Method, device, equipment, medium and program for detecting performance of mine car by edge computing

Abstract

The application discloses a kind of edge computing detection mine car performance method, device, equipment, medium and program, the method includes: receiving performance detection request by cloud platform and extracting the frame number of target mine car;Determine performance detection template according to frame number and return to mobile terminal for template analysis;Receive the performance test instruction generated based on template analysis result returned by mobile terminal, and forward to vehicle terminal for edge computing;Receive the edge computing result returned by vehicle terminal, including the highest speed peak value of mine car and the time used for acceleration in specified speed interval, generate performance detection report according to edge computing result and performance detection template and synchronize to mobile terminal.The technical scheme of the embodiment of the application adopts cloud, mobile terminal, vehicle terminal three-level architecture to realize task cooperation to reduce delay, supports mobile terminal to receive report containing deviation value highlight in real time to shorten decision-making cycle, and simultaneously improves the efficiency and scalability of mine car performance detection through edge computing.

Description

Technical Field

[0001] This invention relates to the field of engineering machinery technology, and in particular to a method, apparatus, equipment, medium and program for edge computing to detect the performance of mining trucks. Background Technology

[0002] The commissioning of mining trucks after production is a crucial step in ensuring vehicle performance meets standards, with top speed and 0-35 km / h acceleration time being two core test indicators. Traditional commissioning processes rely on manual operation: before testing, paper templates must be manually designed and printed for different vehicle models; during testing, the driver estimates the top speed visually from the dashboard and manually records the acceleration time with a stopwatch; after testing, the paper data must be manually transcribed into a spreadsheet and compared with preset thresholds to determine pass / fail status. The entire process involves multiple stages, including vehicle model template management, data collection, recording and analysis, and archiving, all primarily manual and lacking automation tools. As the number of vehicle models and test items increases, the efficiency bottlenecks and potential risks of the traditional model become increasingly apparent.

[0003] The existing debugging methods have the following main problems: ① When vehicle models and test items change, debugging templates need to be manually adjusted, transferred, and printed, resulting in delayed updates and a high risk of errors; ② Maximum speed and acceleration tests need to be performed step by step, leading to repetitive operations and wasted time and resources; ③ Reliance on manual stopwatches and visual inspection instruments makes data accuracy susceptible to subjective influence and poses a risk of misreading; ④ Paper data needs to be manually entered into spreadsheets, and pass / fail judgments rely entirely on manual comparison of thresholds, resulting in low efficiency and a high risk of misjudgment; ⑤ Debugging results are scattered across individual or departmental terminals, lacking unified archiving, making them prone to loss and difficult to trace. These shortcomings restrict debugging efficiency and data reliability, necessitating optimization through automation and digitalization technologies. Summary of the Invention

[0004] Based on this, the present invention provides a method, apparatus, equipment, medium and program for edge computing to detect the performance of mining trucks, so as to solve the problems of low efficiency, inaccurate data, cumbersome process and inconsistent standards in manual testing during the commissioning of mining trucks.

[0005] In a first aspect, embodiments of the present invention provide a method for edge computing to detect the performance of mining trucks, executed by an automated detection system. The automated detection system includes a cloud platform, a mobile terminal, and an on-board terminal, comprising:

[0006] The cloud platform receives performance testing requests for the target mining truck sent by the mobile terminal, and extracts the vehicle frame number of the target mining truck from the performance testing request.

[0007] The cloud platform determines the performance test template corresponding to the target mining truck model based on the chassis number and returns it to the mobile device for template parsing; wherein, the chassis number is obtained by the mobile device scanning the barcode of the mining truck.

[0008] The system receives performance test commands generated from template parsing results returned by the mobile terminal through the cloud platform and forwards the performance test commands to the vehicle terminal for edge computing.

[0009] The cloud platform receives edge computing results from the vehicle terminal, including the peak speed of the mining truck and the acceleration time within a specified speed range. Based on the edge computing results and the performance test template, a performance test report is generated, which includes the judgment results of the test items and the maximum deviation value. The performance test report is then synchronized to the mobile terminal.

[0010] Secondly, embodiments of the present invention also provide an edge computing device for detecting the performance of mining trucks, arranged in an automated detection system, comprising:

[0011] The performance testing request receiving module is used to receive a performance testing request for a target mining truck sent by a mobile terminal through a cloud platform, and to extract the chassis number of the target mining truck from the performance testing request.

[0012] The performance test template determination module is used to determine the performance test template corresponding to the target mining truck model based on the chassis number through the cloud platform and return it to the mobile terminal for template parsing; wherein, the chassis number is obtained by the mobile terminal scanning the whole vehicle barcode of the mining truck;

[0013] The performance test instruction forwarding module is used to receive performance test instructions generated based on template parsing results returned by the mobile terminal through the cloud platform, and forward the performance test instructions to the vehicle terminal for edge computing;

[0014] The performance test report generation module is used to receive edge computing results from the vehicle terminal via the cloud platform, which include the peak speed of the mining truck and the acceleration time within a specified speed range. Based on the edge computing results and the performance test template, it generates a performance test report that includes the judgment results of the test items and the maximum deviation value highlighted, and synchronizes the performance test report to the mobile terminal.

[0015] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0016] 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, the computer program being executed by the at least one processor to enable the at least one processor to perform an edge computing method for detecting the performance of a mining truck as described in any embodiment of the present invention.

[0017] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute a method for edge computing detection of mining truck performance as described in any embodiment of the present invention.

[0018] Fifthly, a computer program product is also provided, the computer program product including a computer program, which, when executed by a processor, implements the edge computing method for detecting the performance of a mining truck as described in any embodiment of the present invention.

