CN112328970A - Accident prediction method and system based on vehicle performance parameters - Google Patents
Accident prediction method and system based on vehicle performance parameters Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, and provides an accident prediction method based on vehicle performance parameters, which comprises the following steps: monitoring a plurality of vehicle performance parameters when a vehicle to be tested runs; establishing a vehicle model to be tested according to a plurality of vehicle performance parameters; generating a parameter vector to be tested according to the vehicle model to be tested; and inputting the parameter vector to be tested into a trained vehicle safety prediction model, outputting the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model and uploading the accident occurrence probability to a block chain. The method and the device establish the vehicle model to be tested according to the vehicle performance parameters of the vehicle to be tested, improve the prediction accuracy of the accident occurrence probability of the vehicle to be tested, and reduce the occurrence probability of the traffic accident.
Description
Technical Field
The embodiment of the invention relates to the technical field of artificial intelligence, in particular to an accident prediction method and system based on vehicle performance parameters, computer equipment and a computer readable storage medium.
Background
As the number of cars used increases year by year, the absolute number and the occurrence probability of vehicle traffic accidents increase year by year. Many of these accidents are due to the vehicle itself. The current common accident prediction method is to predict the probability of car accidents according to the state of the vehicle, the environmental information and other factors during the running process of the vehicle. However, the existing accident prediction methods are based on external factors such as environment and the like, and influence of internal factors such as different brands, models, years, service time and current states of vehicles on accident occurrence probability is ignored. However, it is often the intrinsic and subjective factors that affect the occurrence of accidents to a greater extent. Therefore, how to more comprehensively analyze the factors of the vehicle to improve the accuracy of the traffic accident prediction, so as to further reduce the occurrence probability of the traffic accident becomes one of the technical problems which need to be solved at present.
Disclosure of Invention
In view of the above, it is necessary to provide an accident prediction method, system, computer device and computer readable storage medium based on vehicle performance parameters to solve the technical problem that the prediction of the current traffic accident is easy to be caused by the vehicle itself and affects the accuracy of the accident prediction.
In order to achieve the above object, an embodiment of the present invention provides an accident prediction method based on vehicle performance parameters, where the method includes:
monitoring a plurality of vehicle performance parameters when a vehicle to be tested runs;
establishing a vehicle model to be tested according to a plurality of vehicle performance parameters;
generating a parameter vector to be tested according to the vehicle model to be tested; and
and inputting the parameter vector to be tested into a trained vehicle safety prediction model so as to output the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model.
Illustratively, the step of establishing a vehicle model to be tested according to a plurality of vehicle performance parameters includes;
acquiring a pre-constructed vehicle parameter model frame; the vehicle parameter model framework comprises a plurality of parameter containers, and each parameter container is used for storing corresponding vehicle performance parameters; and
and inputting a plurality of vehicle performance parameters into the vehicle parameter model framework to generate the vehicle model to be tested.
After the step of building a vehicle model under test according to a plurality of vehicle performance parameters, the method further comprises:
monitoring the change conditions of a plurality of vehicle performance parameters; and
and when any one of the plurality of vehicle performance parameters is monitored to be changed, updating the vehicle model to be tested according to the changed vehicle performance parameter.
Illustratively, each parameter container is respectively pre-configured with a corresponding preset threshold value;
after the step of building a vehicle model under test according to a plurality of vehicle performance parameters, the method further comprises:
judging whether the vehicle performance parameter in each of the plurality of parameter containers is greater than a corresponding preset threshold value; and
and when the vehicle performance parameter in any one of the parameter containers is larger than the preset threshold corresponding to the parameter container, generating alarm information corresponding to the parameter container, and sending the alarm information to the vehicle to be tested.
For example, after the step of inputting the parameter vector to be tested into the trained vehicle safety prediction model to output the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model, the method further includes:
and adjusting the vehicle performance parameters of the vehicle to be tested according to the accident occurrence probability of the vehicle to be tested.
