Work order circulation management method and system for automobile maintenance

Abstract

The invention relates to the technical field related to work order management, in particular to a work order circulation management method and system for automobile maintenance, and the method comprises the steps: introducing workshop real-time operation data and work order execution scene parameters, and generating a conflict work order scheduling instruction set; and triggering a work order circulation linkage scheduling mechanism according to the abnormal type and priority level of work order circulation, and performing cooperative execution management and control on a plurality of edge computing nodes and each work order execution link according to the consistency deviation of the system process. The technical problems of exception identification lagging, scheduling response stiffness and work order execution overall efficiency limitation caused by lack of dynamic perception of the whole process of the maintenance task and frequent resource contention are solved, dynamic scheduling path planning of the maintenance task flow is realized, process conflicts are effectively avoided, exception types and priorities are identified, and the efficiency of work order execution is improved. Cooperative management and control are carried out on the plurality of edge nodes and execution links according to the process consistency deviation, and the technical effects of work order circulation robustness and closed-loop processing capability are enhanced.

Description

Technical Field

[0001] This invention relates to the technical field of work order management, specifically to a work order flow management method and system for automobile repair and maintenance. Background Technology

[0002] With the continuous growth of car ownership and the increasing complexity of after-sales service demands, the automotive repair and maintenance industry is accelerating its transformation towards digitalization and intelligence. Work orders need to be efficiently coordinated across multiple stages such as diagnosis, disassembly and assembly, testing, parts replacement, and quality inspection, which places higher demands on the speed of abnormal response and the robustness of the system.

[0003] Work order management, as a core support for maintenance services, directly impacts workshop resource scheduling efficiency, customer waiting time, and service quality consistency. However, current mainstream work order management systems generally adopt a centralized, linear process-driven model, lacking the ability to dynamically perceive the real-time operating status of the workshop. Work order progress relies on simple status markers, making it difficult to build accurate timeliness assessment and process correlation models. When anomalies such as duplicate work assignments, missing logs, or process conflicts occur, the system responds late, leading to process blockages, resource waste, and even service disputes. Furthermore, data silos between edge nodes, transmission interference, and tampering risks further weaken the reliability and collaborative efficiency of work order flow, making it difficult to meet the needs of high-concurrency, high-reliability intelligent maintenance scenarios.

[0004] In summary, existing technologies suffer from the following technical problems: the work order workflow system adopts a linear process-driven mode, lacks real-time dynamic perception of the entire maintenance task process, frequently experiences resource contention, resulting in delayed anomaly identification, rigid scheduling response, and limited overall work order execution efficiency. Summary of the Invention

[0005] This application provides a work order flow management method and system for automobile repair and maintenance, aiming to solve the technical problems of existing work order flow systems that adopt a linear process-driven mode, lack real-time dynamic perception of the entire repair task process, frequently experience resource contention, resulting in delayed anomaly identification, rigid scheduling response, and limited overall work order execution efficiency.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows: In its first aspect, this application provides a method for managing work order flow in automotive repair and maintenance. The method includes: deploying multi-dimensional scheduling nodes to construct a comprehensive work order monitoring network; generating a work order progress heatmap based on the automotive repair and maintenance task flow; and determining the work order processing time efficiency gradient and process correlation matrix through multiple edge computing nodes; introducing real-time workshop operation data and work order execution scenario parameters to perform dynamic scheduling path planning and generate a conflict work order scheduling instruction set; based on a work order process consistency risk cloud map, performing multi-source data fusion analysis at multiple edge computing nodes according to the work order processing time efficiency gradient and process correlation matrix, and dynamically identifying the abnormal types and priority levels of work order flow in conjunction with the conflict work order scheduling instruction set; triggering a work order flow linkage scheduling mechanism based on the abnormal types and priority levels of the work order flow, and performing collaborative execution control of multiple edge computing nodes and each work order execution process based on system process consistency deviations.

[0007] In a second aspect, this application provides a work order flow management system for automobile repair and maintenance, wherein the system includes: a full-domain work order monitoring network construction module: deploying multi-dimensional scheduling nodes to construct a full-domain work order monitoring network, generating a work order progress heatmap based on the automobile repair and maintenance task flow, and determining the work order processing time efficiency gradient and link correlation matrix through multiple edge computing nodes; a scheduling path dynamic planning module: introducing real-time workshop operation data and work order execution scenario parameters, performing dynamic scheduling path planning, and generating a conflict work order scheduling instruction set; a dynamic identification module: based on the work order process consistency risk cloud map, performing multi-source data fusion analysis at multiple edge computing nodes according to the work order processing time efficiency gradient and link correlation matrix, and dynamically identifying the abnormal type and priority level of work order flow in conjunction with the conflict work order scheduling instruction set; and an execution control module: triggering a work order flow linkage scheduling mechanism based on the abnormal type and priority level of the work order flow, and performing collaborative execution control of multiple edge computing nodes and each work order execution link according to the system process consistency deviation.

[0008] In summary, one or more technical solutions provided in this application achieve the technical effect of real-time visualization and dynamic scheduling path planning of maintenance task flow through a full-domain work order monitoring network and work order progress heatmap, effectively avoiding process conflicts, identifying abnormal types and priorities, and implementing collaborative control of multiple edge nodes and execution links based on process consistency deviations, thereby enhancing the robustness and closed-loop handling capability of work order flow. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0010] Figure 1 This application provides a flowchart illustrating a work order flow management method for automobile repair and maintenance.

[0011] Figure 2 This application provides a structural diagram of a work order management system for automobile repair and maintenance.

[0012] Explanation of reference numerals in the attached diagram: M100 is the global work order monitoring network construction module; M200 is the scheduling path dynamic planning module; M300 is the dynamic identification module; and M400 is the execution control module. Detailed Implementation

[0013] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0014] Example 1: The present application will be described in detail below with reference to the accompanying drawings, as follows... Figure 1 As shown, this application provides a work order flow management method for automobile repair and maintenance, wherein the method includes: S1: Deploy multi-dimensional scheduling nodes to build a full-domain work order monitoring network, generate a work order progress heatmap based on the car repair and maintenance task flow, and determine the work order processing time gradient and link correlation matrix through multiple edge computing nodes.

[0015] Specifically, multi-dimensional scheduling nodes refer to scheduling control points distributed in different locations and functional areas of the automotive repair and maintenance workshop, which can monitor and manage the flow of work orders from multiple angles and levels, including their physical location distribution; the full-domain work order monitoring network refers to a monitoring system built through these multi-dimensional scheduling nodes that covers the entire repair and maintenance workshop, and can acquire and process the status information of work orders at each stage in real time; the work order progress heatmap refers to the use of color variations to represent the progress and processing efficiency of work orders at different stages, intuitively displaying the real-time status and potential problems of work orders.

