the data stream based on the importance sampling method to obtain a corresponding first data set; the first data set includes the same M pieces of data related to the air suspension system of the vehicle; the K pieces of data include the M pieces of data; the M pieces of data are used by the server to obtain N second data sets; the N pieces of data Each of the second data sets in the two data sets includes one or more of the M data; the N second data sets correspond to N types of features, and the N types of features include the characteristics of the air suspension system. One or more of adjustment characteristics, life characteristics and material characteristics; the N second data sets are used by the server to determine the corresponding weights based on the N second data sets and the N types of characteristics. The first detection result of the air suspension system; M and N are integers greater than or equal to 1, and K is an integer greater than or equal to M.

With the method provided in the second aspect, when the vehicle (such as the first vehicle) is running or parking, the vehicle can collect data related to the air suspension system in real time (for example, it can be collected during the running of the vehicle, when each adjustment Compressed gas volume, released gas volume and rising temperature), and upload a large amount of collected data to the server in real time in the form of a data stream. Wherein, optionally, the server can sample a large amount of data in the data stream by means of an importance sampling method, and obtain part of the data, so as to reduce the operation cost. Then, the server can classify the obtained large amount of data based on different characteristics of different data, and obtain a data set corresponding to each type of characteristics. Finally, the server can comprehensively consider the data sets corresponding to the various features and the respective weights of the various features, and calculate the detection result of the air suspension system (for example, calculate the current wear rate of the suspension system, etc.), so as to achieve Multi-dimensional, more comprehensive and accurate inspection of the air suspension system. However, in the prior art, when testing the air suspension system, only some components in the air suspension system can only be tested for individual items through the corresponding testing equipment at the local end, resulting in incomplete and inaccurate testing results. Seriously endanger the driver's driving safety, and even cause serious traffic accidents, damage to public property and personal safety. In this way, compared with the prior art, the embodiment of the present application can upload a large amount of data for the air suspension system collected in real time during the driving process of the vehicle to the server, and then, under the support of the large amount of data, based on different characteristics of the data As well as the respective weights of various features (for example, considering the impact of data of different features on the use of the air suspension system), a more accurate and effective multi-dimensional detection system is established, so as to achieve a more comprehensive and accurate real-time detection of the air suspension system. , effectively avoid traffic accidents caused by sudden failure of the air suspension system and ensure driving safety.

It should be understood that the execution subject of the second aspect is the first vehicle, the specific content of the second aspect corresponds to the content of the first aspect, and the corresponding features of the second aspect and the beneficial effects achieved may refer to the description of the first aspect. To avoid repetition, The detailed description is appropriately omitted here.

In a possible implementation manner, the first detection result is used for the server to determine a second detection result of the air suspension system based on the first detection result; the first detection result includes the The wear rate of the air suspension system; the second detection result includes the failure probability of the air suspension system and the usable time of the air suspension system.

In a possible implementation manner, the method further includes: sending a query request to the server; receiving the first detection result and all the air suspension system sent by the server based on the query request. The second test result is described.

In a possible implementation manner, the method further includes: receiving a target terrain sent by the server, and issuing a corresponding control strategy to the air suspension system according to the target terrain; the target terrain is the The terrain corresponding to the first vehicle during driving determined by the server; the target terrain is one of sand, snow, rocks and ice; the control strategy includes corresponding to the air suspension system A control strategy for at least one of the height parameters, vibration parameters and damping parameters of .

In a possible implementation manner, the method further includes: if the first detection result and/or the second detection result satisfy a preset condition, receiving the first detection result sent by the server , the second detection result and corresponding warning information; the warning information is used to warn the user to perform maintenance on the air suspension system; wherein the preset condition includes that the wear rate of the air suspension system is greater than the first A threshold value and/or the failure susceptibility rate of the air suspension system is greater than a second threshold value and/or the usable duration of the air suspension system is less than a third threshold value.

In a possible implementation manner, the method further includes: if the first detection result and/or the second detection result satisfy a preset condition, receiving a message sent by the server in the first vehicle The information of at least one auto repair shop within the preset range of ; the information includes the respective address of the at least one auto repair shop, the distance from the first vehicle, the toll price, user evaluation and driving path planning at least one of.

In a third aspect, an embodiment of the present application provides a detection device, which is applied to a server, and the device includes:

a first obtaining unit, configured to obtain a first data set; the first data set includes M pieces of data related to the air suspension system of the first vehicle; M is an integer greater than or equal to 1;

a second obtaining unit, configured to obtain N second data sets; each second data set in the N second data sets includes one or more data in the M data; the N second data sets The second data set corresponds to N types of characteristics, and the N types of characteristics include one or more of the adjustment characteristics, life characteristics and material characteristics of the air suspension system; N is an integer greater than or equal to 1;

A first determining unit, configured to determine a first detection result of the air suspension system according to the N second data sets and the weights corresponding to the N types of features.

In a possible implementation, the device further includes:

a second determination unit, configured to determine a second detection result of the air suspension system based on the first detection result of the air suspension system; the first detection result includes the wear rate of the air suspension system; the The second detection result includes the failure susceptibility of the air suspension system and the usable time of the air suspension system.

In a possible implementation manner, the first obtaining unit is specifically used for:

receiving a data stream from the first vehicle; the data stream including K data related to the air suspension system;

The K pieces of data included in the data stream are sampled based on the importance sampling device to obtain the first data set; the K pieces of data include the M pieces of data; K is an integer greater than or equal to M.

In a possible implementation, the device further includes:

a receiving unit, configured to receive a query request sent by the first vehicle;

A first sending unit, configured to send the first detection result and the second detection result of the air suspension system to the first vehicle based on the query request.

In a possible implementation, the device further includes:

A second sending unit, configured to determine a target terrain corresponding to the first vehicle during driving, and send the target terrain to the first vehicle; the target terrain is used for the first vehicle to The target terrain issues a corresponding regulation strategy to the air suspension system; the target terrain is one of sand, snow, rocks and ice; the regulation strategy includes height parameters corresponding to the air suspension system , a control strategy for at least one of vibration parameters and damping parameters.

In a possible implementation, the device further includes:

a third sending unit, configured to send the first detection result, the second detection result and the second detection result to the first vehicle if the first detection result and/or the second detection result satisfy a preset condition Corresponding warning information; the warning information is used to warn the user to perform maintenance on the air suspension system; wherein the preset condition includes that the wear rate of the air suspension system is greater than a first threshold and/or the air suspension system The failure-prone rate of the suspension system is greater than a second threshold and/or the usable duration of the air suspension system is less than a third threshold.

In a possible implementation, the device further includes:

The fourth sending unit is configured to acquire the data of at least one auto repair shop within the preset range of the first vehicle if the first detection result and/or the second detection result satisfy the preset condition. information, and send the information of the at least one auto repair shop to the first vehicle; the information includes the respective addresses of the at least one auto repair shop, the distance from the first vehicle, the charged price, the user At least one of evaluation and driving path planning.

In a possible implementation manner, the first determining unit is specifically configured to:

Based on the N second data sets and the preset scoring criteria, respectively calculating the corresponding score values of the N types of features;

The first detection result of the air suspension system is obtained by calculation based on the respective score values corresponding to the N types of features and the respective weights of the N types of features.

In a possible implementation, the device further includes:

a third acquiring unit, configured to acquire a third data set, where the third data set includes P pieces of data related to the respective air suspension systems of the plurality of second vehicles; P is an integer greater than 1;

a third determining unit, configured to determine the respective first detection results of the plurality of second vehicles based on the third data set;

a correction unit, configured to, based on the respective first detection results of the plurality of second vehicles and the first detection results of the first vehicle, perform a correction on the scoring criteria and/or the respective N-type features weights are corrected.

In a possible implementation manner, the second obtaining unit is specifically used for:

Based on the N-type features, the M pieces of data are classified to obtain N second data sets corresponding to the N-type features.

In a fourth aspect, an embodiment of the present application provides a detection device, and the device may include:

an acquisition unit, configured to acquire a data stream and send the data stream to the server; the data stream includes K pieces of data related to the air suspension system of the first vehicle; the data stream is used by the server based on importance The sampling method samples the K data included in the data stream to obtain a corresponding first data set; the first data set includes M data related to the air suspension system of the first vehicle; the The K pieces of data include the M pieces of data; the M pieces of data are used by the server to obtain N second data sets; each of the N second data sets includes the M pieces of data One or more pieces of data; the N second data sets correspond to N types of features, and the N types of features include one or more of adjustment features, life features and material features of the air suspension system; The N second data sets are used by the server to determine the first detection result of the air suspension system based on the N second data sets and the corresponding weights of the N types of features; M and N are greater than Or an integer equal to 1, and K is an integer greater than or equal to M.

In a possible implementation, the device further includes:

a sending unit, configured to send a query request to the server;

A first receiving unit, configured to receive the first detection result and the second detection result of the air suspension system sent by the server based on the query request.

In a possible implementation, the device further includes:

The second receiving unit is configured to receive the target terrain sent by the server, and issue a corresponding control strategy to the air suspension system according to the target terrain; the target terrain is the first terrain determined by the server. A terrain corresponding to a vehicle during driving; the target terrain is one of sand, snow, rocks and ice; the regulation strategy includes height parameters, vibration parameters and damping corresponding to the air suspension system A control strategy for at least one of the parameters.

In a possible implementation, the device further includes:

A third receiving unit, configured to receive the first detection result, the second detection result and the Corresponding warning information; the warning information is used to warn the user to perform maintenance on the air suspension system; wherein the preset condition includes that the wear rate of the air suspension system is greater than a first threshold and/or the air suspension system The failure-prone rate of the suspension system is greater than a second threshold and/or the usable duration of the air suspension system is less than a third threshold.

In a possible implementation, the device further includes:

a fourth receiving unit, configured to receive at least one of the first detection results and/or the second detection results within the preset range of the first vehicle sent by the server if the first detection result and/or the second detection result satisfy a preset condition Information of an automobile repair shop; the information includes at least one of the respective addresses of the at least one automobile repair shop, the distance to the first vehicle, the toll price, user evaluation and driving route planning.

In a fifth aspect, an embodiment of the present application provides a server, where the server includes a processor, and the processor is configured to support the server to implement corresponding functions in the detection method provided in the first aspect. The server may also include a memory for coupling with the processor, which stores necessary program instructions and data for the server. The server may also include a communication interface for the server to communicate with other devices or communication networks.

In a sixth aspect, an intelligent vehicle provided by an embodiment of the present application, the intelligent vehicle is a first vehicle, the intelligent vehicle includes a processor, and the processor is configured to support the intelligent vehicle to implement the detection method provided in the second aspect. function. The intelligent vehicle may also include a memory for coupling with the processor that holds program instructions and data necessary for the intelligent vehicle. The intelligent vehicle may also include a communication interface for the intelligent vehicle to communicate with other devices or a communication network.

In a seventh aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the detection described in any one of the first aspects above is implemented. The flow of the method, or the flow of the detection method described in any one of the second aspect above is implemented.

In an eighth aspect, an embodiment of the present application provides a computer program, where the computer program includes instructions, when the computer program is executed by a computer, the computer can execute the detection method flow described in any one of the first aspect above, or The flow of the detection method described in any one of the second aspect above is executed.

In a ninth aspect, an embodiment of the present application provides a chip system, and the chip system may include the detection device according to any one of the above third aspects, so as to realize the detection method according to any one of the above first aspects The functions involved in the process. Alternatively, the system-on-a-chip may include the detection device described in any one of the foregoing fourth aspects, which is configured to implement the functions involved in the flow of the detection method described in any one of the foregoing second aspects. In a possible design, the chip system further includes a memory for storing necessary program instructions and data for the detection method. The chip system may be composed of chips, or may include chips and other discrete devices.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present application more clearly, the accompanying drawings required in the embodiments of the present application or the background technology will be described below.

Figure 1 is a schematic structural diagram of an air suspension system.

FIG. 2a is a schematic diagram of failure rate analysis of an air suspension system provided by an embodiment of the present application.

FIG. 2b is a schematic diagram of a failure cause analysis of an air suspension system provided by an embodiment of the present application.

FIG. 3 is a schematic diagram of an automatic detection system for an air pump of an automobile air suspension system.

FIG. 4a is a functional block diagram of an intelligent vehicle provided by an embodiment of the present application.

FIG. 4b is a schematic structural diagram of an air suspension system provided by an embodiment of the present application.

FIG. 5 is a schematic diagram of a system architecture of a detection method provided by an embodiment of the present application.

