Heavy-duty diesel vehicle remote monitoring method and system based on edge calculation

Abstract

The invention belongs to the technical field of Internet of Vehicles communication, and discloses a heavy diesel vehicle remote monitoring method and system based on edge computing, and the method comprises the steps: constructing a rule engine according to a predefined transmission rule, and deploying the rule engine to an edge computing node; the data stream of the heavy diesel vehicle is transmitted to an edge computing node, a rule engine is used for evaluating each data field in the data stream, a corresponding transmission level is distributed, and the transmission level comprises a first-level field, a second-level field and a third-level field; and matching a preset transmission strategy according to the data field with the allocated transmission level, executing differentiated data processing, and transmitting a processing result to an enterprise cloud platform through a network transmission layer. According to the method, low-value data is filtered in real time through edge calculation, the invalid data uploading amount is reduced, and data transmission is reduced; in addition, through a high compression ratio and a delay transmission technology, the bandwidth occupation of low-value data is reduced, and the utilization efficiency of network resources is optimized.

Description

Technical Field

[0001] This invention relates to the field of vehicle network communication technology, and in particular to a method and system for remote monitoring of heavy-duty diesel vehicles based on edge computing. Background Technology

[0002] In existing technologies, to meet the requirements for real-time monitoring of the operating status and exhaust emissions of heavy-duty diesel vehicles, remote monitoring vehicle terminals are typically installed on the vehicles to collect data related to vehicle operation and emissions and upload it to the enterprise monitoring platform. The enterprise monitoring platform then forwards the data packets to the remote monitoring platform according to standard requirements. The vehicle terminal and the enterprise monitoring platform use TCP / IP as the underlying communication protocol, and data packets must follow a pre-defined fixed structure to ensure interoperability and accurate data parsing. A typical data packet structure includes a start character, command unit, data encryption method identifier, data unit length field, data unit body, and checksum. Transmission uses big-endian mode (network byte order).

[0003] To ensure the integrity, timeliness, and effectiveness of monitoring data, existing standards set forth clear requirements for data transmission and quality, including but not limited to: Data integrity: The engine data stream uploaded by the vehicle terminal must include specified core fields (19 items in total) to ensure the comprehensiveness of the monitoring information; such as the Vehicle Identification Number (VIN), engine torque, and the concentration of nitrogen oxides (NOx) downstream of the Selective Catalytic Reduction (SCR) system. Timeliness and transmission frequency: The terminal must collect data at a frequency of no less than once per second (≥1Hz); the enterprise monitoring platform must forward the received data to the remote monitoring platform within 10 seconds; simultaneously, the packet loss rate of the entire data transmission link must be controlled within 1% to meet the timeliness and reliability requirements of regulatory and statistical analysis. Data effectiveness: For each core data item in the engine data stream, invalid data types and legal data ranges must be clearly defined, such as no output due to sensor failure, exceeding the range, and discrete outliers, so as to identify and eliminate invalid or abnormal data before reporting or processing, reducing interference with monitoring conclusions.

[0004] Although existing solutions are feasible at the architectural and basic specification levels, they still have the following technical shortcomings in practical applications: 1. Excessive proportion of invalid data transmission: Due to factors such as sensor noise, cold operating conditions (coolant temperature <70℃), high-altitude failure, and brief signal anomalies, the proportion of invalid or low-value data in the original data exceeds 30%. This data is uploaded indiscriminately, which not only consumes bandwidth resources but also increases the burden of invalid data processing on the platform.

[0005] 2. Severe waste of network resources: The existing solution adopts a uniform, high-frequency (collection per second, upload per 10 seconds) transmission strategy for all data items (regardless of their value). Low-value or invalid data (such as routine parameters during stable operation) is continuously transmitted at a high frequency, which not only increases the pressure on the limited wireless communication bandwidth of the vehicle network (such as 4G / 5G), but also wastes resources.

[0006] 3. Insufficient reliability and poor standard compliance in critical data transmission: Although the standard requires a packet loss rate of less than 1%, due to network fluctuations and congestion, the existing full-upload mode cannot ensure the reliable real-time transmission of high-value critical data (such as OBD fault codes and excessive emission data), resulting in a general failure to meet standard requirements in practical applications. A large amount of low-value data occupies network resources, thus crowding out opportunities for critical data transmission, which is the main reason for excessive packet loss rates.

[0007] Therefore, how to provide a remote monitoring method and system for heavy-duty diesel vehicles based on edge computing is an urgent problem to be solved. Summary of the Invention

[0008] This invention provides a method and system for remote monitoring of heavy-duty diesel vehicles based on edge computing, in order to solve the aforementioned technical problems existing in the prior art.

[0009] According to a first aspect of the present invention, a method for remote monitoring of heavy-duty diesel vehicles based on edge computing is provided.

[0010] In one embodiment, a remote monitoring method for heavy-duty diesel vehicles based on edge computing includes: Based on predefined transmission rules, a rule engine is built and deployed to edge computing nodes; The data stream from the heavy-duty diesel vehicle is transmitted to the edge computing node. The rule engine is used to evaluate each data field in the data stream and assign a corresponding transmission level. The transmission level includes first-level fields, second-level fields and third-level fields. Based on the data fields assigned to the transmission level, the system matches the preset transmission strategy, performs differentiated data processing, and transmits the processing results to the enterprise cloud platform through the network transport layer.

