CN118069536A - Vehicle chassis performance test method and system - Google Patents
Vehicle chassis performance test method and system Download PDFInfo
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Abstract
The invention provides a vehicle chassis performance test method and system, and relates to the technical field of vehicle tests, wherein the method comprises the following steps: when an access signal of a vehicle chassis is detected in real time, calling a matlab program in a preset database, and detecting a chassis code corresponding to the vehicle chassis in real time; analyzing and processing the chassis code through a matlab program to output a dbc file corresponding to the chassis code in real time, and creating a corresponding initial test model according to the dbc file; detecting a plurality of electronic elements contained in a vehicle chassis in real time, and compiling corresponding test scripts according to the electronic elements; and reading and writing the test script into the initial test model to generate a corresponding target test model, and completing performance test of the vehicle chassis through the target test model. The invention can omit redundant test fields and test equipment and improves the working efficiency.
Description
Technical Field
The invention relates to the field of vehicle testing, in particular to a vehicle chassis performance testing method and system.
Background
Along with the progress of science and technology and the rapid development of productivity, automobiles are popularized in daily life of people, and become one of the indispensable transportation means for daily travel of people, so that the lives of people are greatly facilitated.
Among them, the chassis of the automobile is one of the important components of the vehicle, and the performance of the chassis directly affects the performance of the vehicle. In addition, because the existing vehicle types are numerous, and the steering gear, the driver and the brake used by each vehicle type are different, after the floor of the vehicle is assembled, the corresponding performance test is required to be carried out on the chassis of the vehicle so as to meet the corresponding production requirements.
Further, with the development of automobile intellectualization, the existing automobile manufacturers choose to integrate various electronic devices in the automobile into the chassis, however, because the electronic components mounted in the chassis of each automobile are different, the prior art needs to specially design a corresponding test site and a corresponding test device, and the test of the chassis of the automobile with different models can be correspondingly completed on the premise of combining the current test site and the test device, so that the test has certain limitation, meanwhile, the test cost is higher, and the working efficiency is correspondingly reduced.
Disclosure of Invention
Based on the above, the invention aims to provide a vehicle chassis performance testing method and system, so as to solve the problem that in the prior art, a testing site and a testing device are required to be specially designed to finish the vehicle chassis testing.
The first aspect of the embodiment of the invention provides:
a method for testing performance of a vehicle chassis, the method comprising:
When an access signal of a vehicle chassis is detected in real time, calling a matlab program in a preset database, and detecting a chassis code corresponding to the vehicle chassis in real time;
Analyzing the chassis code through the matlab program to output a dbc file corresponding to the chassis code in real time, and creating a corresponding initial test model according to the dbc file;
detecting a plurality of electronic elements contained in the vehicle chassis in real time, and compiling corresponding test scripts according to the electronic elements;
And reading and writing the test script into the initial test model to generate a corresponding target test model, and completing performance test of the vehicle chassis through the target test model.
The beneficial effects of the invention are as follows: by detecting the access signal of the vehicle chassis in real time, whether the corresponding test is needed or not can be correspondingly known, further, a needed matlab program is called out, so that the chassis code corresponding to the current vehicle chassis is detected in real time, corresponding analysis processing is carried out, a dbc file for testing is further obtained, a needed initial test model can be further built, on the basis, a set test script is input into the current initial test model, a needed target test model can be finally obtained, and on the basis, whether the performance of the current vehicle chassis is qualified or not can be accurately judged through the target test model, and therefore redundant test sites and equipment are omitted, and the test efficiency is correspondingly improved.
Further, the step of parsing the chassis code by the matlab program to output a dbc file corresponding to the chassis code in real time includes:
when the chassis code is obtained in real time, detecting a code sequence contained in the chassis code in real time, wherein the code sequence has uniqueness;
Carrying out forward maximum step word segmentation processing on the code sequence so as to correspondingly split the code sequence into a plurality of numbers and letters;
and correspondingly generating the dbc file according to the numbers and the letters.
