Intelligent sanitation vehicle whole life cycle management method and system based on digital twinning

Abstract

The application provides a kind of intelligent sanitation vehicle full life cycle management method and system based on digital twinning, which comprises the following steps: based on the static information and real-time dynamic information of intelligent sanitation vehicle, a vehicle digital twin is constructed; if an abnormality occurs, abnormal information of the abnormal equipment is generated, and a corresponding maintenance parts database is associated based on the vehicle digital twin; based on the equipment type of the abnormal equipment, the corresponding parts information is matched in the maintenance parts database, and the abnormal information and parts information are pushed to the maintenance mobile terminal; based on the digital identifier of the vehicle digital twin, the maintenance information fed back by the maintenance mobile terminal and the daily vehicle operation parameters are associated to obtain the daily vehicle electronic file; based on the digital identifier, the daily vehicle electronic file in the full life cycle of intelligent sanitation vehicle from purchase to scrap is summarized to obtain the full life cycle electronic file. The application improves the abnormal processing efficiency and realizes the fine management of the vehicle in the full life cycle.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and system for the full lifecycle management of intelligent sanitation vehicles based on digital twins. Background Technology

[0002] With the continued advancement of urbanization and the rapid growth of the urban population, the amount of urban domestic waste and construction waste generated has increased significantly, leading to a sharp rise in demand for sanitation services.

[0003] In sanitation operations, sanitation vehicles are crucial tools. Currently, most smart sanitation systems have achieved information-based management of multiple functions, including vehicle records and maintenance, which has to some extent solved the problems of wide-ranging "one-to-many" management and information asymmetry. Specifically, in vehicle maintenance management, a preliminary, process-oriented, and information-based maintenance management system centered on vehicle anomaly management has been established. From reporting, analyzing, and assigning tasks for vehicle anomalies to implementing maintenance and confirming results, a basic closed-loop control and electronic records have been formed.

[0004] However, existing smart sanitation systems rely primarily on manual entry for equipment malfunctions, lacking automatic judgment and notification functions, resulting in low efficiency in processing malfunction information. Furthermore, they are not linked to maintenance parts, making it impossible to track individual vehicle maintenance records and providing comprehensive vehicle status reports, hindering refined vehicle management and failing to effectively support cost control and decision-making. Summary of the Invention

[0005] This invention provides a method and system for the full lifecycle management of intelligent sanitation vehicles based on digital twins, aiming to improve the efficiency of anomaly handling and achieve refined management of vehicles throughout their entire lifecycle.

[0006] In a first aspect, the present invention provides a method for the full lifecycle management of intelligent sanitation vehicles based on digital twins, including:

[0007] A digital twin of a smart sanitation vehicle is constructed based on its static and real-time dynamic information. The digital twin reflects the real-time status of the smart sanitation vehicle. The real-time dynamic information is collected from various sensors installed on the smart sanitation vehicle.

[0008] If an anomaly is detected in the smart sanitation vehicle based on the vehicle digital twin, anomaly information of the abnormal equipment is generated, and the corresponding maintenance parts database is associated with the vehicle digital twin.

[0009] Based on the equipment type of the abnormal device, the corresponding part information is matched in the maintenance parts database, and the abnormal information and the part information are pushed to the maintenance mobile terminal so that the maintenance user can perform maintenance on the smart sanitation vehicle based on the information in the maintenance mobile terminal.

[0010] Based on the digital identifier of the vehicle's digital twin, and combined with the maintenance information fed back by the maintenance mobile terminal during the maintenance process and the vehicle operation parameters of the smart sanitation vehicle within each day, a daily vehicle electronic file is obtained.

[0011] Based on the digital identifier, the daily electronic vehicle records of the smart sanitation vehicle throughout its entire lifecycle, from purchase to scrapping, are summarized to obtain a full lifecycle electronic record.

[0012] Secondly, the present invention also provides a digital twin-based intelligent sanitation vehicle lifecycle management system, applied to the digital twin-based intelligent sanitation vehicle lifecycle management method described in the first aspect; the digital twin-based intelligent sanitation vehicle lifecycle management system includes:

[0013] The digital twin construction module is used to construct a vehicle digital twin based on the static information and real-time dynamic information of the smart sanitation vehicle; the vehicle digital twin reflects the real-time status of the smart sanitation vehicle; the real-time dynamic information is collected based on various sensors installed on the smart sanitation vehicle;

[0014] An anomaly information generation module is used to generate anomaly information of the abnormal equipment if an anomaly is detected in the smart sanitation vehicle based on the vehicle digital twin, and associate the corresponding maintenance parts database based on the vehicle digital twin.

[0015] The information push module is used to match the corresponding parts information in the maintenance parts database based on the equipment type of the abnormal equipment, and push the abnormal information and the parts information to the maintenance mobile terminal, so that the maintenance user can perform maintenance on the smart sanitation vehicle based on the information in the maintenance mobile terminal;

[0016] The information association module is used to associate the digital identifier of the vehicle's digital twin with the maintenance information fed back by the maintenance mobile terminal during the maintenance process and the vehicle operation parameters of the smart sanitation vehicle in daily operations to obtain a daily vehicle electronic file.

[0017] The full-lifecycle management module is used to summarize the daily electronic vehicle records of the smart sanitation vehicle throughout its entire lifecycle, from purchase to scrapping, based on the digital identifier, to obtain a full-lifecycle electronic record.

[0018] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the intelligent sanitation vehicle full lifecycle management method based on digital twins as described above.

[0019] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the intelligent sanitation vehicle full lifecycle management method based on digital twins as described above.

[0020] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the intelligent sanitation vehicle full lifecycle management method based on digital twins as described above.

[0021] The present invention provides a digital twin-based intelligent sanitation vehicle lifecycle management method. This method automatically monitors anomalies using digital twins, replacing manual data entry and ensuring timely transmission of anomaly information, thus improving anomaly handling efficiency. Furthermore, by linking the digital identifier of the vehicle's digital twin with maintenance information during the repair process and the vehicle's daily operating parameters, it achieves precise tracking of daily maintenance records for each vehicle, solving the problem of missing maintenance records. Finally, based on the digital identifier, the daily electronic vehicle files throughout the vehicle's lifecycle from purchase to scrapping are summarized to form a unique electronic lifecycle file for each vehicle, compensating for missing reports and achieving refined management of intelligent sanitation vehicles throughout their entire lifecycle. This provides effective support for cost control and decision-making. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the intelligent sanitation vehicle lifecycle management method based on digital twins provided in this embodiment of the invention.

[0023] Figure 2 This is a schematic diagram of the structure of the intelligent sanitation vehicle lifecycle management system based on digital twin provided in an embodiment of the present invention;

[0024] Figure 3 An embodiment diagram of the electronic device provided in this invention;

[0025] Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with the present invention. Detailed Implementation

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

[0027] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0029] Optional, see below Figure 1 , Figure 1 This is a flowchart illustrating the intelligent sanitation vehicle lifecycle management method based on digital twins provided by this invention. In this embodiment, the executing entity of the intelligent sanitation vehicle lifecycle management method based on digital twins is the vehicle management system. Therefore, the intelligent sanitation vehicle lifecycle management method based on digital twins includes:

[0030] Step 10: Construct a digital twin of the smart sanitation vehicle based on its static and real-time dynamic information. The digital twin reflects the real-time status of the smart sanitation vehicle. The real-time dynamic information is collected from various sensors installed on the smart sanitation vehicle.

[0031] Optionally, the vehicle management system collects static information about the vehicle. Static information refers to the vehicle's inherent attributes that do not change dynamically over time, such as the vehicle's model, chassis number, engine model, manufacturing date, rated load capacity, and body dimensions.

[0032] Furthermore, the vehicle management system acquires real-time dynamic information, which is collected by various sensors installed on the vehicle. These sensors are diverse, including GPS positioning sensors, speed sensors, fuel consumption sensors, engine speed sensors, water temperature sensors, tire pressure sensors, and so on.