[0016] Edge computing nodes are computing units deployed at the network edge to quickly process and analyze local data, reduce data transmission latency, and improve system response speed. Work order processing time gradient refers to the difference in processing time for a work order at different stages. Quantitative analysis can identify which stages are bottlenecks. The stage correlation matrix describes the interrelationships and dependencies between different maintenance stages of a work order, helping to optimize the overall process.

[0017] Execution Steps: Edge computing gateways are installed as scheduling nodes at multiple key workstations in 4S stores or comprehensive repair shops. Each node collects data in real time, including work order ID, operator identity, equipment start / stop signals, and process start / end timestamps. These key workstations include the lift station, four-wheel alignment area, and paint booth. Based on this collected data, a comprehensive work order progress heatmap is updated periodically. Simultaneously, the edge nodes use a sliding window algorithm to calculate the timeliness gradient of each work order at each execution stage, and combine this with historical data to construct a work order processing timeliness gradient-stage correlation matrix, quantitatively analyzing the dependencies between stages. Preferably, the work order processing timeliness gradient-stage correlation matrix is ​​determined to provide data support for subsequent dynamic scheduling.

[0018] S2: Introduce real-time workshop operation data and work order execution scenario parameters to perform dynamic planning of scheduling paths and generate conflict work order scheduling instruction sets; S3: Based on the work order process consistency risk cloud map, perform multi-source data fusion analysis at multiple edge computing nodes according to the work order processing time efficiency gradient and link correlation matrix, and combine the conflict work order scheduling instruction sets to dynamically identify the abnormal types and priority levels of work order flow.

[0019] Specifically, real-time workshop operation data refers to dynamic data such as equipment operating status, personnel operation, and material inventory during the automotive repair and maintenance process. This data reflects the actual working environment and resource utilization in the workshop in real time. Work order execution scenario parameters refer to specific parameters related to work order execution, including the complexity of the repair task, required tools and equipment, and technician skill level. These parameters directly affect the efficiency and quality of work order execution. Dynamic scheduling path planning is an intelligent planning based on real-time data that optimizes the flow path of work orders between different stages to improve efficiency and reduce conflicts.

[0020] The conflict work order scheduling instruction set refers to a set of instructions generated when resource competition or execution order conflicts occur between multiple work orders, used to adjust the execution order and resource allocation of work orders; the work order process consistency risk cloud map is used to display the risk points and abnormal situations that may occur in different stages of the work order, and the severity of the risk is indicated by changes in color or graphics; multi-source data fusion analysis refers to the integration and analysis of data from different sources to obtain more comprehensive and accurate information. Data from different sources includes real-time workshop operation data, historical work order data, and edge computing node data; anomaly type and priority level refers to classifying the anomalies that occur during the work order process and assigning different priorities according to their impact on the overall process, so that the system can reasonably allocate resources for processing.

[0021] Execution steps: At a certain frequency, data streams from various workshop IoT devices are fused with scenario parameters from multiple work orders currently awaiting scheduling. A lightweight graph neural network is used to replan the scheduling path at the edge nodes. Furthermore, when two vehicles of the same model are detected to be simultaneously entering the transmission fluid change stage and sharing the same dedicated fluid change equipment, a conflict scheduling instruction set is automatically generated based on the urgency of the work order and the dependence strength of subsequent stages. At the same time, each edge node performs multi-source fusion analysis on this instruction set and the locally stored work order processing time efficiency gradient and stage correlation matrix, and overlays a process consistency risk cloud map.

[0022] For example, "disassembly / reassembly" and "inspection" have a strong sequential dependency. If the process consistency risk cloud map is trained based on historical compliant work orders, and the probability of identifying the abnormal pattern of "entering disassembly / reassembly without completing OBD code reading" exceeds 80%, this fusion analysis can dynamically determine that the current work order, due to skipping diagnosis and directly proceeding to disassembly / reassembly, belongs to a high-risk process jump anomaly, which is set to the highest priority and requires immediate intervention. Preferably, through dynamic planning and intelligent analysis, abnormal situations in work order flow can be identified and handled in real time, optimizing resource allocation, reducing conflicts and delays, and significantly improving the overall efficiency and robustness of work order flow.

[0023] S4: Based on the abnormal type and priority level of the work order flow, trigger the work order flow linkage scheduling mechanism, and perform collaborative execution control of multiple edge computing nodes and each work order execution link according to the consistency deviation of the system process.

[0024] Specifically, the anomaly types and priority levels in work order flow refer to the classification of anomalies identified during the work order flow process and the priority assigned based on their impact on the overall process. For example, anomaly types may include missing maintenance logs, incorrect parts matching, or process timeouts. Priorities are divided into high, medium, and low levels based on the severity of the anomaly and its impact on subsequent processes. The work order flow linkage scheduling mechanism refers to a cross-node collaborative response strategy driven by anomaly events. When a specific anomaly type and its priority are identified, the system automatically activates a preset scheduling rule chain to coordinate multiple edge computing nodes to synchronously adjust work order paths, resource allocation, or personnel tasks. Specific anomaly types include process skipping, resource over-ordering, and missing logs.

[0025] System process consistency deviation refers to the structured difference between the actual execution path of the current work order and the standard maintenance process template, which is usually quantified by graph editing distance, state transition probability offset, etc. Multiple edge computing nodes refer to computing units distributed in different locations in the workshop, used to process local data and execute real-time decisions. Collaborative execution control emphasizes the realization of decentralized closed-loop control at the edge, that is, while maintaining local autonomy, each edge node dynamically negotiates and adjusts the execution strategy of its own link according to the global consistency goal, rather than relying on the central server to issue instructions one by one. The execution strategy of the work order execution link includes delayed start, insertion of checkpoints, and rollback operation.

[0026] Execution steps: When the system determines that a work order is a high-risk process jump anomaly, the linkage scheduling mechanism immediately triggers the work order flow linkage scheduling mechanism. Specifically: First, a forced OBD re-inspection task is inserted at the edge node of the diagnostic area and the access permissions of subsequent workstations are locked; Second, a pause execution command is sent to the edge node of the disassembly and assembly area, and the location of the work order is highlighted in yellow and flashing in the digital twin workshop view; At the same time, the scheduling engine recalculates the resource occupancy sequence of each affected related work order based on the current process consistency deviation value, and pushes new reservation windows to the edge controllers of shared equipment such as lifts and torque wrenches. Preferably, through linkage scheduling and collaborative management, command broadcasting and local policy updates are performed without cloud intervention, enabling rapid response and handling of abnormal situations and reducing the impact of abnormalities on the overall process.

