Python-based vehicle cloud data quality evaluation method and system

Abstract

The invention discloses a vehicle cloud data quality evaluation method and system based on python, and relates to the technical field of cloud processing. The vehicle cloud data quality evaluation method based on python comprises the following steps: obtaining vehicle end CAN data, cloud end CAN data and a DBC protocol database; selecting the vehicle-side CAN data and the cloud-side CAN data in a preset timestamp range, and performing consistency comparison with the content of the DBC protocol database to generate an evaluation result; generating a corresponding result report according to the evaluation result; wherein the result report comprises a data loss rate result report, a data content accuracy result report and a timestamp synchronization evaluation result report. According to the method, the problems that in the process of collecting data from a vehicle end and transmitting the data to a cloud end, the data quality is reduced due to various factors such as interference, errors and the like, the data content accuracy problem, the data loss problem, the timestamp synchronization deviation and the like are caused, and an existing data quality evaluation method mostly depends on manual auditing, and the efficiency is low are solved.

Description

Technical Field

[0001] The present invention relates to the field of cloud processing technology, and in particular to a vehicle cloud data quality assessment method and system based on Python. Background Art

[0002] With the popularization of Internet of Vehicles technology, vehicle-side CAN data has become an important basis for vehicle status monitoring, fault diagnosis and driving behavior analysis.

[0003] However, in the process of data collection and transmission from the vehicle to the cloud, data quality may be degraded due to interference and errors, including data content accuracy, data loss, and timestamp synchronization deviation. In addition, existing data quality assessment methods mostly rely on manual review, which is not only inefficient but also difficult to fully cover. No effective solution has been proposed for the above problems. Summary of the invention

[0004] Purpose of the invention: To provide a vehicle-cloud data quality assessment method and system based on Python to solve the above-mentioned problems existing in the prior art.

[0005] Technical solution: The car cloud data quality assessment method based on Python includes the following steps:

[0006] Obtain vehicle-side CAN data, cloud-side CAN data and DBC protocol database;

[0007] Select the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range, and compare them with the contents of the DBC protocol database for consistency, and generate an evaluation result;

[0008] Generate a corresponding result report based on the evaluation result; wherein the result report includes: a data loss rate result report, a data content accuracy result report and a timestamp synchronization evaluation result report.

[0009] As a preferred method, the vehicle-side CAN data, the cloud-side CAN data and the DBC protocol database are obtained, including:

[0010] The original files collected by the vehicle, the vehicle-cloud data files, the DBC protocol database and the timestamp deviation range; wherein the timestamp deviation range is ±2s.

[0011] Preferably, after generating a corresponding result report from the evaluation result, the method further comprises:

[0012] When the data loss rate result report and the data content accuracy result report meet the preset indicators, the preset timestamp range is narrowed, and the result report within the narrowed timestamp range is compared with the content of the DBC protocol database for consistency to accurately obtain the timestamp synchronization result.

[0013] Preferably, before obtaining the vehicle-side CAN data, the cloud-side CAN data and the DBC protocol database, the following steps are also included:

[0014] Enter the vehicle VIN code and the start and end time of the required test data, issue a remote call task with one click, pull the test data to the cloud database, and download it to the local test terminal.

[0015] As a preferred method, input the vehicle VIN code and the start and end time of the required test data, issue a remote call task with one click, pull the test data to the cloud database, and download it to the local test terminal, and also include:

[0016] Connect the OBD port of the vehicle to be tested through the CAN card hardware device, start the real-time data collection and recording function, and continue for a preset time. At the same time, the vehicle-side data acquisition ECU records the data packet to the local memory.

[0017] Preferably, the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range are selected and compared for consistency with the contents of the DBC protocol database, including:

[0018] Through the preset Python script, the CAN data packets extracted from the cloud database are used as the comparison benchmark data to extract the data ID, data field and data timestamp information, and compare them with the original data collected by the vehicle.

[0019] Obtain all data fields with the same ID as the original data and a timestamp deviation within ±2s, and compare them one by one with the cloud data fields to verify the accuracy of the data content.