[0019] This invention employs a vehicle-specific performance testing template linked to the vehicle identification number (VIN) to ensure a strict match between testing standards and mining truck characteristics, thereby improving the accuracy and relevance of testing results. A three-tiered architecture—cloud platform, mobile terminal, and vehicle-mounted terminal—enables collaborative task distribution, edge computing, and central processing, reducing data transmission latency and improving system response efficiency. Real-time reception of testing reports containing judgment results and highlighted deviation values ​​via mobile terminals allows testing personnel to instantly obtain intuitive performance evaluation results, shortening the decision-making cycle. Based on a template-based design, the system easily adapts to the testing needs of different vehicle models; simply updating the cloud template library supports performance testing for new models, demonstrating strong scalability. Through maximum deviation value highlighting technology, the system automatically marks performance anomalies, assisting testing personnel in quickly identifying key issues and improving fault diagnosis efficiency. A closed-loop management system covering the entire process from testing request initiation and instruction execution to result feedback ensures traceability and controllability of the testing process, improving testing quality and management level. Simultaneously, data preprocessing and some computational tasks are offloaded to the vehicle-mounted terminal, reducing cloud computing pressure, lowering network bandwidth requirements, and ensuring data privacy and security.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for detecting the performance of a mining truck using edge computing, according to Embodiment 1 of the present invention.

[0023] Figure 2This is a flowchart of another edge computing method for detecting the performance of mining trucks according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of an edge computing device for detecting the performance of a mining truck according to Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device that implements an edge computing method for detecting the performance of a mining truck according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1 This is a flowchart of a method for edge computing to detect the performance of mining trucks according to Embodiment 1 of the present invention. This embodiment is applicable to the production and commissioning stage of mining trucks, where an automated detection system replaces traditional manual testing. This method can be executed by an edge computing device for detecting mining truck performance. This device can be implemented in hardware and / or software and can be configured in an IoT-based automated detection system. Figure 1 As shown, the method includes:

[0030] S110. Receive a performance testing request for the target mining truck sent by the mobile terminal through the cloud platform, and extract the chassis number of the target mining truck from the performance testing request.

[0031] In this embodiment of the invention, a three-layer distributed architecture of cloud platform-mobile terminal-vehicle terminal is adopted at the system architecture level: the cloud platform serves as the core hub, undertaking the functions of template management, data analysis and report generation; the mobile terminal serves as the human-computer interaction interface, realizing the digital triggering of the detection process and the visualization of results; and the vehicle terminal serves as the edge node, completing real-time data acquisition and localized calculation.

[0032] The target mining truck refers to the specific mining truck that needs to be tested for performance. Its identity can be distinguished by a unique identifier and chassis number. When the mining truck needs to be tested for performance, the staff will use a mobile device (such as a smartphone or tablet device with a dedicated testing APP) to initiate the testing operation. The mobile device will generate a performance testing request. This request is a digital instruction containing the testing intent and relevant identification information of the target mining truck, which is used to transmit the testing requirements to the cloud platform.

[0033] The performance testing request includes the VIN (Vehicle Identification Number) for uniquely identifying the target mining truck. This VIN is obtained by scanning the vehicle barcode (which stores the VIN information and serves as a visual representation of it) affixed to the target mining truck using a mobile device's camera. After scanning, the mobile device extracts the VIN information from the barcode and embeds it into the performance testing request. Once the cloud platform extracts the VIN from the performance testing request, it can use this unique identifier to perform subsequent operations such as template retrieval, providing an identity benchmark for the entire performance testing process.

[0034] S120. The cloud platform determines the performance test template corresponding to the target mining truck model based on the chassis number and returns it to the mobile terminal for template parsing; wherein, the chassis number is obtained by the mobile terminal scanning the whole vehicle barcode of the mining truck.

[0035] After extracting the chassis number of the target mining truck, the cloud platform will use the chassis number encoding rules to parse out the specific model of the mining truck. The cloud platform has pre-stored performance test templates corresponding to various models. These templates are standardized test files customized for specific models, including a list of test items, the standard parameter range of each test item, data format requirements, and the basic framework for subsequent report generation.

[0036] Based on the parsed vehicle model, the cloud platform accurately retrieves a matching performance test template from the pre-stored template library and returns it to the requesting mobile device via network transmission. After receiving the template, the mobile device performs a template parsing operation, which identifies the priority, parameter type, and interaction command format of the test items according to the template's built-in format rules, laying the foundation for subsequently generating performance test commands that meet the template requirements.

[0037] S130: Receives performance test instructions generated from template parsing results returned by the mobile terminal through the cloud platform, and forwards the performance test instructions to the vehicle terminal for edge computing.

[0038] After the mobile device parses the performance test template, it generates a performance test command based on the priority of the parsed test items, parameter types, and interaction command format. This command is an execution command containing specific test tasks, covering key parameters such as the items to be tested, data sampling frequency, and test duration. Its format perfectly matches the template requirements, ensuring the vehicle-mounted terminal can accurately recognize and execute it. The mobile device sends the generated performance test command to the cloud platform. Upon receiving the command, the cloud platform first verifies its validity. Once confirmed, it forwards the performance test command to the vehicle-mounted terminal installed on the target mining truck through the established communication link.

[0039] As the executor of edge computing, the vehicle-mounted terminal will start the local edge computing module after receiving the performance test command. Based on the parameters in the command, it will collect, analyze and calculate the real-time operating data of the mining truck, thereby completing the edge computing process.

[0040] S140: Receives edge computing results from the vehicle terminal via the cloud platform, including the peak speed of the mining truck and the acceleration time within a specified speed range. Based on the edge computing results and the performance test template, generates a performance test report that includes the judgment results of the test items and the maximum deviation value highlighted, and synchronizes the performance test report to the mobile terminal.

[0041] After performing edge computing, the onboard terminal integrates the calculated core data—the mine truck's highest peak speed (the maximum speed recorded during the detection process) and the acceleration time within a specified speed range (such as the time required to accelerate from 0-35 km / h)—into an edge computing result, which is then transmitted back to the cloud platform via a preset communication protocol. Upon receiving this result, the cloud platform calls upon the previously retrieved performance detection template corresponding to the target mine truck model.

