A vehicle networking performance detection and evaluation system based on data analysis

Abstract

The present invention belongs to the field of vehicle networking and relates to performance testing technology, which is used to solve the problem that the prior art cannot perform networking stability analysis when the vehicle or platform load is high. Specifically, it is a vehicle networking performance detection and evaluation system based on data analysis, including a server, which is communicatively connected to a performance testing module, a terminal load analysis module, a platform load analysis module and a database; the performance testing module is used to test and analyze the vehicle's networking performance: generate a test cycle, divide the test cycle into several test time periods, mark the vehicle to be tested as a test object, execute the test process at the end of the test time period and obtain the performance coefficient of the test time period; the present invention can test and analyze the vehicle's networking performance, and then provide feedback on the networking performance of the test object within the test time period through the performance coefficient, so as to provide data support for the load analysis process.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle networking and relates to performance testing technology, specifically a vehicle networking performance detection and evaluation system based on data analysis. Background Art

[0002] As a core component of modern automotive technology, the importance of in-vehicle network communication cannot be ignored. In-vehicle network communication refers to the process of data exchange and information sharing between various electronic control units (ECUs) inside the car, and between the car and external devices through specific communication protocols.

[0003] The vehicle networking performance detection and evaluation system in the prior art can only detect the vehicle's networking performance parameters and perform performance evaluation based on the parameter detection results. However, this method cannot perform networking stability analysis when the vehicle or platform load is high, nor can it predict the probability of networking anomalies in a vehicle with normal performance during subsequent use.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a vehicle networking performance detection and evaluation system based on data analysis, which is used to solve the problem that the prior art cannot perform networking stability analysis when the vehicle or platform load is high;

[0006] The technical problem to be solved by the present invention is: how to provide a vehicle networking performance detection and evaluation system based on data analysis that can perform networking stability analysis when the vehicle or platform load is high.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A vehicle networking performance detection and evaluation system based on data analysis includes a server, wherein the server is communicatively connected to a performance test module, a terminal load analysis module, a platform load analysis module and a database;

[0009] The performance test module is used to test and analyze the networking performance of the vehicle: generate a test cycle, divide the test cycle into several test periods, mark the vehicle to be tested as a test object, execute the test process at the end of the test period and obtain the performance coefficient XN of the test period, sum and average the performance coefficients XN of all test periods in the test cycle to obtain a performance value, calculate the variance of the performance coefficients XN of all test periods in the test cycle to obtain a test deviation value, and mark the test object as a normal object or an abnormal object according to the performance value and the test deviation value;

[0010] The terminal load analysis module is used to analyze the vehicle load status of the normal object during the test cycle;

[0011] The platform load analysis module is used to analyze the platform load status of a normal object within a test cycle.

[0012] Furthermore, the process of obtaining the performance coefficient XN of the test period includes: obtaining the transmission speed data CS, signal strength data XH and packet loss data DB for data transmission between the test object and the Internet of Vehicles platform during the test period, the transmission speed data CS being the average of the maximum uplink speed and the maximum downlink speed when the test object and the Internet of Vehicles platform perform data transmission during the test period, the signal strength data XH being the maximum signal strength for data transmission between the test object and the Internet of Vehicles platform during the test period, and the packet loss data DB being the minimum packet loss rate for data transmission between the test object and the Internet of Vehicles platform during the test period; the performance coefficient XN of the test object during the test period is obtained by the formula.

[0013] Furthermore, the specific process of marking the test object as a normal object or an abnormal object includes: obtaining the performance threshold and the test deviation threshold through the database, and comparing the performance value and the test deviation value of the test period with the performance threshold and the test deviation threshold respectively: if the performance value is greater than or equal to the performance threshold and the test deviation value is less than the test deviation threshold, then it is determined that the networking performance test result of the test object meets the requirements, and the corresponding test object is marked as a normal object; otherwise, it is determined that the networking performance test result of the test object does not meet the requirements, and the corresponding test object is marked as an abnormal object.

[0014] Furthermore, the specific process of the terminal load analysis module analyzing the vehicle computer load status of a normal object within the test cycle includes: obtaining the vehicle computer CPU occupancy rate of the normal object in real time, marking the maximum value of the vehicle computer CPU occupancy rate in the test period as the terminal occupancy value of the test period, marking the L1 test periods with the largest terminal occupancy values ​​in the test cycle as terminal occupancy periods, marking the difference between the performance value and the performance coefficient XN of the terminal occupancy period as the terminal performance value of the terminal occupancy period, summing and averaging the terminal performance values ​​of all terminal occupancy periods in the test cycle to obtain the terminal influence coefficient, and judging whether the terminal networking performance of the normal object meets the requirements through the terminal influence coefficient.