Illustratively, the step of adjusting the vehicle performance parameter of the vehicle to be tested according to the accident occurrence probability of the vehicle to be tested includes:
pre-configuring a first threshold value and a second threshold value, wherein the first threshold value is smaller than the second threshold value;
if the accident occurrence probability is larger than the threshold and smaller than the second threshold, matching a corresponding accident vehicle model for the vehicle to be tested according to the vehicle model to be tested, generating low-risk reminding information and sending the low-risk reminding information to the vehicle to be tested so as to remind a driver of the vehicle to be tested of adjusting the vehicle performance parameters of the vehicle to be tested according to the accident vehicle model through the low-risk reminding information; and
and if the accident occurrence probability is greater than the second threshold value, directly adjusting the vehicle performance parameters of the vehicle to be detected according to the accident vehicle model, generating high-risk alarm information and sending the high-risk alarm information to the vehicle to be detected.
For example, after the step of inputting the parameter vector to be tested into the trained vehicle safety prediction model to output the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model, the method further includes:
and uploading the accident occurrence probability to a block chain.
In order to achieve the above object, an embodiment of the present invention further provides an accident prediction system based on vehicle performance parameters, including:
the monitoring module is used for monitoring a plurality of vehicle performance parameters when the vehicle to be tested runs;
the building module is used for building a vehicle model to be tested according to the vehicle performance parameters;
the generating module is used for generating a parameter vector to be detected according to the vehicle model to be detected; and
and the prediction module is used for inputting the parameter vector to be tested into a trained vehicle safety prediction model so as to output the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model.
To achieve the above object, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and operable on the processor, and when executed by the processor, the computer program implements the steps of the accident prediction method based on vehicle performance parameters as described above.
To achieve the above object, the present invention also provides a computer-readable storage medium, which stores therein a computer program, which is executable by at least one processor to cause the at least one processor to execute the steps of the accident prediction method based on vehicle performance parameters as described above.
According to the accident prediction method, the accident prediction system, the computer equipment and the computer readable storage medium based on the vehicle performance parameters, provided by the embodiment of the invention, a vehicle model to be tested is established by monitoring a plurality of vehicle performance parameters of the vehicle to be tested during running and according to the plurality of vehicle performance parameters of the vehicle to be tested; the running state is updated in real time through the vehicle model to be tested, and the vehicle performance parameters after the vehicle model to be tested is updated are predicted through vehicle safety prediction, so that the prediction accuracy of the accident occurrence probability of the vehicle to be tested during running is improved, and the traffic accident occurrence probability is reduced.
Drawings
Fig. 1 is a schematic flow chart of an accident prediction method based on vehicle performance parameters according to an embodiment of the present invention.
Fig. 2 is a detailed flowchart of step S102 according to an embodiment of the present invention.
Fig. 3 is another detailed flowchart after step S102 according to an embodiment of the invention.
Fig. 4 is another detailed flowchart after step S102 in the first embodiment of the present invention.
Fig. 5 is another detailed flowchart after step S106 according to an embodiment of the present invention.
FIG. 6 is a block diagram of a second embodiment of an accident prediction system based on vehicle performance parameters.
Fig. 7 is a schematic diagram of a hardware structure of a third embodiment of the computer apparatus according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
In the following embodiments, the computer device 2 will be exemplarily described as an execution subject.
Example one
Referring to fig. 1, a flow chart of steps of a vehicle performance parameter based accident prediction method according to an embodiment of the present invention is shown. It is to be understood that the flow charts in the embodiments of the present method are not intended to limit the order in which the steps are performed. The details are as follows.
And S100, monitoring a plurality of vehicle performance parameters when the vehicle to be tested runs.
In an exemplary embodiment, the vehicle performance parameters may include basic parameters and running parameters, and the basic parameters may include a maximum power, a maximum torque, a maximum climbing gradient, a maximum vehicle speed, a maximum acceleration of 0-100km/h, a shortest braking distance of 100-0km/h, a tire size, and the like of the vehicle under test; the operation parameters are parameters corresponding to the current operation of the vehicle to be tested, such as the current power, the current torque, the current speed, the shortest braking distance corresponding to the current speed and the like of the vehicle to be tested during the operation.