[0033] Furthermore, the vehicle management system integrates static and real-time dynamic information, and constructs a digital twin of the vehicle in virtual space that corresponds one-to-one with the physical vehicle according to the vehicle's actual physical structure and operating logic. The digital twin of the vehicle can map the status of the physical vehicle in real time, including its location, speed, fuel consumption, engine operating status, tire pressure, etc.

[0034] In one embodiment, taking a smart sanitation vehicle of model HW-2023 as an example, the vehicle management system collects its static information, such as the chassis number LC123456789012345, engine model FD450, manufacturing date May 10, 2023, rated load capacity of 5 tons, and vehicle dimensions of 6000mm×2200mm×2800mm. Simultaneously, the vehicle is equipped with various sensors: a GPS positioning sensor collects real-time vehicle location information, a speed sensor collects driving speed, a fuel consumption sensor collects real-time fuel consumption, and an engine speed sensor collects engine speed. This information is integrated to construct a digital twin of the HW-2023 vehicle. When this sanitation vehicle is operating on urban roads, the vehicle digital twin displays its real-time location at 116.4°E, 39.9°N, driving speed of 30km / h, real-time fuel consumption of 8L / h, and engine speed of 2000r / min, accurately reflecting the real-time status of the sanitation vehicle.

[0035] Step 20: If an anomaly is detected in the smart sanitation vehicle based on the vehicle digital twin, anomaly information of the abnormal equipment is generated, and the corresponding maintenance parts database is associated with the vehicle digital twin.

[0036] Furthermore, the vehicle management system continuously monitors its self-constructed vehicle digital twins. By comparing the real-time status reflected in the vehicle digital twins with preset normal status thresholds, it determines whether the smart sanitation vehicles have malfunctioned. Once an anomaly is detected, the vehicle management system immediately generates anomaly information for the malfunctioning device. This information should include the time of the anomaly, the vehicle's digital identifier, the name of the malfunctioning device, and the anomaly phenomenon. Further, the vehicle management system associates the vehicle digital twins with a corresponding maintenance parts database. This database stores parts information for various equipment types, and the association is based on the matching relationship between the relevant attributes of the malfunctioning device and the equipment type of the parts in the database.

[0037] Continuing with the above embodiment, when the vehicle management system monitors the digital twin of the HW-2023 smart sanitation vehicle, it detects that the left front tire pressure data transmitted by its tire pressure sensor has been consistently below the normal threshold (normal threshold is 2.5-3.0 bar, currently 1.8 bar) for more than 5 minutes, thus determining that the left front tire is abnormal. At this time, the generated abnormal information reads: "July 15, 2025, 10:30 AM, vehicle with digital identifier HW2023001, left front tire pressure abnormal, current tire pressure 1.8 bar, below the normal range." Furthermore, based on the vehicle's digital twin, the vehicle management system links to a maintenance parts database, which stores tire-related parts information.

[0038] Step 30: Based on the equipment type of the abnormal equipment, match the corresponding parts information in the maintenance parts database, and push the abnormal information and parts information to the maintenance mobile terminal so that maintenance users can perform maintenance on the smart sanitation vehicle based on the information in the maintenance mobile terminal.

[0039] Furthermore, after obtaining the equipment type of the malfunctioning device, the vehicle management system uses that type as a search criterion to perform a matching query in the repair parts database. The repair parts database is categorized and stored by equipment type, with each type corresponding to multiple possible parts information, including part name, model, specifications, inventory quantity, supplier information, and unit price. After matching the corresponding parts information, the vehicle management system integrates the previously generated malfunction information with the matched parts information and pushes it to the mobile repair app via the network. Upon receiving the push notification, the repair user can prepare the corresponding parts based on the information in the mobile repair app and travel to the vehicle's location for repair.

[0040] Continuing with the above embodiment, the malfunctioning device on the HW-2023 smart sanitation vehicle is the left front wheel, and the device type is "tire". Searching the repair parts database for the "tire" type yields the corresponding part information: part name is vacuum sanitation vehicle tire, model 235 / 75R17.5, inventory quantity 5, supplier is XX Tire Co., Ltd., unit price 800 yuan / piece. The malfunction information "July 15, 2025, 10:30 AM, vehicle with the digital identifier HW2023001, left front tire pressure abnormal, current tire pressure 1.8 bar, below the normal range" is integrated with the above part information and pushed to the maintenance personnel's mobile app. Upon seeing the information, the maintenance personnel understand that they need to bring a 235 / 75R17.5 vacuum sanitation vehicle tire to repair the vehicle's left front wheel.

[0041] Step 40: Based on the digital identifier of the vehicle's digital twin, and combined with the maintenance information fed back from the maintenance mobile terminal during the maintenance process and the vehicle operation parameters of the smart sanitation vehicle within each day, a daily vehicle electronic file is obtained.

[0042] Furthermore, the vehicle management system uses the digital identifier of the vehicle's digital twin as a link to integrate the maintenance information fed back from the maintenance mobile terminal with the daily operating parameters of the smart sanitation vehicles.

[0043] The maintenance information includes the parts repaired, the cause of the malfunction, and the maintenance time, while the vehicle operating parameters include fuel consumption, engine speed, and mileage. Therefore, the vehicle management system organizes the above information by date to form a daily vehicle electronic file. The daily vehicle electronic file clearly records the maintenance status and operating condition of the vehicle on that day, and uses digital identifiers to ensure that the information is accurately associated with the corresponding vehicle, as shown in steps 401 to 404.

[0044] Step 50: Based on digital identification, summarize the daily electronic vehicle records of smart sanitation vehicles throughout their entire lifecycle from purchase to scrapping to obtain a full lifecycle electronic record.

[0045] Furthermore, the vehicle management system uses the digital identifiers of smart sanitation vehicles to collect daily electronic vehicle records throughout the entire lifecycle of the vehicle, from purchase to scrapping. During the collection process, it is crucial to ensure that each daily electronic vehicle record is associated with the correct digital identifier to avoid confusion.

[0046] Furthermore, the vehicle management system summarizes, organizes, and archives all daily vehicle electronic records, arranging them chronologically to form a complete electronic record for the vehicle's entire lifecycle. This lifecycle electronic record covers all important information about the vehicle throughout its entire usage process, including maintenance records, operating parameters, and maintenance details at each stage.

[0047] Continuing with the above embodiment, the smart sanitation vehicle with the digital identifier HW2023001 was purchased on May 10, 2023, and its estimated scrapping date is May 10, 2033. Throughout its 10-year lifespan, the vehicle management system will generate a corresponding daily electronic vehicle file. Upon the vehicle's scrapping on May 10, 2033, all daily electronic vehicle files from these 10 years will be aggregated using the digital identifier HW2023001 to form the vehicle's full lifespan electronic file. This file will record detailed maintenance information, operating parameters, and other information for each day from the date of purchase to the date of scrapping, such as initial information on May 10, 2023, maintenance and operating information on July 15, 2025, and relevant information for other days.

[0048] This invention utilizes digital twins to automatically monitor anomalies, replacing manual data entry, ensuring timely transmission of anomaly information, and improving anomaly handling efficiency. Furthermore, by linking the digital identifiers of the vehicle's digital twin with maintenance information during the repair process and the daily operating parameters of the smart sanitation vehicle, it achieves precise tracking of the daily maintenance records for each vehicle, solving the problem of missing maintenance records. Finally, based on the digital identifiers, daily electronic vehicle files throughout the entire lifecycle of the vehicle, from purchase to scrapping, are compiled into a unique electronic file for each vehicle, enabling refined management of smart sanitation vehicles throughout their entire lifecycle and providing effective support for cost control and decision-making.

[0049] In one embodiment, steps 401 to 404 include:

[0050] Step 401: Based on the physical hierarchy and functional association of devices in the vehicle digital twin, construct a structure tree that includes an aggregation tree of devices of the same type, a dependency tree of devices across types, and a fault propagation path tree.