Further, the step of generating the dbc file according to the numbers and the letters includes:
When a plurality of numbers and a plurality of letters are acquired in real time, calling out a dbc encoder in the preset database, wherein a dbc encoding script is preset in the dbc encoder;
And sequentially carrying out coding processing on each number and each letter through a dbc coding script in the dbc coder so as to generate a plurality of corresponding original dbc codes, and correspondingly generating the dbc file according to the plurality of original dbc codes.
Further, the step of generating the dbc file according to the original dbc codes includes:
When a plurality of original dbc codes are obtained in real time, adding corresponding target identifiers to each original dbc code respectively, wherein each target identifier has uniqueness;
Judging whether the same dbc code appears in a plurality of original dbc codes or not in real time based on the target identifier;
If the same dbc codes appear in the original dbc codes based on the target identification in real time, correspondingly deleting the same dbc codes to generate corresponding target dbc codes, and integrating the target dbc codes to correspondingly generate the dbc file.
Further, the step of creating a corresponding initial test model according to the dbc file includes:
When the dbc file is obtained in real time, calling out an original convolutional neural network in the preset database, and performing full-disk scanning on the dbc file to extract a plurality of dbc values contained in the dbc file in real time;
and correspondingly fusing a plurality of dbc values into the original convolutional neural network to correspondingly generate the initial test model.
Further, the step of correspondingly fusing the dbc values into the original convolutional neural network to correspondingly generate the initial test model includes:
When a plurality of dbc values are obtained in real time, detecting a plurality of neural nodes contained in the original convolutional neural network in real time, wherein each neural node has uniqueness;
Adding a corresponding target sequence number to each neural node, and detecting original network parameters carried in each neural node in real time;
and generating the initial test model according to each dbc value and each neural node.
Further, the step of generating the initial test model according to each dbc value and each neural node comprises:
Replacing original network parameters in each neural node with each dbc value one by one according to the sequence of the target sequence numbers so as to generate a corresponding target convolutional neural network;
And training the model of the target convolutional neural network to correspondingly generate the initial test model, wherein the target convolutional neural network has uniqueness.
A second aspect of an embodiment of the present invention proposes:
a vehicle chassis performance testing system, the system comprising:
The detection module is used for calling a matlab program in a preset database when an access signal of a vehicle chassis is detected in real time, and detecting a chassis code corresponding to the vehicle chassis in real time;
The analysis module is used for analyzing the chassis code through the matlab program so as to output a dbc file corresponding to the chassis code in real time, and creating a corresponding initial test model according to the dbc file;
The processing module is used for detecting a plurality of electronic elements contained in the vehicle chassis in real time and writing corresponding test scripts according to the electronic elements;
And the test module is used for reading and writing the test script into the initial test model to generate a corresponding target test model, and completing the performance test of the vehicle chassis through the target test model.
Further, the parsing module is specifically configured to:
when the chassis code is obtained in real time, detecting a code sequence contained in the chassis code in real time, wherein the code sequence has uniqueness;
Carrying out forward maximum step word segmentation processing on the code sequence so as to correspondingly split the code sequence into a plurality of numbers and letters;
and correspondingly generating the dbc file according to the numbers and the letters.
Further, the parsing module is specifically configured to:
When a plurality of numbers and a plurality of letters are acquired in real time, calling out a dbc encoder in the preset database, wherein a dbc encoding script is preset in the dbc encoder;
And sequentially carrying out coding processing on each number and each letter through a dbc coding script in the dbc coder so as to generate a plurality of corresponding original dbc codes, and correspondingly generating the dbc file according to the plurality of original dbc codes.
Further, the parsing module is specifically configured to:
When a plurality of original dbc codes are obtained in real time, adding corresponding target identifiers to each original dbc code respectively, wherein each target identifier has uniqueness;
Judging whether the same dbc code appears in a plurality of original dbc codes or not in real time based on the target identifier;
If the same dbc codes appear in the original dbc codes based on the target identification in real time, correspondingly deleting the same dbc codes to generate corresponding target dbc codes, and integrating the target dbc codes to correspondingly generate the dbc file.
Further, the parsing module is specifically further configured to:
When the dbc file is obtained in real time, calling out an original convolutional neural network in the preset database, and performing full-disk scanning on the dbc file to extract a plurality of dbc values contained in the dbc file in real time;
and correspondingly fusing a plurality of dbc values into the original convolutional neural network to correspondingly generate the initial test model.
