CN117523706A - Method and device for processing abnormal signals of vehicle - Google Patents
Method and device for processing abnormal signals of vehicle Download PDFInfo
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Abstract
The invention discloses a processing method and a processing device of a vehicle abnormal signal, wherein the processing method comprises the steps of obtaining and storing vehicle end signal data of a vehicle, wherein the vehicle end signal data are used for representing state information of the vehicle, and the vehicle end signal data comprise historical data and real-time data; determining whether the vehicle-end signal data is an abnormal signal or not based on a signal abnormality determination rule, and if the vehicle-end signal data is the abnormal signal, determining the type of the abnormal signal; correcting the abnormal signal based on a correction rule and the type of the abnormal signal to obtain corrected signal data; and merging the vehicle-end signal data determined to be the abnormal signal and the corrected signal data into target signal data, and sending a list of the target signal data to a mobile terminal. According to the cloud storage method and the cloud storage device, the vehicle state stored in the cloud is as close to the real vehicle state as possible, and more accurate service is provided for obtaining the service of the vehicle state in the cloud, so that the functional robustness is enhanced, and the user experience is improved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing an abnormal signal of a vehicle.
Background
With the vigorous development of the automobile industry, more and more vehicles with an intelligent function, intelligent networking technology is continuously developed, and digital twinning is also developed dramatically as a technology based on intelligent networking. And uploading the cloud end to the whole vehicle and the states of all parts due to the service requirement, and establishing a corresponding virtual vehicle state at the cloud end.
The service of the internet of vehicles highly depends on a stable and high-quality mobile network, and low-quality mobile communication, such as no mobile network coverage, frequency band residing in signal difference, frequent inter-cell switching and the like, can be caused due to the reasons of communication facility construction, urban road section high building shielding signals, underground place network shielding and the like; resulting in a loss of signal from the vehicle end reporting the vehicle status. In addition, due to design defects of the vehicle, the vehicle state is abnormal at the vehicle end, and even if the vehicle state is successfully reported to the cloud, the abnormal state exists.
However, intelligent network vehicles have strong dependence on cloud vehicle state accuracy, and many businesses depend on accurate vehicle states, such as mobile phone remote vehicle control, whole vehicle FOTA, remote diagnosis and the like, when abnormal signals occur, the use of the intelligent network vehicles by users is seriously affected. In order to improve the vehicle experience of a user and provide high-quality and high-stability internet-of-vehicles service for the user, a mechanism capable of improving the vehicle state accuracy is needed to be realized.
Disclosure of Invention
The invention provides a method and a device for processing abnormal signals of a vehicle, which are used for improving the quality and stability of vehicle communication by processing abnormal signals of the vehicle.
In a first aspect of the present invention, a method for processing a vehicle abnormal signal is provided, which is applied to a cloud, wherein the cloud stores a vehicle state, and the method includes:
acquiring and storing vehicle-end signal data of a vehicle, wherein the vehicle-end signal data are used for representing state information of the vehicle, and the vehicle-end signal data comprise historical data and real-time data;
determining whether the vehicle-end signal data is an abnormal signal or not based on a signal abnormality determination rule, and if the vehicle-end signal data is the abnormal signal, determining the type of the abnormal signal;
correcting the abnormal signal based on a correction rule and the type of the abnormal signal to obtain corrected signal data;
and fusing the vehicle-end signal data determined to be the abnormal signal and the corrected signal data into target signal data, and sending the target signal data to a mobile terminal.
In an optional embodiment, the determining whether the vehicle-side signal data is an abnormal signal based on a signal abnormality determination rule includes:
triggering signal abnormality judgment based on a preset triggering condition, and judging whether the vehicle-end signal data is abnormal or not according to a signal abnormality judgment condition;
if the vehicle-end signal data is judged to be an abnormal signal, judging whether the abnormal signal executes alarming or not based on an alarming condition rule, and determining the correction rule based on a hit rule.