[0051] Optionally, the vehicle management system conducts in-depth analysis of the equipment in smart sanitation vehicles. Based on the physical hierarchy and functional relationships of the equipment within the vehicle's digital twin, a structure tree is constructed. This structure tree includes a cluster tree for similar equipment, a dependency tree for cross-type equipment, and a fault propagation path tree. The cluster tree for similar equipment uses equipment model as nodes, accurately mapping the spatial location of each device within the vehicle's digital twin using digital identifiers, clearly showing the distribution of equipment of the same model within the vehicle. The dependency tree for cross-type equipment uses equipment as nodes, detailing the functional dependencies between different types of equipment, such as the engine's operation depending on the fuel supply system, and the transmission's operation depending on the engine's power output. The fault propagation path tree uses abnormal equipment as nodes, analyzing and describing the probabilistic relationships of fault propagation between abnormal equipment through historical fault data and the connections between devices, such as the probability that an engine malfunction may lead to a generator failure.

[0052] Continuing with the example of the HW-2023 smart sanitation vehicle with the digital identifier HW2023001, in the same type of equipment aggregation tree, the node "YC6J200-52 engine" maps its spatial location in the vehicle's digital twin to "inside the engine compartment in the middle of the front of the vehicle" via its digital identifier; another node is "235 / 75R17.5 tire," mapped to "at the left front, right front, left rear, and right rear wheels of the vehicle." In the cross-type equipment dependency tree, the node "engine" depends on nodes such as "fuel pump," "air filter," and "spark plug," while the node "transmission" depends on nodes such as "engine" and "clutch." In the fault propagation path tree, the fault propagation probability between the node "engine coolant temperature too high" and the node "water pump failure" is 80%, and the fault propagation probability between the node "engine abnormal noise" and the node "bearing wear" is 60%.

[0053] Step 402: Bind the target nodes in the structure tree to the vehicle digital twin using digital identifiers to generate an association matrix. The target node represents the node corresponding to the abnormal device. The matrix elements in the association matrix represent the mapping strength between the node and the digital identifier; the row index is the node ID from the structure tree, and the column index is the digital identifier. When there is a unique correspondence between a node and a digital identifier, the element value is the digital identifier itself. When there is a one-to-many association between a node and a digital identifier, the element value is the associated identifier.

[0054] Furthermore, the vehicle management system identifies the target node (the node corresponding to the malfunctioning device) from the constructed structure tree and binds the target node to the vehicle's digital twin using a digital identifier, generating an association matrix. In this matrix, the row index is the structure tree node ID, and the column index is the digital identifier. When a node has a unique correspondence with a digital identifier, the element value at that position in the matrix is ​​the digital identifier itself; when a node has a one-to-many association with a digital identifier, the element value is the association identifier, used to reflect this multiple association correspondence and ensure accurate association between structure tree nodes and the vehicle's digital twin.

[0055] Continuing with the structure tree of the HW2023001 smart sanitation vehicle, the target node is "left front tire (235 / 75R17.5)", with node ID JT001. This node has a unique correspondence with the numerical identifier HW2023001. Therefore, in the association matrix, the element with row index JT001 and column index HW2023001 has the value HW2023001. If the structure tree contains the node "tire (235 / 75R17.5)" (node ​​ID JT002), it corresponds to the four wheels of the vehicle, meaning it has a one-to-many association with the numerical identifier HW2023001. In this case, the element in row JT002 and column HW2023001 in the matrix has the association identifier GL001, indicating a one-to-many association between this node and the numerical identifier.

[0056] Step 403: Based on the association matrix, the maintenance information is bound to the tree structure nodes through numerical identifiers to obtain the association graph. Each numerical identifier in the association graph corresponds to a subgraph, and the subgraph information includes the tree structure nodes involved in the maintenance process, as well as the maintenance parts, the cause of the failure, and the maintenance time.

[0057] Furthermore, the vehicle management system utilizes an association matrix to bind maintenance information to tree structure nodes via numerical identifiers. Because the association matrix clearly defines the correspondence between nodes and numerical identifiers, it can accurately map the maintenance parts, fault causes, and maintenance durations within the maintenance information to the corresponding nodes in the tree structure, resulting in an association graph. In this graph, each numerical identifier corresponds to a subgraph, which clearly displays the tree structure nodes involved in the maintenance process, along with the corresponding maintenance parts, fault causes, and maintenance durations, intuitively presenting the relationship between maintenance information and tree structure nodes.

[0058] Continuing with the above embodiment, the maintenance information for the HW2023001 smart sanitation vehicle is as follows: the part to be repaired is the left front tire (235 / 75R17.5), the cause of the malfunction is that the tire was punctured by a sharp object, and the repair time is 1 hour. Based on the association matrix, the vehicle management system binds this maintenance information to the node ID JT001 (left front tire) in the structure tree, generating an association graph. The subgraph corresponding to the numerical identifier HW2023001 in this graph contains the node JT001, and the corresponding maintenance part "left front tire (235 / 75R17.5)," the cause of the malfunction "punctured by a sharp object," and the repair time "1 hour."

[0059] Step 404: Based on the correlation map and the daily vehicle operation parameters, a daily electronic vehicle file is obtained.

[0060] Furthermore, the vehicle management system correlates the association map with the daily vehicle operation parameters to obtain daily vehicle electronic files, as detailed in steps 4041 to 4045.

[0061] This invention utilizes a structure tree to thoroughly analyze the various relationships between devices, an association matrix to ensure precise binding of nodes and digital identifiers, and an association graph to effectively associate maintenance information with structure tree nodes. Finally, combined with the daily electronic archives formed by operating parameters, a clear and accurately associated daily vehicle electronic archive can be constructed, fully recording the daily maintenance and operating status of vehicles. This enables precise tracking of the daily maintenance records of a single vehicle and solves the problem of missing maintenance record tracking.

[0062] In one embodiment, steps 4041 to 4045 include:

[0063] Step 4041: Associate daily fuel consumption, engine speed, and mileage with tree structure nodes through digital identifiers to construct a mapping network with vehicle operating parameter types as layers and tree structure nodes as nodes.

[0064] Optionally, the vehicle management system associates daily fuel consumption, engine speed, and mileage with nodes in the structure tree based on digital identifiers, constructing a mapping network. The mapping network uses different levels based on vehicle operating parameter types, such as fuel consumption, engine speed, and mileage. Each level uses nodes in the structure tree as specific nodes, and each node contains the specific value of the corresponding parameter.

[0065] Continuing with the example of the HW-2023 smart sanitation vehicle with the digital identifier HW2023001, its operating parameters on a certain day are: fuel consumption 80L, average speed 2200r / min, and mileage 150km. The vehicle management system associates these parameters with nodes in the structure tree using the digital identifier HW2023001. In the constructed mapping network, the node for the fuel consumption layer is "engine (node ​​ID: FD001)," corresponding to the value 80L; the node for the speed layer is also "engine (node ​​ID: FD001)," corresponding to the value 2200r / min; and the node for the mileage layer is "wheels (node ​​IDs: CL001-CL004)," corresponding to the value 150km, thus forming a parameter-node mapping relationship.

[0066] Step 4042: Cross-associate the association graph and mapping network using digital identifiers to generate triples. Each triple includes abnormal equipment, maintenance information, and vehicle operating parameters, with each triple corresponding to a node in the structure tree.

[0067] Furthermore, the vehicle management system cross-links the association graph and mapping network using digital identifiers to generate triples, as detailed in steps 40421 to 40424.

[0068] The triplet in this embodiment of the invention includes abnormal equipment, maintenance information (maintenance parts, cause of failure, maintenance time) and vehicle operating parameters (fuel consumption, speed, mileage). Each triplet corresponds to a node in the structure tree, realizing the close association of the three types of information.

[0069] Step 4043: Using a structure tree as a framework, the triples are indexed and allocated according to the hierarchical relationship of the structure tree, resulting in a three-level index chain from the root node to the child node and from the child node to the leaf node. The data corresponding to each index node in the three-level index chain points to the associated record in the triple.