[0020] Preferably, the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range are selected and compared for consistency with the contents of the DBC protocol database, including:

[0021] Through the preset Python script, the total number of data packets and message frames on the vehicle side is compared with the total number of data packets and message frames pulled from the cloud side to calculate the data loss rate; and the original data packets collected on the vehicle side are used as the benchmark data to extract the data ID, data field and timestamp. In the same way as content evaluation, the original data on the vehicle side is compared with the data collected on the cloud side to further confirm whether there is data packet loss.

[0022] Preferably, the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range are selected and compared for consistency with the contents of the DBC protocol database, including:

[0023] Compare the timestamp of the data content recorded by the vehicle-side acquisition controller on the cloud with the timestamp when the vehicle sends it to verify the rationality of the time difference; and preset the timestamp threshold to evaluate the synchronization of the timestamp.

[0024] In order to achieve the above objectives, according to another aspect of the present application, a vehicle-cloud data quality assessment system based on Python is provided.

[0025] The Python-based vehicle cloud data quality assessment system according to this application includes:

[0026] Acquisition module, used to obtain vehicle-side CAN data, cloud-side CAN data and DBC protocol database;

[0027] A vehicle-cloud data quality assessment module is used to select the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range, and compare the consistency with the content of the DBC protocol database to generate an assessment result;

[0028] The result generation module is used to generate a corresponding result report based on the evaluation result; wherein the result report includes: a data loss rate result report, a data content accuracy result report and a timestamp synchronization evaluation result report.

[0029] In order to achieve the above-mentioned purpose, according to another aspect of the present application, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the Python-based vehicle-cloud data quality assessment method described in any one of the present inventions.

[0030] Beneficial effect: In the embodiment of the present application, a Python-based automated evaluation method is adopted to obtain the vehicle-side CAN data, cloud-side CAN data and DBC protocol database; select the vehicle-side CAN data and cloud-side CAN data within a preset timestamp range, and compare them with the content of the DBC protocol database for consistency to generate an evaluation result; generate a corresponding result report for the evaluation result; wherein the result report includes: a data loss rate result report, a data content accuracy result report and a timestamp synchronization evaluation result report, thereby achieving the purpose of verifying data content accuracy, data loss rate evaluation and timestamp synchronization, thereby achieving the technical effect of improving the quality and reliability of vehicle-cloud data, and further solving the problem that in the process of data collection and transmission from the vehicle to the cloud, the data quality may be reduced due to various factors such as interference and errors, including data content accuracy problems, data loss and timestamp synchronization deviation, etc., and the existing data quality evaluation methods mostly rely on manual review, which is not only inefficient but also difficult to fully cover technical problems.

[0031] Compared with the related prior art, the present invention has at least the following main technical advantages, and the beneficial effects of the present invention are:

[0032] Comprehensive evaluation: This invention realizes a multi-dimensional evaluation of the quality of vehicle-cloud data, including key indicators such as content accuracy, data loss rate and timestamp synchronization.

[0033] Automated processing: The Python programming language is used to automate the evaluation process, improving the efficiency and accuracy of large-scale data evaluation.

[0034] Easy to expand: The evaluation method has good scalability, and new evaluation dimensions can be added or existing evaluation algorithms can be optimized according to actual needs.

[0035] The above description provides a comprehensive and reasonable explanation of the beneficial effects of the technical solution, highlighting its advantages in comprehensive evaluation, efficiency improvement and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a test system architecture diagram of a vehicle-cloud data quality assessment method based on Python according to an embodiment of the present application;

[0037] Figure 2 It is a flow chart of a test system of a vehicle-cloud data quality assessment method based on Python according to an embodiment of the present application;

[0038] Figure 3 It is a vehicle cloud data pulling operation interface of the vehicle cloud data quality assessment method based on Python according to an embodiment of the present application;

[0039] Figure 4 It is a flowchart of a vehicle cloud data quality assessment method based on Python according to an embodiment of the present application;

[0040] Figure 5 is a schematic diagram of the structure of a vehicle cloud data quality assessment system based on Python according to an embodiment of the present application; and