[0015] Furthermore, the specific process of determining whether the terminal networking performance of a normal object meets the requirements includes: retrieving the terminal impact threshold through the database, and comparing the terminal impact coefficient with the terminal impact threshold: if the terminal impact coefficient is less than the terminal impact threshold, then it is determined that the terminal networking performance of the normal object meets the requirements; if the terminal impact coefficient is greater than or equal to the terminal impact threshold, then it is determined that the terminal networking performance of the normal object does not meet the requirements, and a terminal optimization signal is generated and sent to the mobile terminal of the administrator through the server.

[0016] Furthermore, the specific process of the platform load analysis module analyzing the platform load status of the normal object during the test cycle includes: obtaining the processor CPU occupancy rate of the Internet of Vehicles platform in real time, marking the maximum value of the processor CPU occupancy rate in the test period as the platform occupancy value of the test period, marking the L1 test periods with the largest platform occupancy values ​​in the test cycle as the platform occupancy period, marking the difference between the performance value and the performance coefficient XN of the platform occupancy period as the platform performance value of the platform occupancy period, summing and averaging the platform performance values ​​of all platform occupancy periods in the test cycle to obtain the platform influence coefficient, and judging whether the platform networking performance of the normal object meets the requirements through the platform influence coefficient.

[0017] Furthermore, the specific process of determining whether the platform networking performance of a normal object meets the requirements includes: retrieving the platform impact threshold through the database, and comparing the platform impact coefficient with the platform impact threshold: if the platform impact coefficient is less than the platform impact threshold, then it is determined that the platform networking performance of the normal object meets the requirements; if the platform impact coefficient is greater than or equal to the platform impact threshold, then it is determined that the platform networking performance of the normal object does not meet the requirements, and a platform optimization signal is generated and sent to the administrator's mobile terminal through the server.

[0018] Furthermore, the server is also communicatively connected to a performance prediction module, and the performance prediction module is used to predict and analyze the probability of abnormal networking performance of normal objects in subsequent operation processes: the terminal influence coefficient and the platform influence coefficient of the normal object are summed and averaged to obtain the performance prediction value of the normal object, the test time periods within the test cycle are numbered, and the ratio of the number of overlapping elements of the number set corresponding to the terminal occupancy time period and the number set corresponding to the platform occupancy time period to L1 is marked as the overlapping value, and the product of the performance prediction value and the overlapping value is marked as the prediction coefficient, and whether the performance prediction result of the normal object meets the requirements is judged by the prediction coefficient.

[0019] Furthermore, the specific process of determining whether the performance prediction results of normal objects meet the requirements includes: retrieving the prediction threshold through the database, and comparing the prediction coefficient with the prediction threshold: if the prediction coefficient is greater than or equal to the prediction threshold, then it is determined that the performance prediction results of the normal object meet the requirements; if the prediction coefficient is less than the prediction threshold, then it is determined that the performance prediction results of the normal object do not meet the requirements, and a prediction abnormality signal is generated and sent to the administrator's mobile phone terminal through the server.

[0020] Furthermore, the working method of the vehicle networking performance detection and evaluation system based on data analysis includes the following steps:

[0021] Step 1: Test and analyze the networking performance of the vehicle: generate a test cycle, divide the test cycle into several test periods, obtain the performance coefficient XN of the test period at the end of the test period, perform numerical calculations on the performance coefficient XN of all test periods in the test cycle to obtain performance values ​​and test deviation values, and mark the test object as a normal object or an abnormal object based on the performance value and the test deviation value;

[0022] Step 2: Analyze the vehicle computer load status of the normal object during the test period: obtain the vehicle computer CPU occupancy rate of the normal object in real time, filter the terminal occupancy period by the vehicle computer CPU occupancy rate, and then calculate the terminal performance value of the terminal occupancy period to obtain the terminal impact coefficient, and evaluate the terminal networking performance of the normal object by the terminal impact coefficient;

[0023] Step 3: Analyze the platform load status of normal objects during the test period: obtain the CPU occupancy rate of the vehicle networking platform in real time, filter the platform occupancy period by the CPU occupancy rate, and then calculate the platform performance value of the platform occupancy period to obtain the platform impact coefficient, and evaluate the platform networking performance of normal objects by the platform impact coefficient;

[0024] Step 4: Predict and analyze the probability of abnormal network performance of normal objects in subsequent operation: sum and average the terminal influence coefficient and platform influence coefficient of the normal object to obtain the performance prediction value of the normal object, mark the product of the performance prediction value and the overlap value as the prediction coefficient, and judge whether the performance prediction result of the normal object meets the requirements through the prediction coefficient.