[0070] Furthermore, using a structure tree as a framework, triples are indexed and assigned according to the hierarchical relationship of the structure tree. The structure tree has a hierarchical structure of root nodes, child nodes, and leaf nodes. Based on the position of the node corresponding to each triple in the structure tree, it is assigned to the corresponding level, forming a three-level index chain from the root node to the child node and then to the leaf node. Each index node has a corresponding data pointer that points to the associated record in the triple. The index chain allows for quick location and querying of the triple information corresponding to each node.

[0071] Continuing in the structure tree of the HW2023001 smart sanitation vehicle, the root node is "HW-2023 Smart Sanitation Vehicle (ID: Root001)", and the child nodes include "Power System (ID: Sub001)" and "Driving System (ID: Sub002)". The leaf node "Left Front Tire (ID: CL001)" belongs to the "Driving System" child node. The vehicle management system assigns the triples of the corresponding leaf nodes to a three-level index chain. The root node Root001 points to the child node Sub002, the child node Sub002 points to the leaf node CL001, and the data of the leaf node CL001 points to the triple (left front tire, (left front tire, punctured by a sharp object, 1 hour), 150km), forming a complete index chain.

[0072] Step 4044: Based on the three-level index chain, mine the temporal correlation between maintenance information and vehicle operating parameters under the same tree structure node, as well as the parameter influence relationship between different nodes, to obtain the association rules.

[0073] Furthermore, the vehicle management system performs time-series analysis on maintenance information and vehicle operating parameters under the same tree structure node based on a three-level index chain, mining the temporal correlation between the two. For example, after a specific maintenance occurs at a node, the change pattern of its corresponding operating parameters over subsequent time is analyzed. Simultaneously, the influence relationships between parameters of different nodes are analyzed, such as the impact of engine speed changes on fuel consumption values ​​at the fuel consumption node.

[0074] Continuing with the three-level index chain of the HW2023001 smart sanitation vehicle, the vehicle management system analyzed the "Engine (ID: FD001)" node. It was found that after this node underwent repair due to "spark plug aging" (repair information), the daily engine speed subsequently decreased from 2500 r / min to 2200 r / min, and fuel consumption decreased from 90L to 80L (operating parameters). This revealed a time-series correlation rule: after spark plug aging repair, engine speed decreases, and fuel consumption decreases. Simultaneously, it was found that for every 500 r / min increase in engine speed, fuel consumption increases by an average of 10L, thus establishing the parameter influence relationship rules between different nodes (engine speed and fuel consumption).

[0075] Step 4045: Based on the tree structure, the three-level index chain and association rules are linked together using digital identifiers to obtain the daily vehicle electronic file.

[0076] Furthermore, the vehicle management system connects the three-level index chain and association rules through digital identifiers according to the structure tree to obtain daily vehicle electronic files, as detailed in steps 40451 to 40454.

[0077] This invention constructs daily vehicle electronic files through mapping networks, triples, three-level index chains, and association rules. This enables the daily vehicle electronic files to achieve deep integration and organic association of abnormal equipment, maintenance information, and vehicle operating parameters. Therefore, the daily vehicle electronic files can not only clearly present the specific condition of the vehicle on that day, but also reveal the inherent connections between information, enabling accurate tracking of the maintenance records of a single vehicle within a day and solving the problem of missing maintenance record tracking.

[0078] In one embodiment, the process of steps 40421 to 40424 includes:

[0079] Step 40421: Based on the subgraph information corresponding to each digital identifier in the association graph and the running parameter type associated with the digital identifier in the mapping network, establish a digital identifier index pool for digital identifiers and structure tree nodes.

[0080] Optionally, the vehicle management system organizes the subgraph information corresponding to each numerical identifier in the association graph. The subgraph information includes the structure tree nodes involved in the maintenance process, the parts to be repaired, the cause of the fault, and the maintenance time. At the same time, it extracts the types of operating parameters associated with the numerical identifier in the mapping network, such as fuel consumption, speed, and mileage, as well as the structure tree nodes corresponding to these parameters.

[0081] Furthermore, the vehicle management system organizes and summarizes the digital identifiers and their corresponding structure tree nodes to establish a digital identifier index pool. This index pool uses the digital identifier as the key and all associated structure tree nodes as the value, forming a set of correspondences between digital identifiers and structure tree nodes.

[0082] Continuing with the example of the smart sanitation vehicle with the digital identifier HW2023001, the subgraph information corresponding to it in the association graph involves the tree structure node "left front tire (node ​​ID: CL001)". The maintenance information includes the repaired part being the left front tire, the cause of the malfunction being a puncture by a sharp object, and the repair time being 1 hour. The operating parameters associated with this digital identifier in the mapping network are mileage (corresponding to nodes CL001-CL004), fuel consumption (corresponding to node engine FD001), and engine speed (corresponding to node engine FD001). After organizing this information, the vehicle management system records in the digital identifier index pool: HW2023001 corresponds to the tree structure nodes CL001, CL002, CL003, CL004, and FD001.

[0083] Step 40422: Based on the digital identifier index pool, extract the maintenance information transmission path in the association graph and the operation parameter transmission path in the mapping network for each digital identifier, and extract the nodes and relationships that exist simultaneously in the maintenance information transmission path and the operation parameter transmission path through link cross-validation to obtain the association links between the digital identifier and the tree structure node.

[0084] Furthermore, the vehicle management system selects a digital identifier from the digital identifier index pool, and then extracts the maintenance information transmission path in the association graph and the operating parameter transmission path in the mapping network for that digital identifier. The maintenance information transmission path refers to the nodes and relationships between the nodes through which maintenance information is transmitted from the faulty equipment node in the structure tree; the operating parameter transmission path refers to the nodes and relationships between the nodes through which the operating parameters are transmitted from the data acquisition source node in the structure tree.

[0085] Furthermore, the vehicle management system performs link cross-validation on these two paths to identify nodes that exist simultaneously in both paths and the relationships between these nodes. These common nodes and relationships constitute the association link between the digital identifier and the nodes in the structure tree, ensuring the accuracy of the association.

[0086] Continuing with the digital identifier HW2023001, the maintenance information transmission path in the association graph is: left front tire (CL001) → driving system (Sub002) → HW-2023 intelligent sanitation vehicle (Root001); the operating parameter transmission path in the mapping network (taking mileage as an example) is: left front tire (CL001) → driving system (Sub002) → HW-2023 intelligent sanitation vehicle (Root001). Through cross-validation, the vehicle management system found that the common nodes in both paths are CL001, Sub002, and Root001, and the relationships between the nodes are hierarchical inclusion relationships. Therefore, the association link is: CL001 → Sub002 → Root001.

[0087] Step 40423: Based on the associated links and target nodes in the structure tree, and combining the spatial location information of the aggregate tree of similar devices and the abnormal propagation probability of the fault propagation path tree, extract the device features of the abnormal devices. The device features include the device model, spatial location identifier, and propagation source identifier of the associated abnormal nodes.

[0088] Furthermore, the vehicle management system extracts the device characteristics of abnormal devices based on the associated links and target nodes (nodes corresponding to abnormal devices) in the structure tree, combined with the aggregation tree of similar devices and the fault propagation path tree. The spatial location information of the abnormal device is obtained from the aggregation tree of similar devices, i.e., the specific spatial location identifier of the device in the vehicle's digital twin; the propagation source identifier of the abnormal node associated with the abnormal device is determined from the fault propagation path tree based on the anomaly propagation probability, i.e., the identifier of the node that initially triggered the fault propagation; these, along with the device's model number, together constitute the device characteristics.

[0089] Continuing with the above embodiment, the target node in the structure tree is CL001 (left front wheel tire), and the associated links include this node. In the same type of device aggregation tree, the spatial location identifier of CL001 is "vehicle left front wheel position"; in the fault propagation path tree, the anomaly of CL001 may be caused by "sharp object on the road surface (propagation source node ID: S001)," with a propagation probability of 90%, therefore the propagation source identifier is S001; the device model is 235 / 75R17.5. The device characteristics extracted by the vehicle management system are: device model 235 / 75R17.5, spatial location identifier "vehicle left front wheel position," and propagation source identifier S001.