In an optional embodiment, the determining whether the vehicle-side signal data is an abnormal signal based on a signal abnormality determination rule includes:
triggering abnormality judgment according to the logic symbol, the signal name, the signal history value and the signal new value of the vehicle-end signal data, and executing whether the abnormality judgment is executed on the vehicle-end signal data according to the outer logic symbol, the inner logic symbol, the signal name, the target signal value and the calculation symbol of the vehicle-end signal data;
if the vehicle-end signal data is judged to be an abnormal signal, determining a hit correction rule based on a fetch function, a logic symbol and a calculation symbol, and sorting a plurality of abnormal signals based on a rule sorting value;
and judging whether the abnormal signal executes alarming or not based on the continuous hit time, the rule judging result, the alarming limit times and the alarming limit period.
In an optional implementation manner, the determining whether the abnormal signal performs the alarm based on the continuous hit time, the rule determination result, the alarm limit number, and the alarm limit period includes:
calculating whether the hit exceeds the alarm limit times in the alarm limit period, and if the hit exceeds the limit times, not giving an alarm; if not, the alarm is put into the pre-alarm queue.
In an optional embodiment, the correcting the abnormal signal based on the correction rule and the type of the abnormal signal to obtain corrected signal data includes:
and determining a correction rule ID based on the hit rule and the type of the abnormal signal, loading the correction rule ID to a corresponding correction rule configuration, and selecting a correction strategy to fill, correct or discard the data of the abnormal signal according to the correction rule configuration.
In an optional embodiment, the correcting the abnormal signal based on the correction rule and the type of the abnormal signal to obtain corrected signal data includes:
if the correction strategy is filling or correcting, acquiring data needed to be used in subsequent numerical correction from a data source through an access function in correction rule configuration, calculating a correction value of an abnormal signal needed to be filled or corrected through a numerical correction function, storing the correction value into a correction signal database, and marking the type of the strategy as filling or correcting; if the correction strategy is discard, the abnormal signal is stored in the correction signal database, and the correction strategy type is marked as discard.
In an optional embodiment, the fusing the vehicle-end signal data determined as the abnormal signal and the corrected signal data into target signal data, and sending the target signal data to a mobile terminal includes:
inquiring the vehicle-end signal data and the corrected signal data from an original signal database and a corrected signal database respectively, and performing signal fusion;
and when signals are fused, filling, replacing or discarding the original vehicle-end signal data in the original signal database according to the correction strategy queried in the correction signal database by the vehicle-end signal data, and returning to the mobile terminal after obtaining a fused signal list.
A second aspect of the present invention provides a processing apparatus for a vehicle abnormality signal, comprising:
the system comprises a number acquisition module, a number acquisition module and a data processing module, wherein the number acquisition module is used for acquiring and storing vehicle end signal data of a vehicle, and the vehicle end signal data comprises historical data and real-time data;
the condition calculation module is used for determining whether the vehicle-end signal data is an abnormal signal or not based on a signal abnormality judgment rule, and judging the type of the abnormal signal if the vehicle-end signal data is the abnormal signal;
the data correction module is used for correcting the abnormal signal based on a correction rule and the type of the abnormal signal to obtain corrected signal data;
and the transmission module is used for fusing the vehicle-end signal data judged to be the abnormal signal and the corrected signal data into target signal data and sending the target signal data to the mobile terminal.
In a third aspect of the present invention, there is provided an electronic apparatus comprising:
at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method according to the first aspect of the embodiments of the invention.
In a fourth aspect of the invention, a computer-readable storage medium is provided, on which a computer program is stored which, when run by a computer, performs the method according to the first aspect of the embodiment of the invention.
According to the invention, the preset signal abnormality judgment rule and the preset correction rule are used for hit and correction of the vehicle-end signal data, so that the vehicle state in the high-frequency scene in the vehicle-to-vehicle service can be obtained, the vehicle state stored in the cloud is as close to the real vehicle state as possible, more accurate service is provided for obtaining the service of the vehicle state in the cloud, and the functional robustness is enhanced, and the user experience is improved.
Drawings
Fig. 1 is a flow chart of a method for processing a vehicle abnormal signal according to an embodiment of the invention.
Fig. 2 is a business flow chart of a method for processing abnormal signals of a vehicle in an embodiment of the invention.
Fig. 3 is a flowchart illustrating another method for processing a vehicle abnormal signal according to an embodiment of the invention.