[0025] The present invention has the following beneficial effects:

[0026] The performance test module can be used to test and analyze the networking performance of the vehicle. By testing in different time periods, multiple data transmission parameters in each test period are obtained and numerically calculated to obtain the performance coefficient. Then, the networking performance of the test object in the test period is fed back through the performance coefficient to provide data support for the load analysis process.

[0027] The terminal load analysis module can analyze the vehicle computer load status of normal objects during the test cycle, screen the test period with high vehicle computer CPU occupancy rate during the test cycle, and then analyze the terminal influence coefficient of the terminal occupancy period, so as to evaluate the stability of networking performance under high vehicle computer load through the terminal influence coefficient;

[0028] The platform load analysis module can analyze the platform load status of normal objects during the test cycle, screen the test periods with high platform processor CPU occupancy during the test cycle, and then analyze the performance coefficient of the platform occupancy period to obtain the platform impact coefficient. The platform impact coefficient can be used to provide feedback on the stability of networking performance under high load conditions of the Internet of Vehicles.

[0029] 4. The performance prediction module can be used to predict and analyze the probability of abnormal networking performance of normal objects in the subsequent operation process. The prediction coefficient is obtained by comprehensive analysis and calculation based on the terminal influence coefficient, platform influence coefficient, and the overlap of the terminal occupancy period and the platform occupancy period. The prediction coefficient can then be used to evaluate the probability of abnormal networking performance of the vehicle during actual use. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0032] Figure 2 is a system block diagram of Embodiment 2 of the present invention;

[0033] Figure 3 This is a flow chart of the method of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Embodiment 1

[0035] See also Figure 1 As shown, a vehicle networking performance detection and evaluation system based on data analysis includes a server, and the server is communicatively connected to a performance testing module, a terminal load analysis module, a platform load analysis module and a database.

[0036] The performance test module is used to test and analyze the networking performance of the vehicle: generate a test cycle, divide the test cycle into several test periods, mark the vehicle to be tested as a test object, and execute the test process at the end of the test period: obtain the transmission speed data CS, signal strength data XH and packet loss data DB of the test object and the Internet of Vehicles platform for data transmission during the test period. The transmission speed data CS is the average value of the maximum uplink speed and the maximum downlink speed when the test object and the Internet of Vehicles platform perform data transmission during the test period. The signal strength data XH is the maximum signal strength of the test object and the Internet of Vehicles platform for data transmission during the test period. The packet loss data DB is the minimum packet loss rate of the test object and the Internet of Vehicles platform for data transmission during the test period. Through the formula The performance coefficient XN of the test object in the test period is obtained, wherein w1, w2 and w3 are all proportional coefficients, and w1>w2>w3>1; the performance coefficients XN of all test periods in the test cycle are summed and averaged to obtain the performance value, the variance of the performance coefficients XN of all test periods in the test cycle is calculated to obtain the test deviation value, the performance threshold and the test deviation threshold are obtained through the database, and the performance value and the test deviation value of the test period are compared with the performance threshold and the test deviation threshold respectively: if the performance value is greater than or equal to the performance threshold and the test deviation value is less than the test deviation threshold, it is determined that the networking performance test result of the test object meets the requirements, and the corresponding test object is marked as a normal object; otherwise, it is determined that the networking performance test result of the test object does not meet the requirements, and the corresponding test object is marked as an abnormal object; the networking performance of the vehicle is tested and analyzed, and multiple data transmission parameters in each test period are obtained by time period testing and numerically calculated to obtain the performance coefficient, and then the networking performance of the test object in the test period is fed back through the performance coefficient to provide data support for the load analysis process.