[0090] Step 40424: Generate triples based on the digital identifier index pool, associated links, and device characteristics.

[0091] Furthermore, the vehicle management system generates triples based on the digital identifier index pool, associated links, and device characteristics, as detailed in steps a1 to a4.

[0092] This invention accurately correlates abnormal equipment, maintenance information, and operating parameters, and incorporates feature information such as the spatial location and propagation source of the equipment. This results in a triplet that not only contains basic correlation information but also has richer background data, providing a data foundation for accurate tracking of daily maintenance records of individual vehicles. Therefore, it enables refined management of smart sanitation vehicles throughout their entire lifecycle, providing effective support for cost control and decision-making.

[0093] In one embodiment, the process of steps a1 to a4 includes:

[0094] Step a1: Based on the digital identifier index pool and associated links, cross-matching is performed between the subgraph corresponding to each digital identifier in the association graph and the associated operating parameter type in the mapping network to obtain the maintenance parameter matching pair for each node under each digital identifier. The maintenance parameter matching pair represents the matching pair where the maintenance information of the subgraph and the parameter type have a temporal co-occurrence relationship.

[0095] Optionally, the vehicle management system is based on a digital identifier index pool, which records the correspondence between digital identifiers and nodes in the structure tree. At the same time, it determines the node path corresponding to the digital identifier in the association graph and mapping network based on the association links.

[0096] Furthermore, the vehicle management system cross-matches each digital identifier with the corresponding subgraph in the association graph (containing maintenance information, such as maintenance parts, fault causes, and maintenance time) and the associated operating parameter types (such as fuel consumption, speed, and mileage) in the mapping network.

[0097] The core of the matching in this embodiment of the invention is to determine whether there is a temporal co-occurrence relationship between the maintenance information and parameter type in the subgraph, that is, whether the time of the maintenance event and the time of parameter type data collection overlap or are sequentially related. If so, a maintenance parameter matching pair is formed for the node under that digital identifier.

[0098] Continuing with the example of digital identifier HW2023001, the nodes associated with its digital identifier index pool include the left front tire (CL001), etc. The association link is CL001→Sub002→Root001. The maintenance information of the subgraph corresponding to CL001 in the association graph is: maintenance part: left front tire; cause of failure: punctured by a sharp object; maintenance duration: 1 hour (occurred on July 15, 2025, from 10:30 to 11:30). The operating parameter type associated with CL001 in the mapping network is mileage (collected throughout the day on July 15, 2025). The vehicle management system determines that there is a temporal co-occurrence relationship between the two (the maintenance time is within the parameter collection time), and obtains the maintenance parameter matching pair: ((left front tire, punctured by a sharp object, 1 hour), mileage).

[0099] Step a2 involves using the feature map of the malfunctioning equipment as the core, binding the maintenance information and vehicle operating parameters in the maintenance parameter matching pairs to the equipment features, thus obtaining the association kernel for each node. The association kernel characterizes the functional matching between the malfunctioning equipment model and the maintenance parts, as well as the scenario adaptability with the vehicle operating parameters.

[0100] Furthermore, the vehicle management system uses the feature map of the abnormal equipment as its core. This feature map includes equipment characteristics such as equipment model, spatial location identifier, and the propagation source identifier of associated abnormal nodes. Further, the vehicle management system binds maintenance information (maintenance parts, fault cause, maintenance duration) and vehicle operating parameters (specific parameter values) from the maintenance parameter matching pairs to these equipment characteristics, obtaining the association core for each node. The association core reflects two aspects: first, the functional compatibility between the abnormal equipment model and the maintenance parts, i.e., whether the maintenance parts are compatible with the equipment model; and second, the scenario adaptability between the abnormal equipment and the vehicle operating parameters, i.e., whether the equipment operates normally under the scenario reflected by the current operating parameters.

[0101] Continuing with the above embodiment, the feature map of the abnormal device's left front tire (CL001) includes: device model 235 / 75R17.5, spatial location identifier "vehicle left front wheel position", and propagation source identifier S001. The maintenance information in the maintenance parameter matching pair is (left front tire, punctured by a sharp object, 1 hour), and the vehicle operating parameter is a mileage of 150km. The vehicle management system binds this information to the feature map to obtain the association kernel: device model 235 / 75R17.5 is functionally matched with the maintenance component left front tire (same model), and the device malfunctions due to being punctured in the scenario of a mileage of 150km (scenario adaptability association). The association kernel integrates this matching and adaptability information.

[0102] Step a3: Determine the first functional dependency of the upstream node and the second functional dependency of the downstream node for each node based on the cross-type device dependency tree.

[0103] Furthermore, the vehicle management system uses a cross-type device dependency tree, which describes the functional dependencies between devices with devices as nodes. Therefore, for each node (the node corresponding to the abnormal device), its upstream and downstream nodes are searched in the dependency tree. Upstream nodes are device nodes that directly support the implementation of the current node's device functions, and the first functional dependency relationship between them is determined (i.e., how the upstream node supports the current node's functions). Downstream nodes are device nodes that depend on the current node's device functions to function properly, and the second functional dependency relationship between them is determined (i.e., how the current node supports the downstream node's functions).

[0104] Continuing with the above embodiments, in the cross-type device dependency tree, the upstream node of the left front tire (CL001) is "rim (CL001-1)", and the first functional dependency relationship is: the rim provides mounting support for the tire, and the tire depends on the rim to maintain its shape; the downstream node is "brake system (Sub003)", and the second functional dependency relationship is: the normal rotation of the tire provides a braking carrier for the brake system, and the brake system depends on the tire to achieve vehicle deceleration. Therefore, the first and second functional dependencies of CL001 are determined.

[0105] Step a4: Generate triples based on each node, combining the first functional dependency and the second functional dependency.

[0106] Furthermore, the vehicle management system generates triples based on each node, combining the first functional dependency and the second functional dependency, as described in steps a41 to a44.

[0107] This invention ensures the compatibility verification of equipment with maintenance components and operating scenarios through the association kernel, while the functional dependency relationship reveals the role of the equipment in the system and its relationship with other equipment. This makes the generated triplet not only a simple combination of information, but also a deep integration of abnormal equipment, maintenance information, operating parameters and equipment functional dependencies. This provides a data foundation for the accurate tracking of the daily maintenance records of a single vehicle. Therefore, it can be used for the refined management of smart sanitation vehicles throughout their entire life cycle, providing effective support for cost control and decision-making.

[0108] In one embodiment, the process of steps a41 to a44 includes:

[0109] Step a41: Determine the upstream node associated kernel based on the first functional dependency relationship, and determine the downstream node associated kernel based on the second functional dependency relationship.

[0110] Optionally, the vehicle management system, based on the first functional dependency relationship (the dependency relationship between the current node and the upstream node), finds the corresponding dependency kernel for the upstream node in the dependency kernel set, i.e., the upstream node dependency kernel. This dependency kernel contains abnormal equipment information, maintenance information, operating parameters, and functional dependency details between the upstream node and the current node. Simultaneously, based on the second functional dependency relationship (the dependency relationship between the current node and the downstream node), it finds the corresponding dependency kernel for the downstream node, i.e., the downstream node dependency kernel, which contains relevant information about the downstream node and functional dependency details between the downstream node and the current node.

[0111] Continuing with the left front tire (node ​​CL001), the first functional dependency is relying on the rim (upstream node CL001-1) for installation support. The vehicle management system finds the associated core of CL001-1: equipment model LW-2023, maintenance information (no maintenance record), and operating parameters (rim diameter adaptation data), forming the upstream node associated core. The second functional dependency is supporting the braking system (downstream node Sub003) to achieve braking. The system finds the associated core of Sub003: equipment model ZD-500, maintenance information (no maintenance record), and operating parameters (120 braking cycles / day), forming the downstream node associated core.