FIG. 6a is a schematic diagram of an application scenario provided by an embodiment of the present application.

FIG. 6b is a schematic diagram of another application scenario provided by an embodiment of the present application.

FIG. 7 is a schematic flowchart of a detection method provided by an embodiment of the present application.

FIG. 8 is a schematic flowchart of another detection method provided by an embodiment of the present application.

FIG. 9 is an overall flowchart of a detection method provided by an embodiment of the present application.

FIG. 10 is a schematic diagram of a data sampling provided by an embodiment of the present application.

FIG. 11 is a schematic flowchart of a terrain recognition provided by an embodiment of the present application.

Fig. 12a is a schematic diagram of a damping adjustment provided by an embodiment of the present application.

FIG. 12b is a schematic diagram of another damping adjustment provided by an embodiment of the present application.

FIG. 13 is an overall flowchart of another detection method provided by an embodiment of the present application.

FIG. 14 is a schematic structural diagram of a detection device provided by an embodiment of the present application.

FIG. 15 is a schematic structural diagram of another detection device provided by an embodiment of the present application.

FIG. 16 is a schematic structural diagram of a server provided by an embodiment of the present application.

FIG. 17 is a schematic structural diagram of an intelligent vehicle provided by an embodiment of the present application.

Detailed ways

The embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

First, some professional terms in this application are explained so as to facilitate the understanding of those skilled in the art.

(1) Air suspension. Please refer to FIG. 1 , which is a schematic structural diagram of an air suspension system. As shown in FIG. 1 , an air suspension system in a vehicle includes an air pump (or an air compressor), an air spring, a shock absorber, a control unit, a control circuit, and the like. Wherein, each air pump can be independent, and the contraction and release of the air pump can be controlled by electrical signals. Optionally, the air suspension system may also include exhaust valves, a dynamic chassis control unit, a plurality of sensors (not shown in FIG. 1 ), and the like. Wherein, the plurality of sensors may specifically include body height sensors of front and rear axles, a plurality of body acceleration sensors in different directions, and a plurality of air spring extension acceleration sensors, etc., which will not be repeated here.

The basic technical scheme of air suspension mainly includes two parts: an air spring with compressed air inside and a shock absorber with variable damping. Compared with the traditional steel car suspension system, air suspension has many advantages. The most important point is that the elastic coefficient of the spring, that is, the softness and hardness of the spring, can be adjusted automatically according to the needs. For example, the suspension can be hardened when driving at high speed to improve the stability of the vehicle body. When driving at a low speed for a long time, the control unit will think that it is passing through a bumpy road, and the suspension will be softer to improve the comfort of shock absorption.

Further, the acceleration caused by the impact of the wheel on the ground can also be one of the parameters considered when the air spring is automatically adjusted. For example, when cornering at high speed, the air springs and shock absorbers of the outer wheels will automatically become stiffer to reduce the body roll, and the electronic module will also strengthen the hardness of the springs and shock absorbers of the front wheels during emergency braking. To reduce the inertia of the body forward. Therefore, models equipped with air springs have higher handling limits and comfort than other cars.

Further, the air suspension can also integrate the traditional chassis lifting technology into it. For example, when the vehicle is driving at high speed, the body height is automatically lowered, thereby improving the ground contact performance and ensuring good high-speed driving stability while reducing wind resistance and fuel consumption. When the vehicle passes over bumps at slow speeds, the chassis automatically raises to improve passing performance. In addition, the air suspension system can also automatically maintain the level of the vehicle body, and the vehicle height can remain constant regardless of whether it is empty or fully loaded. In this way, under any load conditions, the spring stroke of the suspension system remains constant, so that the damping characteristics are basically not affected. affected. As a result, the body is easy to control, even when the vehicle is fully loaded.

However, compared with traditional suspensions (such as coil spring suspension systems), due to the complex structure of air-type adjustable suspensions, the probability and frequency of failures are generally higher. Please refer to FIG. 2a, which is a schematic diagram of a failure rate analysis of an air suspension system provided by an embodiment of the present application. As shown in Figure 2a, the failure rate of air suspension systems tends to increase exponentially with age. Further, please refer to FIG. 2 b , which is a schematic diagram of a failure cause analysis of an air suspension system provided by an embodiment of the present application. As shown in Figure 2b, the air leakage of the distribution valve body (that is, the above-mentioned exhaust valve) itself and the aging of the rubber account for 20% of the causes of the air suspension system failure. Among them, the air leakage of the air pipeline and the air spring Both account for 13% of the causes of air suspension system failures, and so on. The failure of the air suspension system will endanger driving safety to a great extent, resulting in serious traffic accidents. Therefore, how to monitor the air suspension system in the vehicle more comprehensively and accurately in real time, and give users early warnings in time, so as to ensure the safety of users. Driving safety is particularly important.

(2) Importance sampling is one of the variance reduction techniques. Importance sampling is a variance reduction algorithm for rare events. Introduce bias in a controlled way, increasing rare events and reducing runtime. In the system design, the mathematical expectation of the target distribution function is approximated by the random weighted average of a relatively simple distribution function, and the bias function is added to make the system generate more decision errors and thus more important events. This relatively simple distribution function is called the importance density function or bias function, and the weight value is approximately proportional to the likelihood ratio of the two distributions. By modifying the importance density function and introducing importance weights, the number of simulated samples can be greatly reduced, resulting in a simulation result of a given accuracy in a shorter running time. In short, the importance sampling algorithm is to try to make the sampling points cover the points that contribute a lot to the integral within a limited number of sampling times.

First, in order to facilitate the understanding of the embodiments of the present application, the technical problems to be solved by the present application are further analyzed and proposed. In the prior art, the detection technology of the air suspension system includes a variety of technical solutions. The following is an example of a commonly used solution.

Please refer to FIG. 3 . FIG. 3 is a schematic diagram of an automatic detection system for an air pump of an automobile air suspension system. As shown in FIG. 3 , the air pump automatic detection system may include a DC power supply module, a programmable logic controller, an analog quantity acquisition module, a gas circuit leak detection module, and the like. The DC power supply module is electrically connected with the programmable logic controller, the programmable logic controller is electrically connected with the DC motor of the air pump through the DC controller, and the analog quantity acquisition module is electrically connected with the programmable logic controller. As shown in Figure 3, the gas circuit leak detection module includes a balance comparison chamber, a pressure stabilizer chamber and a flow tester. There are gas circuit switching valves between the pressure stabilizer chamber and the balance comparison chamber and the flow tester. A gas path switching valve is arranged between the exhaust ports of the pump. The balance and comparison chamber, the pressure-stabilizing chamber and the flow tester are respectively electrically connected with the analog quantity acquisition module, and pressure sensors are respectively arranged between the analog quantity acquisition module, the pressure-stabilizing chamber and the balance and comparison chamber. Wherein, the analog quantity acquisition module is connected with a current sensor for measuring its current value and/or a voltmeter for measuring its voltage value. The automatic detection system of the air pump has high detection efficiency and good accuracy, can avoid the situation of human misjudgment and missed detection, and effectively improve the quality of the air pump of the air suspension system.

Further, the automobile air suspension system air pump automatic detection system may also include a two-dimensional code generator and a printing device. The two-dimensional code generator is electrically connected to the programmable logic controller and the printing device respectively, and the two-dimensional code graphic is directly generated by directly generating the two-dimensional code. , the product data can be permanently saved with the product, etc., which will not be repeated here.

Optionally, the user can interact with the programmable logic controller through a human-computer interface. Among them, the user can set the detection parameters through the programmable logic controller, or select the manual detection mode to detect individual items of the air pump, or select the automatic detection mode to provide the automatic detection system of the air suspension system of the automobile air pump. All detection items of , are detected in sequence, and so on, which will not be repeated here.

Disadvantages of this solution: As mentioned above, although the automatic detection system for the air pump of the automobile air suspension system provided by this solution can be more accurate and efficient by setting the corresponding module and controller to the existing state of the air pump in the air suspension system. detection. However, for the air suspension system with complex structure and many components, the above solution only involves the detection of the air pump, the detection range is narrow, and the detection results are one-sided, which is not for reference. In short, the above solutions cannot comprehensively and accurately detect and evaluate the overall state of the air suspension system.

To sum up, the above solutions cannot utilize the existing general vehicle hardware architecture and air suspension system to achieve efficient, accurate and comprehensive detection of the air suspension system, and thus cannot guarantee the driving safety of the user when driving a vehicle with an air suspension system. Therefore, in order to solve the problem that the current air suspension system detection technology does not meet the actual business needs, the technical problems to be solved in the embodiments of the present application include the following aspects: (1) Based on a large amount of data collected from the air suspension system during the running of the vehicle , conduct comprehensive and accurate real-time detection of the air suspension system in the vehicle, avoid traffic accidents caused by air suspension system failures, and ensure the user's driving safety, etc. (2) Based on the test results, further predict the use of the air suspension system, and in an emergency (for example, when it is detected that the air suspension system is severely worn, and it is estimated that there is not much of its safe service life left, it is very easy to occur). In case of failure), the car owner will be warned to remind the car owner to repair or replace the air suspension system in time, so as to avoid traffic accidents caused by the failure of the air suspension system during driving, so as to effectively ensure driving safety.

Please refer to FIG. 4a, which is a functional block diagram of an intelligent vehicle provided by an embodiment of the present application. A detection method provided in this embodiment of the present application may be applied to the smart vehicle 200 as shown in FIG. 4a , and in one embodiment, the smart vehicle 200 may be configured in a fully or partially automatic driving mode. When the intelligent vehicle 200 is in an autonomous driving mode, the intelligent vehicle 200 may be set to operate without human interaction.

Intelligent vehicle 200 may include various subsystems such as air suspension system 201 , travel system 202 , sensing system 204 , control system 206 , one or more peripherals 208 and power supply 210 , computer system 212 and user interface 216 . Alternatively, intelligent vehicle 200 may include more or fewer subsystems, and each subsystem may include multiple elements. Additionally, each of the subsystems and elements of the intelligent vehicle 200 may be wired or wirelessly interconnected.

The air suspension system 201 may include various components for air suspension during the driving of the intelligent vehicle 200 . In one embodiment, the air suspension system 201 may include air springs, air compressors, shock absorbers, and the like. Optionally, in one embodiment, the air suspension system 201 may further include a corresponding data collection module, and data collection may be performed on the air suspension system 201 during the driving process of the intelligent vehicle 200 or when the vehicle is parked, for example, collecting data for each air compressor Air compression density, compressed gas volume, released gas volume, and the length of time the air suspension system is used during the second adjustment. Optionally, in an embodiment, the air suspension system 201 may also include a corresponding communication module, which may establish a communication connection with a remote server through a wireless network, and then upload the collected data to the server, so that the The server can perform comprehensive and accurate detection on the air suspension system 201 in the smart vehicle 200 based on the data through a detection method provided in this application. Further, the corresponding communication module in the air suspension system 201 can also receive the detection result sent by the server and so on. Therefore, it can be ensured that when necessary (for example, when it is detected that the wear rate of the air suspension system 201 has exceeded 60%), the user can grasp the health status of the air suspension system 201 in time and repair or replace the air suspension system 201 to ensure driving. Safety. Optionally, in some possible implementation manners, the air suspension system 201 may also be provided in the traveling system 202, etc., which is not specifically limited in this embodiment of the present application.

The travel system 202 may include components that provide powered motion for the intelligent vehicle 200 . In one embodiment, travel system 202 may include engine 218 , energy source 219 , transmission 220 , and wheels 221 . Engine 218 may be an internal combustion engine, an electric motor, an air compression engine, or other types of engine combinations, such as a gasoline engine and electric motor hybrid engine, an internal combustion engine and an air compression engine hybrid engine. Engine 218 may convert energy source 219 into mechanical energy.

Examples of energy sources 219 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. Energy source 219 may also provide energy to other systems of intelligent vehicle 200 .

Transmission 220 may transmit mechanical power from engine 218 to wheels 221 . Transmission 220 may include a gearbox, a differential, and a driveshaft. In one embodiment, transmission 220 may also include other devices, such as clutches. Among other things, the drive shafts may include one or more axles that may be coupled to one or more wheels 221 .