[0042] The cloud platform compares the measured data from the edge computing results with the standard parameter ranges of each test item in the performance testing template. For example, it compares the peak speed with the standard range of the highest speed in the template, and the acceleration time for a specified speed range with the standard range of acceleration performance, thereby generating a test item judgment result. At the same time, it calculates the percentage difference between the measured value and the median of the standard range to obtain the maximum deviation value, and highlights the maximum deviation value that exceeds a certain range according to the template's preset rules (e.g., red indicates severe deviation, yellow indicates slight deviation) to intuitively present the degree of deviation of the test results.

[0043] The test results, the maximum deviation value after highlighting, and the measured data are entered into the corresponding fields of the performance test template to generate a complete performance test report. Finally, the cloud platform pushes the report to the mobile device that initiated the test request through a data synchronization mechanism, enabling staff to view the performance test results of the target mining truck in real time, thus completing the closed loop of the entire test process.

[0044] Optionally, a performance test report can be generated via a cloud platform based on edge computing results and performance test templates, including the judgment results of test items and a highlighted maximum deviation value. This report may include:

[0045] The target mining truck frame number, peak speed, and acceleration time within a specified speed range are extracted from the edge computing results through the cloud platform, and the corresponding performance test template is retrieved based on the frame number.

[0046] The cloud platform compares the peak speed with the preset acceptable range of the highest speed in the performance test template, compares the acceleration time of the specified speed range with the preset standard range of acceleration time in the performance test template, and generates the judgment results for each test item according to the preset acceptance judgment rules.

[0047] The maximum deviation of the highest speed peak value is calculated by using the median of the preset maximum speed acceptable range and the highest speed peak value for the test items that are judged as unqualified through the cloud platform. The maximum deviation of the acceleration time in the specified speed range is calculated by using the median of the preset acceleration time standard range and the acceleration time.

[0048] The cloud platform highlights the calculated maximum deviation value according to the maximum deviation value highlighting rules in the performance test template. The target mining truck frame number, peak speed, acceleration time in the specified speed range, judgment result, and the highlighted maximum deviation value are then filled into the corresponding fields of the performance test template to generate a performance test report.

[0049] The cloud platform accurately extracts the target mining truck's chassis number, peak speed, and acceleration time within a specified speed range from the edge computing results transmitted back from the vehicle terminal. Then, using the chassis number as an index, it retrieves the performance test template corresponding to that mining truck model from the template library stored in the cloud. The template contains core content such as the preset maximum speed acceptable range, the preset acceleration time standard range, the pass / fail judgment rules, the maximum deviation value calculation method, and the highlighting rules.

[0050] The cloud platform compares the extracted peak speed with the preset acceptable range of peak speed in the performance testing template, and also compares the acceleration time within a specified speed range with the preset standard acceleration time range in the template. According to the preset pass / fail rules in the template (if the measured value is within the standard range, it is considered pass; otherwise, it is considered fail), it generates a pass / fail result for each of the two test items. For test items with a fail result, the cloud platform initiates deviation calculation: for the peak speed, the maximum deviation value is calculated using the median of the preset acceptable range of peak speed as the benchmark, and the formula "(peak speed - median) / median × 100%" is used; for the acceleration time within a specified speed range, the maximum deviation value is calculated using the median of the preset standard acceleration time range as the benchmark, and the formula "(acceleration time - median) / median × 100%" is used to quantify the degree of deviation between the measured value and the standard value.

[0051] The cloud platform highlights the calculated maximum deviation value according to the maximum deviation value highlighting rules in the performance test template (e.g., deviation values ​​>10% are marked in red, and 5%-10% are marked in yellow). Then, the target mining truck frame number, the highest peak speed, the acceleration time in the specified speed range, the judgment results of each test item, and the highlighted maximum deviation value are filled into the corresponding fields of the performance test template one by one, and finally integrated into a complete and detailed performance test report.

[0052] Furthermore, the method may also include:

[0053] The cloud platform receives test interruption commands for the target test item sent by the mobile terminal, and extracts the unique identifier and VIN information corresponding to the target test item from the test interruption command.

[0054] The target communication link corresponding to the mining truck associated with the chassis number information is queried through the cloud platform, and the test terminal command containing the unique identifier of the target test item is sent to the target vehicle terminal through the target communication link.

[0055] The cloud platform receives the response information, which includes an interruption confirmation flag, returned by the target vehicle terminal and forwards it to the mobile device.

[0056] When a specific test item needs to be paused during the testing process, the mobile device generates a test interruption command for the target test item. This command includes a unique identifier for the test item and the chassis number of the target mining vehicle. Upon receiving this command, the cloud platform extracts the unique identifier and chassis number to identify the interruption target and associated mining vehicle. Based on the extracted chassis number, the cloud platform queries a pre-stored "chassis number-communication link" mapping table to determine the target communication link corresponding to the mining vehicle. Subsequently, the cloud platform forwards the test interruption command, containing the unique identifier of the target test item, to the onboard terminal of the target mining vehicle via this target communication link, ensuring that the onboard terminal clearly understands the specific test item that needs to be interrupted.

[0057] After receiving and executing the test interruption command, the vehicle-mounted terminal generates a response message containing an interruption confirmation identifier and sends it back to the cloud platform. Upon receiving the response message, the cloud platform immediately forwards it to the mobile terminal that initiated the interruption request, enabling staff to know the execution result of the interruption operation in real time and completing the closed loop of the entire interruption process.

[0058] This invention employs a vehicle-specific performance testing template linked to the vehicle identification number (VIN) to ensure a strict match between testing standards and mining truck characteristics, thereby improving the accuracy and relevance of testing results. A three-tiered architecture—cloud platform, mobile terminal, and vehicle-mounted terminal—enables collaborative task distribution, edge computing, and central processing, reducing data transmission latency and improving system response efficiency. Real-time reception of testing reports containing judgment results and highlighted deviation values ​​via mobile terminals allows testing personnel to instantly obtain intuitive performance evaluation results, shortening the decision-making cycle. Based on a template-based design, the system easily adapts to the testing needs of different vehicle models; simply updating the cloud template library supports performance testing for new models, demonstrating strong scalability. Through maximum deviation value highlighting technology, the system automatically marks performance anomalies, assisting testing personnel in quickly identifying key issues and improving fault diagnosis efficiency. A closed-loop management system covering the entire process from testing request initiation and instruction execution to result feedback ensures traceability and controllability of the testing process, improving testing quality and management level. Simultaneously, data preprocessing and some computational tasks are offloaded to the vehicle-mounted terminal, reducing cloud computing pressure, lowering network bandwidth requirements, and ensuring data privacy and security.