Further, the parsing module is specifically further configured to:
When a plurality of dbc values are obtained in real time, detecting a plurality of neural nodes contained in the original convolutional neural network in real time, wherein each neural node has uniqueness;
Adding a corresponding target sequence number to each neural node, and detecting original network parameters carried in each neural node in real time;
and generating the initial test model according to each dbc value and each neural node.
Further, the parsing module is specifically further configured to:
Replacing original network parameters in each neural node with each dbc value one by one according to the sequence of the target sequence numbers so as to generate a corresponding target convolutional neural network;
And training the model of the target convolutional neural network to correspondingly generate the initial test model, wherein the target convolutional neural network has uniqueness.
A third aspect of an embodiment of the present invention proposes:
A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the vehicle chassis performance test method as described above when executing the computer program.
A fourth aspect of the embodiment of the present invention proposes:
A readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements a vehicle chassis performance testing method as described above.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a vehicle chassis performance testing method provided by a first embodiment of the present invention;
Fig. 2 is a block diagram of a vehicle chassis performance testing system according to a sixth embodiment of the present invention.
The invention will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, a vehicle chassis performance testing method according to a first embodiment of the present invention is shown, and the vehicle chassis performance testing method according to the present embodiment can omit redundant testing sites and devices, thereby improving testing efficiency correspondingly.
Specifically, the present embodiment provides:
the vehicle chassis performance test method specifically comprises the following steps:
step S10, when an access signal of a vehicle chassis is detected in real time, calling a matlab program in a preset database, and detecting a chassis code corresponding to the vehicle chassis in real time;
step S20, analyzing the chassis code through the matlab program to output a dbc file corresponding to the chassis code in real time, and creating a corresponding initial test model according to the dbc file;
step S30, detecting a plurality of electronic elements contained in the vehicle chassis in real time, and compiling corresponding test scripts according to the electronic elements;
And S40, reading and writing the test script into the initial test model to generate a corresponding target test model, and completing performance test of the vehicle chassis through the target test model.
Specifically, in this embodiment, it should be firstly explained that, in order to complete testing of various vehicle chassis without using a testing site and testing equipment, the characteristics of each vehicle chassis need to be correspondingly known, based on this, when an access signal of the vehicle chassis is received in real time, it indicates that the performance of the current chassis needs to be tested, and further, in order to be able to correspondingly obtain the characteristics of the current vehicle chassis, it is necessary to synchronously call a required matlab program in a preset software database, and specifically, it should be explained that the matlab program is a matrix laboratory, and various algorithms and scripts of different types are preset in the matrix laboratory to facilitate subsequent processing.
Furthermore, the chassis code corresponding to the current vehicle chassis can be detected correspondingly according to the received access signal in real time, specifically, the chassis code is composed of a series of numbers and letters, based on the chassis code, the corresponding analysis processing can be further carried out on the current chassis code through the matlab program, the dbc file corresponding to the current chassis code is further output in real time, and meanwhile, a needed initial test model can be further created in real time based on the content contained in the current dbc file. The dbc file is a CAN database file, and CAN define the information of CAN communication in the vehicle completely and clearly, so that the vehicle CAN be further tested through the dbc file. Furthermore, in order to enable the initial test model to meet the test conditions of the vehicle chassis of the current model, it is further required to further detect a plurality of electronic elements contained in the current vehicle chassis in real time, and meanwhile, corresponding test scripts are compiled according to the current plurality of electronic elements, and on the basis, finally, the test scripts are read and written into the initial test model in real time, so that a final required target test model can be correspondingly generated, and finally, performance test of the current vehicle chassis can be accurately and objectively completed through the target test model, test efficiency is improved, and meanwhile, use experience of users is improved.
Second embodiment
Further, the step of parsing the chassis code by the matlab program to output a dbc file corresponding to the chassis code in real time includes:
when the chassis code is obtained in real time, detecting a code sequence contained in the chassis code in real time, wherein the code sequence has uniqueness;
Carrying out forward maximum step word segmentation processing on the code sequence so as to correspondingly split the code sequence into a plurality of numbers and letters;
and correspondingly generating the dbc file according to the numbers and the letters.