[0112] Step a42: Based on the association kernel of each node, the association kernel of the upstream node, and the association kernel of the downstream node, the association kernel is fused to obtain the fused association kernel.

[0113] Furthermore, the vehicle management system merges the association cores of the current node, the upstream node, and the downstream node. During the fusion process, core information from each association core is retained, including abnormal device models, maintenance information, operating parameters, functional compatibility, and scenario adaptability. At the same time, the functional dependencies between the current node and its upstream and downstream nodes are highlighted, resulting in a merged association core. The merged association core comprehensively reflects the current node's position in the device dependency network, its own status, and its interactive relationships with upstream and downstream nodes.

[0114] Continuing with the above embodiment, the associated core of the current node CL001 includes equipment model 235 / 75R17.5, maintenance information (left front tire repair, punctured, 1 hour), and operating parameters (mileage 150km); the associated core of the upstream node CL001-1 includes model LW-2023, no maintenance record, and rim diameter compatibility data; the associated core of the downstream node Sub003 includes model ZD-500, no maintenance record, and 120 braking times / day. The associated core obtained after fusion by the vehicle management system is: the left front tire (235 / 75R17.5) relies on the rim (LW-2023) for support, maintenance due to puncture took 1 hour, and during the 150km journey, it provides a braking carrier for the braking system (ZD-500), with 120 braking times / day.

[0115] Step a43: Based on the abnormal propagation probability relationship of each node in the fault propagation path tree and combined with the equipment characteristics, the maintenance information and vehicle operating parameters that match the downstream fault type caused by the characteristics of the abnormal propagation source are selected from the fused association kernel to obtain the initial triplet constrained by fault propagation.

[0116] Furthermore, the vehicle management system utilizes the anomaly propagation probability relationship of each node in the fault propagation path tree, combined with equipment characteristics (such as propagation source identifier, spatial location, etc.), to filter the fused association kernel. Optionally, the core of the filtering in this embodiment of the invention is to determine whether the fault type and vehicle operating parameters in the maintenance information match the downstream fault type caused by the characteristics of the anomaly propagation source. For example, if the propagation source is a "sharp object on the road surface," then the downstream fault type should be related to tire damage. The system retains the matching information and removes the mismatched content, obtaining the initial triplet constrained by fault propagation.

[0117] Continuing with the above embodiment, in the fault propagation path tree, the abnormal propagation source of the left front tire (CL001) is identified as S001 (sharp object on the road surface), and the downstream fault type it causes is tire damage. The maintenance information in the fused correlation kernel is "punctured by a sharp object" (matching the fault type), and the operating parameter is "mileage 150km" (reflecting driving on roads where sharp objects may exist). After filtering, the vehicle management system retains this information to obtain the initial triplet: (left front tire (235 / 75R17.5), (punctured by a sharp object, repaired for 1 hour), 150km driven).

[0118] Step a44: Bind the initial triples to the digital identifiers based on the digital identifier index pool to obtain the final triples for each node.

[0119] Furthermore, the vehicle management system extracts the digital identifier corresponding to the current node from the digital identifier index pool and binds the initial triplet to the digital identifier. During the binding process, it ensures that the triplet has a unique correspondence with the specific smart sanitation vehicle to avoid information confusion, thus obtaining the final triplet for each node. This triplet contains abnormal equipment, maintenance information, operating parameters, and implicitly contains the vehicle information and equipment dependencies corresponding to the digital identifier.

[0120] Continuing with the above embodiment, in the digital identifier index pool, the digital identifier corresponding to node CL001 is HW2023001. The vehicle management system binds the initial triplet to HW2023001 to obtain the final triplet: (Abnormal device: left front tire (235 / 75R17.5, digital identifier HW2023001), maintenance information: (punctured by a sharp object, 1 hour of maintenance), operating parameters: driven 150km).

[0121] The final triplet generated by this invention achieves deep integration of abnormal equipment, maintenance information, operating parameters, equipment-dependent networks, fault propagation paths, and digital identifiers. This allows the triplet to not only clearly present the status information of a single node, but also to reflect the interactive relationship between devices through the fusion of upstream and downstream association kernels. After fault propagation constraints, the consistency between the information and the source of the fault is ensured, enabling accurate tracing of the source in the future.

[0122] In one embodiment, the process of steps 40451 to 40454 includes:

[0123] Step 40451: Using the spatial location in the structure tree as the spatial coordinate reference, the functional dependency relationship as the functional connection network, and the fault propagation path as the fault evolution time axis, construct the spatial skeleton.

[0124] Optionally, the vehicle management system uses the spatial location of devices in the structure tree as the spatial coordinate reference. That is, it determines the coordinate position based on the actual spatial distribution of each device in the vehicle's digital twin; for example, the engine is in the center of the front of the vehicle, and the tires are at the four corners. Simultaneously, it uses the functional dependencies in the cross-type device dependency tree as a functional connection network, associating devices by function to form a network. For example, the engine and fuel pump are connected through the fuel supply function, and the tires and braking system are connected through the braking function. Furthermore, it uses the fault propagation path in the fault propagation path tree as the fault evolution timeline, constructing a time dimension according to the chronological order of fault occurrence and propagation. For example, a sharp object on the road first causes tire damage, which may then affect braking performance. The system integrates these three elements to construct a spatial skeleton that includes spatial, functional, and temporal dimensions.

[0125] Taking the HW2023001 intelligent sanitation vehicle as an example, in the structure tree, the spatial position of the left front tire (CL001) is "left front wheel coordinates (X1, Y1, Z1)", the rim (CL001-1) is "left front rim coordinates (X1, Y1, Z1)", and the braking system (Sub003) is "left front braking device coordinates (X2, Y2, Z2)". In the functional connection network, CL001 and CL001-1 are connected through "installation support", and CL001 and Sub003 are connected through "brake carrier". In the fault evolution timeline, the time sequence of propagation source S001 (sharp object on the road) → CL001 (tire damage) → Sub003 (decrease in braking efficiency) is t0→t1→t2. The vehicle management system integrates this information to construct a spatial skeleton containing the above spatial coordinates, functional connection edges, and timeline.

[0126] Step 40452: Embed the nodes of the three-level index chain into the spatial skeleton according to the hierarchical correspondence to obtain a three-level mapping chain. The root node index, child node index, and leaf node index in the three-level index chain are respectively associated with the root node, child node, and target node corresponding to the abnormal device in the spatial skeleton.

[0127] Furthermore, the vehicle management system embeds the root node index, child node index, and leaf node index from the three-level index chain into the spatial skeleton according to their hierarchical correspondence. The root node index corresponds to the root node of the spatial skeleton (the entire vehicle), the child node index corresponds to the child nodes of the spatial skeleton (such as the driving system, braking system, etc.), and the leaf node index corresponds to the target node of abnormal equipment in the spatial skeleton (such as the left front wheel tire). During the embedding process, the system ensures that the data pointer of each index node accurately points to the corresponding triplet association record, forming a three-level mapping chain that establishes a connection between the index chain and the spatial, functional, and temporal dimensions of the spatial skeleton.

[0128] Continuing with the above embodiment, the three-level index chain of HW2023001 is Root001 (root node index) → Sub002 (driving system sub-node index) → CL001 (leaf node index). Root001 is embedded into the root node of the spatial skeleton (the entire vehicle space range), Sub002 is embedded into the "driving system" sub-node (containing the spatial set of all wheels, suspension, etc.), and CL001 is embedded into the target node "left front wheel coordinates (X1, Y1, Z1)". The data pointer of each index node points to the corresponding triplet, such as CL001 pointing to (left front tire, (punctured, repaired for 1 hour), driven 150km), forming a three-level mapping chain.

[0129] Step 40453: Map the rules in the association rules to the corresponding nodes and edge relationships of the spatial skeleton according to the device nodes involved in the rule conditions and rule conclusions, and obtain the mapped rules.