[0011] In one embodiment, the data stream from the heavy-duty diesel vehicle is transmitted to an edge computing node. A rule engine is used to evaluate each data field in the data stream and assign a corresponding transmission level. The transmission level includes first-level fields, second-level fields, and third-level fields, including: Collect data streams from heavy-duty diesel vehicles and transmit the data streams to edge computing nodes; Edge computing nodes receive data streams and use the deployed rule engine to evaluate the data fields in the data streams. Based on the evaluation results, corresponding transmission levels are assigned, including first-level fields, second-level fields, and third-level fields.

[0012] In one embodiment, the edge computing node receives a data stream and uses a deployed rule engine to evaluate the data fields in the data stream. Based on the evaluation results, it assigns a corresponding transmission level, which includes first-level fields, second-level fields, and third-level fields, including: The rule engine in the edge computing node reads the data fields in the data stream and performs data transposition on the data fields by combining preset precision and offset; Based on the data transpose result, the rule engine evaluates whether the data fields meet the preset judgment rules, and marks the error code according to the evaluation result to allocate the corresponding transmission level. The transmission level includes first-level fields, second-level fields and third-level fields.

[0013] In one embodiment, the step of matching the data field of the assigned transmission level with a preset transmission strategy, performing differentiated data processing, and transmitting the processing result to the enterprise cloud platform through the network transport layer includes: Based on the first-level, second-level, and third-level fields, analyze the error codes corresponding to each field, match the preset transmission strategy, and trigger the corresponding scenarios; Based on the triggering results of each field, the corresponding compression level is set using a compression algorithm for compression, and a data packet is constructed to integrate the compression results. The data packet is then sent to the enterprise cloud platform through the network transport layer.

[0014] In one embodiment, the step of compressing data based on the triggering results of each field, setting the corresponding compression level using a compression algorithm, constructing a data packet to integrate the compression results, and sending the data packet to the enterprise cloud platform through the network transport layer includes: Based on the triggering result of the first-level field, the compression level is set to the priority mode, the first-level field is compressed, and a first-level data packet is constructed and the compressed first-level field is integrated. The integration results are placed in a first-level priority queue, and the data packets are sent to the enterprise cloud platform through the network transport layer.

[0015] In one embodiment, the step of using a compression algorithm to set the corresponding compression level based on the triggering results of each field, compressing the data packet to integrate the compression results, and sending the data packet to the enterprise cloud platform through the network transport layer further includes: Based on the triggering result of the secondary field, the compression level is set to balanced mode, the secondary field is compressed, a secondary data packet is constructed, and the compressed secondary field is integrated. Real-time monitoring of network bandwidth and target queues in secondary data packets, combined with preset thresholds for the number of backlogged headers and preset bandwidth thresholds, to process secondary data packets; The processed secondary data packets are placed into a secondary priority queue and then sent to the enterprise cloud platform through the network transport layer.

[0016] In one embodiment, the real-time monitoring of network bandwidth and the target queue in secondary data packets, combined with a preset threshold for the number of backlogged headers and a preset bandwidth threshold, to process the secondary data packets includes: If the number of backlogged packet headers in the target queue exceeds the preset threshold and the network bandwidth is lower than the preset bandwidth threshold, the compression level will be upgraded for the first time, and the initial degradation level will be set in the header of the secondary data packet. If the number of backlogged packet headers in the target queue continues to exceed the preset threshold, then the secondary data packets will be quantized a second time, the compression level will be upgraded a second time, and a second degradation level will be set in the header of the secondary data packets.

[0017] In one embodiment, the step of using a compression algorithm to set the corresponding compression level based on the triggering results of each field, compressing the data packet to integrate the compression results, and sending the data packet to the enterprise cloud platform through the network transport layer further includes: Based on the triggering result of the third-level field, the compression level is set to compression mode to compress the third-level field; A three-level data packet is constructed, the compressed three-level fields are integrated, and the integration result is placed into a three-level priority queue. The three-level data packets in the queue are processed using the storage-transmission mode.

[0018] In one embodiment, the storage and transfer mode includes: If the network bandwidth of the wireless or diagnostic port is detected to be higher than a preset threshold before the heavy-duty diesel vehicle is turned off, the interrupted transmission will be resumed; otherwise, it will enter a sleep waiting state. After the breakpoint resume function is started, the cached three-level data packets are sliced ​​and compressed according to preset units, and the slices are verified in combination with the verification information. Based on the verification results, the data is uploaded to the enterprise cloud platform. If the interrupted resume transmission occurs, the resume transmission will continue within a preset time after the heavy-duty diesel vehicle is powered on again, based on the identifier and checksum of the three-level data packet, until the data is uploaded to the enterprise cloud platform.

[0019] According to a second aspect of the present invention, a remote monitoring system for heavy-duty diesel vehicles based on edge computing is provided.