Specifically, in this embodiment, after the matlab program is called in real time through the above steps, in order to accurately and effectively complete the analysis processing of the chassis code, it is necessary to first detect in real time the code sequence included in the current chassis code, where only one chassis code is provided for each vehicle chassis, and thus the code sequence is unique.
Further, the starting point and the ending point of the current code sequence are correspondingly identified, and forward maximum step word segmentation processing is carried out on the current code sequence within the range of the current starting point and the ending point, so that the current code sequence can be correspondingly split into a plurality of numbers and letters, and the dbc file can be further correspondingly generated on the basis of the current numbers and letters, so that subsequent processing is facilitated.
Further, the step of generating the dbc file according to the numbers and the letters includes:
When a plurality of numbers and a plurality of letters are acquired in real time, calling out a dbc encoder in the preset database, wherein a dbc encoding script is preset in the dbc encoder;
And sequentially carrying out coding processing on each number and each letter through a dbc coding script in the dbc coder so as to generate a plurality of corresponding original dbc codes, and correspondingly generating the dbc file according to the plurality of original dbc codes.
Specifically, in this embodiment, it should also be noted that, when a plurality of required numbers and letters are obtained through the above steps in real time, in order to be able to effectively process the current numbers and letters, a required dbc encoder is further called out in the preset database, and each current number and letter is further encoded through a dbc encoding script in the dbc encoder in turn, that is, each number and letter is converted into a corresponding original dbc code in real time, and the dbc file is further generated in real time according to the current original dbc code, so as to facilitate subsequent processing.
Third embodiment
Further, the step of generating the dbc file according to the original dbc codes includes:
When a plurality of original dbc codes are obtained in real time, adding corresponding target identifiers to each original dbc code respectively, wherein each target identifier has uniqueness;
Judging whether the same dbc code appears in a plurality of original dbc codes or not in real time based on the target identifier;
If the same dbc codes appear in the original dbc codes based on the target identification in real time, correspondingly deleting the same dbc codes to generate corresponding target dbc codes, and integrating the target dbc codes to correspondingly generate the dbc file.
In addition, in this embodiment, it should be noted that, after the required dbc codes are obtained in real time through the above steps, because the number of dbc codes is large, based on this, corresponding target identifiers may be added to each original dbc code, further, whether the same dbc code appears in the current several original dbc codes may be sequentially determined in real time according to the sequence of each current target identifier, specifically, if so, it is noted that redundant dbc codes appear, and the same dbc codes need to be deleted correspondingly, so as to finally select the required several target dbc codes, and on this basis, only the integration processing needs to be performed on the current several target dbc codes, so that the dbc file may be correspondingly generated.
Furthermore, after the dbc file is obtained in real time, the initial test model can be further trained based on the content contained in the current dbc file, so that subsequent processing is facilitated.
Further, the step of creating a corresponding initial test model according to the dbc file includes:
When the dbc file is obtained in real time, calling out an original convolutional neural network in the preset database, and performing full-disk scanning on the dbc file to extract a plurality of dbc values contained in the dbc file in real time;
and correspondingly fusing a plurality of dbc values into the original convolutional neural network to correspondingly generate the initial test model.
In addition, in this embodiment, it should be further noted that after the required dbc file is obtained in real time through the foregoing steps, at this time, the original convolutional neural network adapted to the required dbc file may be adjusted correspondingly in the foregoing preset database, further, the current dbc file may be scanned in a full disc, and a plurality of dbc values contained in the current dbc file may be further extracted in real time, where the plurality of dbc values are specific numerical values. Furthermore, the initial test model can be generated only by correspondingly fusing a plurality of current dbc values into the original convolutional neural network so as to facilitate subsequent processing.
Fourth embodiment
Further, the step of correspondingly fusing the dbc values into the original convolutional neural network to correspondingly generate the initial test model includes:
When a plurality of dbc values are obtained in real time, detecting a plurality of neural nodes contained in the original convolutional neural network in real time, wherein each neural node has uniqueness;
Adding a corresponding target sequence number to each neural node, and detecting original network parameters carried in each neural node in real time;
and generating the initial test model according to each dbc value and each neural node.