[0041] Figure 6 It is a structural schematic diagram of an electronic device according to a vehicle-cloud data quality assessment method based on Python according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0043] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0044] In addition, the terms "installed", "set", "provided with", "connected", "connected", and "socketed" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection, or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0045] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] like Figure 1-4As shown, according to an embodiment of the present invention, a vehicle cloud data quality assessment method based on Python is provided, and the method includes the following steps S101 to S103:

[0047] Step S101, obtaining vehicle-side CAN data, cloud-side CAN data and DBC protocol database;

[0048] It can achieve good information collection and reception effects. The data includes but is not limited to: vehicle-side CAN data, cloud-side CAN data and DBC protocol database, thus providing a data basis for subsequent processing or calculation.

[0049] According to an embodiment of the present invention, preferably, obtaining vehicle-side CAN data, cloud-side CAN data and a DBC protocol database includes:

[0050] The original files collected by the vehicle, the vehicle-cloud data files, the DBC protocol database and the timestamp deviation range; wherein the timestamp deviation range is ±2s.

[0051] The original files collected on the vehicle side refer to the data files collected directly from the vehicle's sensors or CAN bus by the vehicle-mounted system (such as the electronic control unit ECU); the original files usually contain unprocessed or compressed vehicle signal data, reflecting the real-time information of the vehicle's operating status.

[0052] The original files serve as the basic data for analysis and processing, and are used for further analysis and comparison to ensure the integrity and accuracy of the data from collection to final application.

[0053] The vehicle-to-cloud data file is a data file that has been processed or converted after the vehicle-side data is uploaded to the cloud, including but not limited to the following processing: formatting, aggregation or other pre-processing steps for storage, analysis or remote diagnosis in the cloud.

[0054] Cloud data files are used for long-term storage, data mining or remote service support; by comparing with the original data on the vehicle side, the data integrity and synchronization during the upload process can be verified.

[0055] The DBC protocol database is a database used to store DBC files, which are used to define the structure and properties of each signal in the vehicle network. The DBC protocol database contains all possible signal definitions that may be collected and transmitted in the vehicle, such as signal name, length, unit, scaling factor, etc.

[0056] The DBC protocol database is crucial in parsing and decoding raw data from the vehicle, ensuring that all signals can be correctly understood and converted into actual physical quantities.

[0057] The timestamp deviation range (±2s) refers to the allowed timestamp difference between the vehicle-side data and the vehicle-cloud data. In this scenario, the allowed timestamp deviation range is ±2 seconds. This means that the timestamps of the data collected by the vehicle-side and the data processed by the cloud can differ by up to 2 seconds.

[0058] This deviation range is used to ensure the time synchronization of data, which is critical in applications such as multi-source data fusion, real-time analysis, and event replay. The timestamp deviation is controlled within the range of ±2 seconds to ensure the time consistency of data and avoid data dislocation or misunderstanding caused by time differences.

[0059] Specifically, vehicle-side data collection and labeling

[0060] The vehicle side has deployed relevant data acquisition controllers such as TBOX or networked domain control to collect and store data on the CAN bus in real time, and add a timestamp and unique identifier to each data packet. The collected data can be reported to the cloud server through a secure and reliable communication link (such as 4G / 5G, Wi-Fi, etc.); at the same time, the vehicle side OBD connects to reliable data acquisition equipment such as CANoe to collect and record the original CAN data files of the vehicle network in real time.

[0061] Remote call and download of cloud data

[0062] Write a Python script to automatically make a remote call, input the vehicle VIN code, call time range and call channel, etc. The cloud server receives the data packet from the vehicle, stores it in the database, and records the receiving time; at the same time, it downloads the readable data packet (asc format) to the local computer.

[0063] Step S102: Select the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range, and compare them with the contents of the DBC protocol database for consistency to generate an evaluation result;

[0064] It can achieve good data traversal and comparison effects, thereby generating the required evaluation results.

[0065] By traversing the cloud data based on the vehicle-side data, comparing the consistency, and generating evaluation results;

[0066] By traversing the vehicle-side data based on the cloud data, comparing the consistency, and generating evaluation results.