[0020] In one embodiment, the edge computing-based heavy-duty diesel vehicle remote monitoring system includes: The rule engine deployment module is used to build a rule engine based on predefined transmission rules and deploy the rule engine to edge computing nodes; The transmission level allocation module is used to transmit the data stream of heavy-duty diesel vehicles to the edge computing node. It uses a rule engine to evaluate each data field in the data stream and allocate a corresponding transmission level. The transmission level includes a first-level field, a second-level field, and a third-level field. The differentiated processing and transmission module is used to match the preset transmission strategy with the data fields assigned to the transmission level, perform differentiated data processing, and transmit the processing results to the enterprise cloud platform through the network transmission layer.

[0021] According to a third aspect of the present invention, a computer device is provided.

[0022] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0023] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0024] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0025] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: 1. This invention uses edge computing to filter low-value data in real time, reducing the amount of invalid data uploaded and reducing data transmission. In addition, through high compression ratio and delayed transmission technology, it reduces the bandwidth occupation of low-value data and optimizes the utilization efficiency of network resources.

[0026] 2. This invention ensures high reliability of critical data transmission and reduces packet loss rate through compression and slicing technologies; at the same time, it supports dynamic loading of standard updates, avoids hardware modifications, reduces implementation costs, and improves system compatibility and scalability.

[0027] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0029] Figure 1 This is a flowchart illustrating a remote monitoring method for heavy-duty diesel vehicles based on edge computing, according to an exemplary embodiment. Figure 2 This is a schematic diagram of a remote monitoring system for heavy-duty diesel vehicles based on edge computing, according to an exemplary embodiment. Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment; Figure 4 This is an architecture diagram of a remote monitoring system for heavy-duty diesel vehicles based on edge computing, according to an exemplary embodiment. Detailed Implementation

[0030] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0031] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0032] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0033] Figure 1 An embodiment of the edge computing-based remote monitoring method for heavy-duty diesel vehicles of the present invention is shown.

[0034] In this optional embodiment, the edge computing-based remote monitoring method for heavy-duty diesel vehicles includes: Step S101: Build a rule engine based on predefined transmission rules and deploy the rule engine to edge computing nodes; Step S102: Transmit the data stream of the heavy-duty diesel vehicle to the edge computing node, use the rule engine to evaluate each data field in the data stream, and assign a corresponding transmission level. The transmission level includes first-level fields, second-level fields, and third-level fields. Step S103: Based on the data fields of the assigned transmission level, match the preset transmission strategy, perform differentiated data processing, and transmit the processing results to the enterprise cloud platform through the network transmission layer.

[0035] In this optional embodiment, the data stream from the heavy-duty diesel vehicle is transmitted to an edge computing node. A rule engine is used to evaluate each data field in the data stream and assign a corresponding transmission level. The transmission levels include first-level fields, second-level fields, and third-level fields, including: Collect data streams from heavy-duty diesel vehicles and transmit the data streams to edge computing nodes; Edge computing nodes receive data streams and use the deployed rule engine to evaluate the data fields in the data streams. Based on the evaluation results, corresponding transmission levels are assigned, including first-level fields, second-level fields, and third-level fields.

[0036] In this optional embodiment, the edge computing node receives a data stream and uses a deployed rule engine to evaluate the data fields in the data stream. Based on the evaluation results, it assigns a corresponding transmission level, which includes first-level fields, second-level fields, and third-level fields, including: The rule engine in the edge computing node reads the data fields in the data stream and performs data transposition on the data fields by combining preset precision and offset; Based on the data transpose result, the rule engine evaluates whether the data fields meet the preset judgment rules, and marks the error code according to the evaluation result to allocate the corresponding transmission level. The transmission level includes first-level fields, second-level fields and third-level fields.

[0037] In this optional embodiment, the step of matching the data field with the assigned transmission level to a preset transmission strategy, performing differentiated data processing, and transmitting the processing result to the enterprise cloud platform through the network transport layer includes: Based on the first-level, second-level, and third-level fields, analyze the error codes corresponding to each field, match the preset transmission strategy, and trigger the corresponding scenarios; Based on the triggering results of each field, the corresponding compression level is set using a compression algorithm for compression, and a data packet is constructed to integrate the compression results. The data packet is then sent to the enterprise cloud platform through the network transport layer.

[0038] In this optional embodiment, the step of compressing based on the triggering results of each field, setting the corresponding compression level using a compression algorithm, constructing a data packet to integrate the compression results, and sending the data packet to the enterprise cloud platform through the network transport layer includes: Based on the triggering result of the first-level field, the compression level is set to the priority mode, the first-level field is compressed, and a first-level data packet is constructed and the compressed first-level field is integrated. The integration results are placed in a first-level priority queue, and the data packets are sent to the enterprise cloud platform through the network transport layer.

[0039] In this optional embodiment, the step of compressing data based on the triggering results of each field, setting the corresponding compression level using a compression algorithm, constructing a data packet to integrate the compression results, and sending the data packet to the enterprise cloud platform through the network transport layer further includes: Based on the triggering result of the secondary field, the compression level is set to balanced mode, the secondary field is compressed, a secondary data packet is constructed, and the compressed secondary field is integrated. Real-time monitoring of network bandwidth and target queues in secondary data packets, combined with preset thresholds for the number of backlogged headers and preset bandwidth thresholds, to process secondary data packets; The processed secondary data packets are placed into a secondary priority queue and then sent to the enterprise cloud platform through the network transport layer.