Fig. 4 is a schematic block diagram of a device for processing abnormal signals of a vehicle according to an embodiment of the invention.
Fig. 5 is a schematic block diagram of another apparatus for processing abnormal signals of a vehicle according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of an electronic device in an embodiment according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
And establishing a corresponding virtual vehicle state at the cloud, establishing communication at two ends of the vehicle cloud, and uploading data by using an MQTT protocol. Typically both structured data and unstructured data. The method comprises the steps that after structured data, such as signal acquisition data, is acquired by a vehicle end in a complete acquisition mode, a periodic acquisition mode and an event acquisition mode, the structured data is compressed in a binary file mode and is uploaded to a cloud end; unstructured data, such as files including pictures, videos and logs, is uploaded to the cloud end by a vehicle end through a file compression package mode. The complete acquisition is to record a continuously-changed signal completely according to the time sequence and frequency of the actual occurrence of the signal; the periodic acquisition is to sample at the same time interval, i.e. a continuously changing signal is sampled at the same time interval; event collection is to monitor and control specific events continuously, and capture data of a period of time before and after the event is triggered.
When the virtual vehicle is stationary, the mobile terminal can perform functions such as remote control on the vehicle. The mobile terminal, such as a mobile phone, a watch TSP background webpage, etc., may obtain the real-time status of the vehicle through the vehicle unique vehicle identification code VIN, for example: vehicle information such as driving/parking status, vehicle position, door lock status, window opening and closing degree, tire pressure, temperature in the vehicle, remaining life, current electric quantity, total mileage, and the like. Meanwhile, based on real-time vehicle state, the remote control of the vehicle is also supported by the user through the mobile terminal or the TSP background webpage, for example: remote unlocking and closing of the door lock, remote flashing and whistling, remote opening and closing of the car window, remote opening of the trunk, remote control of an air conditioner, remote seat ventilation and heating and other operations are performed, and car use experience of a car user is greatly facilitated.
The invention aims at the vehicle state in the high-frequency scene of the vehicle-to-vehicle service, such as: the method comprises the steps of identifying partial conditions of signal missing, signal mutual exclusion and signal distortion based on historical data, service auxiliary data and preset alarm rules, wherein the conditions comprise vehicle running state, door and window state, door lock state, tire state, vehicle positioning, vehicle charging and discharging state, vehicle network quality, vehicle electric quantity duration and the like. And, aiming at the partially missing signals, mutually exclusive signals and distorted signals, the signals are complemented, discarded and modified based on a preset correction rule, so that the dependence of a user on the accuracy and timeliness of the remote vehicle state in the vehicle-to-vehicle service is met; in particular, this can be achieved in the following manner.
Referring to fig. 1, the present invention provides a method for processing an abnormal signal of a vehicle, including:
step 100: and acquiring and storing vehicle-end signal data of the vehicle, wherein the vehicle-end signal data comprises historical data and real-time data.
And the vehicle reports the signal data to the cloud through communication devices such as a T-BOX module, a 4/5G module and the like, wherein the signal data is divided into periodic data and event data. Periodic data has a fixed reporting time interval, and event data is signal data reported when some fixed sensors change, and signal libraries of two data types have no intersection. The same signal data is divided into historical data and real-time data, wherein the real-time data only keeps the latest data value reported by the vehicle end at one time and is stored in the real-time database, and the historical data keeps the track of the data value reported by the vehicle end for a period of time and is stored in the historical database.
In addition, the vehicle-end signal data also comprises cloud end storage and processing signal data, and the mobile terminal acquires the signal data (such as service data) from the cloud end.
Step 200: and determining whether the vehicle-end signal data is an abnormal signal or not based on a signal abnormality determination rule, and if the vehicle-end signal data is the abnormal signal, determining the type of the abnormal signal.
Specifically, different signal abnormality determination rules may be preset for different signal data to determine whether the vehicle-end signal data is abnormal. For example, one or more rule judgment conditions are preset for the vehicle-end signal to judge whether the vehicle-end signal is abnormal, and one or more rule judgment conditions are preset for the vehicle-end service data to judge whether the vehicle-end service data is abnormal.