[0027] Furthermore, the method of this application also includes: During the operation of the multi-dimensional scheduling node, dual-modal data collection of work order system records and workshop operation logs is performed simultaneously, and a work order process consistency risk cloud map is generated by integrating the historical work order anomaly feature library.

[0028] Specifically, the multi-dimensional scheduling node operation process refers to the normal operation of multiple scheduling nodes distributed across different functional areas and stages in the automotive repair and maintenance work order flow management; work order system records refer to the records of information such as work order status, progress, and task allocation stored in the work order management system; workshop operation logs refer to log data generated during actual workshop operations, including equipment operation records, technician operation records, and material usage records; dual-modal data acquisition refers to simultaneously collecting data from two different sources, work order system records and workshop operation logs, to obtain more comprehensive information; the historical work order anomaly feature database refers to a database that stores various anomalies and their characteristics that have occurred in past work orders, used for analysis and identification of potential risks; and the work order process consistency risk cloud map refers to displaying potential risk points and anomalies in different stages of the work order through color or graphic changes, helping to quickly identify potential problems.

[0029] Execution steps: Information such as work order creation, assignment, and execution progress needs to be extracted from the work order system, and log data from various operation points in the workshop needs to be collected at the same time. This log data usually contains noise or redundant information, so preprocessing work such as cleaning and format conversion is required. Based on the historical work order anomaly feature library, key features that have a significant impact on the consistency of the work order process are identified, including the dependencies between processes, resource allocation, and time arrangements. For operation logs, the identification of specific words or patterns may be involved in order to extract potential problem signals.

[0030] Using machine learning algorithms or statistical methods, combined with labeled work order anomaly cases, a model is trained to predict whether new work orders have process consistency risks. This stage requires continuous parameter adjustment and model performance verification to ensure accuracy and generalization ability. The analysis results are then transformed into an intuitive and easy-to-understand form: a work order process consistency risk cloud map. This is typically achieved using heatmaps or geographic information systems, with different colors or symbols representing different levels of risk to quickly locate high-risk areas and implement corresponding measures. As more real-world data accumulates and the business environment changes, the feature library needs to be updated regularly and the model retrained to ensure the timeliness and accuracy of the risk assessment system.

[0031] Furthermore, during the operation of the multi-dimensional scheduling node, dual-modal data acquisition of work order system records and workshop operation logs is performed simultaneously. The method of this application also includes: During the information verification process of automotive repair and maintenance work orders, the data verification accuracy is dynamically adjusted according to the complexity of the repair project. At the same time, formatting correction is performed on the workshop operation log data, and a nonlinear regression function is established for the integrity of the operation log and the work order scheduling pass rate. Based on the nonlinear regression function of the integrity of the operation log and the work order scheduling pass rate, anomaly samples of work order flow are generated by an adversarial generative network. The deep residual network trained is used to classify work order-operation record mismatch, missing maintenance log information, and duplicate work assignment faults.

[0032] Specifically, the complexity of a repair project refers to a quantitative indicator that comprehensively evaluates factors such as vehicle type, fault category, required working hours, and the number of trades involved. It is used to characterize the technical difficulty and process depth of a single work order. Dynamically adjusting data verification accuracy refers to adaptively increasing or decreasing the granularity and fault tolerance threshold of verification rules based on this complexity when verifying work order information. Work order information includes part codes and process sequences. Formatting correction refers to standardizing workshop operation logs, including unifying time formats, completing missing fields, and parsing key action semantics in free text.

[0033] Operation log completeness is defined as the proportion of logs containing necessary operation elements, including equipment, start and end times, and result status. A nonlinear regression function is used to model the complex mapping relationship between log completeness and work order scheduling pass rate, reflecting the impact of log quality on process smoothness. Operation log completeness is the input variable, and work order scheduling pass rate is the output variable. An adversarial generative network generates high-fidelity data samples through adversarial training between the generator and discriminator, which is used here to generate abnormal work order flow samples. A deep residual network acts as a classifier, identifying three typical anomalies: work order-operation record mismatch, missing maintenance log information, and duplicate work assignment faults. For example, a work order-operation record mismatch might include a work order requesting brake pad replacement but the log only showing cleaning.

[0034] Execution steps: The complexity of maintenance projects is calculated in real time by a preset rule engine. For example, maintenance of the three-electric system of new energy vehicles triggers a high-precision verification mode, which requires that all key fields must be complete and logically consistent. If the verification fails, the work order will be blocked. Key fields include VIN code, high-voltage power failure confirmation, and insulation test value. Ordinary maintenance only verifies the core fields and allows a certain time stamp deviation. At the same time, the log cleaning module deployed on the edge node formats and corrects the original operation logs and uses a BERT-based named entity recognition model to extract action semantics, thereby improving the log structuring rate.

[0035] The workshop operation log data undergoes formatting correction, and a nonlinear regression function is fitted to correlate the completeness of the operation logs with the first-time pass rate of work order scheduling. This nonlinear regression function indicates that as log completeness improves, the scheduling pass rate also improves. Guided by this nonlinear regression function, a conditional GAN ​​is used to generate data samples, which are then mixed with real abnormal samples to train a deep residual network. This enables the network to accurately classify and identify abnormal situations in the actual work order flow. Furthermore, dynamic verification is used to improve the accuracy of work order information.

[0036] Furthermore, this application's method also includes triggering a work order flow linkage scheduling mechanism and conducting collaborative execution control of multiple edge computing nodes and various work order execution stages based on system process consistency deviations. The multi-dimensional scheduling node is equipped with a multi-source interference monitoring unit to construct a spatial distribution map of scheduling harmonic interference caused by legacy issues from the workshop system upgrade. Simultaneously, it employs an encrypted communication protocol to combat interference noise generated by cross-process work order data transmission and dynamically adjusts the transmission frequency of the flow scheduling signal. When the data transmission signal-to-noise ratio is detected to be lower than a preset signal-to-noise ratio threshold, a data relay synchronization unit is activated to construct a mesh network topology. The relay nodes of the mesh network topology are selected to conform to the spatial distribution map of scheduling harmonic interference.