[0040] In this optional embodiment, the real-time monitoring of network bandwidth and the target queue in secondary data packets, combined with a preset threshold for the number of backlogged headers and a preset bandwidth threshold, to process the secondary data packets includes: If the number of backlogged packet headers in the target queue exceeds the preset threshold and the network bandwidth is lower than the preset bandwidth threshold, the compression level will be upgraded for the first time, and the initial degradation level will be set in the header of the secondary data packet. If the number of backlogged packet headers in the target queue continues to exceed the preset threshold, then the secondary data packets will be quantized a second time, the compression level will be upgraded a second time, and a second degradation level will be set in the header of the secondary data packets.

[0041] In this optional embodiment, the step of compressing data based on the triggering results of each field, setting the corresponding compression level using a compression algorithm, constructing a data packet to integrate the compression results, and sending the data packet to the enterprise cloud platform through the network transport layer further includes: Based on the triggering result of the third-level field, the compression level is set to compression mode to compress the third-level field; A three-level data packet is constructed, the compressed three-level fields are integrated, and the integration result is placed into a three-level priority queue. The three-level data packets in the queue are processed using the storage-transmission mode.

[0042] In this optional embodiment, the storage and transfer mode includes: If the network bandwidth of the wireless or diagnostic port is detected to be higher than a preset threshold before the heavy-duty diesel vehicle is turned off, the interrupted transmission will be resumed; otherwise, it will enter a sleep waiting state. After the breakpoint resume function is started, the cached three-level data packets are sliced ​​and compressed according to preset units, and the slices are verified in combination with the verification information. Based on the verification results, the data is uploaded to the enterprise cloud platform. If the interrupted resume transmission occurs, the resume transmission will continue within a preset time after the heavy-duty diesel vehicle is powered on again, based on the identifier and checksum of the three-level data packet, until the data is uploaded to the enterprise cloud platform.

[0043] Figure 2 An embodiment of the edge computing-based remote monitoring system for heavy-duty diesel vehicles of the present invention is shown.

[0044] In this optional embodiment, the edge computing-based heavy-duty diesel vehicle remote monitoring system includes: The rule engine deployment module 201 is used to build a rule engine based on predefined transmission rules and deploy the rule engine to edge computing nodes; The transmission level allocation module 202 is used to transmit the data stream of the heavy-duty diesel vehicle to the edge computing node, and to use the rule engine to evaluate each data field in the data stream and allocate the corresponding transmission level. The transmission level includes a first-level field, a second-level field and a third-level field. The differentiated processing and transmission module 203 is used to match the preset transmission strategy with the data fields of the assigned transmission level, perform differentiated data processing, and transmit the processing results to the enterprise cloud platform through the network transmission layer.

[0045] To facilitate understanding of the above technical solutions of the present invention, the following further describes the above technical solutions of the present invention from the perspectives of architecture and principle, as follows: Step 1: Real-time data quality assessment and grading at the edge: A dynamic rule engine is deployed and runs on the vehicle-mounted edge computing node. This engine collects the raw data stream output by the sensors of China VI heavy-duty diesel vehicles in real time (including the core fields in the engine data stream and OBD data stream required by HJ1239-2021). The engine performs real-time and dynamic evaluation on each data field in the collected data stream based on the following dimensions: 1. Rule Presetting and Loading: When edge nodes are initialized or updated, the following two types of rules are pre-set as structured rules that the rule engine can recognize.

[0046] (1) As shown in Table 1, invalid rules and out-of-bounds rules for each transmission field defined in the HJ1239-2021 standard.

[0047] Table 1: Invalid and Out-of-bounds Rules for Transmitted Fields

[0048] (2) As shown in Table 2, establish a configurable threshold parameter library for the following fields related to vehicle status and business logic.

[0049] Table 2: Threshold Parameter Library

[0050] 2. Real-time Matching and Triggering: The engine receives each data point from the engine data stream (including field ID, original value, and timestamp). After transposing the data according to the precision and offset in HJ1239.3-2021, it matches the pre-defined fields with the preset rule standards in real time. The data transposition calculation formula is as follows: Actual value = (original integer value × precision) + offset.

[0051] 3. Output error code and corresponding transmission quality level: Once the threshold rule standard conditions are met, the engine immediately outputs the corresponding error code and transmission quality level L1-L3 for the data point; the classification and grading rules for transmission quality levels L1-L3 are as follows: L1 (High Priority - Critical Emissions Data): Indicates anomalies or risks in key parameters that are directly related to emissions and require timely monitoring.

[0052] L2 (Medium Priority - Routine Monitoring Data): Refers to valid data that is within the standard range, has no immediate risk, but requires routine monitoring.

[0053] L3 (Lowest Priority - Degraded / Invalid Data): Refers to data that has been determined to be invalid or of very low value.