The sensing system 204 may include a number of sensors that may be used to sense the environment surrounding the smart vehicle 200 (eg, may include terrain, motor vehicles, non-motor vehicles, pedestrians, roadblocks, traffic signs around the smart vehicle 200 ) , traffic lights, animals, buildings and plants, etc.). As shown in FIG. 4a, the sensing system 204 may include a positioning system 222 (the positioning system may be a global positioning system (GPS) system, a Beidou system or other positioning systems), an inertial measurement unit (inertial measurement unit) , IMU) 224, radar 226, laser rangefinder 228, camera 230, and computer vision system 232, among others. The sensing system 204 may also include one or more sensors of the interior systems of the intelligent vehicle 200 , eg, an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, and the like. In one embodiment, the sensor system 204 may further include one or more sensors for collecting data on the air suspension system 201 , such as sensors for collecting air pressure or rising temperature in the air spring, etc., the collected data The data can be uploaded to the server to test the air suspension system to ensure driving safety.

The positioning system 222 may be used to estimate the geographic location of the intelligent vehicle 200 . The IMU 224 is used to sense position and orientation changes of the intelligent vehicle 200 based on inertial acceleration. In one embodiment, IMU 224 may be a combination of an accelerometer and a gyroscope.

Radar 226 may utilize radio signals to sense objects within the surrounding environment of intelligent vehicle 200 . In some embodiments, the radar 226 may also be used to sense the speed and/or direction of travel of vehicles surrounding the intelligent vehicle 200, among others.

The laser rangefinder 228 may utilize laser light to sense objects in the environment in which the intelligent vehicle 200 is located. In some embodiments, the laser rangefinder 228 may include one or more laser sources, one or more laser scanners, and one or more detectors, among other system components.

Camera 230 may be used to capture multiple images of the surrounding environment of intelligent vehicle 200 . Camera 230 may be a still camera or a video camera.

Computer vision system 232 is operable to process and analyze images captured by camera 230 in order to identify objects and/or features in the environment surrounding intelligent vehicle 200 . The objects and/or features may include terrain, motor vehicles, non-motor vehicles, pedestrians, buildings, traffic signals, road boundaries and obstacles, and the like. Computer vision system 232 may use object recognition algorithms, structure from motion (SFM) algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system 232 may send the recognized terrain to the air suspension system 201 , and the air suspension system 201 may issue a corresponding regulation strategy to its internal components based on the terrain. For example, if it is recognized that the terrain that the intelligent vehicle 200 is currently driving on is rocky terrain, the air suspension system 201 can correspondingly raise the vehicle base of the intelligent vehicle 200 and increase damping to improve driving comfort, and so on.

The control system 206 controls the operation of the intelligent vehicle 200 and its components. Control system 206 may include various elements, including throttle 234 , braking unit 236 , and steering system 240 .

The throttle 234 is used to control the operating speed of the engine 218 and thus the speed of the intelligent vehicle 200 .

The braking unit 236 is used to control the deceleration of the intelligent vehicle 200 . The braking unit 236 may use friction to slow the wheels 221 . In other embodiments, the braking unit 236 may convert the kinetic energy of the wheels 221 into electrical current. The braking unit 236 may also take other forms to slow down the wheels 221 to control the speed of the smart vehicle 200 .

Steering system 240 is operable to adjust the heading of intelligent vehicle 200 .

Of course, in one example, the control system 206 may additionally or alternatively include components other than those shown and described. Alternatively, some of the components shown above may be reduced.

The intelligent vehicle 200 interacts with external sensors, other vehicles, other computer systems, or users through peripheral devices 208 . Peripherals 208 may include a wireless communication system 246 , an onboard computer 248 , a microphone 250 and/or a speaker 252 . In some embodiments, the collected data of the air suspension system 201 can also be uploaded to the server through the wireless communication system 246, and the detection result of the air suspension system 201 can also be requested from the server through the wireless communication system 246 and received from the server. , etc., which are not specifically limited in the embodiments of the present application.

In some embodiments, peripherals 208 provide a means for a user of intelligent vehicle 200 to interact with user interface 216 . For example, the onboard computer 248 may provide information to the user of the smart vehicle 200 . User interface 216 may also operate on-board computer 248 to receive user input. The onboard computer 248 can be operated via a touch screen. In other cases, peripheral devices 208 may provide a means for intelligent vehicle 200 to communicate with other devices located within the vehicle. For example, microphone 250 may receive audio (eg, voice commands or other audio input) from a user of intelligent vehicle 200 . Similarly, speaker 252 may output audio to a user of intelligent vehicle 200 .

Wireless communication system 246 may wirelessly communicate with one or more devices, either directly or via a communication network. For example, wireless communication system 246 may use 3rd generation mobile networks (3G) cellular communications, such as code division multiple access (CDMA), global system for mobile communications, GSM)/General Packet Radio Service (GPRS), or 4th Generation Mobile Networks (4G) cellular communications, such as Long Term Evolution (LTE). Or 5th generation mobile networks (5G) cellular communications. The wireless communication system 246 may also utilize wireless-fidelity (WIFI) to communicate with a wireless local area network (WLAN). In some embodiments, the wireless communication system 246 may communicate directly with the device using an infrared link, Bluetooth, or the like. Other wireless protocols, such as various vehicle communication systems, for example, wireless communication system 246 may include one or more dedicated short range communications (DSRC) devices, which may include a combination of vehicle and/or roadside stations. between public and/or private data communications.

Power supply 210 may provide power to various components of intelligent vehicle 200 . In one embodiment, the power source 210 may be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such batteries may be configured as a power source to provide power to various components of the intelligent vehicle 200. In some embodiments, power source 210 and energy source 219 may be implemented together, such as in some all-electric vehicles.

Some or all of the functions of intelligent vehicle 200 are controlled by computer system 212 . Computer system 212 may include at least one processor 213 that executes instructions 215 stored in a non-transitory computer-readable medium such as memory 214 . Computer system 212 may also be multiple computing devices that control individual components or subsystems of intelligent vehicle 200 in a distributed fashion.

The processor 213 may be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor may be a dedicated device such as an application-specific integrated circuit (ASIC) or other hardware-based processor. Although FIG. 4a functionally illustrates the processor, memory, and other elements of the computer system 212 in the same block, one of ordinary skill in the art will understand that the processor or memory may actually include a processor or memory that is not stored in the same physical enclosure multiple processors or memories within. For example, the memory may be a hard drive or other storage medium located within an enclosure other than computer system 212 . Thus, a reference to a processor or memory will be understood to include a reference to a collection of processors or memories that may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, for example, some of the components in sensing system 204 may each have its own processor that only performs computations related to component-specific functions .

In various aspects described herein, the processor 213 may be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle while others are performed by a remote processor.

In some embodiments, memory 214 may include instructions 215 (eg, program logic) executable by processor 213 to perform various functions of intelligent vehicle 200, including those described above. Memory 214 may also contain additional instructions, including air suspension system 201 , sending data to, receiving data from, interacting with, and/or one or more of travel system 202 , sensing system 204 , control system 206 , and peripherals 208 or an instruction to control it.

In addition to the instructions 215, the memory 214 can also store data, such as the product specifications and product models of the various components in the air suspension system 201 (for example, the product model of the air spring is rubber A-001, etc.), the usage time of the air suspension system 201, Terrain models (such as ice, snow, sand, rocks, etc.), as well as air suspension control strategies corresponding to different terrain models, etc. In some embodiments, the memory 214 may also store, for example, road maps, route information, the vehicle's position, direction, speed, and other such vehicle data, as well as other information, among others. Such information may be used by the air suspension system 201 or the computer system 212 in the intelligent vehicle 200 while the intelligent vehicle 200 is traveling. For example, the corresponding terrain model may be determined according to the current driving road conditions, etc., and then the regulation strategy of the air suspension system 201 may be further determined to obtain a better driving experience.

User interface 216 for providing information to or receiving information from a user of intelligent vehicle 200 . Optionally, the user interface 216 may include one or more input/output devices within the set of peripheral devices 208 , such as a wireless communication system 246 , an onboard computer 248 , a microphone 250 and a speaker 252 .

Optionally, one or more of these components described above may be installed or associated with the intelligent vehicle 200 separately. For example, memory 214 may exist partially or completely separate from intelligent vehicle 200 . The above-described components may be communicatively coupled together in a wired and/or wireless manner.

To sum up, the smart vehicle 200 can be a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a lawn mower, a recreational vehicle, a playground vehicle, construction equipment, a tram, a golf cart, a train, and carts, etc., which are not specifically limited in the embodiments of the present application.

It can be understood that the functional block diagram of the smart vehicle in FIG. 4a is only an exemplary implementation in the embodiment of the present application, and the smart vehicle in the embodiment of the present application includes but is not limited to the above structures.

Please refer to FIG. 4b, which is a schematic structural diagram of an air suspension system provided by an embodiment of the present application. The air suspension system 10 may be the air suspension system 201 in the smart vehicle 200 shown in FIG. 4a. As shown in FIG. 4b, the air suspension system 10 may include an air spring 101, an air compressor 102, a shock absorber 103, a data acquisition module 104, a communication module 105, a control module 106, and the like. The specific functions of the air spring 101 , the air compressor 102 and the shock absorber 103 can be referred to the descriptions in the above-mentioned professional terminology explanation, which will not be repeated here. It can be understood that some or all of the data collection unit 104 , the communication unit 105 , and the control unit 106 may also be integrated together, which is not specifically limited in this embodiment of the present application.

Wherein, the control module 106 can control various components in the air suspension system 10 to adjust (for example, control the air spring 101, the air compressor 102, the shock absorber 103, etc. to adjust).

Wherein, the data collection module 104 can periodically collect the corresponding data of the air suspension system 10 in real time during the driving process of the intelligent vehicle, for example, collect the released gas volume, the compressed gas volume, and the air compression density of the air compressor 102 during each adjustment. and rising temperature, etc. The usage time and adjustment frequency of the air suspension system 10 may also be collected, wherein the adjustment frequency may be the adjustment frequency of the air compressor, specifically the damping adjustment frequency, etc., which are not specifically limited in the embodiments of the present application.

The communication module 105 can communicate through various wireless communication methods not limited to 2G, 3G, 4G, 5G, etc., and can also be WIFI, dedicated short range communications (DSRC), or long-term evolution-vehicle technology (long-term evolution-vehicle technology). term evolution-vehicle, LTE-V), etc., it can also be a wired communication mode connected by a data line, and so on. The communication module 105 can establish a communication connection with the remote server, and the communication module 105 can receive the raw data collected by the above-mentioned data collection module 104, or the data obtained after preprocessing the original sensor data by the data collection module 104, and use the data collected by the data collection module 104. The data is uploaded to the server. The server can comprehensively and accurately detect the air suspension system 10 in the smart vehicle 200 based on the large amount of collected data.

Optionally, the data collection module 104 may also periodically collect the power signal of the air suspension system 10 , and send the collected power signal to the control module 106 . Correspondingly, the control module 106 may receive the power signal, and calculate the corresponding power spectrum, power spectral density, spectral density, and Gaussian pulse value statistics per unit time based on the power signal. Then, the control module 106 can determine the current driving process of the intelligent vehicle 200 based on the power spectrum, power spectral density, spectral density and Gaussian pulse value statistics per unit time obtained by the above calculation, as well as the respective model parameters of the preset multiple terrain models. the corresponding terrain. Finally, the control module 106 can issue a corresponding regulation strategy based on the current terrain to ensure driving comfort in any terrain. Optionally, the data collection module 104 can also send the collected power signal to the communication module 105, and the communication module 105 can send the received power signal to the server, and then the server determines the current terrain based on the power signal. , and feedback the terrain to the intelligent vehicle 200 (for example, the server can send the determined terrain to the communication module 105, and the communication module 105 sends the terrain to the control module 106), and finally realizes different regulation of the air suspension system under different terrains strategies to ensure driving comfort in any terrain.

Optionally, in some possible embodiments, the inside of the air spring 101, the air compressor 102 and the shock absorber 103 may also be independently provided with respective data acquisition modules, communication modules, control modules, etc., to achieve corresponding functions, This embodiment of the present application does not specifically limit this.

It can be understood that the structure of the air suspension system in FIG. 4b is only an exemplary implementation in the embodiment of the present application, and the structure of the air suspension system in the embodiment of the present application includes but is not limited to the above structures.