[0059] Example 2

[0060] Figure 2 This is a flowchart of another edge computing method for detecting the performance of mining trucks provided in Embodiment 2 of the present invention. This embodiment is a refinement based on Embodiment 1, specifically as follows: Figure 2 As shown, the method includes:

[0061] S210. Receive a performance testing request for the target mining truck sent by the mobile terminal through the cloud platform, and extract the chassis number of the target mining truck from the performance testing request.

[0062] S220. The cloud platform determines the performance test template corresponding to the target mining truck model based on the chassis number and returns it to the mobile terminal for template parsing; wherein, the chassis number is obtained by the mobile terminal scanning the whole vehicle barcode of the mining truck.

[0063] S230: Receives and parses the performance test template from the cloud platform via the mobile terminal, extracting the target mining truck frame number, test item configuration parameters, dynamic threshold adjustment rules, and the maximum execution time of each test item.

[0064] The mobile device first receives a performance test template sent from the cloud platform. This template is a unique file matched by the cloud based on the target minecart's frame number. The mobile device then parses the template, reading and extracting key information according to its format rules. This includes: the target minecart frame number, used to confirm the specific minecart being tested, ensuring accuracy; test item configuration parameters, containing a list of items to be tested (such as maximum speed, acceleration performance, etc.) and specific testing requirements for each item; dynamic threshold adjustment rules, allowing for adjustments to testing standards based on environmental factors (such as rules on the impact of temperature changes on the acceptable range); and the maximum execution time for each test item, ensuring that excessively long execution times for individual items do not negatively impact overall testing efficiency.

[0065] S240. The execution sequence of the detection items is determined by the mobile terminal based on the correlation and priority of each detection item in the detection item configuration parameters, and the detection calculation logic in the detection item configuration parameters is extracted as the detection calculation logic parameters.

[0066] The mobile terminal analyzes the correlation and priority of each detection item in the configuration parameters and plans an orderly execution sequence according to the principle of collecting basic data first, followed by complex calculations, and prioritizing high-priority items. For example: wheel speed sensor data collection → acceleration sensor data collection → peak speed calculation → acceleration time calculation for a specified interval, ensuring that the detection process is free of logical conflicts. The mobile terminal extracts the specific calculation rules for each detection item from the configuration parameters, such as the formula for converting wheel speed values ​​to speed and the algorithm for calculating speed by integrating acceleration data. These rules are organized into standardized detection calculation logic parameters, which serve as the basis for subsequent data processing by the vehicle terminal.

[0067] S250: Collects the current ambient temperature value through the mobile terminal as the initial parameter for the dynamic threshold, and generates the detection start time based on the system clock.

[0068] The mobile device acquires the real-time ambient temperature of the testing site through a built-in or external temperature sensor, using it as the initial parameter for the dynamic threshold. This parameter is subsequently combined with dynamic threshold adjustment rules to provide basic data for the dynamic adaptation of testing standards. The mobile device records the precise start time of the testing process based on its own system clock, serving as the time reference for the entire testing process and facilitating subsequent tracking of the execution duration of each testing item and data timestamp alignment.

[0069] S260 encapsulates the target mining truck frame number, the execution sequence of detection items, the detection calculation logic parameters, the initial parameters of the dynamic threshold, the detection start time, and the maximum execution time of each detection item into a performance test instruction and sends it to the cloud platform.

[0070] In this embodiment of the invention, the information to be encapsulated is not simply piled up, but each has a specific function: the target mining truck chassis number is used to accurately correspond to the detection object; the execution sequence of the detection items specifies the order of detection; the detection calculation logic parameters are the specific rules for data processing; the initial parameters of the dynamic threshold provide the basis for standard adjustment; the detection start time is the time starting point of the process; and the maximum execution time of each detection item is a constraint to prevent timeouts. Next, the mobile terminal integrates this information in a unified format to form a complete performance test instruction. This instruction is like a detailed detection task list, containing all the key information needed to perform the detection, ensuring that the vehicle-mounted terminal clearly understands what to do and how to do it after receiving it.

[0071] S270 receives performance test instructions generated based on template parsing results returned by the mobile terminal through the cloud platform, and forwards the performance test instructions to the vehicle terminal for edge computing.

[0072] Optionally, after forwarding performance test commands to the in-vehicle terminal for edge computing via the cloud platform, it may also include:

[0073] The vehicle terminal receives and parses the performance test instructions forwarded by the cloud platform to obtain the target mining truck frame number, the execution sequence of the test items, the test calculation logic parameters, and the initial parameters of the dynamic threshold.

[0074] The vehicle terminal reads the vehicle component data of the target mining truck transmitted via the CAN bus in real time according to the detection item execution sequence, and preprocesses the vehicle component data by applying detection calculation logic parameters; wherein, the vehicle component data includes wheel speed sensor data, acceleration sensor data and engine torque;

[0075] The vehicle terminal uses a sliding window algorithm to segment the preprocessed wheel speed sensor data into time segments to obtain the wheel speed curve corresponding to each segment of wheel speed sensor data. The dynamic time warping algorithm is used to match the wheel speed curves of adjacent time windows. When the matching degree exceeds a preset threshold, the wheel speed value in the current window is recorded and converted into the vehicle speed as the highest peak speed.

[0076] The vehicle terminal performs integration calculations on the pre-processed acceleration sensor data along the time dimension to obtain the vehicle speed value corresponding to each time point and form a speed-time curve. The start and end time points of a specified speed range are determined from the curve, and the acceleration time of that speed range is obtained by calculating the difference between the time points.