[0054] The judgment rules, results, and corresponding classification and grading methods for each field are shown in Table 3.

[0055] Table 3: Classification and Grading Table

[0056] Step 2: Dynamic hierarchical transmission decision-making and execution based on quality level: Based on the transmission quality levels L1-L3 assigned to each field in Step 1, differentiated data transmission strategies are implemented at the edge nodes. All three layers, L1, L2, and L3, use the Zstandard compression algorithm, but the level, queue, triggering conditions, and header fields are dynamically adjusted according to the fields and error codes. Each packet carries 1 Byte of [Field ID] + 1 Byte of [Error Code] + 1 Byte of [Transmission Level]. The compression level, queue number, and drop policy are automatically indexed by the [Transmission Level] in the following table, as shown in Table 4.

[0057] Table 4: Transmission Strategy Table

[0058] 1. L1 level fields: (1) Transmission principle: high priority, high fidelity, and extremely low latency transmission.

[0059] (2) Applicable fields and error codes: 0x07-SCR upstream NO x NO x _WARNING; 0x08 - SCR downstream NO x NO x _WARNING; 0x0D - Reactive agent balance: UREA_LOW.

[0060] (3) Compression trigger: ZSTD_compressCCtx(ctx,…,-2) is triggered when any of the above error codes are met. Compression level -2 corresponds to the ultra-high speed setting, with a compression speed ≥1GB / s, a compression ratio ≥1.8:1, and lossless compression format is maintained.

[0061] (4) Packet construction: Packet header 12 bytes, as shown in Table 5.

[0062] Table 5: Data Packet Table

[0063] (5) Transmission strategy: Force entry into Queue-0; allow 8KBSRAM buffer when bandwidth <1Mbps, delay <10s.

[0064] 2. Level 2 fields: (1) Transmission principle: medium priority, moderately high fidelity transmission; end-to-end delay ≤ 500ms; quality can be degraded during congestion.

[0065] (2) Applicable fields and error codes: 0x01 - Vehicle Speed: SENSOR_FAULT; 0x02 - Atmospheric Pressure: INVALID_ALTITUDE; 0x09 / 0x0A - SCR Inlet / Outlet Temperature: INVALID_DPF; 0x0B - DPF Pressure Difference: SENSOR_FAULT; 0x0C - Intake Air Volume: SENSOR_FAULT; 0x0E - Fuel Tank Level: FUEL_FAULT; 0x0F - Coolant Temperature: SENSOR_FAULT; 0x10 - Cumulative Mileage: SENSOR_FAULT.

[0066] (3) Compression trigger: It is triggered immediately when any error code in (2) above is met. By default, ZSTD_compressCCtx(ctx,…,3) is called, and ZSTD_compressCCtx(ctx,…,6) is dynamically increased when congestion occurs.

[0067] (4) Packet construction: 13-byte packet header, as shown in Table 6.

[0068] Table 6: Data Packet Table

[0069] (5) Transmission strategy: The data is carried by the Queue-1 secondary priority queue, and Zstdlevel-3 is used for real-time transmission by default. When the network bandwidth is less than 500kbps and the queue backlog is ≥20 packets, it will be automatically upgraded to level-6 (slight degradation, the 3rd byte of the packet header is set to 0x01). If the congestion is still more than 3 seconds and the backlog is ≥40 packets, the original data will be quantized twice before compression (shifted right by 2 bits, moderate degradation, the 3rd byte of the packet header is set to 0x02) to ensure that the end-to-end delay is always ≤500ms.

[0070] 3. Level 3 fields: (1) Transmission principle: “Invalid or low value”, only upload in batches when the vehicle is stationary and the diagnostic port / Wi-Fi ≥ 100kbps, with no hard delay requirement.

[0071] (2) Applicable fields and error codes: The error is triggered immediately when a field is marked with an L3 error code such as INVALID, OUT_OF_RANGE, INVALID_COOLANT, INVALID_STOP, INVALID_ALTITUDE, or INVALID_DPF.

[0072] (3) Compression trigger: Triggered immediately when any error code in (2) above is met, and ZSTD_compressCCtx(ctx,…,19) is called by default.

[0073] (4) Data packet construction: 15 bytes, including checksum, as shown in Table 7.

[0074] Table 7: Data Packet Table

[0075] (5) Transmission strategy: The lowest priority queue of Queue-7 is used for transmission, and the data is compressed to the maximum level of Zstdlevel-19. The data is directly written to the disk and cached. When the Wi-Fi / diagnostic port is detected to be ≥100kbps before the vehicle is turned off, the interruption resume transmission is started immediately. If there is no network, the transmission will be put into hibernation and wait. The transmission will be automatically resumed within 30 seconds after the next power-on until all slice verification is completed, ensuring that the data is 100% delivered to the cloud and never lost. Slicing: Every 32MB of raw data is compressed into a .zst slice. The file name includes the sequence number and the first 4 bytes of SHA-256, which is convenient for continuing to transmit after power failure.