[0067] Step S103, generating a corresponding result report based on the evaluation result; wherein the result report includes: a data loss rate result report, a data content accuracy result report and a timestamp synchronization evaluation result report.

[0068] A corresponding result report can be generated based on the evaluation results, so that the status of the entire vehicle can be accurately known.

[0069] The report may include evaluation methods, evaluation process, evaluation results and improvement suggestions, providing a basis for the continuous optimization of data quality.

[0070] According to an embodiment of the present invention, preferably, selecting the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range and comparing them for consistency with the content of the DBC protocol database includes:

[0071] Through the preset Python script, the CAN data packets extracted from the cloud database are used as the comparison benchmark data to extract the data ID, data field and data timestamp information, and compare them with the original data collected by the vehicle.

[0072] Obtain all data fields with the same ID as the original data and a timestamp deviation within ±2s, and compare them one by one with the cloud data fields to verify the accuracy of the data content.

[0073] According to an embodiment of the present invention, preferably, selecting the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range and comparing them for consistency with the content of the DBC protocol database includes:

[0074] Through the preset Python script, the total number of data packets and message frames on the vehicle side is compared with the total number of data packets and message frames pulled from the cloud side to calculate the data loss rate; and the original data packets collected on the vehicle side are used as the benchmark data to extract the data ID, data field and timestamp. In the same way as content evaluation, the original data on the vehicle side is compared with the data collected on the cloud side to further confirm whether there is data packet loss.

[0075] According to an embodiment of the present invention, preferably, selecting the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range and comparing them for consistency with the content of the DBC protocol database includes:

[0076] Compare the timestamp of the data content recorded by the vehicle-side acquisition controller on the cloud with the timestamp when the vehicle sends it to verify the rationality of the time difference; and preset the timestamp threshold to evaluate the synchronization of the timestamp.

[0077] From the above description, it can be seen that the present application achieves the following technical effects:

[0078] In an embodiment of the present application, a Python-based automated evaluation method is adopted to obtain vehicle-side CAN data, cloud-side CAN data and DBC protocol database; select the vehicle-side CAN data and cloud-side CAN data within a preset timestamp range, and compare them with the content of the DBC protocol database for consistency to generate an evaluation result; generate a corresponding result report for the evaluation result; wherein the result report includes: a data loss rate result report, a data content accuracy result report and a timestamp synchronization evaluation result report, thereby achieving the purpose of verifying data content accuracy, data loss rate evaluation and timestamp synchronization, thereby achieving the technical effect of improving the quality and reliability of vehicle-cloud data, and further solving the problem that in the process of data collection and transmission from the vehicle to the cloud, the data quality may be degraded due to various factors such as interference and errors, including data content accuracy problems, data loss and timestamp synchronization deviation, etc., and the existing data quality evaluation methods mostly rely on manual review, which is not only inefficient but also difficult to fully cover technical problems.

[0079] According to an embodiment of the present invention, preferably, after generating a corresponding result report from the evaluation result, the method further includes:

[0080] When the data loss rate result report and the data content accuracy result report meet the preset indicators, the preset timestamp range is narrowed, and the result report within the narrowed timestamp range is compared with the content of the DBC protocol database for consistency to accurately obtain the timestamp synchronization result.

[0081] On the premise that the data content accuracy and loss rate test results meet the indicators, the input timestamp deviation range can be further narrowed, the content consistency can be compared, and the timestamp synchronization results can be further refined.

[0082] According to an embodiment of the present invention, preferably, before obtaining the vehicle-side CAN data, the cloud-side CAN data and the DBC protocol database, the method further includes:

[0083] Enter the vehicle VIN code and the start and end time of the required test data, issue a remote call task with one click, pull the test data to the cloud database, and download it to the local test terminal.