[0037] Specifically, a multi-source interference monitoring unit refers to a device or module installed on a multi-dimensional scheduling node to monitor interference signals from different sources, which may originate from workshop equipment operation, legacy issues from system upgrades, or other external factors; a scheduling harmonic interference spatial distribution map is used to display the degree and distribution of harmonic interference affecting scheduling signals at different locations within the workshop, helping to identify interference sources and high-interference areas; an encrypted communication protocol uses encryption technology to protect the integrity and security of data during transmission, preventing data tampering or leakage; cross-process work order data transmission refers to the transmission of work order data between different processes, and this data transmission may be affected by the complex electromagnetic environment of the workshop.

[0038] Signal-to-noise ratio (SNR) is the ratio of signal power to noise power, used to measure the quality of signal transmission. A low SNR indicates that the signal is subject to more noise interference. The preset SNR threshold is a pre-set SNR value. When the actual SNR is lower than this threshold, a corresponding processing mechanism is triggered. The data relay synchronization unit is used as a relay point during data transmission to enhance signal strength and synchronize data, ensuring the reliability of data transmission. In a mesh network topology, each node is connected to multiple other nodes, forming a mesh structure with high reliability and redundancy.

[0039] Execution steps: The multi-dimensional scheduling node is responsible for coordinating the flow of work orders between different processes within the workshop, ensuring that each task can be completed efficiently and orderly. It collects information through onboard sensors and other monitoring equipment, including equipment status, work order progress, and environmental parameters. To identify and quantify potential scheduling harmonic interference caused by system upgrades or other factors, the scheduling node is equipped with a multi-source interference monitoring unit. This unit can analyze data streams from different sources in real time and determine interference patterns that may affect the efficiency and accuracy of work order flow. Based on the collected data, an interference spatial distribution map is drawn, showing the interference intensity and type at various locations within the workshop, and providing a basis for subsequent targeted measures.

[0040] To prevent information leakage or errors caused by interference noise during cross-process work order data transmission, an encrypted communication protocol is used for protection. This not only enhances the security of data transmission but also reduces the impact of external noise to a certain extent. Based on the current signal-to-noise ratio, the transmission frequency of the flow scheduling signal is dynamically adjusted. When the signal-to-noise ratio is detected to be low, the transmission frequency is reduced to reduce the bit error rate; conversely, the frequency is appropriately increased to speed up data flow.

[0041] Once the signal-to-noise ratio of data transmission is found to be lower than the preset threshold, the built-in data relay synchronization unit is automatically activated. The role of the data relay synchronization unit is to establish a temporary data transmission path when necessary, ensuring that important information can reach its destination smoothly. When selecting relay nodes, the previously constructed spatial distribution map of scheduling harmonic interference is used as a reference, and nodes located in low-interference areas or with good anti-interference capabilities are given priority as relay nodes to form an efficient mesh network topology. This effectively avoids the impact of high-interference areas on data transmission and further improves the stability and reliability of the entire network.

[0042] Furthermore, the method of this application also includes: A work order verification sensor array is deployed at the scheduling correction node to detect deviation fluctuation characteristics and perform multi-source data positioning and scheduling logic collaborative control. At the same time, to meet the system process consistency requirements of automobile repair and maintenance work orders, the control instructions issued by multiple edge computing nodes are synchronously verified based on the operational health status of workshop repair equipment and parts inventory dynamics.

[0043] Specifically, the scheduling correction node is a node used to correct and adjust scheduling decisions. It is usually located in critical process links and has the ability to intervene and provide feedback adjustment in real time. The work order verification sensor array refers to a group of heterogeneous sensing devices integrated into the scheduling correction node, including RFID readers, vision cameras, device communication interfaces and time synchronization modules, which together constitute a multimodal monitoring system for the work order execution process. Deviation fluctuation characteristics refer to the dynamic deviation pattern of the actual execution trajectory of the work order relative to the standard process template. Its fluctuation can be quantified by indicators such as time series variance, state transition anomaly rate or entropy value. Multi-source data positioning refers to the fusion of information from multiple channels such as sensor arrays, work order systems, and device IoT interfaces to accurately locate the specific link, equipment or personnel where the deviation occurs.

[0044] The scheduling logic collaborative control emphasizes that the correction node not only identifies deviations but also links with the scheduling engine to adjust subsequent paths or trigger corrective actions, achieving a closed loop of perception, positioning, and control. The system process consistency requirement refers to the standardization and consistency requirements that the work order flow system must meet to ensure the smooth flow of work orders between different stages. The operational health status of workshop maintenance equipment refers to the real-time operating status of maintenance equipment in the workshop, including predictive maintenance indicators such as equipment vibration, temperature, fault codes, and calibration validity period. The dynamic inventory of spare parts reflects the real-time available inventory, the status of replenishment in transit, and the compatibility of replacement parts. Synchronous verification refers to the need to jointly verify whether the control instructions issued by multiple edge computing nodes are compatible with the current equipment health and inventory status before issuing them, so as to avoid global conflicts caused by local optimization.

[0045] Execution steps: The scheduling and calibration node installs work order verification sensor arrays at high-risk workstations such as the four-wheel alignment area and the high-voltage electrical inspection area to collect data including vehicle VIN identification results, lift positioning signals, and torque wrench force curves. It analyzes deviation fluctuation characteristics through a sliding window. For example, if the operation sequence entropy value of a work order suddenly increases during the battery removal process, and visual recognition reveals that the insulation detection step has been skipped, it is determined to be a high-risk process jump. At this point, the calibration node uses multi-source data to locate the specific anomaly source and triggers the scheduling logic collaborative control: requesting the insertion of a forced detection node from the central scheduler, while simultaneously locking the access permissions of the downstream high-voltage connection workstation.

[0046] Meanwhile, before generating control commands, all edge nodes must jointly assess the health and inventory status of their equipment through a consistency verification module. For example, when an edge node intends to issue a command to immediately change the transmission fluid, the verification module will query the remaining lifespan of the automatic fluid changer and the inventory of specialized fluids. If either condition is not met, the command is rejected and an alternative solution is triggered. Preferably, through real-time monitoring and collaborative control, the security and feasibility of scheduling decisions in high-concurrency scenarios are ensured.

[0047] Furthermore, the method of this application also includes: A deep scan is performed on the implicit deviations in the flow of automotive repair and maintenance work orders. Digital signature verification is used to enhance the consistency and contrast of work order data across processes. A convolutional neural network mapping sequence of work order flow anomaly degree and data deviation characteristics is constructed to determine the predicted value of work order correction priority. When the work order flow deviation rate is detected to exceed the deviation value benchmark M times, the multi-dimensional scheduling node is linked with the multiple edge computing nodes to perform cross-process collaborative verification operation, where M≥2.