[0076] In summary, this invention achieves a paradigm shift of "data quality-driven transmission": in the field of heavy-duty vehicle remote monitoring, at the source of data generation (vehicle edge node), real-time, dynamic, and multi-dimensional (standard, status, historical) quality assessments are performed on the smallest data unit (field) required by regulations, and refined differentiated transmission strategies are implemented accordingly. By filtering invalid L3 data at the source and implementing high compression ratio + delayed transmission for low-value data, invalid network traffic is significantly reduced (estimated >40% bandwidth savings). By allocating lossless / low-loss compression + highest priority queues + fast retransmission + 5G slicing resources to L0 / L1 level critical data, its transmission is centrally guaranteed, thereby significantly reducing its packet loss rate (<0.1%) and meeting stringent standards. Pre-filtering invalid data and marking degraded data at the edge significantly improves the overall quality of data transmitted to the platform (invalid data volume reduced by >50%), improving the efficiency and accuracy of data analysis on the platform side. This fundamentally solves the problem of excessive packet loss (>1%) caused by the existing full upload mode, enabling the system as a whole to meet the HJ1239-2021 requirement of ≤1% packet loss. The dynamic rule engine supports remote updates and can flexibly adapt to revisions of the GB17691 / HJ1239 standards without requiring modifications to the vehicle hardware.

[0077] like Figure 4 As shown, the system architecture of the present invention includes: 1. Data Acquisition Layer: Raw data is acquired by sensors such as the OBD interface and engine ECU.

[0078] 2. Edge computing layer: Quality assessment engine: Field-level assessment based on GB17691 / HJ1239 rules; Hierarchical transmission strategy: Differentiated transmission is performed according to L1-L3.

[0079] 3. Network transport layer: Transport channels are allocated according to level.

[0080] 4. Platform layer: After classifying and processing the data, the enterprise platform forwards it to the national platform.

[0081] In specific embodiments, the present invention includes the following: Step 1: Data Acquisition and Reporting: The vehicle terminal periodically collects multiple parameter fields from the engine data stream and OBD data stream on the vehicle's CAN bus, including but not limited to vehicle speed (0x01), atmospheric pressure (0x02), NOx values ​​upstream and downstream of SCR (0x07, 0x08), and reactant balance (0x0D). The collected raw data is aggregated to the edge computing node within the vehicle terminal.

[0082] Step 2: Edge-side data quality assessment and classification: The edge computing node has a built-in rule base to perform real-time verification and dynamic modeling analysis on each collected field value, and assign a transmission quality level (L1, L2, L3) to it according to predefined rules.

[0083] 1. Taking the NOx value (0x07) upstream of SCR as an example: The edge node reads its byte value. If it is 0xFF,0xFF, then the error code INVALID is marked and L3 level is assigned. If the byte value is valid, then the actual value is calculated. If the actual value is >300ppm, then the error code NOX_WARNING is marked, which indicates that a critical emission exceedance event has occurred, and L1 level is assigned immediately. If the coolant temperature is <70℃, then the error code INVALID_COOLANT is marked and L3 level is assigned.

[0084] 2. Taking the reactant balance (0x0D) as an example: If the actual value is <5%, the error code UREA_LOW is marked, indicating that the urea is about to be exhausted and needs to be dealt with in time, and L1 level is allocated.

[0085] Step 3: Execution of hierarchical dynamic transmission strategy: The edge node indexes the pre-configured transmission strategy table and performs differentiated data processing and transmission preparation based on the transmission level determined by the edge side data quality assessment and hierarchical classification.

[0086] 1. L1 level data processing (high priority): Triggering scenario: NOX_WARNING or UREA_LOW error code is detected.

[0087] Data compression: Immediately invoke the Zstandard compression library, use the ZSTD_compressCCtx function, set the compression level to -2 (ultra-high speed mode), and perform lossless compression on the data.

[0088] Packet construction: Generate a 12-byte dedicated header, followed by the compressed data body.

[0089] Packet header structure: 0xA1 (L1 type identifier); 0x07 (field ID); 0x04 (error code, representing NOX_WARNING); original length (2 bytes); compressed length (2 bytes); high-precision timestamp (4 bytes); serial number (1 byte).

[0090] Transmission scheduling: Place the data packet into the highest priority Queue-0 send queue; the network stack prioritizes sending data in this queue, ensuring that the end-to-end latency is less than 10ms and preventing packet loss.

[0091] 2. Level 2 data processing (medium priority): Triggering scenario: Error codes such as vehicle speed SENSOR_FAULT and DPF pressure difference SENSOR_FAULT are detected.

[0092] Data compression: Zstandard compression is invoked immediately, with an initial compression level of 3 (balanced mode). The system monitors network bandwidth and queue status in real time; if more than 20 packets are backlogged in Queue-1 and the bandwidth is below 500kbps, the compression level is automatically increased to 6 (higher compression ratio), and the degradation level is set to 0x01 (mild degradation) in the packet header; if congestion persists, the data is first quantized twice (e.g., right-shifted by 2 bits) before compression, and the degradation level is set to 0x02 (moderate degradation).

[0093] Packet construction: Generate a 13-byte packet header.

[0094] Packet header structure: 0xA2 (L2 type identifier); Field ID; Error code; Degradation level (1 byte, dynamically reflecting the compression strategy); Original length (2 bytes); Compressed length (2 bytes); Timestamp (4 bytes); Serial number (1 byte).