In this step, an abnormal signal trigger condition, a rule determination condition, a rule hit condition, an abnormal type determination condition, and the like may be preset. It should be understood that, the foregoing conditions are that the judging conditions are formed according to the rules determined by the attribute of the signal data, so as to realize automatic judgment of the signal data of the vehicle end, and the content of the judging conditions is not limited in the invention, and can be set according to specific requirements or service states; it is important to identify signal anomalies and determine the type of the anomalies by specifying one or more rule judgment conditions.
Step 300: and correcting the abnormal signal based on a correction rule and the type of the abnormal signal to obtain corrected signal data.
Specifically, different correction rules may be preset for the signal anomaly type. The types of abnormal signals generally comprise signal missing, signal mutual exclusion and signal distortion, and the corresponding correction modes comprise signal insufficiency, signal discarding and signal modification. In this step, the abnormal signal is corrected based on the result of the abnormality determination in the above step, the corrected signal data is stored in the corrected signal database, and the original vehicle-end signal data is stored in the original signal database.
Step 400: and fusing the vehicle-end signal data determined to be the abnormal signal and the corrected signal data into target signal data, and sending the target signal data to a mobile terminal.
In the step, the vehicle-end signal data and the corrected signal data are respectively searched from the original signal database and the corrected signal database, and the signals are fused. For example, signal values are respectively searched from an original signal database and a corrected signal database, the vehicle-end signal data and the corrected signal data are determined based on the signal values, and the two signal data are associated based on the signal values.
When the signals are fused, the correction strategy searched in the correction signal database according to the vehicle-end signal data can be used for determining that the vehicle-end signal data is an abnormal signal and determining the abnormal type of the vehicle-end signal data as described in the steps 200 to 300. And then filling, replacing or discarding the original vehicle-end signal data in the original signal database according to a preset correction rule based on the abnormal type, obtaining a fused signal list, and returning to the mobile terminal. Therefore, the signal data acquired by the mobile terminal are accurate, and more accurate vehicle states can be provided based on the scheme of the invention, so that the functional robustness is enhanced, and the user experience is improved.
It should be noted that the traditional signal abnormality judgment and correction technology is mainly focused on the inside of the whole vehicle, and the judgment of the authenticity and accuracy of the signals is carried out based on various signals fed back by the sensor and various logics under different states of the vehicle. And finally, the device is used for displaying the state of the instrument vehicle/warning lamp and reminding other links of the whole vehicle running. It is apparent that the conventional technology has been difficult to satisfy the new demand for users to acquire the state of a vehicle through a mobile terminal. In three data interactions of reporting a vehicle signal to the cloud, storing and processing signal data by the cloud and acquiring the signal data by the mobile terminal, signal loss exists in any link, or the signal is inaccurate, so that inaccurate display of a remote vehicle state can occur, or a service depending on the vehicle state can not be normally performed. For example, when inquiring, the vehicle running state, door and window state, door lock state, tire state, vehicle positioning, vehicle charging and discharging state, vehicle network quality, vehicle electric quantity duration and the like are inaccurate in vehicle state information acquired by a user because of abnormal signals.
As will be understood with reference to fig. 2, the cloud end obtains periodic signal data and time signal data through signal data reported by the vehicle end, and service data obtained by the cloud end through service processing processes of the vehicle end and the mobile terminal, such as charging, mobile phone vehicle control, etc., where the signal data is stored to form a plurality of databases, including a historical signal database, a real-time signal database, and a service database. And the cloud end judges whether the signal reported in real time hits a preset signal abnormality judgment rule, and if yes, judges which type the signal is in the absence, mutual exclusion and true according to the rule. And carrying out signal completion, signal discarding and signal modification on the abnormal signal according to the type of the abnormality and the established correction rule, thereby ensuring the authenticity of the signal and strengthening the robustness of functions (signal acquisition, signal processing and signal receiving).
Referring to fig. 3, fig. 3 is another method for processing abnormal signals of a vehicle according to the present invention, which specifically includes the following steps:
step 10: configuring an abnormal signal rule;
the method specifically comprises the steps of configuring abnormal alarm rules, configuring hit rules and configuring correction rules. The hit rule is used for judging whether the signal is abnormal, the alarm rule is used for judging whether the signal is abnormal and the alarm is needed, and the correction rule is used for correcting the abnormal signal.