[0048] Specifically, implicit deviations in workflow refer to logical or temporal anomalies that exist in the cross-process execution of work orders but are not explicitly marked, such as minor adjustments to the process sequence, abnormally extended operation time, and substitution of non-standard parts. These deviations usually do not trigger traditional state machine alarms, but may accumulate and lead to a decline in service quality or rework. Deep scanning refers to using time series modeling and graph structure analysis techniques to perform fine-grained mining of work order data throughout its entire lifecycle to identify potential non-compliant patterns. Digital signature verification is used to ensure that the source of work order data transmitted across processes is trustworthy and has not been tampered with. By attaching a digital signature based on hash and private key to the output of each process, the signature is verified at the receiving end, thereby enhancing the consistency and contrast of data between different links and improving the distinguishability between genuine and tampered data.

[0049] The convolutional neural network mapping sequence refers to organizing the multidimensional deviation features of work orders into two-dimensional tensors resembling images, inputting them into a lightweight CNN model, and learning the nonlinear mapping relationship between them and the degree of anomaly. The single correction priority prediction value is the continuous or discrete score output by the lightweight CNN model, used to quantify the urgency of the current work order requiring manual intervention or system rescheduling. A deviation rate exceeding the deviation value benchmark M times indicates that when the measured deviation index reaches twice or more of the historical average or a preset threshold, it is judged as a serious anomaly, requiring the initiation of a cross-node collaborative response. Furthermore, the deviation value benchmark is used to measure whether the deviation in the work order flow has reached the level requiring intervention.

[0050] Execution steps: Perform a deep scan of latent deviations on automotive repair and maintenance work orders: First, use a graph attention network to model a process dependency graph to identify non-standard paths in the chain of diagnosis, disassembly, replacement, and quality inspection; simultaneously, after each process is completed, edge nodes use the ECDSA algorithm to generate digital signatures for key data, which are then transferred to the next stage along with the work order; before processing, the receiving node verifies the signature. If the signature is invalid or missing, the consistency contrast is significantly reduced, and this event is marked as high-risk data inconsistency. These multi-source deviation features include signature validity, process deviation degree, and time gradient. Furthermore, these multi-source deviation features are reconstructed into a grayscale deviation feature map, which is input into a multi-layer deep separable convolutional neural network. After training, it outputs a corrected priority prediction value.

[0051] For example, when the system detects that the daily process skipping rate in a certain workshop reaches 4.6%, while the baseline value is 1.8%, M = 4.6% ÷ 1.8% > 2, cross-process collaborative verification is immediately triggered: multi-dimensional scheduling nodes broadcast verification requests to multiple edge computing nodes, each node returns local signature logs and operation snapshots, and the scheduling center completes joint verification through a three-stage consistency comparison. Preferably, through deep scanning and intelligent analysis, hidden deviations in work order flow are promptly discovered and processed, effectively preventing hidden deviations from evolving into explicit service incidents.

[0052] Furthermore, the method of this application also includes: A lightweight blockchain sharding architecture is deployed on the multiple edge computing nodes. The lightweight blockchain sharding architecture is used to store the execution feature fingerprint of automobile repair and maintenance work orders and the scheduling operation timestamp. Based on the lightweight blockchain sharding architecture, a tamper-proof work order compliance verification certificate is generated through a zero-knowledge proof protocol.

[0053] Specifically, lightweight blockchain sharding architecture refers to a scalable, low-overhead distributed ledger structure deployed on resource-constrained edge computing nodes. By dividing the global ledger into multiple logically independent shards according to work order type, workshop area, or time window, each shard is maintained by a set of edge nodes and only stores work order data related to it, thereby reducing storage and consensus overhead. The work order execution feature fingerprint is a hash digest of the key attributes of a single work order during execution, including multi-dimensional information such as process sequence, operator ID, equipment usage record, time distribution, and parts batch, which is standardized to generate a unique digital fingerprint.

[0054] The scheduling operation timestamp refers to the precise time stamp generated by a trusted clock source each time a scheduling instruction is issued, a work order status changes, or an abnormal intervention event occurs. It is used to build a non-repudiable operation sequence chain. The zero-knowledge proof protocol is used to prove to a third party that the work order is compliant without disclosing the original work order details. The tamper-proof work order compliance verification certificate is a certificate generated by the zero-knowledge proof protocol to prove the integrity and compliance of the work order data and that it cannot be tampered with.

[0055] Execution Steps: A lightweight sharding consensus protocol based on PBFT optimization runs on multiple edge computing nodes in each repair workshop. Automotive repair and maintenance work orders are divided into multiple shards according to vehicle model and repair category, such as new energy high-voltage repair, routine maintenance, and bodywork / painting. Each shard requires only 3 nodes to reach consensus, and the block size is limited to 256KB. When a new energy vehicle battery repair work order is completed, the edge node extracts its execution characteristics, generates a feature fingerprint, and writes it along with the scheduling operation timestamp into the corresponding shard's block. This write operation is signed by the local hardware security module to ensure source trustworthiness. Subsequently, when a customer or quality inspector requests compliance verification, a zero-knowledge proof protocol is invoked, using private work order data as witness, to generate a compliance verification certificate. The verifier only needs to use the public verification key and rule hash to confirm the work order's compliance within milliseconds, without knowing the specific operational details. Preferably, blockchain technology and the zero-knowledge proof protocol ensure the authenticity and immutability of work order data, meeting the service data security requirements of process verifiability, content confidentiality, and accountability traceability.

[0056] Furthermore, the method of this application also includes: When a data deviation is detected in N consecutive verifications of the same car repair and maintenance work order, the edge computing node under the linkage control of the neighboring scheduling node is automatically activated to perform cross-validation operation. The neighboring scheduling node belongs to the multi-dimensional scheduling node and N≥3. At the same time, a hybrid consensus cross-validation mechanism is established, which is jointly executed by a core consensus node and multiple participating consensus nodes.

[0057] Specifically, if the same car repair and maintenance work order has data deviations in N consecutive verifications, it means that during the work order process, the system performs multiple independent verifications on key fields such as execution status, operation logs, or resource usage. If inconsistencies or anomalies exceeding the tolerance threshold are detected in N consecutive verifications, the work order is determined to have a systemic data credibility risk. Proximity scheduling nodes are nodes in the multi-dimensional scheduling node network that are geographically or logically adjacent to the current work order execution location, such as adjacent workstations, the front and rear sections of the same assembly line, or areas with shared parts warehouses, and have the ability to locally perceive the context of the work order.