[0095] Transmission scheduling: Packets are placed into Queue-1 with the goal of reliable delivery within a 500ms delay, allowing for lossy compression to ensure latency.

[0096] 3. Level 3 data processing (lowest priority): Triggering scenario: Data is determined to be invalid (such as INVALID, OUT_OF_RANGE) or of extremely low value.

[0097] Data compression: Invoke Zstandard compression, using level 19 (extreme compression mode) to minimize data size.

[0098] Packet construction: Generate a 15-byte packet header, emphasizing offline verification.

[0099] Packet header structure: 0xA3 (L3 type identifier); field ID; error code; original length (4 bytes); timestamp (4 bytes); first 4 bytes of SHA-256 checksum (4 bytes, used for resuming interrupted downloads).

[0100] Transmission scheduling: The data packet is placed in the lowest priority Queue-7 and persistently cached in local storage. The transmission strategy is "storage-transmission mode". Transmission is initiated only when the vehicle is off and Wi-Fi or diagnostic port bandwidth is detected to be higher than 100kbps. During transmission, the cached data is sliced, compressed, and uploaded in units of 32MB raw data. If transmission is interrupted, it can resume transmission from the breakpoint based on the file name sequence number and checksum after the next power-on, ensuring that the data is eventually delivered 100% reliably.

[0101] Step 4: Data Upload and Application All data packets processed through different queues and strategies are ultimately transmitted to the enterprise cloud platform via the network transport layer (4G / 5G, Wi-Fi, etc.). The enterprise platform can quickly parse and classify the packets based on the type identifier (0xA1, 0xA2, 0xA3) in the packet header.

[0102] L1 data: Immediately triggers the platform's alarm mechanism, notifying administrators or the after-sales service system for emergency handling.

[0103] L2 data: used to generate routine vehicle health reports and early warning work orders, and to schedule planned maintenance.

[0104] L3 data: Stored in cold storage for long-term trend analysis, big data model training, and compliance auditing. After processing, the enterprise platform can further upload the relevant data to the national regulatory platform.

[0105] In summary, this invention prioritizes the most critical (Level 1) data within limited wireless communication bandwidth, achieving optimal utilization of network resources; it enables near real-time reporting and alarms for emergency events such as excessive emissions and depleted reactants, meeting regulatory requirements for real-time monitoring; it performs data quality assessment at the edge before transmission, filtering out a large amount of invalid data (Level 3), avoiding network congestion and wasted storage space caused by "data floods"; and through differentiated queue management, packet loss strategies, and breakpoint resumption mechanisms, it ensures that data of different levels can be reliably delivered under agreed-upon rules.

[0106] Furthermore, the judgment logic of the dynamic rule engine in this invention, besides explicit if-else rules, can also be implemented using decision trees, state machines, or lightweight machine learning models (such as decision tree ensembles). The core lies in field-level evaluation based on standards, states, and thresholds, emphasizing that the core is the evaluation logic, not the specific programming implementation. For compression algorithms, L1 level lossless compression can use LZ4, Snappy, or ZIP; L2 level lossy compression can use JPEG2000 (for specific data), DeltaEncoding+EntropyCoding; and L3 level high compression ratio can use Brotli, LZMA, etc., instead of Zstandard. The core is to select the matching compression strength and algorithm type according to the level. Parameters such as real-time transmission bandwidth threshold and data transmission latency can be dynamically configured and adjusted according to specific vehicle models, network environments, and platform requirements.

[0107] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0108] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0109] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0110] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0111] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0112] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A method for remote monitoring of heavy-duty diesel vehicles based on edge computing, characterized in that, The method includes: Based on predefined transmission rules, a rule engine is built and deployed to edge computing nodes; The data stream from the heavy-duty diesel vehicle is transmitted to the edge computing node. The rule engine is used to evaluate each data field in the data stream and assign a corresponding transmission level. The transmission level includes first-level fields, second-level fields and third-level fields. Based on the data fields assigned to the transmission level, the system matches the preset transmission strategy, performs differentiated data processing, and transmits the processing results to the enterprise cloud platform through the network transport layer.

2. The remote monitoring method for heavy-duty diesel vehicles based on edge computing according to claim 1, characterized in that, The process involves transmitting the data stream from the heavy-duty diesel vehicle to an edge computing node, using a rule engine to evaluate each data field in the data stream, and assigning a corresponding transmission level. The transmission levels include first-level fields, second-level fields, and third-level fields. Collect data streams from heavy-duty diesel vehicles and transmit the data streams to edge computing nodes; Edge computing nodes receive data streams and use the deployed rule engine to evaluate the data fields in the data streams. Based on the evaluation results, corresponding transmission levels are assigned, including first-level fields, second-level fields, and third-level fields.