[0077] The system acquires ambient temperature sensor data and real-time engine torque values ​​as dynamic operating condition parameters through the vehicle terminal. These dynamic operating condition parameters, along with the target mining truck chassis number, peak speed, and acceleration time within a specified speed range, are then encapsulated into an edge computing result and transmitted back to the cloud platform. The judgment result includes whether each detection item meets the standard and the maximum deviation value of each detection item.

[0078] The vehicle-mounted terminal receives performance test commands forwarded from the cloud platform. These commands are integrated by the mobile terminal, sent to the cloud, and then forwarded by the cloud. The vehicle-mounted terminal parses the received commands and extracts key information: the target mining truck frame number is used to confirm the test object and ensure that the executed test task corresponds to a specific mining truck; the test item execution sequence specifies the order of the tests, guiding the vehicle-mounted terminal to perform the tests in sequence; the test calculation logic parameters are the specific rules for subsequent data processing, clarifying how to calculate and analyze the collected data; and the dynamic threshold initial parameters provide a basis for the dynamic adjustment of the test standards.

[0079] Based on the parsed detection item execution sequence, the on-board terminal begins real-time reading of the target mining truck's component data transmitted via the CAN bus. The CAN bus is the communication channel between various components within the mining truck, from which the on-board terminal obtains wheel speed sensor data, acceleration sensor data, and engine torque information. After acquiring this raw data, the on-board terminal applies the previously parsed detection calculation logic parameters to preprocess this component data, such as filtering noise data and standardizing data formats, preparing for subsequent deep learning calculations. Subsequently, the on-board terminal uses a sliding window algorithm to segment the preprocessed wheel speed sensor data over time. The sliding window algorithm acts like a window sliding across the data sequence, extracting data at fixed time intervals, with each segment corresponding to a wheel speed curve.

[0080] The vehicle-mounted terminal uses a dynamic time warping algorithm to match wheel speed curves between adjacent time windows. This algorithm can find the optimal matching path between two curves even when their time axes are inconsistent. When the matching degree exceeds a preset threshold, it indicates that the wheel speed data within this time period is stable and representative. The vehicle-mounted terminal records the wheel speed value within the current window and converts it into vehicle speed according to the conversion rules in the detection and calculation logic parameters, using this as the highest peak speed.

[0081] For the preprocessed acceleration sensor data, the on-board terminal performs integration calculations over time. Integrating acceleration over time yields velocity. Through this calculation, the on-board terminal obtains the vehicle's velocity value at each time point, thus generating a velocity-time curve. From this curve, the on-board terminal can determine the start and end times of a specified speed range (e.g., acceleration from 0 to 35 km / h). By calculating the difference between these two times, the acceleration time for that speed range is obtained.

[0082] Finally, the onboard terminal acquires ambient temperature sensor data and real-time engine torque values, using them as dynamic operating condition parameters. Ambient temperature affects vehicle performance, while engine torque reflects the vehicle's power output. The onboard terminal encapsulates these dynamic operating condition parameters along with data such as the target mining truck's chassis number, the previously calculated peak speed, and acceleration time within a specified speed range to form edge computing results, which are then transmitted back to the cloud platform. The judgment result portion of the edge computing results includes whether each detection item meets the standard and the maximum deviation value for each detection item. This information is derived by the onboard terminal after analyzing the detection data based on the detection calculation logic parameters and the initial parameters of the dynamic threshold.

[0083] S280 receives edge computing results from the vehicle terminal via the cloud platform, including the peak speed of the mining truck and the acceleration time within the specified speed range. Based on the edge computing results and the performance test template, it generates a performance test report that includes the judgment results of the test items and the maximum deviation value highlighted, and synchronizes the performance test report to the mobile terminal.

[0084] Furthermore, the performance test report generated by the cloud platform based on edge computing results and performance test templates, which includes pass / fail criteria for test items and highlights of the maximum deviation value, may also include:

[0085] The system extracts ambient temperature sensor data and real-time engine torque values ​​from edge computing results via a cloud platform.

[0086] The cloud platform dynamically adjusts the preset maximum speed pass range and preset acceleration time standard range based on the preset temperature and speed correction curves and torque and acceleration time correction coefficient table in the performance test template. The adjusted dynamic standard values ​​are then used to determine the pass / fail status of each test item and calculate the maximum deviation value.

[0087] The cloud platform extracts ambient temperature sensor data and real-time engine torque values ​​from the edge computing results transmitted back from the vehicle-mounted terminal. These data reflect the actual operating conditions of the mining truck during testing and are crucial for adjusting subsequent testing standards. The cloud platform references pre-set rules in the performance testing template: firstly, it adjusts the preset maximum speed acceptable range based on the "temperature and speed correction curve" (e.g., for every 1°C increase in temperature, the upper limit of the acceptable maximum speed range decreases by 0.3 km / h); secondly, it adjusts the preset acceleration time standard range based on the "torque and acceleration time correction coefficient table" (e.g., for every 50 N·m increase in torque, the standard acceleration time range widens by 0.2 seconds). The cloud platform uses these adjusted dynamic standard values ​​to evaluate the peak maximum speed, acceleration time, and other test results calculated by the vehicle-mounted terminal, determining whether each item meets the standards and calculating the maximum deviation of each test item from the standard value.

[0088] This invention, through template parsing to extract key information, plan detection sequences, integrate dynamic parameters, and generate standardized instructions, achieves precision and orderliness in the detection process. This ensures both the compliance of the execution logic of detection items and the clarity of calculation rules, while also incorporating dynamic environmental factors and time benchmarks to provide a complete and adaptable basis for subsequent stages. The onboard terminal then parses the instructions, sequentially collects and preprocesses vehicle data, and uses algorithms such as sliding windows, dynamic time warping, and integral calculations to accurately calculate core performance indicators. Dynamic operating condition parameters are then encapsulated and transmitted back to the cloud, enabling real-time edge computing of performance data. This embodiment ensures the timeliness and accuracy of data processing, reduces cloud load, reflects the actual operating status of the mining truck, and lays the foundation for generating accurate detection reports, comprehensively improving the targeting, organization, execution efficiency, and reliability of mining truck performance testing.