[0037] The terminal load analysis module is used to analyze the vehicle computer load status of normal objects within the test cycle: obtain the vehicle computer CPU occupancy rate of normal objects in real time, mark the maximum value of the vehicle computer CPU occupancy rate in the test period as the terminal occupancy value of the test period, mark the L1 test periods with the largest terminal occupancy values ​​in the test cycle as the terminal occupancy period, mark the difference between the performance value and the performance coefficient XN of the terminal occupancy period as the terminal performance value of the terminal occupancy period, sum and average the terminal performance values ​​of all terminal occupancy periods in the test cycle to obtain the terminal impact coefficient, retrieve the terminal impact threshold through the database, and compare the terminal impact coefficient with the terminal impact threshold Comparison is performed: if the terminal impact coefficient is less than the terminal impact threshold, the terminal networking performance of the normal object is determined to meet the requirements; if the terminal impact coefficient is greater than or equal to the terminal impact threshold, the terminal networking performance of the normal object is determined to not meet the requirements, and a terminal optimization signal is generated and sent to the mobile terminal of the administrator through the server; the vehicle computer load status of the normal object during the test cycle is analyzed, the test period with a high vehicle computer CPU occupancy rate during the test cycle is screened, and then the performance coefficient of the terminal occupancy period is analyzed to obtain the terminal impact coefficient, so as to evaluate the stability of the networking performance under high vehicle computer load conditions through the terminal impact coefficient.

[0038] The platform load analysis module is used to analyze the platform load status of normal objects during the test cycle: obtain the processor CPU occupancy rate of the Internet of Vehicles platform in real time, mark the maximum value of the processor CPU occupancy rate in the test period as the platform occupancy value of the test period, mark the L1 test periods with the largest platform occupancy values ​​in the test cycle as the platform occupancy period, mark the difference between the performance value and the performance coefficient XN of the platform occupancy period as the platform performance value of the platform occupancy period, sum and average the platform performance values ​​of all platform occupancy periods in the test cycle to obtain the platform impact coefficient, retrieve the platform impact threshold through the database, and compare the platform impact coefficient with the platform impact threshold Comparison is performed: if the platform impact coefficient is less than the platform impact threshold, the platform networking performance of the normal object is determined to meet the requirements; if the platform impact coefficient is greater than or equal to the platform impact threshold, the platform networking performance of the normal object is determined to not meet the requirements, and a platform optimization signal is generated and sent to the administrator's mobile terminal through the server; the platform load status of the normal object during the test cycle is analyzed, the test period with a high CPU occupancy rate of the platform processor during the test cycle is screened, and then the performance coefficient of the platform occupancy period is analyzed to obtain the platform impact coefficient, and the platform impact coefficient is used to provide feedback on the stability of the networking performance under high load conditions of the Internet of Vehicles. Embodiment 2

[0039] See also Figure 2As shown, the server is also communicatively connected to a performance prediction module, which is used to predict and analyze the probability of abnormal networking performance of normal objects in the subsequent operation process: the terminal influence coefficient and the platform influence coefficient of the normal object are summed and averaged to obtain the performance prediction value of the normal object, the test time periods in the test cycle are numbered, and the ratio of the number of overlapping elements of the number set corresponding to the terminal occupancy time period and the number set corresponding to the platform occupancy time period to L1 is marked as the overlap value, and the product of the performance prediction value and the overlap value is marked as the prediction coefficient, and the prediction threshold is retrieved through the database, and the prediction coefficient is compared with the prediction threshold: if the prediction coefficient is greater than or equal to the prediction threshold, it is determined that the performance prediction result of the normal object meets the requirements; if the prediction coefficient is less than the prediction threshold, it is determined that the performance prediction result of the normal object does not meet the requirements, and a prediction abnormality signal is generated and sent to the mobile terminal of the administrator through the server; the probability of abnormal networking performance of the normal object in the subsequent operation process is predicted and analyzed, and the prediction coefficient is obtained by combining the terminal influence coefficient, the platform influence coefficient, and the overlap degree of the terminal occupancy time period and the platform occupancy time period. The probability of abnormal networking performance of the vehicle in actual use is evaluated through the prediction coefficient. Embodiment 3

[0040] Referring to Figure 3, a vehicle networking performance detection and evaluation method based on data analysis includes the following steps:

[0041] Step 1: Test and analyze the networking performance of the vehicle: generate a test cycle, divide the test cycle into several test periods, obtain the performance coefficient XN of the test period at the end of the test period, perform numerical calculations on the performance coefficient XN of all test periods in the test cycle to obtain performance values ​​and test deviation values, and mark the test object as a normal object or an abnormal object based on the performance value and the test deviation value;