In this embodiment, it should be noted that, after obtaining a plurality of dbc values in real time through the above steps, a plurality of neural nodes respectively included in the current original convolutional neural network may be further detected in real time correspondingly. Based on the above, different target serial numbers can be added to each neural node correspondingly, and at the same time, the original network parameters carried in each current neural node are synchronously detected, and finally the initial test model is generated according to each current dbc value and each neural node, so that subsequent processing is facilitated.
Fifth embodiment
Further, the step of generating the initial test model according to each dbc value and each neural node comprises:
Replacing original network parameters in each neural node with each dbc value one by one according to the sequence of the target sequence numbers so as to generate a corresponding target convolutional neural network;
And training the model of the target convolutional neural network to correspondingly generate the initial test model, wherein the target convolutional neural network has uniqueness.
In this embodiment, it should be noted that, after each target sequence number and each neural node are obtained in real time through the above steps, the original network parameters in each neural node may be replaced with each dbc value one by one directly according to the sequence of each target sequence number, and the required target convolutional neural network is finally generated.
Further, the current target convolutional neural network is used as a model framework, a corresponding training set and a corresponding verification set are further manufactured according to the dbc file, based on the training set and the corresponding model training of the current target convolutional neural network are performed through the training set and the verification set, so that the initial test model is correspondingly generated, a special test site and test equipment can be omitted, the test cost is correspondingly reduced, and meanwhile the use experience of a user is improved.
Referring to fig. 2, a sixth embodiment of the present invention provides:
a vehicle chassis performance testing system, the system comprising:
The detection module is used for calling a matlab program in a preset database when an access signal of a vehicle chassis is detected in real time, and detecting a chassis code corresponding to the vehicle chassis in real time;
The analysis module is used for analyzing the chassis code through the matlab program so as to output a dbc file corresponding to the chassis code in real time, and creating a corresponding initial test model according to the dbc file;
The processing module is used for detecting a plurality of electronic elements contained in the vehicle chassis in real time and writing corresponding test scripts according to the electronic elements;
And the test module is used for reading and writing the test script into the initial test model to generate a corresponding target test model, and completing the performance test of the vehicle chassis through the target test model.
Further, the parsing module is specifically configured to:
when the chassis code is obtained in real time, detecting a code sequence contained in the chassis code in real time, wherein the code sequence has uniqueness;
Carrying out forward maximum step word segmentation processing on the code sequence so as to correspondingly split the code sequence into a plurality of numbers and letters;
and correspondingly generating the dbc file according to the numbers and the letters.
Further, the parsing module is specifically configured to:
When a plurality of numbers and a plurality of letters are acquired in real time, calling out a dbc encoder in the preset database, wherein a dbc encoding script is preset in the dbc encoder;
And sequentially carrying out coding processing on each number and each letter through a dbc coding script in the dbc coder so as to generate a plurality of corresponding original dbc codes, and correspondingly generating the dbc file according to the plurality of original dbc codes.
Further, the parsing module is specifically configured to:
When a plurality of original dbc codes are obtained in real time, adding corresponding target identifiers to each original dbc code respectively, wherein each target identifier has uniqueness;
Judging whether the same dbc code appears in a plurality of original dbc codes or not in real time based on the target identifier;
If the same dbc codes appear in the original dbc codes based on the target identification in real time, correspondingly deleting the same dbc codes to generate corresponding target dbc codes, and integrating the target dbc codes to correspondingly generate the dbc file.
Further, the parsing module is specifically further configured to:
When the dbc file is obtained in real time, calling out an original convolutional neural network in the preset database, and performing full-disk scanning on the dbc file to extract a plurality of dbc values contained in the dbc file in real time;
and correspondingly fusing a plurality of dbc values into the original convolutional neural network to correspondingly generate the initial test model.
Further, the parsing module is specifically further configured to:
When a plurality of dbc values are obtained in real time, detecting a plurality of neural nodes contained in the original convolutional neural network in real time, wherein each neural node has uniqueness;
Adding a corresponding target sequence number to each neural node, and detecting original network parameters carried in each neural node in real time;
and generating the initial test model according to each dbc value and each neural node.