[0084] According to an embodiment of the present invention, preferably, before inputting the vehicle VIN code and the start and end time of the required test data, issuing a remote call task with one click, pulling the test data to the cloud database, and downloading it to the local test terminal, it also includes:

[0085] Connect the OBD port of the vehicle to be tested through the CAN card hardware device, start the real-time data collection and recording function, and continue for a preset time. At the same time, the vehicle-side data acquisition ECU records the data packet to the local memory.

[0086] The present invention can solve the following technical problems:

[0087] 1. Verification of data content accuracy:

[0088] Problem description: During the process of collecting CAN data from the vehicle and reporting it to the cloud, due to hardware failure, software error, communication interference and other factors, there may be deviations or errors between the data reported to the cloud and the CAN data on the actual bus. If such deviations or errors are not discovered and corrected in time, they will seriously affect the subsequent data analysis and application effects.

[0089] Solution: This invention writes Python scripts to realize the automatic comparison of CAN data packets received by the cloud and the original data on the vehicle side to verify the accuracy of the data content. This includes a one-by-one comparison of key information such as data ID, data length, data byte content, etc. at the same time to ensure that the cloud data is completely consistent with the vehicle data.

[0090] 2. Evaluation of data loss rate:

[0091] Problem description: During data transmission, due to network instability, device failure or configuration error, some CAN data packets may fail to be successfully reported to the cloud, resulting in data loss. Data loss not only affects the integrity of the data, but may also mislead subsequent data analysis and decision-making.

[0092] Solution: The present invention calculates the data loss rate by counting the total number of data packets sent by the vehicle and the total number of data packets received by the cloud. At the same time, combined with the continuity check of the continuous life signal or timestamp in the data content, it further confirms whether there is data packet loss and gives a specific loss rate indicator.

[0093] 3. Verification of timestamp synchronization:

[0094] Problem description: In the Internet of Vehicles system, timestamp is an important basis for evaluating data freshness and synchronization. However, due to factors such as network delay and device clock asynchrony, there may be a deviation between the time when the data packet is received recorded in the cloud and the time when it is sent by the vehicle. If this deviation is not properly controlled, it will affect the time series analysis and real-time requirements of the data.

[0095] Solution: This invention verifies the rationality of the time difference by comparing the time when the data packet is received recorded in the cloud with the timestamp when it is sent by the vehicle. Considering factors such as network delay and server processing time, a reasonable threshold range is set to evaluate the synchronization of timestamps. If the time difference exceeds the threshold range, it is considered that there is a problem with the synchronization of timestamps, and corresponding measures need to be taken to adjust and optimize.

[0096] In summary, the invention provides a comprehensive, efficient and automated solution for data quality assessment in the field of Internet of Vehicles by solving key technical problems such as data content accuracy verification, data loss rate assessment and timestamp synchronization verification. This not only helps to improve the quality and reliability of vehicle-to-cloud data, but also provides a solid foundation for subsequent data analysis and application.

[0097] The present invention aims to solve the difficult problems of multi-dimensional evaluation such as data content accuracy, data loss rate and timestamp synchronization in vehicle-to-cloud data quality assessment, and provide a Python-based automated evaluation method to ensure the high quality and reliability of vehicle-to-cloud data.

[0098] like Figure 5 As shown, in order to achieve the above purpose, according to another aspect of the present application, a vehicle cloud data quality assessment system based on Python is provided. The vehicle cloud data quality assessment system based on Python includes:

[0099] The acquisition module 501 is used to acquire vehicle-side CAN data, cloud-side CAN data and DBC protocol database;

[0100] It can achieve good information collection and reception effects. The data includes but is not limited to: vehicle-side CAN data, cloud-side CAN data and DBC protocol database, thus providing a data basis for subsequent processing or calculation.

[0101] The vehicle-cloud data quality assessment module 502 is used to select the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range, and compare the consistency with the content of the DBC protocol database to generate an assessment result;

[0102] It can achieve good data traversal and comparison effects, thereby generating the required evaluation results.

[0103] By traversing the cloud data based on the vehicle-side data, comparing the consistency, and generating evaluation results;

[0104] By traversing the vehicle-side data based on the cloud data, comparing the consistency, and generating evaluation results.