The computer device 2 may acquire the basic parameters of the vehicle to be tested in advance and update the basic parameters at a certain time frequency. It can be understood that, as the vehicle to be tested is used for a long time, the wear of the wheels increases, and the degree of the wear of the wheels affects the braking distance of the vehicle to be tested, so the basic parameters of the vehicle to be tested can be updated at a certain time frequency.
The computer device 2 may monitor the operating parameters of the vehicle under test while operating, and read and store operating parameters of systems related to the power and braking of the vehicle under test (e.g., an engine control system, a transmission control system, a vehicle steer-by-wire system, a brake control system, a dashboard control system, a speed sensing system, etc.). For example, the current vehicle speed may be obtained by at least one of a speed sensing system and a transmission control system; the shortest braking distance corresponding to the current vehicle speed can be obtained through a braking system or the running speed.
In the course of vehicle driving, it is a common accident prediction method to predict the probability of car accidents according to the state of the vehicle and the information of the environment. However, the existing accident prediction methods are based on external factors such as environment and the like, and influence of internal factors such as different brands, models, years, service time and current states of vehicles on accident occurrence probability is ignored. However, it is often the intrinsic and subjective factors that affect the occurrence of accidents to a greater extent.
And S102, establishing a vehicle model to be tested according to the vehicle performance parameters.
As shown in fig. 2, the step S102 may further include steps S200 to S202, where: step S200, acquiring a pre-constructed vehicle parameter model frame; and step S202, inputting a plurality of vehicle performance parameters into the vehicle parameter model frame to generate the vehicle model to be tested.
The computer device 2 may input a plurality of vehicle performance parameters into the constructed vehicle parameter model frame to obtain a vehicle model to be tested corresponding to the vehicle to be tested. The parametric model is used for determining the current running state of the vehicle according to the input performance parameters. And the vehicle model to be tested is used for determining the running state of the vehicle to be tested. For example, the vehicle model under test may calculate what state the vehicle power of the current vehicle of the vehicle power under test is in (e.g., a normal state, an excess state, etc.) according to the maximum power and the current power of the vehicle under test.
Wherein the vehicle model under test may be a set of vehicle performance parameters of the vehicle under test. The vehicle model to be tested can be subjected to visual processing, and the computer equipment 2 can send the vehicle model to be tested after the visual processing to the display module, so that a driver of the vehicle to be tested can watch the running state of the vehicle to be tested when the vehicle runs.
The computer device 2 may further select one or more target vehicle models that are closer to the running state of the vehicle to be tested from a plurality of accident vehicle models in which vehicle models are pre-established according to the vehicle model to be tested, and adjust the vehicle performance parameters of the vehicle model to be tested according to the accident occurrence probability and the performance parameters corresponding to the target vehicle models. Taking the number of the vehicle performance parameters of the vehicle to be detected equal to N as an example, when the difference value between the (N-1) vehicle performance parameters of the vehicle to be detected and the (N-1) vehicle performance parameters of the accident vehicle exceeds a preset value, the running state of the accident vehicle can be determined to be closer to the running state of the vehicle to be detected.
It is understood that the accident occurrence probability of vehicles in different running states under the same road section environment is different. For example, taking the wear rate of the wheel as an example, if the wear rate of the wheel is higher and the braking distance is longer under the condition that other vehicle performance parameters are the same, the accident occurrence probability that the wear rate of the wheel is high under the same road section environment is high. And judging the accident occurrence probability of the vehicle to be detected corresponding to each road section environment according to the road section environments corresponding to the target vehicle models. For example, the accident occurrence probability of the vehicle to be tested in the road segment environment can be determined according to the accident occurrence probability of the target vehicle model in the road segment environment. Namely, the higher the recognition degree between the vehicle to be detected and the target vehicle is, the closer the accident occurrence probability of the vehicle to be detected in the road section environment is to the accident occurrence probability of the target vehicle model in the road section environment. It should be noted that the accident occurrence probability obtained in this way can be rounded.