[0130] Furthermore, the vehicle management system analyzes the device nodes involved in the rule conditions and conclusions of the association rules, mapping the rules to the corresponding nodes and edge relationships in the spatial skeleton. If the rule condition is "tire damage" (related to CL001) and the conclusion is "increased braking frequency" (related to Sub003), then the rule is mapped to the CL001 node, the Sub003 node, and the "brake carrier" edge relationship between them. Through this mapping, the association rules are integrated with the spatial location, functional connections, and temporal evolution of the spatial skeleton, resulting in mapped rules that enhance the spatial and temporal context of the rules.

[0131] Continuing with the above embodiment, the association rule is "After the left front tire is punctured by a sharp object (CL001), the number of braking actions of the braking system increases by 10% (Sub003)". The vehicle management system maps the rule condition "CL001 is punctured" to the CL001 node of the spatial skeleton and the time point t1, and the rule conclusion "Sub003 increases the number of braking actions" to the Sub003 node and the time point t2. The rule as a whole is mapped to the "braking carrier" edge relationship between CL001 and Sub003, resulting in the mapped rule, which includes the association of spatial coordinates, time points, and functional edges.

[0132] Step 40454: Based on the digital identifier, the three-level mapping chain and the mapped rules are connected in series to obtain the daily vehicle electronic file.

[0133] Furthermore, the vehicle management system, based on the vehicle's digital identifier (such as HW2023001), connects the three-level mapping chain and the mapped rules. During the connection process, the digital identifier serves as the unique identifier, ensuring that the index data of the three-level mapping chain and the association information of the mapped rules all point to the same vehicle and form an organic whole within the framework of the spatial skeleton. Ultimately, this integration forms a daily vehicle electronic file, which contains multi-dimensional information on spatial distribution, functional associations, and temporal evolution, as well as corresponding triplet data and association rules.

[0134] Continuing with the above embodiment, the digital identifier of HW2023001 is associated with a three-level mapping chain (containing the triplet information of CL001) and the mapped rules (the correlation between tire damage and braking frequency). The vehicle management system connects the two through the digital identifier to form a daily vehicle electronic file for July 15, 2025. In the file, the spatial skeleton displays the location and connection of each device, the three-level mapping chain marks the maintenance and operation data of CL001, and the mapped rules explain the impact of faults on downstream devices, comprehensively presenting the vehicle status for the day.

[0135] The daily vehicle electronic records generated by this invention achieve deep integration of spatial, functional, and temporal dimensions. They not only record vehicle maintenance information and operating parameters, but also intuitively display equipment distribution, functional relationships, and fault evolution processes through a spatial skeleton. This upgrades the records from simple information collections to dynamic records with spatial context, functional logic, and time sequence, enabling precise tracking of daily maintenance records for individual vehicles and solving the problem of missing maintenance record tracking. Subsequently, it can realize refined management of smart sanitation vehicles throughout their entire life cycle, providing effective support for cost control and decision-making.

[0136] The following describes the intelligent sanitation vehicle lifecycle management system based on digital twins provided by this invention. The intelligent sanitation vehicle lifecycle management system based on digital twins described below can be referred to in correspondence with the intelligent sanitation vehicle lifecycle management method based on digital twins described above.

[0137] Optional, refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of the intelligent sanitation vehicle lifecycle management system based on digital twins provided by the present invention. The intelligent sanitation vehicle lifecycle management system based on digital twins includes:

[0138] The digital twin construction module 210 is used to construct a vehicle digital twin based on the static information and real-time dynamic information of the smart sanitation vehicle; the vehicle digital twin reflects the real-time status of the smart sanitation vehicle; the real-time dynamic information is collected based on various sensors installed on the smart sanitation vehicle;

[0139] The abnormal information generation module 220 is used to generate abnormal information of the abnormal equipment if an abnormality is detected in the smart sanitation vehicle based on the vehicle digital twin, and associate the corresponding maintenance parts database based on the vehicle digital twin.

[0140] The information push module 230 is used to match the corresponding parts information in the maintenance parts database based on the equipment type of the abnormal equipment, and push the abnormal information and parts information to the maintenance mobile terminal so that maintenance users can perform maintenance on the smart sanitation vehicle based on the information in the maintenance mobile terminal.

[0141] The information association module 240 is used to associate the digital identifier of the vehicle digital twin with the maintenance information fed back from the maintenance mobile terminal during the maintenance process and the vehicle operation parameters of the smart sanitation vehicle in daily life to obtain the daily vehicle electronic file.

[0142] The full lifecycle management module 250 is used to summarize the daily electronic vehicle records of smart sanitation vehicles throughout their entire lifecycle, from purchase to scrapping, based on digital identification, to obtain a full lifecycle electronic record.

[0143] This invention utilizes digital twins to automatically monitor anomalies, replacing manual data entry, ensuring timely transmission of anomaly information, and improving anomaly handling efficiency. Furthermore, by linking the digital identifiers of the vehicle's digital twin with maintenance information during the repair process and the daily operating parameters of the smart sanitation vehicle, it achieves precise tracking of the daily maintenance records for each vehicle, solving the problem of missing maintenance records. Finally, based on the digital identifiers, daily electronic vehicle files throughout the entire lifecycle of the vehicle, from purchase to scrapping, are compiled into a unique electronic file for each vehicle, enabling refined management of smart sanitation vehicles throughout their entire lifecycle and providing effective support for cost control and decision-making.

[0144] Please see Figure 3 , Figure 3 An embodiment diagram of an electronic device provided in accordance with the present invention. For example... Figure 3 As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it performs the following steps:

[0145] Based on the static and real-time dynamic information of smart sanitation vehicles, a digital twin of the vehicle is constructed; the digital twin of the vehicle reflects the real-time status of the smart sanitation vehicle; the real-time dynamic information is collected based on a variety of sensors installed on the smart sanitation vehicle.

[0146] If an anomaly is detected in a smart sanitation vehicle based on the vehicle's digital twin, anomaly information of the abnormal equipment is generated, and the corresponding maintenance parts database is associated with the vehicle's digital twin.

[0147] Based on the equipment type of the abnormal equipment, the corresponding parts information is matched in the maintenance parts database, and the abnormal information and parts information are pushed to the maintenance mobile terminal so that maintenance users can perform maintenance on the smart sanitation vehicle based on the information in the maintenance mobile terminal;

[0148] Based on the digital identifier of the vehicle's digital twin, and combined with the maintenance information fed back from the maintenance mobile terminal during the maintenance process and the vehicle operation parameters of the smart sanitation vehicle in daily, a daily vehicle electronic file is obtained.

[0149] Based on digital identification, the daily electronic records of smart sanitation vehicles throughout their entire lifecycle, from purchase to scrapping, are compiled to obtain a full lifecycle electronic record.

[0150] Please see Figure 4 , Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with an embodiment of the present invention is shown. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400 on which a computer program 311 is stored. When the computer program 311 is executed by a processor, it performs the following steps:

[0151] Based on the static and real-time dynamic information of smart sanitation vehicles, a digital twin of the vehicle is constructed; the digital twin of the vehicle reflects the real-time status of the smart sanitation vehicle; the real-time dynamic information is collected based on a variety of sensors installed on the smart sanitation vehicle.

[0152] If an anomaly is detected in a smart sanitation vehicle based on the vehicle's digital twin, anomaly information of the abnormal equipment is generated, and the corresponding maintenance parts database is associated with the vehicle's digital twin.

[0153] Based on the equipment type of the abnormal equipment, the corresponding parts information is matched in the maintenance parts database, and the abnormal information and parts information are pushed to the maintenance mobile terminal so that maintenance users can perform maintenance on the smart sanitation vehicle based on the information in the maintenance mobile terminal;

[0154] Based on the digital identifier of the vehicle's digital twin, and combined with the maintenance information fed back from the maintenance mobile terminal during the maintenance process and the vehicle operation parameters of the smart sanitation vehicle in daily, a daily vehicle electronic file is obtained.