In order to facilitate understanding of the embodiments of the present application, the following first describes the system architecture of one of the air suspension system detection methods on which the embodiments of the present application are based. Please refer to FIG. 5, which is a schematic diagram of a system architecture of a detection method provided by an embodiment of the present application. The detection method provided by the embodiment of the present application may be applied to the system architecture shown in FIG. 5 or a similar system architecture. As shown in FIG. 5 , the system architecture may include a server 100 and a plurality of smart vehicles, and specifically may include smart vehicles 200a, 200b, and 200c, and so on. Wherein, the intelligent vehicles 200a, 200b and 200c may be the intelligent vehicles 200 described in the embodiment corresponding to FIG. 4a. Optionally, as shown in FIG. An air suspension system (for example, the air suspension system 10 described in the embodiment corresponding to FIG. 4b ). As shown in FIG. 5 , the smart vehicles 200a, 200b and 200c can establish a communication connection with the server through a wireless network (eg, WIFI, Bluetooth, mobile network, etc.). Optionally, a communication connection may also be established between the smart vehicles 200a, 200b, and 200c through a network, which is not specifically limited in this embodiment of the present application.

In the following, a detection method provided by an embodiment of the present application is described in detail by taking the server 100 and the smart vehicle 200a as examples. As shown in FIG. 5 , when the user drives the smart vehicle 200a, the air suspension system in the vehicle may be in an activated state. When encountering an uneven road surface, the air suspension system will automatically adjust accordingly to dampen the body and ensure the user's driving comfort. Every time the air suspension system is adjusted, the intelligent vehicle 200a can collect data on various aspects of the air suspension system, and upload a large amount of collected data to the server 100 in real time through the network. After receiving the large amount of data uploaded by the smart vehicle 200a, the server 100 can input the received large amount of data into a pre-built detection model, and then obtain the detection result of the air suspension system in the smart vehicle 200a. Optionally, the detection model can firstly classify a large amount of received data based on preset multi-class data features (eg, adjustment features, material features, and life features, etc.) to obtain data sets corresponding to each of the multi-class features. Then, the score value corresponding to each data set can be calculated based on the preset scoring standard, and finally, the air suspension in the intelligent vehicle 200a can be calculated based on the score value corresponding to each data set and the weight corresponding to each type of feature. System test results (for example, calculating the wear rate of the air suspension system, etc.). Further, the server 100 can also formulate corresponding maintenance suggestions based on the detection results, and push the maintenance suggestions and the corresponding detection results to the smart vehicle 200a through the network as shown in FIG. The health status of the internal air suspension system and timely maintenance. So far, the server 100 has completed a large amount of data collected and uploaded in real time based on the vehicle, and comprehensively and accurately tested the air suspension system by comprehensively considering the impact of different types of data on the health of the air suspension system.

Optionally, as shown in FIG. 5 , the server 100 can also receive data uploaded by other multiple vehicles such as the smart vehicles 200b and 200c, and obtain the air in the other multiple vehicles such as the smart vehicles 200b and 200c based on the above detection model. Test results of the suspension system. Then, the server can optimize the detection model based on the obtained large number of detection results. For example, if the calculated wear rates are almost equal, such as in the range of 10%-12%, one or more parameters in the detection model can be corrected, specifically, the above-mentioned scoring criteria and / or the weights of various features, etc., so that the detection results are more accurate.

It is understandable that, with the development of vehicle electrification and intelligence, more and more vehicle data is going to the cloud, and the cloud (ie, the server as shown in Figure 5) can analyze the status of the vehicle based on big data analysis. Evaluate. In the embodiment of the present application, the data uploaded by the vehicle to the cloud can be used to perform comprehensive and accurate detection on the air suspension system in the vehicle, and further, accurate maintenance and repair suggestions can be given, which can greatly reduce the fault zone caused by the air suspension system. traffic accident, ensure driving safety.

To sum up, the smart vehicles 200a, 200b and 200c in the embodiments of the present application may be cars, trucks, motorcycles, buses, boats, airplanes, helicopters, lawn mowers, recreational vehicles, amusement vehicles with the above functions field vehicles, construction equipment, trams, golf carts, trains and carts, and more. Optionally, the smart vehicles 200a, 200b and 200c can also be smart cars with an assisted driving system or a fully automatic driving system (smart cars use technologies such as computers, modern sensing, information fusion, communication, artificial intelligence and automatic control. , is a high-tech complex that integrates functions such as environment perception, planning decision-making, and multi-level assisted driving), and may also be a wheeled mobile robot or other machinery and equipment, which is not specifically limited in the embodiments of the present application. The server 100 in this embodiment of the present application may be a server with the above functions or a chip in the server, may be a server, a server cluster composed of multiple servers, or a cloud computing service center, etc., Optionally, the server 100 may also be a related application for performing air suspension system detection on the smart vehicles 200a, 200b, and 200c, etc., which is not specifically limited in this embodiment of the present application. Optionally, the server 100 may also be terminal devices such as smart phones, tablet computers, notebook computers, and desktop computers, and the like.

It can be understood that the system architecture of the detection method shown in FIG. 5 above is only an exemplary implementation in the embodiment of the present application, and the system architecture of the detection method in the embodiment of the present application includes, but is not limited to, the system architecture shown in FIG. 5 above. system architecture shown.

To facilitate understanding of the embodiments of the present application, the following exemplarily enumerates application scenarios to which a detection method in the present application is applicable.

Please refer to FIG. 6a. FIG. 6a is a schematic diagram of an application scenario provided by an embodiment of the present application. As shown in FIG. 6 a , the application scenario may be a sandy land (or called a desert), including the smart vehicle 200 and the server 100 . Wherein, the smart vehicle 200 may have a built-in air suspension system, including multiple devices for air suspension (such as air springs, air compressors, shock absorbers, etc.), and optionally, the air suspension system may be shown in Figure 4b Air suspension system 10 is shown. As shown in FIG. 6a, a communication connection can be established between the smart vehicle 200 and the server 100 through a network. When the intelligent vehicle 200 is running or parking, the intelligent vehicle 200 may collect data on the air suspension system in the vehicle, and upload the collected data to the server 100 through the network. Then, the server 100 can detect the air suspension system in the smart vehicle 200 based on the uploaded data through a detection method provided in the embodiment of the present application, and obtain a corresponding detection result. Optionally, if the user wants to know the current health status of the air suspension system, he can use the intelligent vehicle 200 (for example, through a related application program running in the intelligent vehicle 200, or a related button set in the intelligent vehicle 200, etc.) Send a query request to the server 100 . Then, the server 100 may send the corresponding detection result to the smart vehicle 200 based on the query request. Optionally, the user may also send a query request to the server 100 through a related application running on the smartphone, and accordingly, the server 100 may also push the detection result to the smartphone. Optionally, the server 100 can also actively send the detection result to the smart vehicle 200. For example, when it is detected that the air suspension system is severely worn and is on the verge of service life, the server 100 can immediately send the detection result to the smart vehicle. Vehicle 200, and send corresponding maintenance suggestions and safety warnings, etc., to remind the user that the current air suspension system has a high degree of harm. Safety.

Optionally, in order to meet driving comfort requirements under different terrains, interpretable modeling of each terrain can be performed in advance to obtain multiple terrain models. Optionally, both the server 100 and the smart vehicle 200 can maintain the multiple terrain models, that is, both can store the multiple terrain models. During the driving process of the intelligent vehicle 200, the intelligent vehicle 200 can periodically collect the power signal of its air suspension system, and upload the power signal to the server 100, and the server 100 can use the pre-built algorithm model based on the power signal. (For example, the algorithm model may include power spectrum calculation, power spectrum density calculation, Gaussian pulse value statistics per unit time, spectrum density calculation, etc. based on the power signal) to determine the corresponding terrain model (that is, to identify the terrain that the smart vehicle 200 is currently driving on) . Then, the server 100 can send the terrain model to the smart vehicle 200. After receiving the terrain model, the smart vehicle 200 can issue a corresponding control strategy to each device in the air suspension system based on the terrain model, so as to ensure that the Driving comfort and safety in different terrains. For example, as shown in FIG. 6a , the current terrain is sandy terrain, and the smart vehicle 200 can issue corresponding control strategies to each device in the air suspension system according to the sandy terrain, such as triggering the air suspension to perform high-frequency Active vibration, thereby preventing the smart vehicle 200 from sinking into a sand pit or the like.

Please refer to FIG. 6b. FIG. 6b is a schematic diagram of another application scenario provided by an embodiment of the present application. As shown in Figure 6b, the application scenario may be a snowy road, including the smart vehicle 200 and the server 100. For the introduction of each part, reference may be made to the relevant descriptions in the corresponding embodiment of Figure 6a, which will not be repeated here. As shown in Figure 6b, the current terrain is snow terrain, and the smart vehicle can issue corresponding control strategies to each device in the air suspension system according to the snow terrain, such as triggering the air suspension to lower the height of the vehicle base, thereby improving driving Stability ensures safe driving on slippery roads such as snow. For another example, if the current road is thick with snow, the air suspension can also be triggered to raise the height of the base of the vehicle, thereby preventing the smart vehicle from falling into a snow pit, and so on.

Optionally, the server 100 may also iteratively update each terrain model and the above-mentioned algorithm model for recognizing terrain, and continuously optimize it, so as to better ensure driving comfort and safety in different terrains and meet user needs.

It should be noted that the above scenarios are only illustrative, and a detection method provided in this embodiment of the present application may also be applied to other scenarios other than the two application scenarios exemplified above, and so on. There is no specific limitation.

Please refer to FIG. 7 . FIG. 7 is a schematic flowchart of a detection method provided by an embodiment of the present application. The method can be applied to the system architecture of the detection method described in FIG. 5 above. Any one of the intelligent vehicles 200a, 200b and 200c in the system architecture described in 5, wherein the air suspension system can be the air suspension system 10 described in the above-mentioned FIG. 4b, and the server can be the above-mentioned FIG. 5. The server 100 in the system architecture described above can be used to support and execute the method flow shown in FIG. 7 . The following will be described from the server side with reference to FIG. 7, the method may include the following steps S701-S703:

Step S701: Obtain a first data set; the first data set includes M pieces of data related to the air suspension system of the first vehicle.

Specifically, the server obtains a first data set, where the first data set may include M pieces of data related to the air suspension system of the first vehicle. The M pieces of data may be data related to the air suspension system collected during the driving process or in the parking state of the first vehicle, and M is an integer greater than or equal to 1.

Optionally, the M pieces of data may include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density, etc. collected when the air suspension system is adjusted, and may also include the air suspension system. The adjustment frequency of the system, the duration of use, the number of product models and product specifications, etc., are not specifically limited in this embodiment of the present application. In this way, a large number of different types of data can provide effective support for the subsequent detection process, greatly improving the comprehensiveness and accuracy of the detection results.

Step S702: Acquire N second data sets; each of the N second data sets includes one or more data among the M data sets, and the N second data sets correspond to N types of features.

Specifically, after acquiring the first data set, the server can classify the M pieces of data in the first data set based on the preset N types of features, and obtain N second data sets corresponding to the N types of features . Obviously, each of the N second data sets includes one or more data in the M data. Optionally, the N types of characteristics may include one or more of adjustment characteristics, life characteristics and material characteristics of the air suspension system, where N is an integer greater than or equal to 1.

Table 1

Wherein, as shown in Table 1 above, the second data set corresponding to the adjustment feature may include one of the above-mentioned at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one air compression density and adjustment frequency or more; the second data set corresponding to the life feature may include the above-mentioned operating time of the air suspension system (for example, 128 hours, 58 days, or 1 year, etc.); the second data set corresponding to the material feature may include Including the above-mentioned product model and product specification, etc. (for example, the material used for the air spring is rubber with product model A-001, product specification is B-001, etc.).

Step S703: Determine the first detection result of the air suspension system according to the N second data sets and the weights corresponding to the N types of features.

Specifically, after classifying and obtaining the N second data sets, the server can calculate and obtain the first detection result of the air suspension system based on the N second data sets and the respective weights of the N types of features. Optionally, the first detection result may be the wear rate of the air suspension system or the like. For example, the weight of the adjustment feature can be 40%, the weight of the material feature can be 30%, the weight of the life feature can be 30%, etc., that is, it can be considered that the adjustment characteristics (such as adjustment frequency and temperature rise, etc.) The quality of the air suspension system, or the state of health, has a greater impact. For another example, the weight of the adjustment feature may be 20%, the weight of the material feature may be 50%, the weight of the life feature may be 30%, etc., that is, material features (such as product model and product specification, etc.) The impact on the quality or health status of the air suspension system is relatively heavy. For example, if the quality is poor or the product model is too old, the wear or failure rate of the air suspension system may be high, etc., and will not be carried out here. Repeat.