As shown in fig. 3, after the step S102, the accident prediction method based on vehicle performance parameters may further include steps S300 to S302, in which: step S300, monitoring the change conditions of a plurality of vehicle performance parameters; and step S302, when any one of the vehicle performance parameters is monitored to be changed, updating the vehicle model to be tested according to the changed vehicle performance parameter.
It can be understood that the vehicle to be tested does not always keep a running state during the running process of the vehicle to be tested. In order to update the running state of the vehicle to be tested in time, the computer device 2 may monitor a plurality of changes of the vehicle performance parameters, so as to ensure that the vehicle performance parameters in the vehicle model to be tested are the parameters of the current running state of the vehicle to be tested.
Illustratively, the vehicle parameter model framework comprises a plurality of parameter containers, each parameter container is used for storing vehicle performance parameters, wherein each parameter container is respectively pre-configured with a corresponding preset threshold; as shown in fig. 4, after the step S102, the accident prediction method based on vehicle performance parameters may further include steps S400 to S402, in which: step S400, judging whether the vehicle performance parameter in each parameter container in the plurality of parameter containers is larger than a corresponding preset threshold value; step S402, when the vehicle performance parameter in any one of the parameter containers is larger than the preset threshold corresponding to the parameter container, generating alarm information corresponding to the parameter container, and sending the alarm information to the vehicle to be tested.
The parameter container may be configured to configure a corresponding preset threshold for a vehicle performance parameter corresponding to the parameter container. In order to further reduce the accident occurrence probability of the vehicle to be tested during operation, the computer device 2 may further configure a preset threshold for each parameter container, so as to avoid an increase in the accident occurrence probability due to an excessively high parameter during operation of the vehicle to be tested. It can be understood that if a certain parameter value is too large during the running of the vehicle, the accident occurrence probability of the vehicle is higher, such as the vehicle speed, the tire wear rate, the maximum braking distance and the like. Therefore, in order to improve the safety of the vehicle in the operation process, the computer device 2 may monitor the vehicle performance parameters in each parameter container, and if the vehicle performance parameters are greater than the preset threshold corresponding to the parameter container, generate alarm information and send the alarm information to the vehicle to be tested, so as to remind the driver to adjust the corresponding vehicle performance parameters.
And step S104, generating a parameter vector to be measured according to the vehicle model to be measured.
In an exemplary embodiment, the parameter vector of the vehicle model to be tested is extracted according to the sequence of the vehicle performance parameters in the vehicle model to be tested, so as to obtain the parameter vector to be tested, wherein each position element in the parameter vector corresponds to one of the vehicle performance parameters.
And S106, inputting the parameter vector to be tested into a trained vehicle safety prediction model so as to output the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model.
In an exemplary embodiment, the vehicle safety prediction model is a pre-trained convolutional neural network model, and may be configured to output, according to an input parameter vector, an accident occurrence probability of a vehicle model corresponding to the parameter vector. According to the scheme, the safety of the vehicle to be tested is determined by extracting the parameter vector to be tested corresponding to the vehicle to be tested and predicting the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model. If the accident occurrence probability of the vehicle to be tested is higher, namely, when the safety of the vehicle to be tested is lower, the safety of the vehicle to be tested can be improved by adjusting the vehicle performance parameters.
In an exemplary embodiment, the training of the vehicle safety prediction model includes:
step 1, obtaining a plurality of sample vehicle models corresponding to a plurality of sample vehicles of which vehicle models are established in advance.
Selecting a plurality of sample vehicle models from a plurality of accident vehicle models of a plurality of accident vehicles which are established in advance according to the vehicle model to be tested; the vehicle performance parameters of a plurality of accident vehicles can be obtained in advance, and a corresponding accident vehicle model is established for each accident vehicle according to the vehicle performance parameters of each accident vehicle so as to obtain a plurality of accident vehicle models. The vehicle performance parameters of the accident vehicles can be obtained according to insurance data after the accident of the vehicle or traffic accident data provided by a traffic control.
And 2, acquiring the accident occurrence probability of each sample vehicle model, and extracting a sample parameter vector corresponding to each sample vehicle model.