[0058] Edge computing nodes under linkage control refer to edge devices that are temporarily called to participate in cross-validation under the coordination of nearby scheduling nodes. They locally store some work order context or operation records. Cross-validation operation refers to multiple edge nodes performing multi-angle consistency comparisons on the same work order based on data from their respective perspectives to identify whether it is a local data error or a global process anomaly. The hybrid consensus cross-validation mechanism is led by a dynamically elected or pre-designated core consensus node, which coordinates with multiple participating consensus nodes to jointly achieve a credible determination of the work order status. It has both efficiency and resistance to single point of failure. Furthermore, the core consensus node is responsible for aggregating evidence, triggering proof generation, and outputting conclusions, while participating consensus nodes provide local data evidence and partial verification results.

[0059] Execution steps: A dynamic verification window is set for each work order. For example, in a high-voltage battery replacement work order, if the operation log is found to be missing insulation test results and the digital signature verification fails in three consecutive verifications during the insulation test, disassembly, and BMS reset stages, the activation condition of N=3 is met. At this time, the system automatically activates the nearby scheduling nodes, such as the high-voltage connection re-inspection area scheduling node located downstream of the battery disassembly area and the diagnostic terminal scheduling node located upstream, and links the edge computing nodes under their jurisdiction to perform cross-verification. Furthermore, the linked edge computing nodes respectively store disassembly torque records and OBD initial fault codes. These edge nodes encrypt and upload relevant local data to the hybrid consensus group, which consists of a core consensus node and multiple participating consensus nodes.

[0060] An improved Raft+PBFT hybrid protocol is adopted: core nodes initiate verification proposals, and participating nodes return local evidence hashes and local consistency scores. If more than 50% of the node scores are higher than the work order consistency reliability threshold, the core node generates a structured cross-validation report and updates the work order status to high-risk and pending review. The work order consistency reliability threshold is a dynamic boundary value determined by fitting a Gaussian mixture model based on the local verification score distribution of historical compliant work orders, used to distinguish between normal fluctuations and systematic deviations. Preferably, when a single edge node suffers physical interference or software anomalies, the hybrid consensus mechanism can still maintain verification reliability through a majority of participating nodes, significantly improving the fault tolerance and anti-interference capability of the work order data governance system.

[0061] Furthermore, the method of this application also includes: Extract the workshop load fluctuation vector and data transmission attenuation gradient parameters; perform dynamic interference analysis based on the data mutation characteristics corresponding to cross-process work order data to generate an error avoidance probability distribution map of the scheduling path; determine multiple work order flow modes adapted to different workshop load scenarios based on the error avoidance probability distribution map, the workshop load fluctuation vector and data transmission attenuation gradient parameters; and perform switching scheduling processing based on the multiple work order flow modes.

[0062] Specifically, the workshop load fluctuation vector refers to the load changes in different time periods or different areas within the workshop, representing the direction and magnitude of load changes in vector form; the data transmission attenuation gradient parameter refers to the parameter of signal strength or quality attenuation during data transmission, used to describe the stability and reliability of data transmission; the data mutation characteristics corresponding to cross-process work order data refer to the abnormal change characteristics that may occur in the data when the work order flows between different processes, such as data loss, errors, or inconsistencies.

[0063] Dynamic interference analysis refers to the real-time analysis of interference encountered during data transmission and processing to identify potential problems and take corresponding measures; error avoidance probability distribution map is used to show the probability distribution of errors that may occur during work order flow under different scheduling paths; work order flow mode refers to the work order flow strategy designed based on workshop load and data transmission characteristics to optimize work order flow paths and resource allocation; switching scheduling processing refers to dynamically switching scheduling strategies in actual operation according to different work order flow modes to adapt to the current workshop load and data transmission conditions.

[0064] Execution steps: Real-time monitoring and recording of the load in each workshop can be achieved through sensors and equipment status monitoring systems deployed within the workshops. Data such as the workload of each machine, the number of pending work orders, and waiting time can be collected. After analysis, this data forms a vector representing the changes in workshop load over different time periods, i.e., the workshop load fluctuation vector. Changes in signal strength or quality during data transmission also need to be measured using appropriate network monitoring tools, including indicators such as network latency, packet loss rate, and signal-to-noise ratio. By analyzing the data on the time and spatial distribution of these indicators, data transmission attenuation gradient parameters can be obtained.

[0065] Based on the data mutation characteristics corresponding to cross-process work order data, combined with historical and real-time data, statistical methods or machine learning algorithms are applied to perform dynamic interference analysis. Data mutation characteristics include a sudden increase in work order processing time and an increase in error rate. Based on the analysis results, an error avoidance probability distribution map of scheduling paths is constructed, which reflects the probability of avoiding errors under different paths. Using the workshop load fluctuation vector, data transmission attenuation gradient parameters, and error avoidance probability distribution map obtained above as input, optimization algorithms such as genetic algorithms and simulated annealing algorithms are used to find the set of work order flow patterns most suitable for the current workshop load conditions. These flow patterns consider how to minimize error risk while ensuring production efficiency and adapt to different workshop load conditions.

[0066] Based on actual workshop operation and predicted future load trends, the system switches between multiple defined work order flow modes. This switching can be executed automatically according to preset rules. For example, during high-load periods, the system is designed to prioritize urgent work orders; during low-load periods, resource allocation is optimized to improve overall efficiency. Preferably, through dynamic analysis and optimized scheduling strategies, the flexibility and stability of workshop production are effectively improved, and the negative impacts caused by load fluctuations or data transmission problems are reduced.

[0067] In summary, the beneficial effects of the embodiments of this application are: By deploying multi-dimensional scheduling nodes to construct a comprehensive work order monitoring network, a work order progress heatmap is generated based on the automotive repair and maintenance task flow. Multiple edge computing nodes are used to determine the work order processing time efficiency gradient and the correlation matrix between stages. Real-time workshop operation data and work order execution scenario parameters are introduced to perform dynamic scheduling path planning and generate a conflict work order scheduling instruction set. Based on the work order process consistency risk cloud map, multi-source data fusion analysis is performed on multiple edge computing nodes according to the work order processing time efficiency gradient and stage correlation matrix. Combined with the conflict work order scheduling instruction set, the abnormal types and priority levels of work order flow are dynamically identified. The anomaly type and priority level of the single-transfer process trigger the work order transfer linkage scheduling mechanism, and based on the system process consistency deviation, it performs collaborative execution control of multiple edge computing nodes and each work order execution link. This application provides a work order transfer management method and system for automobile repair and maintenance, which realizes the technical effect of real-time visualization and dynamic scheduling path planning of repair task flow through a full-domain work order monitoring network and work order progress heat map, effectively avoiding process conflicts, identifying anomaly types and priorities, and implementing collaborative control of multiple edge nodes and execution links based on process consistency deviation, thereby enhancing the robustness and closed-loop handling capability of work order transfer.