The signal configuration content comprises: the cloud can analyze the message according to the configuration content and acquire the signal value.
Step 20: acquiring vehicle end signal data;
the vehicle-end signal data comprises a vehicle-end signal and service signal, and real-time vehicle-end signal data, historical vehicle-end signal data and historical service signal data. The method comprises the steps of interaction signal data among a vehicle end, a cloud end and a mobile terminal.
Step 30: signal abnormality identification and alarm;
and determining an abnormal signal based on the hit rule, completing the identification of the abnormal signal, and alarming the abnormal signal based on the alarm rule.
In one embodiment, triggering signal abnormality determination based on a preset triggering condition, and determining whether the vehicle-end signal data is abnormal according to a signal abnormality determination condition; if the vehicle-end signal data is judged to be an abnormal signal, judging whether the abnormal signal executes alarming or not based on an alarming condition rule, and determining the correction rule based on a hit rule.
In particular, different rule trigger conditions may be configured according to the category of the vehicle data, such as trigger conditions composed of logical symbols, signal names, signal history values, and signal latest values. The cloud end adds the vehicle end signal data into the judging row and column according to a preset triggering condition, when the vehicle end uploads the signal data in the door lock state, the cloud end identifies the logic symbol, the signal name, the signal history value and the signal latest value of the signal data, judges according to the rule of the type of the data, and triggers an abnormal judging rule when judging that the signal data belongs to the data needing to be judged.
After the abnormality judgment rule is triggered, comparing the vehicle-end signal data by using the abnormality judgment condition, judging the vehicle-end signal data as an abnormal signal if the target signal value or the calculated symbol of the vehicle-end signal data are abnormal relative to the abnormality judgment condition, and storing the signal data into a correction signal database.
Specifically, whether the abnormality determination is performed on the vehicle-end signal data according to the outer layer logical symbol, the inner layer logical symbol, the signal name, the target signal value and the calculation symbol of the vehicle-end signal data; if the vehicle-end signal data is judged to be the abnormal signal, a hit correction rule is determined based on a fetch function, a logic symbol and a calculation symbol, and a plurality of abnormal signals are ordered based on a rule ordering value.
In some embodiments, the cloud end identifies the data reported by the vehicle end, loads an abnormal alarm rule corresponding to the signal contained in the data, sequentially calculates rule conditions of the signal, calculates a boolean value as a rule calculation result, and judges that the signal is abnormal and needs to be alarmed if the rule calculation result is true. And sending out an alarm signal, and if the calculation result of the rule is false, judging that the signal is normal and does not need to be alarmed. In addition, the signal abnormality determination condition, the alarm condition rule and the hit rule may be set in the cloud alarm module to be executed.
In some embodiments, at the time of alerting, it is determined whether the abnormal signal performs alerting based on the continuous hit time, rule determination result, number of alerting limits, alerting limit period.
For example, whether the hit exceeds the alarm limit times in the alarm limit period is calculated, and if the hit exceeds the limit times, the alarm is not carried out any more; if not, the alarm is put into the pre-alarm queue.
Step 40: correcting signal data;
in one embodiment, the correction rule ID is determined based on the hit rule and the type of the abnormal signal, the correction rule ID is loaded to a corresponding correction rule configuration, and the correction policy is selected to fill, correct, or discard the data of the abnormal signal according to the correction rule configuration.
Determining the type of the abnormal signal according to a preset hit rule, and giving the abnormal signal an ID for determining a correction rule for determining that the correction rule to be executed later is found; and simultaneously giving a shaping value to the abnormal signal, wherein the shaping value is used for sequencing a plurality of hit rules of one signal.
For example, a signal hit rule sequence corresponding to the alarm signal is loaded according to the correction rule, and hit rule processing is sequentially started from large to small according to the size of the ordering value of the rule. And analyzing the access expression by an expression analyzer and acquiring data needed to be used in subsequent calculation from a data source according to an access function in the expression. And (3) carrying out hit condition calculation according to the signal hit calculation function, wherein the calculation result of the condition is a Boolean value, if the calculation result of the condition is true, entering a data correction stage, and if the calculation result of the condition is false, executing signal transmission. And (3) carrying out hit condition calculation according to the signal hit calculation function, wherein the calculation result of the condition is a Boolean value, if the calculation result of the condition is true, entering a data correction stage, and if the calculation result of the condition is false, executing signal transmission.