[0089] Example 3

[0090] Figure 3 This is a schematic diagram of a device for detecting the performance of a mining truck using edge computing, provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0091] The performance testing request receiving module 310 is used to receive a performance testing request for a target mining truck sent by a mobile terminal through a cloud platform, and extract the chassis number of the target mining truck from the performance testing request.

[0092] The performance test template determination module 320 is used to determine the performance test template corresponding to the target mining truck model based on the chassis number through the cloud platform and return it to the mobile terminal for template parsing; wherein, the chassis number is obtained by the mobile terminal scanning the whole vehicle barcode of the mining truck;

[0093] The performance test instruction forwarding module 330 is used to receive performance test instructions generated based on template parsing results returned by the mobile terminal through the cloud platform, and forward the performance test instructions to the vehicle terminal for edge computing;

[0094] The performance test report generation module 340 is used to receive edge computing results from the vehicle terminal via the cloud platform, which include the peak speed of the mining truck and the acceleration time within a specified speed range. Based on the edge computing results and the performance test template, it generates a performance test report that includes the judgment results of the test items and the maximum deviation value highlighted, and synchronizes the performance test report to the mobile terminal.

[0095] This invention employs a vehicle-specific performance testing template linked to the vehicle identification number (VIN) to ensure a strict match between testing standards and mining truck characteristics, thereby improving the accuracy and relevance of testing results. A three-tiered architecture—cloud platform, mobile terminal, and vehicle-mounted terminal—enables collaborative task distribution, edge computing, and central processing, reducing data transmission latency and improving system response efficiency. Real-time reception of testing reports containing judgment results and highlighted deviation values ​​via mobile terminals allows testing personnel to instantly obtain intuitive performance evaluation results, shortening the decision-making cycle. Based on a template-based design, the system easily adapts to the testing needs of different vehicle models; simply updating the cloud template library supports performance testing for new models, demonstrating strong scalability. Through maximum deviation value highlighting technology, the system automatically marks performance anomalies, assisting testing personnel in quickly identifying key issues and improving fault diagnosis efficiency. A closed-loop management system covering the entire process from testing request initiation and instruction execution to result feedback ensures traceability and controllability of the testing process, improving testing quality and management level. Simultaneously, data preprocessing and some computational tasks are offloaded to the vehicle-mounted terminal, reducing cloud computing pressure, lowering network bandwidth requirements, and ensuring data privacy and security.

[0096] Optionally, based on the above embodiments, it may also include: a performance test instruction encapsulation unit, used to receive and parse the performance test template from the cloud platform after determining the performance test template corresponding to the target mining truck model through the cloud platform according to the chassis number and returning it to the mobile terminal for template parsing, and extract the target mining truck chassis number, test item configuration parameters, dynamic threshold adjustment rules and the maximum execution time of each test item;

[0097] The execution sequence of detection items is determined by the mobile terminal based on the correlation and priority of each detection item in the detection item configuration parameters, and the detection calculation logic in the detection item configuration parameters is extracted as the detection calculation logic parameters.

[0098] The current ambient temperature value is collected by the mobile device as the initial parameter for the dynamic threshold, and the detection start time is generated based on the system clock.

[0099] The target mining truck frame number, the execution sequence of the detection items, the detection calculation logic parameters, the initial parameters of the dynamic threshold, the detection start time, and the maximum execution time of each detection item are encapsulated into a performance test instruction and sent to the cloud platform.

[0100] Optionally, based on the above embodiments, it may also include: an edge computing result encapsulation unit, used to receive and parse the performance test instructions forwarded by the cloud platform through the vehicle terminal after the performance test instructions are forwarded to the vehicle terminal for edge computing through the cloud platform, and obtain the target mining truck frame number, the execution sequence of the detection item, the detection calculation logic parameters and the initial parameters of the dynamic threshold.

[0101] The vehicle terminal reads the vehicle component data of the target mining truck transmitted via the CAN bus in real time according to the detection item execution sequence, and preprocesses the vehicle component data by applying detection calculation logic parameters; wherein, the vehicle component data includes wheel speed sensor data, acceleration sensor data and engine torque;

[0102] The vehicle terminal uses a sliding window algorithm to segment the preprocessed wheel speed sensor data into time segments to obtain the wheel speed curve corresponding to each segment of wheel speed sensor data. The dynamic time warping algorithm is used to match the wheel speed curves of adjacent time windows. When the matching degree exceeds a preset threshold, the wheel speed value in the current window is recorded and converted into the vehicle speed as the highest peak speed.

[0103] The vehicle terminal performs integration calculations on the pre-processed acceleration sensor data along the time dimension to obtain the vehicle speed value corresponding to each time point and form a speed-time curve. The start and end time points of a specified speed range are determined from the curve, and the acceleration time of that speed range is obtained by calculating the difference between the time points.

[0104] The system acquires ambient temperature sensor data and real-time engine torque values ​​as dynamic operating condition parameters through the vehicle terminal. These dynamic operating condition parameters, along with the target mining truck chassis number, peak speed, and acceleration time within a specified speed range, are then encapsulated into an edge computing result and transmitted back to the cloud platform. The judgment result includes whether each detection item meets the standard and the maximum deviation value of each detection item.

[0105] Optionally, based on the above embodiments, the performance test report generation module 340 may include:

[0106] The edge computing result extraction unit is used to extract the target mining truck frame number, peak speed and acceleration time in a specified speed range from the edge computing results through the cloud platform, and retrieve the corresponding performance test template according to the frame number.

[0107] The judgment result generation unit is used to compare the peak speed with the preset maximum speed acceptable range in the performance test template through the cloud platform, compare the acceleration time of the specified speed range with the preset acceleration time standard range in the performance test template, and generate judgment results for each test item according to the preset acceptance judgment rules.