And 3, training the deep neural network model through the accident occurrence probability and the sample parameter vectors corresponding to the plurality of sample vehicle models to obtain a vehicle safety prediction model for predicting road safety.
For example, after the step S106, the accident prediction method based on vehicle performance parameters may further include: and S108, adjusting the vehicle performance parameters of the vehicle to be tested according to the accident occurrence probability of the vehicle to be tested.
As shown in fig. 5, the step S108 may further include steps S500 to S504, where: step S500, a first threshold value and a second threshold value are configured in advance, wherein the first threshold value is smaller than the second threshold value; step S502, if the accident occurrence probability is larger than the threshold and smaller than the second threshold, matching a corresponding accident vehicle model for the vehicle to be tested according to the vehicle model to be tested, generating low-risk reminding information and sending the low-risk reminding information to the vehicle to be tested so as to remind a driver of the vehicle to be tested to adjust the vehicle performance parameters of the vehicle to be tested according to the accident vehicle model through the low-risk reminding information; and step S504, if the accident occurrence probability is greater than the second threshold value, directly adjusting the vehicle performance parameters of the vehicle to be detected according to the accident vehicle model, generating high-risk alarm information and sending the high-risk alarm information to the vehicle to be detected.
For example, the first threshold may be set to 20% and the first threshold is set to 50%: and when the accident occurrence probability is smaller than the threshold value, namely the accident occurrence probability is lower than 20%, the accident occurrence probability of the vehicle to be tested is lower, and the vehicle can normally run. When the accident occurrence probability is greater than the threshold and smaller than the second threshold, namely the accident occurrence probability is between 20% and 50%, the accident occurrence probability of the vehicle to be tested is general; in order to reduce the accident occurrence probability of the vehicle to be detected, the computer device 2 may match the corresponding accident vehicle model with the vehicle to be detected according to the vehicle model to be detected, and generate low-risk reminding information to be sent to the vehicle to be detected, so as to remind a driver of the vehicle to be detected according to the accident vehicle model through the low-risk reminding information to adjust the vehicle performance parameters of the vehicle to be detected. When the accident occurrence probability is greater than the second threshold value, namely the accident occurrence probability is higher than 50%, the accident occurrence probability of the vehicle to be detected is higher; in order to reduce the accident probability of the vehicle to be tested, improve the security of the vehicle to be tested, computer equipment 2 can be according to accident vehicle model direct adjustment the vehicle performance parameter of the vehicle to be tested and generate high-risk alarm information to send the vehicle to be tested so as to remind the driver that the vehicle to be tested runs at high risk at this moment, and has passed through computer equipment 2 has adjusted the vehicle performance parameter of the vehicle to be tested, the security of the vehicle to be tested is improved.
Illustratively, the vehicle performance parameter-based accident prediction method further comprises: and uploading the accident occurrence probability to a block chain. And the safety and the fair transparency of the block chain can be ensured by uploading the accident occurrence probability to the block chain. The blockchain referred to in this example is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm, and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Example two
FIG. 6 is a block diagram of a second embodiment of an accident prediction system based on vehicle performance parameters. The vehicle performance parameter based accident prediction system 20 may include or be divided into one or more program modules stored in a storage medium and executed by one or more processors to implement the present invention and implement the vehicle performance parameter based accident prediction methods described above. The program modules referred to in the embodiments of the present invention are a series of computer program instruction segments that can perform specific functions. The following description will specifically describe the functions of the program modules of the present embodiment:
the monitoring module 200 is configured to monitor a plurality of vehicle performance parameters of the vehicle to be tested during operation.
The establishing module 202 is configured to establish a vehicle model to be tested according to the plurality of vehicle performance parameters.
Illustratively, the establishing module 202 is further configured to: acquiring a pre-constructed vehicle parameter model frame; the vehicle parameter model framework comprises a plurality of parameter containers, and each parameter container is used for storing corresponding vehicle performance parameters; and inputting a plurality of vehicle performance parameters into the vehicle parameter model framework to generate the vehicle model to be tested.