Using the embodiment of the present application, a large amount of data for the air suspension system collected in real time during the driving process of the vehicle can be uploaded to the server, and then, with the support of the large amount of data through the server, based on the different characteristics of the data and the respective characteristics of each type (for example, considering the influence of data of different characteristics on the use of the air suspension system), establish a more accurate and effective multi-dimensional detection system, so as to achieve a more comprehensive and accurate real-time detection of the air suspension system, and effectively avoid the air suspension system. Traffic accidents caused by sudden failure of the suspension system ensure driving safety.

Please refer to FIG. 8 . FIG. 8 is a schematic flowchart of another detection method provided by an embodiment of the present application. The method can be applied to the system architecture of the detection method described in FIG. 5 , wherein the first vehicle may be the above-mentioned detection method. In any one of the intelligent vehicles 200a, 200b and 200c in the system architecture shown in FIG. 5, the air suspension system may be the air suspension system 10 described in the above-mentioned FIG. 4b, and the service end may be the above-mentioned FIG. 5 The server 100 in the system architecture can be used to support and execute the method flow shown in FIG. 8 . The following will be described from the interaction side between the server and the first vehicle with reference to FIG. 8 . The method may include the following steps S801-S809:

Step S801: Acquire a data stream.

Specifically, in order to ensure the real-time performance of the detection result, the embodiment of the present application may adopt the method of stream computing to process the data stream. The first vehicle may collect data related to the air suspension system while driving or parking, so as to obtain a corresponding data stream. The data stream may include K pieces of data. Optionally, for step S801, reference may be made to step S701 in the above-mentioned embodiment corresponding to FIG. 7 , which will not be repeated here.

Step S802: The first vehicle sends the data stream to the server.

Specifically, the first vehicle can upload the data stream obtained by continuously collecting data for the air suspension system during the driving process to the server in real time. Optionally, please refer to FIG. 9 together. FIG. 9 is an overall flowchart of a detection method provided by an embodiment of the present application. Step S802 may refer to step S11 in FIG. 9 . As shown in step S11 in FIG. 9 , the smart vehicle (ie, the above-mentioned first vehicle) reports data to the server.

Step S803: Based on the importance sampling method, the server samples K pieces of data included in the data stream to obtain a first data set; the first data set includes M pieces of data.

Specifically, the server may, based on the importance sampling method, sample K pieces of data included in the data stream to obtain a first data set, where the first data set includes M pieces of data. It can be understood that the K pieces of data include the M pieces of data, and K is an integer greater than or equal to M. Optionally, as described above, in order to reduce the calculation amount and operating cost of the server and improve the detection efficiency, the server may detect the air suspension system based on a part of the data collected and uploaded by the first vehicle.

Please refer to FIG. 10. FIG. 10 is a schematic diagram of a data sampling provided by an embodiment of the present application. It can be understood that the time point of air suspension adjustment is often highly random, and the adjustment frequency is high during busy hours (that is, the first vehicle collects and uploads data extremely frequently during busy hours), and often reaches a peak value. As shown in Figure 10, it can be constructed through big data analysis. Assuming that the actual adjustment distribution probability function is p(z), the peak point of the function is the busy time of the vehicle's dynamic adjustment of the air suspension. In the process of sampling the uploaded data stream, the naive Bayesian model can be used to classify the weights, such as kq(z) shown in the peak value in Figure 10, so that the server can adjust the air suspension during the busy time (that is, the air suspension). The frequency of suspension adjustment is high, and then the first vehicle collects and uploads data more frequently) to increase the data, and when the air suspension adjustment is idle, the data sampling is reduced to obtain the first data set. The data included in the first data set obtained by sampling may be shown in the table in FIG. 10 , which will not be repeated here. For example, if the air suspension system has a high frequency of adjustment from the 5th minute to the 20th minute, and the first vehicle has collected and uploaded 40 data, the server can sample 30 of them; if the air suspension system is in the 40th The frequency of adjustment is extremely low from minute to minute 55. The first vehicle has collected and uploaded only 5 data, and the server can sample 3 of them. In this way, the importance sampling method can be used to make the distribution of sampling points more in line with the actual situation within a limited sampling time or sampling number, and the sampling efficiency is higher, providing a large amount of effective data support for the subsequent detection process.

Step S804: The server classifies the M pieces of data based on the preset N types of features to obtain N second data sets corresponding to the N types of features.

Specifically, for step S804, reference may be made to step S702 in the above-mentioned embodiment corresponding to FIG. 7 , which will not be repeated here. Optionally, developers can build interpretable classification models based on algorithms such as support vector machine (SVM) and neural network (NN) on the server in advance. As described above, by inputting M pieces of data into the classification model, the features of each of the M pieces of data can be analyzed and extracted, and then they are classified based on different features, and finally the N second data sets are obtained, and so on. . In this way, feature analysis through big data, in-depth analysis and integration of corresponding features, and a large amount of data behind the features can improve the interpretability of features.

Step S805: The server determines the first detection result of the air suspension system based on the N second data sets and the respective weights of the N types of features.

Specifically, for step S805, reference may be made to step S703 in the above-mentioned embodiment corresponding to FIG. 7 , and details are not repeated here.

Optionally, step S805 can also refer to step S12 in FIG. 9. As shown in FIG. 9, the developer can build a calculation model on the server in advance, and the calculation model can be based on the preset scoring standard and the corresponding characteristics of each feature. In the second data set, the score value corresponding to the adjustment feature is calculated to be a1 (for example, if the full score is 10 points, the a1 can be 5 points. In general, the higher the score value, the more serious the damage to the air suspension system) , the score value corresponding to the material feature is a2, and the score value corresponding to the life feature is a3. Further, as shown in Figure 9, the weight of the adjustment feature is p1, the weight of the material feature is p2, and the weight of the life feature is p3, then the wear rate of the air suspension system (that is, the first detection result) can be calculated. ) is a1*p1+a2*p2+a3*p3.

To sum up, in order to realize a detection method provided by the embodiments of the present application, a developer may construct a detection model on the server side in advance, and the detection model may include, for example, the above-mentioned classification model and calculation model, etc., and can realize the above-mentioned data Classification and calculation according to different weights to obtain the first detection result and other functions. In this way, the server can efficiently and accurately obtain the first detection result of the air suspension system by inputting the collected data uploaded by the vehicle in real time into the detection model, realize real-time monitoring of the status of the air suspension system, and greatly reduce Incident rate due to air suspension system failure.

Optionally, the server can periodically detect the air suspension system through the above calculation method based on a preset period (for example, 1 hour or 30 minutes, etc.) and the data continuously collected and uploaded by the first vehicle, and periodically The detection results are updated periodically, so as to ensure the real-time and validity of the detection results.

Optionally, the server may also acquire a third data set, where the third data set may include P pieces of data related to the respective air suspension systems of a plurality of second vehicles, and the P pieces of data may be, for example, the plurality of second vehicles. Data collected for the respective air suspension systems in the plurality of second vehicles when the vehicle is running or parking, etc., where P may be an integer greater than 1. Then, the server can obtain the respective first detection results of the plurality of second vehicles based on the third data set and through the above-mentioned calculation method of the first detection results. Secondly, the server may analyze and compare the respective first detection results of the plurality of second vehicles and the first detection results of the first vehicle. For example, you can analyze and compare the respective wear rates of a large number of vehicles calculated to check whether they conform to the actual distribution of wear rates. Obviously, if a large number of calculated wear rates are distributed in the same interval, for example, they are all about 10%. , it can be considered that there is a problem in the current detection process, specifically, there is a problem with the above-mentioned classification model, scoring standard or weight distribution, which is not perfect. Therefore, based on the support of big data (that is, the respective wear rates of a large number of vehicles), the classification model and/or scoring standard in the above-mentioned detection process and/or the respective weights of the N types of features can be further revised, so that the detection results It is more accurate and more effective to avoid the dangerous situation of traffic accidents due to inaccurate detection results, which leads to users not grasping the status of the air suspension system in time and correctly. Optionally, as mentioned above, since the detection result of the first vehicle can be periodically updated based on the continuously uploaded data, the server can also classify the classification in the above detection process based on the detection results obtained by the first vehicle at different times. The model and/or the scoring criteria and/or the respective weights of the N types of features are modified. For example, if the wear rate detected by the server at 9 am for the first vehicle is 30%, the wear rate detected at 10 am is 50%, and the wear rate detected at 11 am is 10%, based on the following Changes in the wear rate that do not conform to real-time conditions can determine that there is a problem in the current detection process, and developers can further optimize the detection process, etc., which will not be repeated here.

Step S806: the server determines the second detection result of the air suspension system based on the first detection result of the air suspension system

Specifically, the server may further calculate the second detection result of the air suspension system based on the calculated first detection result. For example, the server can further evaluate or predict the failure-prone rate and usable duration of the air suspension system based on the wear rate of the air suspension system (or assess whether the usage duration is within a safe duration range, etc.) and so on. The embodiment does not specifically limit this. Optionally, the second detection result may further include evaluating whether the air suspension system needs to be repaired and so on. Optionally, the calculated first detection result and the second detection result may be stored in the server, and may carry a corresponding unique identifier for recording that the first detection result and the second detection result correspond to the first vehicle, etc., the embodiments of the present application do not specifically limit this.

Step S807: The first vehicle sends a query request to the server.

Specifically, if the user wants to know the health status of the air suspension system in the first vehicle, a query request can be sent to the server through the first vehicle. Optionally, step S807 may also refer to step S13a in FIG. 9 .

Step S808: The server sends the first detection result and the second detection result to the first vehicle.

Specifically, after receiving the query request sent by the first vehicle, the server can determine the first detection result and the second detection result corresponding to the first vehicle based on the query request, and use the first detection result and the second detection result The detection result is sent to the first vehicle. Optionally, the server may also send only the first detection result or only the second detection result based on the actual needs of the user, and so on, which is not specifically limited in this embodiment of the present application. Optionally, step S808 may also refer to step S13b in FIG. 9 .

Step S809: if the first detection result and/or the second detection result meet the preset conditions, the server sends the first detection result and the second detection result to the first vehicle

Specifically, if the first detection result and/or the second detection result satisfy the preset condition, the server may also actively send the first detection result and the second detection result to the first vehicle. Optionally, step S809 may refer to step S14 in FIG. 9 . Optionally, for example, it is detected that the wear rate of the air suspension system is greater than the first threshold (for example, 40%) and/or the failure-prone rate is greater than the second threshold (for example, 50%) and/or the usable time is less than the first threshold. In the case of three thresholds (for example, 12 hours), that is, in the case that the air suspension system is severely worn, is prone to failure, and is not suitable for continued use, in order to ensure the user's driving safety, the server can immediately report to the third A vehicle sends its corresponding first detection result, second detection result, and warning information. Optionally, after receiving the warning information, the first vehicle may remind the user through a central display screen, an instrument panel or a voice warning, so that the user can repair the air suspension system in time to avoid traffic accidents.

Optionally, if the first detection result and/or the second detection result satisfy the preset conditions, the service terminal may further formulate a corresponding maintenance plan and obtain information of at least one auto repair shop within the preset range of the first vehicle. , and push the maintenance plan and the information of at least one automobile maintenance shop to the first vehicle, so that the user can timely and accurately and efficiently maintain the air suspension system to ensure driving safety. The information may include the respective name, address, distance to the first vehicle, charging price, user evaluation, and driving path planning of the at least one auto repair shop, which are not specifically limited in the embodiments of the present application.

Optionally, in order to ensure driving comfort and safety under different terrains, the server in this embodiment of the present application may also determine the target terrain corresponding to the current driving process of the first vehicle, and send the target terrain to the first vehicle. vehicle. So that the first vehicle can obtain the optimal air suspension mode under the target terrain based on the target terrain and issue corresponding control strategies to adapt to different terrain driving needs, and can also reduce the wear and tear of the air suspension system caused by extreme terrain. , prolong the service life of the air suspension system. Optionally, the target terrain can be any one of sand, snow, rocks and ice, and the control strategy can include at least one of height parameters, vibration parameters and damping parameters corresponding to the air suspension system. control strategy for various parameters.