Specifically, when the correction strategy is filling or correcting, acquiring data needed to be used in subsequent numerical correction from a data source through an access function in correction rule configuration, calculating a correction value of an abnormal signal needed to be filled or corrected through a numerical correction function, storing the correction value into a correction signal database, and marking the strategy type as filling or correcting; if the correction strategy is discard, the abnormal signal is stored in the correction signal database, and the correction strategy type is marked as discard.
When the signal transmission is executed, the abnormal signal value is transmitted to a signal hit rule processing program corresponding to the next sorting value in the link for processing until the execution of the last rule processing program in the link is finished or any one of the links carries out data correction processing.
Step 50: and (5) signal fusion.
The cloud end can analyze the message according to the configuration content and acquire the signal value, and when signals are fused, the cloud end fills, replaces or discards the original vehicle-end signal data in the original signal database according to the correction strategy which is inquired in the correction signal database by the signal value, and returns to the mobile terminal after a fused signal list is acquired.
Referring to fig. 4, the present invention further provides a device for processing abnormal signals of a vehicle, including:
the number taking module 41 is configured to obtain and store vehicle end signal data of a vehicle, where the vehicle end signal data includes historical data and real-time data. The vehicle-end signal data comprise signal data of a cloud end uploaded by the vehicle end, the cloud end stores and processes the signal data, and the mobile terminal acquires the signal data from the cloud end.
The condition calculation module 42 is configured to determine whether the vehicle-end signal data is an abnormal signal based on a signal abnormality determination rule, and if the vehicle-end signal data is an abnormal signal, determine a type of the abnormal signal. For example, the condition calculation module 42 triggers signal abnormality determination based on a preset trigger condition, and determines whether the vehicle-side signal data is abnormal according to a signal abnormality determination condition; if the vehicle-end signal data is judged to be an abnormal signal, judging whether the abnormal signal executes alarming or not based on an alarming condition rule, and determining the correction rule based on a hit rule.
The data correction module 43 is configured to correct the abnormal signal based on a correction rule and a type of the abnormal signal to obtain corrected signal data. For example, the data correction module 43 determines a correction rule ID based on the hit rule and the type of the abnormal signal, loads the correction rule ID into a corresponding correction rule configuration, and selects a correction policy to fill, correct, or discard the abnormal signal with data according to the correction rule configuration.
And a transmission module 44, configured to fuse the vehicle-end signal data determined as the abnormal signal and the corrected signal data into target signal data, and send the target signal data to a mobile terminal. For example, the transmission module 44 queries the vehicle-end signal data and the corrected signal data from the original signal database and the corrected signal database, respectively, and performs signal fusion; and when signals are fused, filling, replacing or discarding the original vehicle-end signal data in the original signal database according to the correction strategy queried in the correction signal database by the vehicle-end signal data, and returning to the mobile terminal after obtaining a fused signal list.
Further, the condition calculating module 42 is further configured to trigger an anomaly determination according to a logical symbol, a signal name, a signal history value, and a signal new value of the vehicle-end signal data, and execute the anomaly determination on the vehicle-end signal data according to an outer logical symbol, an inner logical symbol, a signal name, a target signal value, and a calculation symbol of the vehicle-end signal data; if the vehicle-end signal data is judged to be an abnormal signal, determining a hit correction rule based on a fetch function, a logic symbol and a calculation symbol, and sorting a plurality of abnormal signals based on a rule sorting value; and judging whether the abnormal signal executes alarming or not based on the continuous hit time, the rule judging result, the alarming limit times and the alarming limit period. When the condition calculation module 42 alarms, calculating whether the hit exceeds the alarm limit times in the alarm limit period, and if the hit exceeds the limit times, not performing alarm; if not, the alarm is put into the pre-alarm queue.