Further, the parsing module is specifically further configured to:
Replacing original network parameters in each neural node with each dbc value one by one according to the sequence of the target sequence numbers so as to generate a corresponding target convolutional neural network;
And training the model of the target convolutional neural network to correspondingly generate the initial test model, wherein the target convolutional neural network has uniqueness.
A seventh embodiment of the invention provides a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the vehicle chassis performance test method as described above when executing the computer program.
An eighth embodiment of the present invention provides a readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor implements the vehicle chassis performance test method as described above.
In summary, the method and the system for testing the performance of the vehicle chassis provided by the embodiment of the invention can omit redundant testing sites and devices, and correspondingly improve the testing efficiency.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. A method for testing performance of a vehicle chassis, the method comprising:
When an access signal of a vehicle chassis is detected in real time, calling a matlab program in a preset database, and detecting a chassis code corresponding to the vehicle chassis in real time;
Analyzing the chassis code through the matlab program to output a dbc file corresponding to the chassis code in real time, and creating a corresponding initial test model according to the dbc file;
detecting a plurality of electronic elements contained in the vehicle chassis in real time, and compiling corresponding test scripts according to the electronic elements;
And reading and writing the test script into the initial test model to generate a corresponding target test model, and completing performance test of the vehicle chassis through the target test model.
2. The vehicle chassis performance testing method according to claim 1, wherein: the step of analyzing the chassis code by the matlab program to output the dbc file corresponding to the chassis code in real time includes:
when the chassis code is obtained in real time, detecting a code sequence contained in the chassis code in real time, wherein the code sequence has uniqueness;
Carrying out forward maximum step word segmentation processing on the code sequence so as to correspondingly split the code sequence into a plurality of numbers and letters;
and correspondingly generating the dbc file according to the numbers and the letters.
3. The vehicle chassis performance testing method according to claim 2, wherein: the step of generating the dbc file according to the numbers and the letters comprises the following steps:
When a plurality of numbers and a plurality of letters are acquired in real time, calling out a dbc encoder in the preset database, wherein a dbc encoding script is preset in the dbc encoder;
And sequentially carrying out coding processing on each number and each letter through a dbc coding script in the dbc coder so as to generate a plurality of corresponding original dbc codes, and correspondingly generating the dbc file according to the plurality of original dbc codes.
4. A vehicle chassis performance testing method according to claim 3, wherein: the step of correspondingly generating the dbc file according to the original dbc codes comprises the following steps:
When a plurality of original dbc codes are obtained in real time, adding corresponding target identifiers to each original dbc code respectively, wherein each target identifier has uniqueness;
Judging whether the same dbc code appears in a plurality of original dbc codes or not in real time based on the target identifier;
If the same dbc codes appear in the original dbc codes based on the target identification in real time, correspondingly deleting the same dbc codes to generate corresponding target dbc codes, and integrating the target dbc codes to correspondingly generate the dbc file.
5. The vehicle chassis performance testing method according to claim 1, wherein: the step of creating a corresponding initial test model according to the dbc file comprises the following steps:
When the dbc file is obtained in real time, calling out an original convolutional neural network in the preset database, and performing full-disk scanning on the dbc file to extract a plurality of dbc values contained in the dbc file in real time;
and correspondingly fusing a plurality of dbc values into the original convolutional neural network to correspondingly generate the initial test model.
6. The vehicle chassis performance testing method of claim 5, wherein: the step of correspondingly fusing the dbc values into the original convolutional neural network to correspondingly generate the initial test model comprises the following steps:
When a plurality of dbc values are obtained in real time, detecting a plurality of neural nodes contained in the original convolutional neural network in real time, wherein each neural node has uniqueness;
Adding a corresponding target sequence number to each neural node, and detecting original network parameters carried in each neural node in real time;
and generating the initial test model according to each dbc value and each neural node.
7. The vehicle chassis performance testing method of claim 6, wherein: the step of generating the initial test model according to each dbc value and each neural node comprises the following steps:
Replacing original network parameters in each neural node with each dbc value one by one according to the sequence of the target sequence numbers so as to generate a corresponding target convolutional neural network;
And training the model of the target convolutional neural network to correspondingly generate the initial test model, wherein the target convolutional neural network has uniqueness.