[0068] Example 2, based on the same inventive concept as the work order flow management method for automobile repair and maintenance in the foregoing examples, such as... Figure 2 As shown in the figure, this application embodiment provides a work order workflow management system for automobile repair and maintenance, wherein the system includes: M100, the module for building a full-domain work order monitoring network, deploys multi-dimensional scheduling nodes to build a full-domain work order monitoring network, generates a work order progress heatmap based on the automotive repair and maintenance task flow, and determines the work order processing time gradient and the link correlation matrix through multiple edge computing nodes.

[0069] The scheduling path dynamic planning module M200: It introduces real-time workshop operation data and work order execution scenario parameters to perform dynamic scheduling path planning and generate conflict work order scheduling instruction sets.

[0070] Dynamic identification module M300: Based on the work order process consistency risk cloud map, it performs multi-source data fusion analysis at multiple edge computing nodes according to the work order processing time efficiency gradient and link correlation matrix, and combines the conflict work order scheduling instruction set to dynamically identify the abnormal type and priority level of work order flow.

[0071] The execution control module M400 triggers the work order flow linkage scheduling mechanism based on the abnormal type and priority level of the work order flow, and performs collaborative execution control of multiple edge computing nodes and each work order execution link according to the system process consistency deviation.

[0072] Furthermore, the work order management system for automobile repair and maintenance performs the following methods: During the operation of the multi-dimensional scheduling node, dual-modal data collection of work order system records and workshop operation logs is performed simultaneously, and a work order process consistency risk cloud map is generated by integrating the historical work order anomaly feature library.

[0073] Furthermore, during the operation of the multi-dimensional scheduling node, dual-modal data acquisition of work order system records and workshop operation logs is performed simultaneously. The work order flow management system for automobile repair and maintenance executes the following methods: During the information verification process of automotive repair and maintenance work orders, the data verification accuracy is dynamically adjusted according to the complexity of the repair project. At the same time, formatting correction is performed on the workshop operation log data, and a nonlinear regression function is established for the integrity of the operation log and the work order scheduling pass rate. Based on the nonlinear regression function of the integrity of the operation log and the work order scheduling pass rate, anomaly samples of work order flow are generated by an adversarial generative network. The deep residual network trained is used to classify work order-operation record mismatch, missing maintenance log information, and duplicate work assignment faults.

[0074] Furthermore, the work order flow linkage scheduling mechanism is triggered, and collaborative execution control is performed on multiple edge computing nodes and each work order execution link based on the system process consistency deviation. The execution control module M400 is also used to execute the following methods: The multi-dimensional scheduling node is equipped with a multi-source interference monitoring unit to construct a spatial distribution map of scheduling harmonic interference caused by legacy issues from the workshop system upgrade. Simultaneously, it employs an encrypted communication protocol to combat interference noise generated by cross-process work order data transmission and dynamically adjusts the transmission frequency of the flow scheduling signal. When the data transmission signal-to-noise ratio is detected to be lower than a preset signal-to-noise ratio threshold, a data relay synchronization unit is activated to construct a mesh network topology. The relay nodes of the mesh network topology are selected to conform to the spatial distribution map of scheduling harmonic interference.

[0075] Furthermore, the execution control module M400 is also used to execute the following methods: A work order verification sensor array is deployed at the scheduling correction node to detect deviation fluctuation characteristics and perform multi-source data positioning and scheduling logic collaborative control. At the same time, to meet the system process consistency requirements of automobile repair and maintenance work orders, the control instructions issued by multiple edge computing nodes are synchronously verified based on the operational health status of workshop repair equipment and parts inventory dynamics.

[0076] Furthermore, the execution control module M400 is also used to execute the following methods: A deep scan is performed on the implicit deviations in the flow of automotive repair and maintenance work orders. Digital signature verification is used to enhance the consistency and contrast of work order data across processes. A convolutional neural network mapping sequence of work order flow anomaly degree and data deviation characteristics is constructed to determine the predicted value of work order correction priority. When the work order flow deviation rate is detected to exceed the deviation value benchmark M times, the multi-dimensional scheduling node is linked with the multiple edge computing nodes to perform cross-process collaborative verification operation, where M≥2.

[0077] Furthermore, the work order management system for automobile repair and maintenance is also used to perform the following methods: A lightweight blockchain sharding architecture is deployed on the multiple edge computing nodes. The lightweight blockchain sharding architecture is used to store the execution feature fingerprint of automobile repair and maintenance work orders and the scheduling operation timestamp. Based on the lightweight blockchain sharding architecture, a tamper-proof work order compliance verification certificate is generated through a zero-knowledge proof protocol.

[0078] Furthermore, the work order management system for automobile repair and maintenance is also used to perform the following methods: When a data deviation is detected in N consecutive verifications of the same car repair and maintenance work order, the edge computing node under the linkage control of the neighboring scheduling node is automatically activated to perform cross-validation operation. The neighboring scheduling node belongs to the multi-dimensional scheduling node and N≥3. At the same time, a hybrid consensus cross-validation mechanism is established, which is jointly executed by a core consensus node and multiple participating consensus nodes.

[0079] Furthermore, the work order management system for automobile repair and maintenance is also used to perform the following methods: Extract the workshop load fluctuation vector and data transmission attenuation gradient parameters; perform dynamic interference analysis based on the data mutation characteristics corresponding to cross-process work order data to generate an error avoidance probability distribution map of the scheduling path; determine multiple work order flow modes adapted to different workshop load scenarios based on the error avoidance probability distribution map, the workshop load fluctuation vector and data transmission attenuation gradient parameters; and perform switching scheduling processing based on the multiple work order flow modes.

[0080] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The work order management method and specific examples for automobile repair and maintenance in Example 1 are also applicable to the work order management system for automobile repair and maintenance in this example. Through the foregoing detailed description of the work order management method for automobile repair and maintenance, those skilled in the art can clearly understand the work order management system for automobile repair and maintenance in this example. Therefore, for the sake of brevity, it will not be described in detail here.