Further, the data correction module 43 is further configured to obtain data needed for subsequent numerical correction from the data source through the access function in the correction rule configuration if the correction policy is filling or correction, calculate a correction value of an abnormal signal needed for filling or correction through the numerical correction function, store the correction value in the correction signal database, and mark the policy type as filling or correction; if the correction strategy is discard, the abnormal signal is stored in the correction signal database, and the correction strategy type is marked as discard.
The function of each module may refer to the description of the method for processing the abnormal signal of the vehicle according to the present invention, and will not be repeated. As an embodiment of a processing device for a vehicle abnormality signal in the present invention, a vehicle abnormality signal module allocation may be constructed with reference to the functional modules described in fig. 5.
The processing device comprises a signal storage module, a signal management module, a data source management module, a signal query module, an alarm module and a correction module.
The signal storage module comprises an original signal storage unit for storing original vehicle-end signal data and a corrected signal storage unit for storing corrected vehicle-end signal data.
The signal management module comprises a signal definition unit and a signal introduction unit, and is used for defining and classifying signal data information and introducing signals into different storage units.
The data source management module comprises a signal data acquisition unit for acquiring signal data of the vehicle end and the mobile terminal, and a service data acquisition unit for acquiring the vehicle end or the mobile terminal.
The alarm module comprises an alarm configuration unit, a rule hit unit and a rule alarm unit, wherein the alarm configuration unit is used for configuring alarm rule condition judgment abnormal signals, the rule hit unit is used for determining abnormal signal types and the like according to hit rules, and the rule alarm unit is used for judging alarm conditions.
The correction module comprises a hit rule configuration unit, a correction rule configuration unit and a data correction unit. The hit rule configuration unit is used for configuring the condition configuration of the correction rule for different signal data, the correction rule configuration unit is used for configuring the correction strategy for different signal data, and the data correction unit is used for correcting the signal data according to the correction strategy.
The signal query module comprises a fusion signal query unit, an original signal query unit and a correction signal query unit. The original signal, the correction signal and the fusion signal are inquired based on the signal value of the vehicle-end signal data.
As shown in fig. 6, the present invention further provides an electronic device, including:
at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, and the processor invokes the program instructions to perform the method of processing the vehicle abnormality signal.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described method of processing a vehicle abnormality signal.
It is understood that the computer-readable storage medium may include: any entity or device capable of carrying a computer program, a recording medium, a USB flash disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a software distribution medium, and so forth. The computer program comprises computer program code. The computer program code may be in the form of source code, object code, executable files, or in some intermediate form, among others. The computer readable storage medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a software distribution medium, and so forth.
In some embodiments of the invention, the electronic device may include a controller or processor, the controller being a single chip microcomputer chip incorporating a processor, memory, communication module, etc. The processor may refer to a processor comprised by the controller. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The method for processing the vehicle abnormal signal is applied to a cloud end, and the cloud end stores the vehicle state, and is characterized by comprising the following steps:
acquiring and storing vehicle-end signal data of a vehicle, wherein the vehicle-end signal data are used for representing state information of the vehicle, and the vehicle-end signal data comprise historical data and real-time data;
determining whether the vehicle-end signal data is an abnormal signal or not based on a signal abnormality determination rule, and if the vehicle-end signal data is the abnormal signal, determining the type of the abnormal signal;
correcting the abnormal signal based on a correction rule and the type of the abnormal signal to obtain corrected signal data;
and merging the vehicle-end signal data determined to be the abnormal signal and the corrected signal data into target signal data, and sending a list of the target signal data to a mobile terminal.
2. The method for processing a vehicle abnormality signal according to claim 1, characterized in that said determining whether the vehicle-side signal data is an abnormality signal based on a signal abnormality determination rule includes:
triggering signal abnormality judgment based on a preset triggering condition, and judging whether the vehicle-end signal data is abnormal or not according to a signal abnormality judgment condition;
if the vehicle-end signal data is judged to be an abnormal signal, judging whether the abnormal signal executes alarming or not based on an alarming condition rule, and determining the correction rule based on a hit rule.