[0108] The maximum deviation value calculation unit is used to calculate the maximum deviation value of the highest speed peak value for the test items whose judgment result is unqualified through the cloud platform, using the median of the preset highest speed qualified range and the highest speed peak value, and to calculate the maximum deviation value of the acceleration time in a specified speed range using the median of the preset acceleration time standard range and the acceleration time.

[0109] The performance test template input unit is used to highlight the calculated maximum deviation value according to the maximum deviation value highlighting rules in the performance test template through the cloud platform, and fill the target mining car frame number, the highest peak speed, the acceleration time in the specified speed range, the judgment result, and the highlighted maximum deviation value into the corresponding fields of the performance test template to generate a performance test report.

[0110] Optionally, based on the above embodiments, it may also include: an interruption command receiving unit, used to receive a test interruption command for the target test item sent by the mobile terminal through the cloud platform, and extract the unique identifier and vehicle identification number information corresponding to the target test item in the test interruption command;

[0111] The communication link determination unit is used to query the target communication link corresponding to the mining car associated with the chassis number information through the cloud platform, and use the target communication link to send the test terminal instruction containing the unique identifier of the target test item to the target vehicle terminal.

[0112] The response information receiving unit is used to receive response information containing an interruption confirmation identifier returned by the target vehicle terminal through the cloud platform and forward it to the mobile terminal.

[0113] Optionally, based on the above embodiments, the performance test report generation module 340 may further include:

[0114] The real-time parameter acquisition unit is used to extract ambient temperature sensor data and real-time engine torque values ​​from edge computing results via a cloud platform.

[0115] The dynamic standard value adjustment unit is used to dynamically adjust the preset maximum speed pass range and preset acceleration time standard range based on the preset temperature and speed correction curves and torque and acceleration time correction coefficient table in the performance test template through the cloud platform, and uses the adjusted dynamic standard value to determine the pass and pass of each test item and calculate the maximum deviation value.

[0116] The edge computing device for detecting the performance of mining trucks provided in this embodiment of the invention can execute the edge computing method for detecting the performance of mining trucks provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0117] Example 4

[0118] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0119] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0120] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0121] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a method for edge computing to detect the performance of a mining truck.

[0122] That is: receiving performance testing requests for the target mining truck sent by the mobile terminal through the cloud platform, and extracting the chassis number of the target mining truck from the performance testing request;

[0123] The cloud platform determines the performance test template corresponding to the target mining truck model based on the chassis number and returns it to the mobile device for template parsing; wherein, the chassis number is obtained by the mobile device scanning the barcode of the mining truck.

[0124] The system receives performance test commands generated from template parsing results returned by the mobile terminal through the cloud platform and forwards the performance test commands to the vehicle terminal for edge computing.

[0125] The cloud platform receives edge computing results from the vehicle terminal, including the peak speed of the mining truck and the acceleration time within a specified speed range. Based on the edge computing results and the performance test template, a performance test report is generated, which includes the judgment results of the test items and the maximum deviation value. The performance test report is then synchronized to the mobile terminal.

[0126] In some embodiments, a method for edge computing to detect the performance of a mining truck can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via read-only memory (ROM) 12 and / or communication unit 19. When the computer program is loaded into random access memory (RAM) 13 and executed by processor 11, one or more steps of the method for edge computing to detect the performance of a mining truck described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform a method for edge computing to detect the performance of a mining truck by any other suitable means (e.g., by means of firmware).

[0127] Various embodiments 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-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] Computer programs used to implement 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, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM) 13, read-only memory (ROM) 12, erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0130] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0131] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0132] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0133] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0134] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for detecting the performance of mining trucks using edge computing, executed by an automated detection system, the automated detection system comprising a cloud platform, a mobile terminal, and an on-board terminal, characterized in that, include: The cloud platform receives performance testing requests for the target mining truck sent by the mobile terminal, and extracts the vehicle frame number of the target mining truck from the performance testing request. The cloud platform determines the performance test template corresponding to the target mining truck model based on the chassis number and returns it to the mobile device for template parsing; wherein, the chassis number is obtained by the mobile device scanning the barcode of the mining truck. The system receives performance test commands generated from template parsing results returned by the mobile terminal through the cloud platform and forwards the performance test commands to the vehicle terminal for edge computing. The cloud platform receives edge computing results from the vehicle terminal, including the peak speed of the mining truck and the acceleration time within a specified speed range. Based on the edge computing results and performance test template, a performance test report is generated, which includes the judgment results of the test items and the maximum deviation value. The performance test report is then synchronized to the mobile device. After determining the performance test template corresponding to the target mining truck model based on the chassis number through the cloud platform and returning it to the mobile terminal for template parsing, the process also includes: The system receives and parses a performance testing template from a cloud platform via a mobile device, extracting the target mining truck frame number, testing item configuration parameters, dynamic threshold adjustment rules, and the maximum execution time of each testing item. The performance testing template is a unique file matched by the cloud platform based on the target mining truck frame number. The execution sequence of detection items is determined by the mobile terminal based on the correlation and priority of each detection item in the detection item configuration parameters, and the detection calculation logic in the detection item configuration parameters is extracted as the detection calculation logic parameters. The current ambient temperature value is collected by the mobile device as the initial parameter for the dynamic threshold, and the detection start time is generated based on the system clock. The target mining truck frame number, the execution sequence of the detection items, the detection calculation logic parameters, the initial parameters of the dynamic threshold, the detection start time, and the maximum execution time of each detection item are encapsulated into a performance test instruction and sent to the cloud platform.