8. A vehicle chassis performance testing system, the system comprising:
The detection module is used for calling a matlab program in a preset database when an access signal of a vehicle chassis is detected in real time, and detecting a chassis code corresponding to the vehicle chassis in real time;
The analysis module is used for analyzing the chassis code through the matlab program so as to output a dbc file corresponding to the chassis code in real time, and creating a corresponding initial test model according to the dbc file;
The processing module is used for detecting a plurality of electronic elements contained in the vehicle chassis in real time and writing corresponding test scripts according to the electronic elements;
And the test module is used for reading and writing the test script into the initial test model to generate a corresponding target test model, and completing the performance test of the vehicle chassis through the target test model.
9. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the vehicle chassis performance testing method of any of claims 1 to 7 when the computer program is executed.
10. A readable storage medium having stored thereon a computer program, which when executed by a processor implements the vehicle chassis performance testing method according to any one of claims 1 to 7.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101797005B1 (en) * | 2017-03-09 | 2017-12-13 | 슈어소프트테크주식회사 | Method for producing test case by vehicle type |
| CN113740077A (en) * | 2021-09-13 | 2021-12-03 | 广州文远知行科技有限公司 | Vehicle chassis testing method, device, equipment and storage medium |
| CN115616936A (en) * | 2022-11-09 | 2023-01-17 | 国汽智控(北京)科技有限公司 | Simulation test method, device and equipment for vehicle |
| CN115629553A (en) * | 2022-08-18 | 2023-01-20 | 西安交通大学 | High-spatial-temporal-resolution IGBT module electric heating stress calculation method |
| CN116125830A (en) * | 2023-01-20 | 2023-05-16 | 天津主线科技有限公司 | Vehicle simulation test method and device |
| CN116793706A (en) * | 2023-06-21 | 2023-09-22 | 梅赛德斯-奔驰集团股份公司 | Vehicle chassis testing method, device and system |
| CN116882125A (en) * | 2023-05-26 | 2023-10-13 | 广州文远知行科技有限公司 | Method, device, equipment and storage medium for testing performance of vehicle chassis |
| CN117651012A (en) * | 2024-01-25 | 2024-03-05 | 江铃汽车股份有限公司 | Vehicle central gateway testing method and system |
| CN117742288A (en) * | 2023-12-18 | 2024-03-22 | 北京易控智驾科技有限公司 | Performance test method and device of drive-by-wire chassis domain controller and electronic equipment |
-
2024
- 2024-04-12 CN CN202410439049.0A patent/CN118069536A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101797005B1 (en) * | 2017-03-09 | 2017-12-13 | 슈어소프트테크주식회사 | Method for producing test case by vehicle type |
| CN113740077A (en) * | 2021-09-13 | 2021-12-03 | 广州文远知行科技有限公司 | Vehicle chassis testing method, device, equipment and storage medium |
| CN115629553A (en) * | 2022-08-18 | 2023-01-20 | 西安交通大学 | High-spatial-temporal-resolution IGBT module electric heating stress calculation method |
| CN115616936A (en) * | 2022-11-09 | 2023-01-17 | 国汽智控(北京)科技有限公司 | Simulation test method, device and equipment for vehicle |
| CN116125830A (en) * | 2023-01-20 | 2023-05-16 | 天津主线科技有限公司 | Vehicle simulation test method and device |
| CN116882125A (en) * | 2023-05-26 | 2023-10-13 | 广州文远知行科技有限公司 | Method, device, equipment and storage medium for testing performance of vehicle chassis |
| CN116793706A (en) * | 2023-06-21 | 2023-09-22 | 梅赛德斯-奔驰集团股份公司 | Vehicle chassis testing method, device and system |
| CN117742288A (en) * | 2023-12-18 | 2024-03-22 | 北京易控智驾科技有限公司 | Performance test method and device of drive-by-wire chassis domain controller and electronic equipment |
| CN117651012A (en) * | 2024-01-25 | 2024-03-05 | 江铃汽车股份有限公司 | Vehicle central gateway testing method and system |
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