3. The remote monitoring method for heavy-duty diesel vehicles based on edge computing according to claim 2, characterized in that, The edge computing node receives the data stream and uses the deployed rule engine to evaluate the data fields in the data stream. Based on the evaluation results, it assigns a corresponding transmission level, which includes first-level fields, second-level fields, and third-level fields, including: The rule engine in the edge computing node reads the data fields in the data stream and performs data transposition on the data fields by combining preset precision and offset; Based on the data transpose result, the rule engine evaluates whether the data fields meet the preset judgment rules, and marks the error code according to the evaluation result to allocate the corresponding transmission level. The transmission level includes first-level fields, second-level fields and third-level fields.

4. The remote monitoring method for heavy-duty diesel vehicles based on edge computing according to claim 1, characterized in that, The process of matching the data fields assigned to the transmission level with a preset transmission strategy, performing differentiated data processing, and transmitting the processing results to the enterprise cloud platform through the network transport layer includes: Based on the first-level, second-level, and third-level fields, analyze the error codes corresponding to each field, match the preset transmission strategy, and trigger the corresponding scenarios; Based on the triggering results of each field, the corresponding compression level is set using a compression algorithm for compression, and a data packet is constructed to integrate the compression results. The data packet is then sent to the enterprise cloud platform through the network transport layer.

5. The remote monitoring method for heavy-duty diesel vehicles based on edge computing according to claim 4, characterized in that, The process of compressing data based on the trigger results of each field using a compression algorithm to set the corresponding compression level, constructing a data packet to integrate the compression results, and sending the data packet to the enterprise cloud platform through the network transport layer includes: Based on the triggering result of the first-level field, the compression level is set to the priority mode, the first-level field is compressed, and a first-level data packet is constructed and the compressed first-level field is integrated. The integration results are placed in a first-level priority queue, and the data packets are sent to the enterprise cloud platform through the network transport layer.

6. The remote monitoring method for heavy-duty diesel vehicles based on edge computing according to claim 5, characterized in that, The process of compressing data based on the trigger results of each field, setting corresponding compression levels using compression algorithms, constructing data packets to integrate the compression results, and sending the data packets to the enterprise cloud platform through the network transport layer also includes: Based on the triggering result of the secondary field, the compression level is set to balanced mode, the secondary field is compressed, a secondary data packet is constructed, and the compressed secondary field is integrated. Real-time monitoring of network bandwidth and target queues in secondary data packets, combined with preset thresholds for the number of backlogged headers and preset bandwidth thresholds, to process secondary data packets; The processed secondary data packets are placed into a secondary priority queue and then sent to the enterprise cloud platform through the network transport layer.

7. The remote monitoring method for heavy-duty diesel vehicles based on edge computing according to claim 6, characterized in that, The real-time monitoring of network bandwidth and target queues in secondary data packets, combined with preset thresholds for the number of backlogged headers and preset bandwidth thresholds, involves processing secondary data packets as follows: If the number of backlogged packet headers in the target queue exceeds the preset threshold and the network bandwidth is lower than the preset bandwidth threshold, the compression level will be upgraded for the first time, and the initial degradation level will be set in the header of the secondary data packet. If the number of backlogged packet headers in the target queue continues to exceed the preset threshold, then the secondary data packets will be quantized a second time, the compression level will be upgraded a second time, and a second degradation level will be set in the header of the secondary data packets.

8. The remote monitoring method for heavy-duty diesel vehicles based on edge computing according to claim 7, characterized in that, The process of compressing data based on the trigger results of each field, setting corresponding compression levels using compression algorithms, constructing data packets to integrate the compression results, and sending the data packets to the enterprise cloud platform through the network transport layer also includes: Based on the triggering result of the third-level field, the compression level is set to compression mode to compress the third-level field; A three-level data packet is constructed, the compressed three-level fields are integrated, and the integration result is placed into a three-level priority queue. The three-level data packets in the queue are processed using the storage-transmission mode.

9. The remote monitoring method for heavy-duty diesel vehicles based on edge computing according to claim 8, characterized in that, The storage and transfer mode includes: If the network bandwidth of the wireless or diagnostic port is detected to be higher than a preset threshold before the heavy-duty diesel vehicle is turned off, the interrupted transmission will be resumed; otherwise, it will enter a sleep waiting state. After the breakpoint resume function is started, the cached three-level data packets are sliced ​​and compressed according to preset units, and the slices are verified in combination with the verification information. Based on the verification results, the data is uploaded to the enterprise cloud platform. If the interrupted resume transmission occurs, the resume transmission will continue within a preset time after the heavy-duty diesel vehicle is powered on again, based on the identifier and checksum of the three-level data packet, until the data is uploaded to the enterprise cloud platform.

10. A remote monitoring system for heavy-duty diesel vehicles based on edge computing, characterized in that, The system includes: The rule engine deployment module is used to build a rule engine based on predefined transmission rules and deploy the rule engine to edge computing nodes; The transmission level allocation module is used to transmit the data stream of heavy-duty diesel vehicles to the edge computing node. It uses a rule engine to evaluate each data field in the data stream and allocate a corresponding transmission level. The transmission level includes a first-level field, a second-level field, and a third-level field. The differentiated processing and transmission module is used to match the preset transmission strategy with the data fields assigned to the transmission level, perform differentiated data processing, and transmit the processing results to the enterprise cloud platform through the network transmission layer.

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