[0105] The result generation module 503 is used to generate a corresponding result report based on the evaluation result; wherein the result report includes: a data loss rate result report, a data content accuracy result report and a timestamp synchronization evaluation result report.

[0106] A corresponding result report can be generated based on the evaluation results, so that the status of the entire vehicle can be accurately known.

[0107] Specifically, the car cloud data remote call and download API module: it contains a car cloud verification login API, which is used to log in to the car cloud webpage using a secure account and password; it contains a car cloud post / get interface, which is used to upload / request data information to the webpage; it contains a remote call API, which is used to actively pull CAN data within a specific time range on the vehicle side; it contains a database file download API, which is used to download the database file stored in the remote call cloud to the local computer.

[0108] Vehicle-cloud data quality assessment API module: external input files, including collected data files obtained from the cloud, original asc files collected by the vehicle-side CAN card, and database dbc parsing files; it contains a time format conversion API to unify different time formats such as UTC-0, UTC-8, and timestamp; contains a start and end time acquisition API to obtain the time range of vehicle-side and cloud-side data content; contains an intersection time data screening API to filter data content with time intersections between the vehicle-side and the cloud; contains a data dictionary extraction API to extract timestamps, IDs, and data fields from the filtered data content to generate a dictionary format that is easy to read and index; includes a DBC parsing API to parse the signal content of data fields; contains a content comparison API to compare the data content consistency of vehicle-side and cloud-side data dictionaries.

[0109] From the above description, it can be seen that the present application achieves the following technical effects:

[0110] In an embodiment of the present application, a Python-based automated evaluation method is adopted to obtain vehicle-side CAN data, cloud-side CAN data and DBC protocol database; select the vehicle-side CAN data and cloud-side CAN data within a preset timestamp range, and compare them with the content of the DBC protocol database for consistency to generate an evaluation result; generate a corresponding result report for the evaluation result; wherein the result report includes: a data loss rate result report, a data content accuracy result report and a timestamp synchronization evaluation result report, thereby achieving the purpose of verifying data content accuracy, data loss rate evaluation and timestamp synchronization, thereby achieving the technical effect of improving the quality and reliability of vehicle-cloud data, and further solving the problem that in the process of data collection and transmission from the vehicle to the cloud, the data quality may be degraded due to various factors such as interference and errors, including data content accuracy problems, data loss and timestamp synchronization deviation, etc., and the existing data quality evaluation methods mostly rely on manual review, which is not only inefficient but also difficult to fully cover technical problems.

[0111] Specifically, further explanation is given by the following examples;

[0112] Embodiment 1:

[0113] When the vehicle is in dynamic driving: collect the original data of the vehicle through the external CAN card device connected to the OBD port, pull the data of the same time period through the automation module, and perform data content comparison to generate a data quality assessment report for the dynamic scenario of the vehicle.

[0114] Embodiment 2:

[0115] When the vehicle key is turned off and the vehicle is charging: collect the original vehicle data through the external CAN card device through the OBD port, pull the data of the same time period through the automation module, and perform data content comparison to generate a data quality assessment report for the vehicle charging scenario.

[0116] Embodiment 3:

[0117] When the vehicle is in an underground garage or remote mountainous area with poor network signals: collect the original data of the vehicle through an external CAN card device through the OBD port, move the vehicle to an environment with good signals, pull data from the same time period through the automation module, perform data content comparison, and generate a data quality assessment report for the abnormal vehicle network scenario.

[0118] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0119] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0120] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the vehicle cloud data quality assessment method based on Python.

[0121] In some embodiments, the Python-based vehicle cloud data quality assessment method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the Python-based vehicle cloud data quality assessment method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the Python-based vehicle cloud data quality assessment method in any other appropriate manner (e.g., by means of firmware).

[0122] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0124] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein may be implemented in a computing system that includes a backend component (e.g., as a data server), or a computing system that includes a middleware component (e.g., an application server), or a computing system that includes a frontend component (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LA), a wide area network (WA), a blockchain network, and the Internet.