Optionally, please refer to FIG. 11 , which is a schematic flowchart of a terrain recognition provided by an embodiment of the present application. As shown in Figure 11, developers can use the least squares method to fit the features under different terrains in advance in the cloud (that is, the above-mentioned server), and perform interpretive driving descriptions, so as to perform interpretable modeling for each terrain, and obtain Multiple terrain models. Optionally, as shown in FIG. 11 , during the running process of the first vehicle, the first vehicle may periodically collect the power signal of its air suspension system (for example, the carrier envelope phase (CEP) shown in FIG. 11 . ) schematic diagram), and upload the power signal to the cloud, the cloud can use a pre-built algorithm model (as shown in Figure 11, the algorithm model can be "Rin = model (model) (power spectrum (power spectrum) spectral, PS), power spectral density (power spectral density, PSD), gauss plus (Gaussian pulse), frequency density (spectral density)", can include power spectrum calculation based on power signal, power spectral density calculation, unit time Gaussian pulse value statistics and spectral density calculation, etc.) to determine the corresponding terrain model under the current driving conditions. Then, the cloud can send the terrain model to the first vehicle. After receiving the terrain model, the first vehicle can maintain the terrain model locally, formulate corresponding control strategies based on the terrain model, and report it to the air suspension system. Each device in the system issues corresponding control strategies to ensure real-time dynamic adjustment of each device in the air suspension system under different terrains, thereby ensuring driving comfort and safety. Optionally, as shown in FIG. 11 , the cloud can also iteratively update the algorithm model to improve the accuracy and efficiency of terrain recognition. Optionally, the cloud can also optimize each terrain model, and so on.

Optionally, when the vehicle does not establish a communication connection with the cloud (that is, when the vehicle is not connected to the Internet), the vehicle can also perform terrain based on the power signal collected by itself, the algorithm model maintained locally by the vehicle, and each terrain model. Identify, and issue corresponding control strategies to multiple devices in the air suspension system according to the identified terrain, and so on. Optionally, the vehicle may also include one or more sensors (such as radars and cameras, etc.), and the vehicle may perform terrain recognition through the one or more sensors, for example, the current terrain may be analyzed through images collected by the cameras, etc. etc., which are not specifically limited in the embodiments of the present application. Optionally, please refer to Table 2 below.

Table 2

地形terrain	高度(毫米)Height (mm)	震动(次/秒)Vibration (times/second)	阻尼(牛顿/(米/秒))Damping (N/(m/s))
沙地sand	A1A1	B1B1	C1C1
雪地snow	A2A2	不调节not regulated	C2C2
岩石rock	A3A3	B3B3	C3C3
冰面Ice	A4A4	不调节not regulated	C4C4

As shown in Table 2 above, the regulation strategy can mainly include regulation at different strategy levels for the three parameters of height, vibration and damping. After receiving the control strategy for each parameter, the corresponding equipment in the air suspension system can perform corresponding adjustment. The following is a detailed description of the control strategies for each parameter:

Height (mm): The height is the actual distance between the vehicle (specifically, the vehicle base) and the ground. Generally, the distance from the ground to the vehicle is different depending on the size of the vehicle, but the usual range is: 430mm-460mm, adjustable range The interval is: -25mm～+25mm. The corresponding electronic components in the air suspension system can be adjusted according to the high parameters issued by different terrains. For example, under uneven terrain such as rocks, the adjustment strategy of the height parameter can be +25mm, so as to increase the distance between the vehicle and the ground as much as possible, so as to avoid the vehicle base being scratched or stuck by the rocks, and so on. As such, A3 in Table 2 may be larger than A1, A2, and A4.

Vibration (times/second): The full name can be vibration frequency, that is, the number of times the air suspension is actively triggered to vibrate per second. The triggering terrain of the vibration may be desert, rock and other terrain. When it is recognized that the current terrain is sand or rock, the first vehicle can issue an instruction to trigger the air suspension to perform active vibration, thereby preventing the vehicle from falling into sand pits and muddy roads and ensuring driving safety. Optionally, the vibration frequencies and/or vibration amplitudes determined based on different terrains may be different, which are not specifically limited in this embodiment of the present application. As shown in Table 2 above, in relatively stable terrain such as snow and ice, vibration adjustment may not be performed, that is, the air suspension will not be triggered to perform active vibration.

Damping (N/(m/s)): The value of force per unit velocity. In general, in order to achieve different damping, different volumes of air need to be filled into the air springs of the air suspension system. For example, when driving on unstable terrain such as rocks or mountains, it is often necessary to fill a larger volume of air. Increase the damping to maintain a smooth ride. However, the adjustment of the damping often has certain differences due to the difference in the equipment materials (that is, the materials used in the air springs) in the air suspension. Therefore, the embodiments of the present application can also be standardized and unified after the damping measurement is performed based on different equipment materials. Optionally, please refer to FIG. 12a, which is a schematic diagram of a damping adjustment provided by an embodiment of the present application. As shown in Fig. 12a, in some possible embodiments, the optimal damping adjustment strategy can be obtained by fitting the rebound damping and compression damping respectively, so as to better adapt to different terrains. Optionally, please refer to FIG. 12b, which is a schematic diagram of another damping adjustment provided by an embodiment of the present application. As shown in Figure 12b, the dotted line is the fitting curve of temperature/pressure without damping adjustment (ie, under real driving conditions), and each dot is the temperature/pressure measured after damping adjustment is performed , the solid line is the fitting curve after fitting the multiple temperatures/pressures measured after the damping adjustment. It can be understood that, in general, the higher the temperature/pressure, the greater the damping. As shown in Figure 12b, the straight line above the dashed line can represent increased damping, while the straight line below the dashed line can represent reduced damping. Case. Optionally, the influence of pressure, temperature, and material on damping can also be comprehensively analyzed to obtain the particle size value of each pulse adjustment (that is, each time the air spring is filled with gas to adjust the damping) (for example, pulse = model (temperature), pressure (pressure), material (material), etc., so as to obtain a better damping adjustment strategy to adapt to different terrains, etc., which are not specifically limited in this embodiment of the present application.

Optionally, in addition to the above-mentioned terrain recognition active triggering for damping adjustment, users can also manually adjust damping based on their own driving needs. For example, if the user wants a smooth driving experience, the damping can be increased manually. Correspondingly, in addition to the above manual adjustment of damping, users can also manually switch terrain modes according to their own needs. For example, the default road terrain mode can be selected during rocky terrain driving, thereby reducing the damping of the air suspension and enhancing the driving experience. Authentic experience and control, and more.

Please refer to FIG. 13 . FIG. 13 is an overall flowchart of another detection method provided by an embodiment of the present application. As shown in FIG. 13 , to sum up, the embodiment of the present application is completed through the interaction between the vehicle end (that is, the above-mentioned first vehicle) and the cloud (that is, the above-mentioned server end). Among them, the data collection and reporting of the air suspension system are carried out at the vehicle end, and the data feature extraction and model analysis are carried out in the cloud. Among them, as shown in Figure 13, the server can use big data analysis technology to analyze the health status of the air suspension system and the terrain model. As shown in Figure 13, the vehicle terminal can receive the health status information sent by the cloud mainly through active query and active push from the cloud to synchronize the health status information. After synchronization, it can be pushed to the owner and provide corresponding query functions. For the terrain selection (or terrain recognition) function, the vehicle end can synchronize the terrain model with the cloud, and then issue commands based on the current terrain model at the vehicle end to adaptively adjust the air suspension. In this way, the embodiments of the present application can effectively monitor the state of the air suspension system in real time based on big data analysis, and reduce the accident rate caused by the failure of the air suspension system. Further, the embodiments of the present application can also bring better driving experience and riding experience based on terrain recognition and intelligent adjustment, and can also prolong the service life of the air suspension system, and so on. It can be understood that, in the process of electrification of the air suspension, data endows it with intelligence, and the value of the data can be better exerted through the embodiments of the present application.

Please refer to FIG. 14 . FIG. 14 is a schematic structural diagram of a detection apparatus provided by an embodiment of the present application. The detection apparatus 30 may be applied to the above-mentioned server. As shown in FIG. 14 , the detection apparatus 30 may include a first acquisition unit 301, a second obtaining unit 302, and a first determining unit 303, wherein the detailed description of each unit is as follows.

a first obtaining unit 301, configured to obtain a first data set; the first data set includes M pieces of data related to the air suspension system of the first vehicle; M is an integer greater than or equal to 1;

The second obtaining unit 302 is configured to obtain N second data sets; each second data set in the N second data sets includes one or more data in the M data; the N second data sets The second data set corresponds to N types of characteristics, and the N types of characteristics include one or more of adjustment characteristics, life characteristics and material characteristics of the air suspension system; N is an integer greater than or equal to 1;

The first determining unit 303 is configured to determine the first detection result of the air suspension system according to the N second data sets and the weights corresponding to the N types of features.

In a possible implementation manner, the apparatus 30 further includes:

a second determination unit 304, configured to determine a second detection result of the air suspension system based on the first detection result of the air suspension system; the first detection result includes the wear rate of the air suspension system; The second detection result includes the failure susceptibility of the air suspension system and the usable time of the air suspension system.

In a possible implementation manner, the first obtaining unit 301 is specifically configured to:

In a possible implementation manner, the apparatus 30 further includes:

a receiving unit 305, configured to receive a query request sent by the first vehicle;

A first sending unit 306, configured to send the first detection result and the second detection result of the air suspension system to the first vehicle based on the query request.

In a possible implementation manner, the apparatus 30 further includes:

The second sending unit 307 is configured to determine the target terrain corresponding to the first vehicle during driving, and send the target terrain to the first vehicle; the target terrain is used by the first vehicle according to the The target terrain issues a corresponding regulation strategy to the air suspension system; the target terrain is one of sand, snow, rocks and ice; the regulation strategy includes a height corresponding to the air suspension system A control strategy for at least one of parameters, vibration parameters and damping parameters.

In a possible implementation manner, the apparatus 30 further includes:

The third sending unit 308 is configured to send the first detection result and the second detection result to the first vehicle if the first detection result and/or the second detection result satisfy a preset condition and corresponding warning information; the warning information is used to warn the user to perform maintenance on the air suspension system; wherein the preset condition includes that the wear rate of the air suspension system is greater than a first threshold and/or the The failure-prone rate of the air suspension system is greater than a second threshold and/or the usable duration of the air suspension system is less than a third threshold.

In a possible implementation manner, the apparatus 30 further includes:

The fourth sending unit 309 is configured to acquire at least one auto repair shop within the preset range of the first vehicle if the first detection result and/or the second detection result satisfy the preset condition and send the information of the at least one auto repair shop to the first vehicle; the information includes the respective addresses of the at least one auto repair shop, the distance from the first vehicle, the charged price, At least one of user evaluation and driving path planning.

In a possible implementation manner, the first determining unit 303 is specifically configured to:

In a possible implementation manner, the apparatus 30 further includes:

The third obtaining unit 310 is configured to obtain a third data set, where the third data set includes P pieces of data related to the respective air suspension systems of the plurality of second vehicles; P is an integer greater than 1;

a third determining unit 311, configured to determine the respective first detection results of the plurality of second vehicles based on the third data set;

A correcting unit 312, configured to, based on the respective first detection results of the plurality of second vehicles and the first detection results of the first vehicle, perform a correction on each of the scoring criteria and/or the N-type features weights are corrected.

In a possible implementation manner, the second obtaining unit 302 is specifically configured to:

It should be noted that, for the functions of each functional unit in the detection device described in the embodiment of the present application, reference may be made to the relevant descriptions of steps S701 to S703 in the method embodiment described in FIG. The related descriptions of steps S801 to S809 in the method embodiments described above will not be repeated here.

Each unit in FIG. 14 may be implemented in software, hardware, or a combination thereof. Units implemented in hardware may include circuits and electric furnaces, algorithm circuits or analog circuits, and the like. A unit implemented in software may include program instructions, is regarded as a software product, is stored in a memory, and can be executed by a processor to implement relevant functions, see the previous introduction for details.

Please refer to FIG. 15 . FIG. 15 is a schematic structural diagram of a detection device provided by an embodiment of the present application. The detection device 40 can be applied to the above-mentioned first vehicle. As shown in FIG. 15 , the detection device 40 may include an acquisition unit 401 . The detailed description of each unit is as follows.

The acquiring unit 401 is configured to acquire a data stream and send the data stream to a server; the data stream includes K pieces of data related to the air suspension system of the first vehicle; the data stream is used by the server based on important The characteristic sampling method samples the K data included in the data stream to obtain a corresponding first data set; the first data set includes M data related to the air suspension system of the first vehicle; the The K pieces of data include the M pieces of data; the M pieces of data are used by the server to obtain N second data sets; each second data set in the N second data sets includes the One or more pieces of data among the M pieces of data; the N second data sets correspond to N types of features, and the N types of features include one or more of adjustment features, life features and material features of the air suspension system The N second data sets are used for the server to determine the first detection result of the air suspension system based on the N second data sets and the corresponding weights of the N class features; M and N are An integer greater than or equal to 1, and K is an integer greater than or equal to M.