Illustratively, the establishing module 202 is further configured to: monitoring the change conditions of a plurality of vehicle performance parameters; and when any one of the plurality of vehicle performance parameters is monitored to be changed, updating the vehicle model to be tested according to the changed vehicle performance parameter.
Illustratively, each parameter container is respectively pre-configured with a corresponding preset threshold value; the establishing module 202 is further configured to: judging whether the vehicle performance parameter in each of the plurality of parameter containers is greater than a corresponding preset threshold value; and when the vehicle performance parameter in any one of the parameter containers is larger than the preset threshold corresponding to the parameter container, generating alarm information corresponding to the parameter container, and sending the alarm information to the vehicle to be tested.
And the generating module 204 is configured to generate a parameter vector to be detected according to the vehicle model to be detected.
And the prediction module 206 is configured to input the parameter vector to be tested into a trained vehicle safety prediction model, so as to output the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model.
The accident prediction system 20 further includes an adjusting module, configured to adjust the vehicle performance parameter of the vehicle to be tested according to the accident occurrence probability of the vehicle to be tested.
Illustratively, the adjusting module (not shown) is further configured to: pre-configuring a first threshold value and a second threshold value, wherein the first threshold value is smaller than the second threshold value; if the accident occurrence probability is larger than the threshold and smaller than the second threshold, matching a corresponding accident vehicle model for the vehicle to be tested according to the vehicle model to be tested, generating low-risk reminding information and sending the low-risk reminding information to the vehicle to be tested so as to remind a driver of the vehicle to be tested of adjusting the vehicle performance parameters of the vehicle to be tested according to the accident vehicle model through the low-risk reminding information; and if the accident occurrence probability is larger than the second threshold value, directly adjusting the vehicle performance parameters of the vehicle to be detected according to the accident vehicle model, generating high-risk alarm information and sending the high-risk alarm information to the vehicle to be detected.
The vehicle performance parameter based accident prediction system 20 further includes an upload module (not shown) for uploading the accident occurrence probability into a block chain.
EXAMPLE III
Fig. 7 is a schematic diagram of a hardware architecture of a computer device according to a third embodiment of the present invention. In the present embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a preset or stored instruction. The computer device 2 may be a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers), and the like. As shown, the computer device 2 includes, but is not limited to, at least a memory 21, a processor 22, a network interface 23, and an accident prediction system 20 based on vehicle performance parameters, which may be communicatively coupled to each other via a system bus.
In this embodiment, the memory 21 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the computer device 2. Of course, the memory 21 may also comprise both internal and external memory units of the computer device 2. In this embodiment, the memory 21 is generally used for storing an operating system and various application software installed on the computer device 2, such as the program code of the accident prediction system 20 based on vehicle performance parameters in the second embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
The network interface 23 may comprise a wireless network interface or a wired network interface, and the network interface 23 is generally used for establishing communication connection between the computer device 2 and other electronic apparatuses. For example, the network interface 23 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication i/On (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.
It is noted that fig. 7 only shows the computer device 2 with components 20-23, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.
In this embodiment, the vehicle performance parameter based accident prediction system 20 stored in the memory 21 may also be divided into one or more program modules, which are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 22) to accomplish the present invention.
For example, fig. 6 is a schematic diagram of program modules for implementing the vehicle performance parameter based accident prediction system 20 according to the second embodiment of the present invention, in which the vehicle performance parameter based accident prediction system 20 may be divided into a monitoring module 200, an establishing module 202, a generating module 204, and a prediction module 206. The program modules referred to herein refer to a series of computer program instruction segments that can perform specific functions, and are more suitable than programs for describing the execution of the vehicle performance parameter based accident prediction system 20 in the computer device 2. The specific functions of the program modules 200 and 206 have been described in detail in the second embodiment, and are not described herein again.