[0127] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0128] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0129] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A car-cloud data quality assessment method based on Python, characterized in that: The following steps are involved: Obtain vehicle-side CAN data, cloud-side CAN data and DBC protocol database; Select the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range, and compare them with the contents of the DBC protocol database for consistency, and generate an evaluation result; Generate a corresponding result report based on the evaluation result; wherein the result report includes: a data loss rate result report, a data content accuracy result report and a timestamp synchronization evaluation result report.

2. The vehicle-cloud data quality assessment method based on Python according to claim 1 is characterized in that: Obtain vehicle-side CAN data, cloud-side CAN data, and DBC protocol database, including: The original files collected by the vehicle, the vehicle-cloud data files, the DBC protocol database and the timestamp deviation range; wherein the timestamp deviation range is ±2s.

3. The vehicle cloud data quality assessment method based on Python according to claim 1 is characterized in that: After generating a corresponding result report from the evaluation result, the following is also included: When the data loss rate result report and the data content accuracy result report meet the preset indicators, the preset timestamp range is narrowed, and the result report within the narrowed timestamp range is compared with the content of the DBC protocol database for consistency to accurately obtain the timestamp synchronization result.

4. The vehicle-cloud data quality assessment method based on Python according to claim 1 is characterized in that: Before obtaining vehicle-side CAN data, cloud CAN data and DBC protocol database, it also includes: Enter the vehicle VIN code and the start and end time of the required test data, issue a remote call task with one click, pull the test data to the cloud database, and download it to the local test terminal.

5. The vehicle-cloud data quality assessment method based on Python according to claim 1 is characterized in that: Enter the vehicle VIN code and the start and end time of the required test data, issue a remote call task with one click, pull the test data to the cloud database, and download it to the local test terminal. It also includes: Connect the OBD port of the vehicle to be tested through the CAN card hardware device, start the real-time data collection and recording function, and continue for a preset time. At the same time, the vehicle-side data acquisition ECU records the data packet to the local memory.

6. The vehicle-cloud data quality assessment method based on Python according to claim 1 is characterized in that: The vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range are selected, and a consistency comparison is performed with the contents of the DBC protocol database, including: Through the preset Python script, the CAN data packets extracted from the cloud database are used as the comparison benchmark data to extract the data ID, data field and data timestamp information, and compare them with the original data collected by the vehicle. Obtain all data fields with the same ID as the original data and a timestamp deviation within ±2s, and compare them one by one with the cloud data fields to verify the accuracy of the data content.

7. The vehicle-cloud data quality assessment method based on Python according to claim 1 is characterized in that: The vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range are selected, and a consistency comparison is performed with the contents of the DBC protocol database, including: Through the preset Python script, the total number of data packets and message frames on the vehicle side is compared with the total number of data packets and message frames pulled from the cloud side to calculate the data loss rate; and the original data packets collected on the vehicle side are used as the benchmark data to extract the data ID, data field and timestamp. In the same way as content evaluation, the original data on the vehicle side is compared with the data collected on the cloud side to further confirm whether there is data packet loss.

8. The vehicle-cloud data quality assessment method based on Python according to claim 1 is characterized in that: The vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range are selected, and a consistency comparison is performed with the contents of the DBC protocol database, including: Compare the timestamp of the data content recorded by the vehicle-side acquisition controller on the cloud with the timestamp when it was sent by the vehicle to verify the rationality of the time difference; and preset the timestamp threshold to evaluate the synchronization of the timestamp.

9. Python-based car cloud data quality assessment system, characterized by: include: The acquisition module is used to obtain vehicle-side CAN data, cloud-side CAN data and DBC protocol database; A vehicle-cloud data quality assessment module is used to select the vehicle-side CAN data and the cloud-side CAN data within a preset timestamp range, and compare the consistency with the content of the DBC protocol database to generate an assessment result; The result generation module is used to generate a corresponding result report based on the evaluation result; wherein the result report includes: a data loss rate result report, a data content accuracy result report and a timestamp synchronization evaluation result report.

10. An electronic device, characterized in that The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the Python-based vehicle-cloud data quality assessment method described in any one of claims 1-8.

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