[0042] Step 2: Analyze the vehicle computer load status of the normal object during the test period: obtain the vehicle computer CPU occupancy rate of the normal object in real time, filter the terminal occupancy period by the vehicle computer CPU occupancy rate, and then calculate the terminal performance value of the terminal occupancy period to obtain the terminal impact coefficient, and evaluate the terminal networking performance of the normal object by the terminal impact coefficient;

[0043] Step 3: Analyze the platform load status of normal objects during the test period: obtain the CPU occupancy rate of the vehicle networking platform in real time, filter the platform occupancy period by the CPU occupancy rate, and then calculate the platform performance value of the platform occupancy period to obtain the platform impact coefficient, and evaluate the platform networking performance of normal objects by the platform impact coefficient;

[0044] Step 4: Predict and analyze the probability of abnormal network performance of normal objects in subsequent operation: sum and average the terminal influence coefficient and platform influence coefficient of the normal object to obtain the performance prediction value of the normal object, mark the product of the performance prediction value and the overlap value as the prediction coefficient, and judge whether the performance prediction result of the normal object meets the requirements through the prediction coefficient.

[0045] A vehicle networking performance detection and evaluation system based on data analysis generates a test cycle when working, divides the test cycle into several test time periods, obtains the performance coefficient XN of the test time period at the end of the test time period, performs numerical calculation on the performance coefficient XN of all test time periods in the test cycle to obtain a performance value and a test deviation value, and marks the test object as a normal object or an abnormal object according to the performance value and the test deviation value; obtains the vehicle computer CPU occupancy rate of the normal object in real time, screens the terminal occupancy time period according to the vehicle computer CPU occupancy rate, and then calculates the terminal performance value of the terminal occupancy time period to obtain the terminal influence coefficient, and evaluates the terminal networking performance of the normal object according to the terminal influence coefficient; obtains the processor CPU occupancy rate of the vehicle networking platform in real time, screens the platform occupancy time period according to the processor CPU occupancy rate, and then calculates the platform performance value of the platform occupancy time period to obtain the platform influence coefficient, and evaluates the platform networking performance of the normal object according to the platform influence coefficient; sums and averages the terminal influence coefficient and the platform influence coefficient of the normal object to obtain the performance prediction value of the normal object, marks the product of the performance prediction value and the coincidence value as the prediction coefficient, and determines whether the performance prediction result of the normal object meets the requirements according to the prediction coefficient.

[0046] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0047] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula ; A technician in this field collects multiple groups of sample data and sets a corresponding performance coefficient for each group of sample data; Substitute the set performance coefficient and the collected sample data into the formula, any three formulas constitute a three-variable linear equation system, screen the calculated coefficients and take the average, and obtain the values ​​of w1, w2 and w3, which are 5.28, 3.47 and 3.02 respectively;

[0048] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the technical personnel in this field preliminarily setting the corresponding performance coefficient for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the performance coefficient is proportional to the value of the transmission speed data.