Example four
The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer readable storage medium of the embodiment is used for the vehicle performance parameter based accident prediction system 20, and when executed by the processor, the accident prediction method based on the vehicle performance parameter of the first embodiment is implemented.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method of accident prediction based on vehicle performance parameters, the method comprising:
monitoring a plurality of vehicle performance parameters when a vehicle to be tested runs;
establishing a vehicle model to be tested according to a plurality of vehicle performance parameters;
generating a parameter vector to be tested according to the vehicle model to be tested; and
and inputting the parameter vector to be tested into a trained vehicle safety prediction model so as to output the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model.
2. The vehicle performance parameter based accident prediction method of claim 1, wherein the step of building a vehicle model under test based on a plurality of the vehicle performance parameters comprises;
acquiring a pre-constructed vehicle parameter model frame; and
and inputting a plurality of vehicle performance parameters into the vehicle parameter model framework to generate the vehicle model to be tested.
3. The vehicle performance parameter based accident prediction method of claim 2, wherein after the step of building a vehicle model under test from a plurality of the vehicle performance parameters, the method further comprises:
monitoring the change conditions of a plurality of vehicle performance parameters; and
and when any one of the plurality of vehicle performance parameters is monitored to be changed, updating the vehicle model to be tested according to the changed vehicle performance parameter.
4. The vehicle performance parameter based accident prediction method according to claim 3, wherein the vehicle parameter model framework comprises a plurality of parameter containers, each parameter container is used for storing the vehicle performance parameters, and each parameter container is pre-configured with a preset threshold corresponding to the parameter container;
after the step of building a vehicle model under test according to a plurality of vehicle performance parameters, the method further comprises:
judging whether the vehicle performance parameter in each parameter container in the plurality of parameter containers is larger than a preset threshold corresponding to the parameter container; and
and when the vehicle performance parameter in any one of the parameter containers is larger than the preset threshold corresponding to the parameter container, generating alarm information corresponding to the parameter container, and sending the alarm information to the vehicle to be tested.
5. The vehicle performance parameter based accident prediction method of claim 1, wherein after the step of inputting the parameter vector to be tested into a trained vehicle safety prediction model to output the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model, the method further comprises:
and adjusting the vehicle performance parameters of the vehicle to be tested according to the accident occurrence probability of the vehicle to be tested.
6. The vehicle performance parameter based accident prediction method of claim 5, wherein the step of adjusting the vehicle performance parameter of the vehicle under test according to the accident occurrence probability of the vehicle under test comprises:
pre-configuring a first threshold value and a second threshold value, wherein the first threshold value is smaller than the second threshold value;
if the accident occurrence probability is larger than the threshold and smaller than the second threshold, matching a corresponding accident vehicle model for the vehicle to be tested according to the vehicle model to be tested, generating low-risk reminding information and sending the low-risk reminding information to the vehicle to be tested so as to remind a driver of the vehicle to be tested of adjusting the vehicle performance parameters of the vehicle to be tested according to the accident vehicle model through the low-risk reminding information; and
and if the accident occurrence probability is greater than the second threshold value, directly adjusting the vehicle performance parameters of the vehicle to be detected according to the accident vehicle model, generating high-risk alarm information and sending the high-risk alarm information to the vehicle to be detected.
7. The vehicle performance parameter based accident prediction method according to any one of claims 1 to 6, wherein after the step of inputting the parameter vector to be tested into a trained vehicle safety prediction model to output the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model, the method further comprises:
and uploading the accident occurrence probability to a block chain.
8. An accident prediction system based on vehicle performance parameters, comprising:
the monitoring module is used for monitoring a plurality of vehicle performance parameters when the vehicle to be tested runs;
the building module is used for building a vehicle model to be tested according to the vehicle performance parameters;
the generating module is used for generating a parameter vector to be detected according to the vehicle model to be detected; and
and the prediction module is used for inputting the parameter vector to be tested into a trained vehicle safety prediction model so as to output the accident occurrence probability of the vehicle to be tested through the vehicle safety prediction model.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program, when executed by the processor, carries out the steps of the method of vehicle performance parameter based accident prediction according to any one of claims 1 to 7.
10. A computer-readable storage medium, having stored therein a computer program which is executable by at least one processor to cause the at least one processor to perform the steps of the vehicle performance parameter based accident prediction method according to any one of claims 1 to 7.
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