3. The method for processing a vehicle abnormality signal according to claim 2, characterized in that said determining whether the vehicle-side signal data is an abnormality signal based on a signal abnormality determination rule includes:
triggering abnormality judgment according to the logic symbol, the signal name, the signal history value and the signal new value of the vehicle-end signal data, and executing whether the abnormality judgment is executed on the vehicle-end signal data according to the outer logic symbol, the inner logic symbol, the signal name, the target signal value and the calculation symbol of the vehicle-end signal data;
if the vehicle-end signal data is judged to be an abnormal signal, determining a hit correction rule based on a fetch function, a logic symbol and a calculation symbol, and sorting a plurality of abnormal signals based on a rule sorting value;
and judging whether the abnormal signal executes alarming or not based on the continuous hit time, the rule judging result, the alarming limit times and the alarming limit period.
4. The method for processing the vehicle abnormality signal according to claim 3, characterized in that said judging whether the abnormality signal performs an alarm based on a continuous hit time, a rule judgment result, an alarm limit number, an alarm limit period, includes:
calculating whether the hit exceeds the alarm limit times in the alarm limit period, and if the hit exceeds the limit times, not giving an alarm; if not, the alarm is put into the pre-alarm queue.
5. The method according to claim 2, wherein the correcting the abnormal signal based on the correction rule and the type of the abnormal signal to obtain corrected signal data includes:
and determining a correction rule ID based on the hit rule and the type of the abnormal signal, loading the correction rule ID to a corresponding correction rule configuration, and selecting a correction strategy to fill, correct or discard the data of the abnormal signal according to the correction rule configuration.
6. The method according to claim 5, wherein the correcting the abnormal signal based on the correction rule and the type of the abnormal signal to obtain corrected signal data includes:
if the correction strategy is filling or correcting, acquiring data needed to be used in subsequent numerical correction from a data source through an access function in correction rule configuration, calculating a correction value of an abnormal signal needed to be filled or corrected through a numerical correction function, storing the correction value into a correction signal database, and marking the type of the strategy as filling or correcting; if the correction strategy is discard, the abnormal signal is stored in the correction signal database, and the correction strategy type is marked as discard.
7. The method according to claim 6, wherein the fusing the vehicle-end signal data determined as the abnormal signal and the corrected signal data into target signal data, and transmitting the target signal data to a mobile terminal, comprises:
inquiring the vehicle-end signal data and the corrected signal data from an original signal database and a corrected signal database respectively, and performing signal fusion;
and when signals are fused, filling, replacing or discarding the original vehicle-end signal data in the original signal database according to the correction strategy queried in the correction signal database by the vehicle-end signal data, and returning to the mobile terminal after obtaining a fused signal list.
8. A processing apparatus for a vehicle abnormality signal, comprising:
the system comprises a number acquisition module, a number acquisition module and a data processing module, wherein the number acquisition module is used for acquiring and storing vehicle end signal data of a vehicle, and the vehicle end signal data comprises historical data and real-time data;
the condition calculation module is used for determining whether the vehicle-end signal data is an abnormal signal or not based on a signal abnormality judgment rule, and judging the type of the abnormal signal if the vehicle-end signal data is the abnormal signal;
the data correction module is used for correcting the abnormal signal based on a correction rule and the type of the abnormal signal to obtain corrected signal data;
and the transmission module is used for fusing the vehicle-end signal data judged to be the abnormal signal and the corrected signal data into target signal data and sending the target signal data to the mobile terminal.
9. An electronic device, comprising:
at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, the processor invoking the program instructions capable of performing the method of processing a vehicle anomaly signal according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when executed by a computer, performs the method of processing a vehicle abnormality signal according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117719440A (en) * | 2024-02-08 | 2024-03-19 | 零束科技有限公司 | Automobile signal detection method, system and readable storage medium |
| CN118260171A (en) * | 2024-05-29 | 2024-06-28 | 蒲惠智造科技股份有限公司 | Service early warning method, system, medium and equipment based on custom pain sense signals |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117719440A (en) * | 2024-02-08 | 2024-03-19 | 零束科技有限公司 | Automobile signal detection method, system and readable storage medium |
| CN117719440B (en) * | 2024-02-08 | 2024-05-03 | 零束科技有限公司 | Automobile signal detection method, system and readable storage medium |
| CN118260171A (en) * | 2024-05-29 | 2024-06-28 | 蒲惠智造科技股份有限公司 | Service early warning method, system, medium and equipment based on custom pain sense signals |
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