[0049] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0050] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A vehicle networking performance detection and evaluation system based on data analysis, characterized in that: It includes a server, wherein the server is communicatively connected with a performance test module, a terminal load analysis module, a platform load analysis module and a database; The performance test module is used to test and analyze the networking performance of the vehicle: generate a test cycle, divide the test cycle into several test periods, mark the vehicle to be tested as a test object, execute the test process at the end of the test period and obtain the performance coefficient XN of the test period, sum and average the performance coefficients XN of all test periods in the test cycle to obtain a performance value, calculate the variance of the performance coefficients XN of all test periods in the test cycle to obtain a test deviation value, and mark the test object as a normal object or an abnormal object according to the performance value and the test deviation value; The terminal load analysis module is used to analyze the vehicle load status of the normal object during the test cycle; The platform load analysis module is used to analyze the platform load status of the normal object during the test cycle; The process of obtaining the performance coefficient XN during the test period includes: obtaining transmission speed data CS, signal strength data XH and packet loss data DB during the test period for data transmission between the test object and the Internet of Vehicles platform, wherein the transmission speed data CS is the average value of the maximum uplink speed and the maximum downlink speed when the test object and the Internet of Vehicles platform perform data transmission during the test period, the signal strength data XH is the maximum signal strength during the data transmission between the test object and the Internet of Vehicles platform during the test period, and the packet loss data DB is the minimum packet loss rate during the data transmission between the test object and the Internet of Vehicles platform during the test period; and obtaining the performance coefficient XN of the test object during the test period by a formula; The calculation formula of the performance coefficient XN of the test object during the test period is: , where w1, w2 and w3 are all proportional coefficients, and w1>w2>w3>1; The specific process of the terminal load analysis module analyzing the vehicle computer load state of the normal object in the test cycle includes: obtaining the vehicle computer CPU occupancy rate of the normal object in real time, marking the maximum value of the vehicle computer CPU occupancy rate in the test period as the terminal occupancy value of the test period, marking the L1 test periods with the largest terminal occupancy value in the test period as the terminal occupancy period, marking the difference between the performance value and the performance coefficient XN of the terminal occupancy period as the terminal performance value of the terminal occupancy period, summing and averaging the terminal performance values ​​of all terminal occupancy periods in the test cycle to obtain the terminal influence coefficient, and judging whether the terminal networking performance of the normal object meets the requirements through the terminal influence coefficient; The specific process of determining whether the terminal networking performance of a normal object meets the requirements includes: retrieving the terminal impact threshold through the database, and comparing the terminal impact coefficient with the terminal impact threshold: if the terminal impact coefficient is less than the terminal impact threshold, it is determined that the terminal networking performance of the normal object meets the requirements; if the terminal impact coefficient is greater than or equal to the terminal impact threshold, it is determined that the terminal networking performance of the normal object does not meet the requirements, and a terminal optimization signal is generated and sent to the mobile terminal of the administrator through the server; The specific process of the platform load analysis module analyzing the platform load status of the normal object in the test cycle includes: obtaining the CPU occupancy rate of the processor of the Internet of Vehicles platform in real time, marking the maximum value of the CPU occupancy rate of the processor in the test period as the platform occupancy value of the test period, marking the L1 test periods with the largest platform occupancy values ​​in the test cycle as the platform occupancy period, marking the difference between the performance value and the performance coefficient XN of the platform occupancy period as the platform performance value of the platform occupancy period, summing and averaging the platform performance values ​​of all platform occupancy periods in the test cycle to obtain the platform influence coefficient, and judging whether the platform networking performance of the normal object meets the requirements through the platform influence coefficient; The specific process of determining whether the platform networking performance of a normal object meets the requirements includes: retrieving the platform impact threshold through the database, and comparing the platform impact coefficient with the platform impact threshold: if the platform impact coefficient is less than the platform impact threshold, then it is determined that the platform networking performance of the normal object meets the requirements; if the platform impact coefficient is greater than or equal to the platform impact threshold, then it is determined that the platform networking performance of the normal object does not meet the requirements, and a platform optimization signal is generated and sent to the administrator's mobile terminal through the server.

2. A vehicle networking performance detection and evaluation system based on data analysis according to claim 1, characterized in that: The specific process of marking the test object as a normal object or an abnormal object includes: obtaining the performance threshold and the test deviation threshold through the database, and comparing the performance value and the test deviation value of the test period with the performance threshold and the test deviation threshold respectively: if the performance value is greater than or equal to the performance threshold and the test deviation value is less than the test deviation threshold, then it is determined that the networking performance test result of the test object meets the requirements, and the corresponding test object is marked as a normal object; otherwise, it is determined that the networking performance test result of the test object does not meet the requirements, and the corresponding test object is marked as an abnormal object.

3. A vehicle networking performance detection and evaluation system based on data analysis according to claim 2, characterized in that: The server is also communicatively connected to a performance prediction module, which is used to predict and analyze the probability of abnormal networking performance of normal objects in subsequent operation: the terminal influence coefficient and the platform influence coefficient of the normal object are summed and averaged to obtain the performance prediction value of the normal object, the test time periods within the test cycle are numbered, and the ratio of the number of overlapping elements of the number set corresponding to the terminal occupancy time period and the number set corresponding to the platform occupancy time period to L1 is marked as the overlapping value, and the product of the performance prediction value and the overlapping value is marked as the prediction coefficient, and the prediction coefficient is used to determine whether the performance prediction result of the normal object meets the requirements.

4. A vehicle networking performance detection and evaluation system based on data analysis according to claim 3, characterized in that: The specific process of determining whether the performance prediction results of normal objects meet the requirements includes: retrieving the prediction threshold through the database, and comparing the prediction coefficient with the prediction threshold: if the prediction coefficient is greater than or equal to the prediction threshold, then it is determined that the performance prediction results of the normal object meet the requirements; if the prediction coefficient is less than the prediction threshold, then it is determined that the performance prediction results of the normal object do not meet the requirements, and a prediction abnormality signal is generated and sent to the administrator's mobile terminal through the server.

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