In a possible implementation manner, the apparatus 40 further includes:

a sending unit 402, configured to send a query request to the server;

The first receiving unit 403 is configured to receive the first detection result and the second detection result of the air suspension system sent by the server based on the query request.

In a possible implementation manner, the apparatus 40 further includes:

The second receiving unit 406 is configured to receive the target terrain sent by the server, and issue a corresponding control strategy to the air suspension system according to the target terrain; the target terrain is the target terrain determined by the server The terrain corresponding to the first vehicle during driving; the target terrain is one of sand, snow, rocks and ice; the control strategy includes height parameters, vibration parameters and A control strategy for at least one of the damping parameters.

In a possible implementation manner, the apparatus 40 further includes:

The third receiving unit 404 is configured to receive the first detection result and the second detection result sent by the server if the first detection result and/or the second detection result satisfy a preset condition and corresponding warning information; the warning information is used to warn the user to perform maintenance on the air suspension system; wherein the preset condition includes that the wear rate of the air suspension system is greater than a first threshold and/or the The failure-prone rate of the air suspension system is greater than a second threshold and/or the usable duration of the air suspension system is less than a third threshold.

In a possible implementation manner, the apparatus 40 further includes:

The fourth receiving unit 405 is configured to receive, if the first detection result and/or the second detection result satisfy a preset condition, receive at least the information sent by the server within the preset range of the first vehicle. Information of one auto repair shop; the information includes at least one of a respective address of the at least one auto repair shop, a distance to the first vehicle, a toll price, user evaluation, and driving route planning.

Each unit in FIG. 15 may be implemented in software, hardware, or a combination thereof. Units implemented in hardware may include circuits and electric furnaces, algorithm circuits or analog circuits, and the like. A unit implemented in software may include program instructions, is regarded as a software product, is stored in a memory, and can be executed by a processor to implement relevant functions, see the previous introduction for details.

Based on the descriptions of the foregoing method embodiments and apparatus embodiments, an embodiment of the present application further provides a server. Please refer to FIG. 16. FIG. 16 is a schematic structural diagram of a server provided by an embodiment of the present application. The server includes at least a processor 1001, an input device 1002, an output device 1003, and a computer-readable storage medium 1004. The server can also Including other general components, which will not be described in detail here. The processor 1001, the input device 1002, the output device 1003, and the computer-readable storage medium 1004 in the server may be connected through a bus or other means.

The processor 1001 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the above programs.

The memory in the server can be read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or can store information and Other types of dynamic storage devices for instructions, which can also be Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical discs storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage medium or other magnetic storage device, or capable of carrying or storing desired program code in the form of instructions or data structures and any other medium that can be accessed by a computer, but is not limited thereto. The memory can exist independently and be connected to the processor through a bus. The memory can also be integrated with the processor.

The computer-readable storage medium 1004 can be stored in the memory of the server, the computer-readable storage medium 1004 is used for storing a computer program, and the computer program includes program instructions, and the processor 1001 is used for executing the computer-readable storage medium Program instructions stored by the medium 1004 . The processor 1001 (or called CPU (Central Processing Unit, central processing unit)) is the computing core and control core of the server, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve the corresponding Method flow or corresponding function; in one embodiment, the processor 1001 described in this embodiment of the present application may be used to perform a series of processing for air suspension system detection, including: acquiring a first data set; the first data set includes M pieces of data related to the air suspension system of the first vehicle; M is an integer greater than or equal to 1; N second data sets are acquired; each of the N second data sets includes the One or more pieces of data among the M pieces of data; the N second data sets correspond to N types of features, and the N types of features include one or more of adjustment features, life features and material features of the air suspension system ; N is an integer greater than or equal to 1; the first detection result of the air suspension system is determined according to the N second data sets and the corresponding weights of the N types of features, and so on.

It should be noted that, for the functions of each functional unit in the server described in the embodiment of the present application, reference may be made to the relevant descriptions of steps S701 to S703 in the method embodiment described in FIG. 7 above, and reference may also be made to the above-mentioned FIG. 8 The relevant descriptions of steps S801 to S809 in the method embodiments described in , are not repeated here.

In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

Embodiments of the present application further provide a computer-readable storage medium (Memory), where the computer-readable storage medium is a memory device in the server, used to store programs and data. It can be understood that the computer-readable storage medium here may include both a built-in storage medium in the server, and of course, an extended storage medium supported by the server. The computer-readable storage medium provides storage space, and the storage space stores the operating system of the server. In addition, one or more instructions suitable for being loaded and executed by the processor 1001 are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory; A computer-readable storage medium for the processor.

The embodiments of the present application also provide a computer program, the computer program includes instructions, when the computer program is executed by the computer, the computer can execute part or all of the steps of any detection method.

Based on the descriptions of the foregoing method embodiments and apparatus embodiments, the embodiments of the present application further provide an intelligent vehicle. Please refer to FIG. 17 . FIG. 17 is a schematic structural diagram of an intelligent vehicle provided by an embodiment of the present application. The intelligent vehicle may be the above-mentioned first vehicle, and may include an air suspension system. As shown in FIG. 17 , the intelligent vehicle includes at least a processor 1101 , an input device 1102 , an output device 1103 and a computer-readable storage medium 1104 , and the intelligent vehicle may also include other general components, which will not be described in detail here. Among them, the processor 1101, the input device 1102, the output device 1103 and the computer-readable storage medium 1104 in the intelligent vehicle may be connected by a bus or other means.

The processor 1101 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the above programs.

The memory in the intelligent vehicle can be read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or can store information and Other types of dynamic storage devices for instructions, which can also be Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical discs storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage medium or other magnetic storage device, or capable of carrying or storing desired program code in the form of instructions or data structures and any other medium that can be accessed by a computer, but is not limited thereto. The memory can exist independently and be connected to the processor through a bus. The memory can also be integrated with the processor.

A computer readable storage medium 1104 may be stored in the memory of the intelligent vehicle, the computer readable storage medium 1104 for storing a computer program including program instructions, the processor 1101 for executing the computer readable storage medium 1104 Storage medium 1104 stores program instructions. The processor 1101 (or called CPU (Central Processing Unit, central processing unit)) is the computing core and the control core of the intelligent vehicle, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve Corresponding method flow or corresponding function; in one embodiment, the processor 1101 described in this embodiment of the present application may be used to perform a series of processing of air suspension system detection, including: acquiring a data stream, and sending the data stream to the service The data stream includes K pieces of data related to the air suspension system of the first vehicle; the data stream is used by the server to sample the K pieces of data included in the data stream based on an importance sampling method, Obtain a corresponding first data set; the first data set includes M pieces of data related to the air suspension system of the first vehicle; the K pieces of data include the M pieces of data; the M pieces of data are used for Obtaining N second data sets from the server; each second data set in the N second data sets includes one or more data in the M data; the N second data sets The set corresponds to N types of characteristics, and the N types of characteristics include one or more of adjustment characteristics, life characteristics and material characteristics of the air suspension system; the N second data sets are used by the server based on the The N second data sets and the weights corresponding to the N types of features determine the first detection result of the air suspension system; M and N are integers greater than or equal to 1, K is an integer greater than or equal to M, etc. .

It should be noted that, for the functions of each functional unit in the smart vehicle described in the embodiments of the present application, reference may be made to the relevant descriptions of steps S701 to S703 in the method embodiment described in the foregoing FIG. 7 , and reference may also be made to the foregoing FIG. 8 . The relevant descriptions of steps S801 to S809 in the method embodiments described in , are not repeated here.

Embodiments of the present application further provide a computer-readable storage medium (Memory), where the computer-readable storage medium is a memory device in an intelligent vehicle, used to store programs and data. It can be understood that the computer-readable storage medium here may include both a built-in storage medium in the smart vehicle, and certainly also an extended storage medium supported by the smart vehicle. The computer-readable storage medium provides storage space that stores the operating system of the intelligent vehicle. In addition, one or more instructions suitable for being loaded and executed by the processor 1101 are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory; A computer-readable storage medium for the processor.

The terms "first", "second", "third" and "fourth" in the description and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order . Furthermore, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes unlisted steps or units, or optionally also includes For other steps or units inherent to these processes, methods, products or devices.

Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor a separate or alternative embodiment that is mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

The terms "component", "module", "system" and the like are used in this specification to refer to a computer-related entity, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a computing device and the computing device may be components. One or more components may reside within a process and/or thread of execution, and a component may be localized on one computer and/or distributed between 2 or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. A component may, for example, be based on a signal having one or more data packets (eg, data from two components interacting with another component between a local system, a distributed system, and/or a network, such as the Internet interacting with other systems via signals) Communicate through local and/or remote processes.

It should be noted that, for the sake of simple description, the foregoing method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence. Because in accordance with the present application, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the device embodiments described above are only illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical or other forms.

The units described above as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

If the above-mentioned integrated units are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, and the computer software products are stored in a storage medium , including several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc., specifically a processor in the computer device) to execute all or part of the steps of the above methods in various embodiments of the present application. Wherein, the aforementioned storage medium may include: U disk, mobile hard disk, magnetic disk, optical disk, read-only memory (Read-Only Memory, abbreviation: ROM) or random access memory (Random Access Memory, abbreviation: RAM) and other various storage media that can store medium of program code.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: The technical solutions described in the embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the present application.

Claims

A detection method, characterized in that, applied to a server, the method comprising:

acquiring a first data set; the first data set includes M pieces of data related to the air suspension system of the first vehicle; M is an integer greater than or equal to 1;

Obtain N second data sets; each of the N second data sets includes one or more data in the M data; the N second data sets correspond to N types of features , the N-type characteristics include one or more of the adjustment characteristics, life characteristics and material characteristics of the air suspension system; N is an integer greater than or equal to 1;

The first detection result of the air suspension system is determined according to the N second data sets and the weights corresponding to the N types of features.
The method according to claim 1, wherein the method further comprises:

Based on the first detection result of the air suspension system, a second detection result of the air suspension system is determined; the first detection result includes the wear rate of the air suspension system; the second detection result includes all the The failure susceptibility of the air suspension system and the usable time of the air suspension system.
The method according to claim 2, wherein the acquiring the first data set comprises:

receiving a data stream from the first vehicle; the data stream including K data related to the air suspension system;

The K pieces of data included in the data stream are sampled based on an importance sampling method to obtain the first data set; the K pieces of data include the M pieces of data; K is an integer greater than or equal to M.
The method according to any one of claims 2-3, wherein the method further comprises:

receiving a query request sent by the first vehicle;

Based on the query request, the first detection result and the second detection result of the air suspension system are sent to the first vehicle.
The method according to any one of claims 1-4, wherein the method further comprises:

determining the target terrain corresponding to the first vehicle during driving, and sending the target terrain to the first vehicle; the target terrain is used by the first vehicle to suspend the air according to the target terrain The system issues a corresponding control strategy; the target terrain is one of sand, snow, rock and ice; the control strategy includes height parameters, vibration parameters and damping parameters corresponding to the air suspension system The control strategy of at least one parameter.
The method according to any one of claims 2-4, wherein the method further comprises:

If the first detection result and/or the second detection result satisfy a preset condition, send the first detection result, the second detection result and corresponding warning information to the first vehicle; the The warning information is used to warn the user to perform maintenance on the air suspension system; wherein, the preset condition includes that the wear rate of the air suspension system is greater than a first threshold and/or the failure-prone of the air suspension system The occurrence rate is greater than a second threshold and/or the usable period of the air suspension system is less than a third threshold.
The method according to claim 6, wherein the method further comprises:

If the first detection result and/or the second detection result satisfy the preset condition, acquire information of at least one auto repair shop within the preset range of the first vehicle, and report to the first vehicle A vehicle sends the information of the at least one auto repair shop; the information includes the respective address of the at least one auto repair shop, the distance from the first vehicle, the toll price, user evaluation and driving route planning. at least one.
The method according to any one of claims 1-7, wherein the first detection result of the air suspension system is determined according to the N second data sets and the weights corresponding to the N types of features ,include:

Based on the N second data sets and the preset scoring criteria, respectively calculating the corresponding score values of the N types of features;