[0081] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0082] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A work order management method for automobile repair and maintenance, characterized in that, The method includes: Deploy multi-dimensional scheduling nodes to build a full-domain work order monitoring network, generate a work order progress heatmap based on the car repair and maintenance task flow, and determine the work order processing time efficiency gradient and link correlation matrix through multiple edge computing nodes; By introducing real-time workshop operation data and work order execution scenario parameters, dynamic planning of scheduling paths is performed to generate conflict work order scheduling instruction sets; Based on the work order process consistency risk cloud map, multi-source data fusion analysis is performed at multiple edge computing nodes according to the work order processing time efficiency gradient and link correlation matrix. Combined with the conflict work order scheduling instruction set, the abnormal type and priority level of work order flow are dynamically identified. Based on the abnormal type and priority level of the work order flow, a work order flow linkage scheduling mechanism is triggered, and collaborative execution control is carried out on multiple edge computing nodes and each work order execution link according to the consistency deviation of the system process.

2. The work order management method for automobile repair and maintenance as described in claim 1, characterized in that, The method includes: During the operation of the multi-dimensional scheduling node, dual-modal data collection of work order system records and workshop operation logs is performed simultaneously, and a work order process consistency risk cloud map is generated by integrating the historical work order anomaly feature library.

3. The work order management method for automobile repair and maintenance as described in claim 2, characterized in that, During the operation of the multi-dimensional scheduling node, dual-modal data acquisition of work order system records and workshop operation logs is performed simultaneously. The method also includes: During the information verification process of automobile repair and maintenance work orders, the data verification accuracy is dynamically adjusted according to the complexity of the repair project. At the same time, the workshop operation log data is formatted and corrected, and a nonlinear regression function is established between the integrity of the operation log and the work order scheduling pass rate. Based on the nonlinear regression function of the operation log integrity and work order scheduling pass rate, an adversarial generative network is used to generate abnormal work order flow samples. The trained deep residual network is used to classify work order-operation record mismatch, missing maintenance log information, and duplicate work assignment faults.

4. The work order management method for automobile repair and maintenance as described in claim 1, characterized in that, The method further includes triggering a work order flow linkage scheduling mechanism and performing collaborative execution control of multiple edge computing nodes and various work order execution stages based on system process consistency deviations. The multi-dimensional scheduling node is equipped with a multi-source interference monitoring unit to construct a spatial distribution map of scheduling harmonic interference caused by legacy issues from the workshop system upgrade. Meanwhile, encrypted communication protocols are used to combat interference noise generated by cross-process work order data transmission, and the transmission frequency of flow scheduling signals is dynamically adjusted. When the signal-to-noise ratio of data transmission is detected to be lower than the preset signal-to-noise ratio threshold, the data relay synchronization unit is activated to construct a mesh network topology. The relay nodes of the mesh network topology are selected in accordance with the spatial distribution map of the scheduling harmonic interference.

5. The work order management method for automobile repair and maintenance as described in claim 4, characterized in that, The method further includes: Deploy a work order verification sensor array at the scheduling correction node to detect deviation fluctuation characteristics and perform multi-source data positioning and scheduling logic collaborative control. Meanwhile, to ensure system process consistency for automotive repair and maintenance work orders, control instructions issued by multiple edge computing nodes are synchronously verified based on the operational health status of workshop repair equipment and parts inventory dynamics.

6. The work order management method for automobile repair and maintenance as described in claim 5, characterized in that, The method includes: Deep scan of the hidden deviations in the flow of automobile repair and maintenance work orders is performed. Digital signature verification is combined to enhance the consistency and contrast of work order data across processes. A convolutional neural network mapping sequence of the degree of work order flow abnormality and data deviation characteristics is constructed to determine the predicted value of work order correction priority. When the deviation rate of the work order flow is detected to exceed the deviation value benchmark M times, the multi-dimensional scheduling node is linked with the multiple edge computing nodes to perform cross-process collaborative verification operation, where M≥2.

7. The work order management method for automobile repair and maintenance as described in claim 1, characterized in that, The method includes: A lightweight blockchain sharding architecture is deployed on the multiple edge computing nodes. The lightweight blockchain sharding architecture is used to store the execution feature fingerprint of automobile repair and maintenance work orders and the timestamp of scheduling operations. Based on the aforementioned lightweight blockchain sharding architecture, a tamper-proof work order compliance verification certificate is generated through a zero-knowledge proof protocol.

8. The work order management method for automobile repair and maintenance as described in claim 7, characterized in that, The method includes: When a data deviation is detected in N consecutive verifications of the same car repair and maintenance work order, the edge computing node under the linkage control of the neighboring scheduling node is automatically activated to perform cross-validation operation. The neighboring scheduling node belongs to the multi-dimensional scheduling node and N≥3. Simultaneously, a hybrid consensus cross-verification mechanism is established, which is jointly executed by a core consensus node and multiple participating consensus nodes.

9. The work order management method for automobile repair and maintenance as described in claim 8, characterized in that, The method includes: Extract the workshop load fluctuation vector and data transmission attenuation gradient parameters; Dynamic interference analysis is performed based on the data mutation characteristics corresponding to cross-process work order data to generate a fault avoidance probability distribution map of the scheduling path; Based on the error avoidance probability distribution map, the workshop load fluctuation vector, and the data transmission attenuation gradient parameter, multiple work order flow modes adapted to different workshop load scenarios are determined. The multiple work order flow modes are switched and scheduled.

10. A work order management system for automobile repair and maintenance, characterized in that, The system is used to implement the work order flow management method for automobile repair and maintenance as described in any one of claims 1-9, comprising: Full-domain work order monitoring network construction module: Deploy multi-dimensional scheduling nodes to build a full-domain work order monitoring network, generate work order progress heatmaps based on the car repair and maintenance task flow, and determine the work order processing time efficiency gradient and link correlation matrix through multiple edge computing nodes; The scheduling path dynamic planning module: introduces real-time workshop operation data and work order execution scenario parameters to perform dynamic scheduling path planning and generate conflict work order scheduling instruction sets; Dynamic identification module: Based on the work order process consistency risk cloud map, multi-source data fusion analysis is performed at multiple edge computing nodes according to the work order processing time efficiency gradient and link correlation matrix. Combined with the conflict work order scheduling instruction set, the abnormal type and priority level of work order flow are dynamically identified. Execution control module: Based on the abnormal type and priority level of the work order flow, trigger the work order flow linkage scheduling mechanism, and perform collaborative execution control of multiple edge computing nodes and each work order execution link according to the system process consistency deviation.

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