2. The method according to claim 1, characterized in that, After forwarding performance test commands to the in-vehicle terminal for edge computing via the cloud platform, the process also includes: The vehicle terminal receives and parses the performance test instructions forwarded by the cloud platform to obtain the target mining truck frame number, the execution sequence of the test items, the test calculation logic parameters, and the initial parameters of the dynamic threshold. The vehicle terminal reads the vehicle component data of the target mining truck transmitted via the CAN bus in real time according to the detection item execution sequence, and preprocesses the vehicle component data by applying detection calculation logic parameters; wherein, the vehicle component data includes wheel speed sensor data, acceleration sensor data and engine torque; The vehicle terminal uses a sliding window algorithm to segment the preprocessed wheel speed sensor data into time segments to obtain the wheel speed curve corresponding to each segment of wheel speed sensor data. The dynamic time warping algorithm is used to match the wheel speed curves of adjacent time windows. When the matching degree exceeds a preset threshold, the wheel speed value in the current window is recorded and converted into the vehicle speed as the highest peak speed. The vehicle terminal performs integration calculations on the pre-processed acceleration sensor data along the time dimension to obtain the vehicle speed value corresponding to each time point and form a speed-time curve. The start and end time points of a specified speed range are determined from the curve, and the acceleration time of that speed range is obtained by calculating the difference between the time points. The system acquires ambient temperature sensor data and real-time engine torque values ​​as dynamic operating condition parameters through the vehicle terminal. These dynamic operating condition parameters, along with the target mining truck chassis number, peak speed, and acceleration time within a specified speed range, are then encapsulated into an edge computing result and transmitted back to the cloud platform. The judgment result includes whether each detection item meets the standard and the maximum deviation value of each detection item.

3. The method according to claim 1, characterized in that, The cloud platform generates a performance test report based on edge computing results and performance test templates, including the results of test item judgments and a highlighted maximum deviation value. The target mining truck frame number, peak speed, and acceleration time within a specified speed range are extracted from the edge computing results through the cloud platform, and the corresponding performance test template is retrieved based on the frame number. The cloud platform compares the peak speed with the preset acceptable range of the highest speed in the performance test template, compares the acceleration time of the specified speed range with the preset standard range of acceleration time in the performance test template, and generates the judgment results for each test item according to the preset acceptance judgment rules. The maximum deviation of the highest speed peak value is calculated by using the median of the preset maximum speed acceptable range and the highest speed peak value for the test items that are judged as unqualified through the cloud platform. The maximum deviation of the acceleration time in the specified speed range is calculated by using the median of the preset acceleration time standard range and the acceleration time. The cloud platform highlights the calculated maximum deviation value according to the maximum deviation value highlighting rules in the performance test template. The target mining truck frame number, peak speed, acceleration time in the specified speed range, judgment result, and the highlighted maximum deviation value are then filled into the corresponding fields of the performance test template to generate a performance test report.

4. The method according to claim 1, characterized in that, The method further includes: The cloud platform receives test interruption commands for the target test item sent by the mobile terminal, and extracts the unique identifier and VIN information corresponding to the target test item from the test interruption command. The target communication link corresponding to the mining truck associated with the chassis number information is queried through the cloud platform, and the test terminal command containing the unique identifier of the target test item is sent to the target vehicle terminal through the target communication link. The cloud platform receives the response information, which includes an interruption confirmation flag, returned by the target vehicle terminal and forwards it to the mobile device.

5. The method according to claim 2, characterized in that, The cloud platform generates a performance test report based on edge computing results and performance test templates, including pass / fail criteria for each test item and a highlighted maximum deviation value. This report also includes: The system extracts ambient temperature sensor data and real-time engine torque values ​​from edge computing results via a cloud platform. The cloud platform dynamically adjusts the preset maximum speed pass range and preset acceleration time standard range based on the preset temperature and speed correction curves and torque and acceleration time correction coefficient table in the performance test template. The adjusted dynamic standard values ​​are then used to determine the pass / fail status of each test item and calculate the maximum deviation value.

6. A device for detecting the performance of mining trucks using edge computing, arranged in an automated detection system, characterized in that, include: The performance testing request receiving module is used to receive a performance testing request for a target mining truck sent by a mobile terminal through a cloud platform, and to extract the chassis number of the target mining truck from the performance testing request. The performance test template determination module is used to determine the performance test template corresponding to the target mining truck model based on the chassis number through the cloud platform and return it to the mobile terminal for template parsing; wherein, the chassis number is obtained by the mobile terminal scanning the whole vehicle barcode of the mining truck; The performance test instruction forwarding module is used to receive performance test instructions generated based on template parsing results returned by the mobile terminal through the cloud platform, and forward the performance test instructions to the vehicle terminal for edge computing; The performance test report generation module is used to receive edge computing results from the vehicle terminal via the cloud platform, which include the peak speed of the mining truck and the acceleration time within a specified speed range. Based on the edge computing results and the performance test template, it generates a performance test report that includes the judgment results of the test items and the maximum deviation value highlighted, and synchronizes the performance test report to the mobile terminal. The performance test instruction encapsulation unit is used to receive and parse the performance test template from the cloud platform after determining the performance test template corresponding to the target mining truck model based on the chassis number through the cloud platform and returning it to the mobile terminal for template parsing. The unit extracts the target mining truck chassis number, test item configuration parameters, dynamic threshold adjustment rules, and the maximum execution time of each test item. The performance test template is a unique file matched by the cloud platform based on the target mining truck chassis number. The execution sequence of detection items is determined by the mobile terminal based on the correlation and priority of each detection item in the detection item configuration parameters, and the detection calculation logic in the detection item configuration parameters is extracted as the detection calculation logic parameters. The current ambient temperature value is collected by the mobile device as the initial parameter for the dynamic threshold, and the detection start time is generated based on the system clock. The target mining truck frame number, the execution sequence of the detection items, the detection calculation logic parameters, the initial parameters of the dynamic threshold, the detection start time, and the maximum execution time of each detection item are encapsulated into a performance test instruction and sent to the cloud platform.

7. An electronic device, characterized in that, The electronic device includes: 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, the computer program being executed by the at least one processor to enable the at least one processor to perform a method for edge computing to detect the performance of a mining truck according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the edge computing method for detecting the performance of a mining truck according to any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements a method for edge computing to detect the performance of a mining truck according to any one of claims 1-5.

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