The first detection result of the air suspension system is obtained by calculation based on the respective score values corresponding to the N types of features and the respective weights of the N types of features.
The method according to claim 8, wherein the method further comprises:

acquiring a third data set, where the third data set includes P pieces of data related to the respective air suspension systems of the plurality of second vehicles; P is an integer greater than 1;

determining the respective first detection results of the plurality of second vehicles based on the third data set;

Based on the respective first detection results of the plurality of second vehicles and the first detection results of the first vehicle, the scoring criteria and/or the respective weights of the N-type features are modified.
The method according to any one of claims 1-9, wherein the M pieces of data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one time of rising temperature, at least one time of the air suspension system related to the air suspension system. The density of primary air compression, and the adjustment frequency, duration of use, product model, and product specification of the air suspension system; wherein, the second data set corresponding to the adjustment feature includes the at least one compressed gas volume, one or more of the at least one release gas volume, the at least one rise temperature, the at least one air compression density, and the adjustment frequency; the second data set corresponding to the life characteristic includes the The duration of use; the second data set corresponding to the material feature includes one or more of the product model and the product specification.
The method according to any one of claims 1-10, wherein the acquiring N second data sets comprises:

Based on the N-type features, the M pieces of data are classified to obtain N second data sets corresponding to the N-type features.
A detection method, comprising:

Acquire a data stream, and send the data stream to the server; the data stream includes K pieces of data related to the air suspension system of the first vehicle; the data stream is used by the server to The K pieces of data included in the data stream are sampled to obtain a corresponding first data set; the first data set includes M pieces of data related to the air suspension system of the first vehicle; the K pieces of data include the M pieces of data; the M pieces of data are used by the server to obtain N second data sets; each of the N second data sets includes one of the M pieces of data or multiple pieces of data; the N second data sets correspond to N types of characteristics, and the N types of characteristics include one or more of adjustment characteristics, life characteristics and material characteristics of the air suspension system; The second data set is used by the server to determine the first detection result of the air suspension system based on the N second data sets and the corresponding weights of the N types of features; M and N are integers greater than or equal to 1 , K is an integer greater than or equal to M.
The method according to claim 11, wherein the first detection result is used for the server to determine a second detection result of the air suspension system based on the first detection result; the first detection The result includes the wear rate of the air suspension system; the second detection result includes the failure susceptibility rate of the air suspension system and the usable time of the air suspension system.
The method of claim 13, wherein the method further comprises:

sending a query request to the server;

The first detection result and the second detection result of the air suspension system sent by the server based on the query request are received.
The method according to any one of claims 12-14, wherein the method further comprises:

Receive the target terrain sent by the server, and issue a corresponding control strategy to the air suspension system according to the target terrain; the target terrain corresponds to the first vehicle determined by the server during driving the terrain; the target terrain is one of sand, snow, rocks and ice; the control strategy includes at least one of the height parameters, vibration parameters and damping parameters corresponding to the air suspension system control strategy.
The method according to any one of claims 13 and 14, wherein the method further comprises:

If the first detection result and/or the second detection result satisfy a preset condition, receive the first detection result, the second detection result and the corresponding warning information sent by the server; the The warning information is used to warn the user to perform maintenance on the air suspension system; wherein, the preset condition includes that the wear rate of the air suspension system is greater than a first threshold and/or the failure-prone of the air suspension system The occurrence rate is greater than a second threshold and/or the usable period of the air suspension system is less than a third threshold.
The method of claim 16, wherein the method further comprises:

If the first detection result and/or the second detection result satisfy a preset condition, receive the information of at least one auto repair shop within the preset range of the first vehicle sent by the server; The information includes at least one of a respective address of the at least one auto repair shop, a distance to the first vehicle, a toll price, user evaluation, and driving route planning.
The method according to any one of claims 12-17, wherein the M pieces of data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one time of rising temperature, at least one time of air suspension system related to the air suspension system. The density of primary air compression, and the adjustment frequency, duration of use, product model, and product specification of the air suspension system; wherein, the second data set corresponding to the adjustment feature includes the at least one compressed gas volume, one or more of the at least one release gas volume, the at least one rise temperature, the at least one air compression density, and the adjustment frequency; the second data set corresponding to the life characteristic includes the The duration of use; the second data set corresponding to the material feature includes one or more of the product model and the product specification.
A detection device, characterized in that, applied to a server, the device comprising:

a first acquisition unit, configured to acquire a first data set; the first data set includes M data related to the air suspension system of the first vehicle; M is an integer greater than or equal to 1;

a second obtaining unit, configured to obtain N second data sets; each second data set in the N second data sets includes one or more data in the M data; the N second data sets The second data set corresponds to N types of characteristics, and the N types of characteristics include one or more of the adjustment characteristics, life characteristics and material characteristics of the air suspension system; N is an integer greater than or equal to 1;

A first determining unit, configured to determine a first detection result of the air suspension system according to the N second data sets and the weights corresponding to the N types of features.
The apparatus of claim 19, wherein the apparatus further comprises:

a second determination unit, configured to determine a second detection result of the air suspension system based on the first detection result of the air suspension system; the first detection result includes the wear rate of the air suspension system; the The second detection result includes the failure susceptibility of the air suspension system and the usable time of the air suspension system.
The device according to claim 20, wherein the first obtaining unit is specifically configured to:

receiving a data stream from the first vehicle; the data stream including K data related to the air suspension system;

The K pieces of data included in the data stream are sampled based on the importance sampling device to obtain the first data set; the K pieces of data include the M pieces of data; K is an integer greater than or equal to M.
The device according to any one of claims 20-21, wherein the device further comprises:

a receiving unit, configured to receive a query request sent by the first vehicle;

A first sending unit, configured to send the first detection result and the second detection result of the air suspension system to the first vehicle based on the query request.
The device according to any one of claims 19-22, wherein the device further comprises:

A second sending unit, configured to determine a target terrain corresponding to the first vehicle during driving, and send the target terrain to the first vehicle; the target terrain is used for the first vehicle to The target terrain issues a corresponding regulation strategy to the air suspension system; the target terrain is one of sand, snow, rocks and ice; the regulation strategy includes height parameters corresponding to the air suspension system , a control strategy for at least one of vibration parameters and damping parameters.
The device according to any one of claims 20-22, wherein the device further comprises:

a third sending unit, configured to send the first detection result, the second detection result and the second detection result to the first vehicle if the first detection result and/or the second detection result satisfy a preset condition Corresponding warning information; the warning information is used to warn the user to perform maintenance on the air suspension system; wherein the preset condition includes that the wear rate of the air suspension system is greater than a first threshold and/or the air suspension system The failure-prone rate of the suspension system is greater than a second threshold and/or the usable duration of the air suspension system is less than a third threshold.
The apparatus of claim 24, wherein the apparatus further comprises:

The fourth sending unit is configured to acquire the data of at least one auto repair shop within the preset range of the first vehicle if the first detection result and/or the second detection result satisfy the preset condition. information, and send the information of the at least one auto repair shop to the first vehicle; the information includes the respective addresses of the at least one auto repair shop, the distance from the first vehicle, the charged price, the user At least one of evaluation and driving path planning.
The device according to any one of claims 19-25, wherein the first determining unit is specifically configured to:

Based on the N second data sets and the preset scoring criteria, respectively calculating the corresponding score values of the N types of features;

The first detection result of the air suspension system is obtained by calculation based on the respective score values corresponding to the N types of features and the respective weights of the N types of features.
The apparatus of claim 26, wherein the apparatus further comprises:

a third acquiring unit, configured to acquire a third data set, where the third data set includes P pieces of data related to the respective air suspension systems of the plurality of second vehicles; P is an integer greater than 1;

a third determining unit, configured to determine the respective first detection results of the plurality of second vehicles based on the third data set;

a correction unit, configured to, based on the respective first detection results of the plurality of second vehicles and the first detection results of the first vehicle, perform a correction on the scoring criteria and/or the respective N-type features weights are corrected.
The device according to any one of claims 19-27, wherein the M pieces of data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one time of rising temperature, and at least one time of the air suspension system related to the air suspension system. The density of primary air compression, and the adjustment frequency, duration of use, product model, and product specification of the air suspension system; wherein, the second data set corresponding to the adjustment feature includes the at least one compressed gas volume, one or more of the at least one release gas volume, the at least one rise temperature, the at least one air compression density, and the adjustment frequency; the second data set corresponding to the life characteristic includes the The duration of use; the second data set corresponding to the material feature includes one or more of the product model and the product specification.
The device according to any one of claims 19-28, wherein the second acquiring unit is specifically configured to:

Based on the N-type features, the M pieces of data are classified to obtain N second data sets corresponding to the N-type features.
A detection device, characterized in that the device comprises:

an acquisition unit, configured to acquire a data stream and send the data stream to the server; the data stream includes K pieces of data related to the air suspension system of the first vehicle; the data stream is used by the server based on importance The sampling method samples the K data included in the data stream to obtain a corresponding first data set; the first data set includes M data related to the air suspension system of the first vehicle; the The K pieces of data include the M pieces of data; the M pieces of data are used by the server to obtain N second data sets; each of the N second data sets includes the M pieces of data One or more pieces of data; the N second data sets correspond to N types of features, and the N types of features include one or more of adjustment features, life features and material features of the air suspension system; The N second data sets are used by the server to determine the first detection result of the air suspension system based on the N second data sets and the corresponding weights of the N types of features; M and N are greater than Or an integer equal to 1, and K is an integer greater than or equal to M.
The device according to claim 30, wherein the first detection result is used for the server to determine a second detection result of the air suspension system based on the first detection result; the first detection The result includes the wear rate of the air suspension system; the second detection result includes the failure susceptibility rate of the air suspension system and the usable time of the air suspension system.
The apparatus of claim 31, wherein the apparatus further comprises:

a sending unit, configured to send a query request to the server;

A first receiving unit, configured to receive the first detection result and the second detection result of the air suspension system sent by the server based on the query request.
The device according to any one of claims 30-32, wherein the device further comprises:

The second receiving unit is configured to receive the target terrain sent by the server, and issue a corresponding control strategy to the air suspension system according to the target terrain; the target terrain is the first terrain determined by the server. A terrain corresponding to a vehicle during driving; the target terrain is one of sand, snow, rocks and ice; the regulation strategy includes height parameters, vibration parameters and damping corresponding to the air suspension system A control strategy for at least one of the parameters.
The device according to any one of claims 31 and 32, wherein the device further comprises:

A third receiving unit, configured to receive the first detection result, the second detection result and the Corresponding warning information; the warning information is used to warn the user to perform maintenance on the air suspension system; wherein the preset condition includes that the wear rate of the air suspension system is greater than a first threshold and/or the air suspension system The failure-prone rate of the suspension system is greater than a second threshold and/or the usable duration of the air suspension system is less than a third threshold.
The apparatus of claim 34, wherein the apparatus further comprises:

a fourth receiving unit, configured to receive at least one of the first detection results and/or the second detection results within the preset range of the first vehicle sent by the server if the first detection result and/or the second detection result satisfy a preset condition Information of an automobile repair shop; the information includes at least one of the respective addresses of the at least one automobile repair shop, the distance to the first vehicle, the toll price, user evaluation and driving route planning.
The device according to any one of claims 30-35, wherein the M pieces of data include at least one compressed gas volume, at least one released gas volume, at least one rising temperature, at least one time of rising temperature, at least one time of the air suspension system related to the air suspension system. The density of primary air compression, and the adjustment frequency, duration of use, product model, and product specification of the air suspension system; wherein, the second data set corresponding to the adjustment feature includes the at least one compressed gas volume, one or more of the at least one release gas volume, the at least one rise temperature, the at least one air compression density, and the adjustment frequency; the second data set corresponding to the life characteristic includes the The duration of use; the second data set corresponding to the material feature includes one or more of the product model and the product specification.
A server, characterized in that it includes a processor and a memory, the processor and the memory are connected, wherein the memory is used to store program codes, and the processor is used to call the program codes to execute the program code as claimed in the claims The method of any one of 1 to 11.
An intelligent vehicle, characterized in that the intelligent vehicle is a first vehicle, and includes a processor and a memory, the processor and the memory are connected, wherein the memory is used for storing program codes, and the processor is used for calling all the said program code to perform the method as claimed in any one of claims 12 to 18.
A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, implements the method described in any one of claims 1 to 11, or implements the above The method of any one of claims 12 to 18.
A computer program, characterized in that the computer program includes instructions, which, when the computer program is executed by a computer, cause the computer to execute the method according to any one of claims 1 to 11, or implement the above claims The method of any one of 12 to 18.