[0155] Based on digital identification, the daily electronic records of smart sanitation vehicles throughout their entire lifecycle, from purchase to scrapping, are compiled to obtain a full lifecycle electronic record.

[0156] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the intelligent sanitation vehicle full lifecycle management method based on digital twins provided by the above methods. The method includes:

[0157] Based on the static and real-time dynamic information of smart sanitation vehicles, a digital twin of the vehicle is constructed; the digital twin of the vehicle reflects the real-time status of the smart sanitation vehicle; the real-time dynamic information is collected based on a variety of sensors installed on the smart sanitation vehicle.

[0158] If an anomaly is detected in a smart sanitation vehicle based on the vehicle's digital twin, anomaly information of the abnormal equipment is generated, and the corresponding maintenance parts database is associated with the vehicle's digital twin.

[0159] Based on the equipment type of the abnormal equipment, the corresponding parts information is matched in the maintenance parts database, and the abnormal information and parts information are pushed to the maintenance mobile terminal so that maintenance users can perform maintenance on the smart sanitation vehicle based on the information in the maintenance mobile terminal;

[0160] Based on the digital identifier of the vehicle's digital twin, and combined with the maintenance information fed back from the maintenance mobile terminal during the maintenance process and the vehicle operation parameters of the smart sanitation vehicle in daily, a daily vehicle electronic file is obtained.

[0161] Based on digital identification, the daily electronic records of smart sanitation vehicles throughout their entire lifecycle, from purchase to scrapping, are compiled to obtain a full lifecycle electronic record.

[0162] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0163] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for full lifecycle management of intelligent sanitation vehicles based on digital twins, characterized in that, include: Based on the static and real-time dynamic information of smart sanitation vehicles, a digital twin of the vehicle is constructed; the digital twin of the vehicle reflects the real-time status of the smart sanitation vehicle. The real-time dynamic information is collected based on multiple sensors installed on the smart sanitation vehicle; If an anomaly is detected in the smart sanitation vehicle based on the vehicle digital twin, anomaly information of the abnormal equipment is generated, and the corresponding maintenance parts database is associated with the vehicle digital twin. Based on the equipment type of the abnormal device, the corresponding part information is matched in the maintenance parts database, and the abnormal information and the part information are pushed to the maintenance mobile terminal so that the maintenance user can perform maintenance on the smart sanitation vehicle based on the information in the maintenance mobile terminal. Based on the digital identifier of the vehicle's digital twin, and combined with the maintenance information fed back by the maintenance mobile terminal during the maintenance process and the vehicle operation parameters of the smart sanitation vehicle within each day, a daily vehicle electronic file is obtained. Based on the digital identifier, the daily electronic vehicle records of the smart sanitation vehicle throughout its entire life cycle from purchase to scrapping are summarized to obtain a full life cycle electronic record. The maintenance information includes the parts being repaired, the cause of the malfunction, and the repair duration; the steps for obtaining the daily vehicle electronic record include: Based on the physical hierarchy and functional association of devices in the vehicle digital twin, a structure tree is constructed that includes an aggregation tree of devices of the same type, a dependency tree of devices across types, and a fault propagation path tree. The target node in the structure tree is bound to the vehicle digital twin through the digital identifier to generate an association matrix; the target node represents the node corresponding to the abnormal device; the matrix elements in the association matrix represent the mapping strength between the node and the digital identifier, the matrix row index is the structure tree node ID, and the column index is the digital identifier; when there is a unique correspondence between the node and the digital identifier, the element value is the digital identifier itself; when there is a one-to-many association between the node and the digital identifier, the element value is the association identifier. Based on the association matrix, the maintenance information is bound to the tree structure nodes through the digital identifiers to obtain an association graph; each digital identifier in the association graph corresponds to a subgraph, and the subgraph information includes the tree structure nodes involved in the maintenance process, as well as the maintenance parts, the cause of the failure, and the maintenance time; Based on the correlation map and the daily vehicle operation parameters, the daily vehicle electronic file is obtained; The same type of device aggregation tree represents the spatial location of the vehicle digital twin with the device model as the node and the digital identifier as the mapping; the cross-type device dependency tree represents the functional dependency relationship between devices with the device as the node; and the fault propagation path tree represents the probability relationship of fault propagation between abnormal devices with abnormal devices as the node.

2. The method for full lifecycle management of intelligent sanitation vehicles based on digital twins according to claim 1, characterized in that, Vehicle operating parameters include fuel consumption, engine speed, and mileage; The process of associating the data with the correlation map and daily vehicle operating parameters to obtain the daily vehicle electronic file includes: The daily fuel consumption, engine speed, and mileage are associated with the tree structure nodes through the digital identifiers, constructing a mapping network with vehicle operating parameter types as layers and tree structure nodes as nodes; The association graph and the mapping network are cross-linked using the digital identifier to generate triples; each triple includes abnormal equipment, maintenance information and vehicle operating parameters, and each triple corresponds to a node in the structure tree. Using a structure tree as a framework, triples are indexed and allocated according to the hierarchical relationship of the structure tree, resulting in a three-level index chain from the root node to the child node and from the child node to the leaf node; the data corresponding to each index node in the three-level index chain points to the associated record in the triple; Based on the three-level index chain, the temporal correlation between maintenance information and vehicle operating parameters under the same tree structure node, as well as the parameter influence relationship between different nodes, are mined to obtain the association rules; Based on the structure tree, the three-level index chain and the association rules are linked together using the digital identifier to obtain the daily vehicle electronic file.

3. The method for full lifecycle management of intelligent sanitation vehicles based on digital twins according to claim 2, characterized in that, The step of cross-associating the association graph and the mapping network using the digital identifier to generate triples includes: Based on the subgraph information corresponding to each digital identifier in the association graph and the running parameter type associated with the digital identifier in the mapping network, a digital identifier index pool of digital identifier and structure tree node is established; Based on the digital identifier index pool, the maintenance information transmission path of each digital identifier in the association graph and the operation parameter transmission path in the mapping network are extracted. Then, the nodes and relationships that exist simultaneously in the maintenance information transmission path and the operation parameter transmission path are extracted through link cross-validation to obtain the association links between digital identifiers and structure tree nodes. Based on the target nodes in the associated links and structure tree, and combined with the spatial location information of the aggregate tree of the same type of devices and the abnormal propagation probability of the fault propagation path tree, the device features of the abnormal devices are extracted; the device features include the device model, spatial location identifier, and propagation source identifier of the associated abnormal nodes; The triplet is generated based on the digital identifier index pool, the associated link, and the device characteristics.

4. The method for full lifecycle management of intelligent sanitation vehicles based on digital twins according to claim 3, characterized in that, The process of generating the triplet based on the digital identifier index pool, the associated link, and the device characteristics includes: Based on the digital identifier index pool and the associated links, the subgraph corresponding to each digital identifier in the associated graph is cross-matched with the operating parameter type associated in the mapping network to obtain the maintenance parameter matching pair of the node under each digital identifier; the maintenance parameter matching pair represents the matching pair where the maintenance information of the subgraph and the parameter type have a temporal co-occurrence relationship; Using the feature map of the abnormal equipment as the core, the maintenance information and vehicle operating parameters in the maintenance parameter matching pair are bound to the equipment features to obtain the association kernel of each node; the association kernel characterizes the functional matching between the abnormal equipment model and the maintenance parts, as well as the scenario adaptability with the vehicle operating parameters. Based on the cross-type device dependency tree, determine the first functional dependency relationship of the upstream node and the second functional dependency relationship of the downstream node for each node; Triples are generated based on each node, combining the first and second functional dependencies.

5. The method for full lifecycle management of intelligent sanitation vehicles based on digital twins according to claim 4, characterized in that, The process of generating triples based on each node in combination with the first and second functional dependencies includes: The upstream node associated core is determined based on the first functional dependency relationship, and the downstream node associated core is determined based on the second functional dependency relationship; The association kernels of each node, the upstream node association kernels, and the downstream node association kernels are fused to obtain the fused association kernel; Based on the abnormal propagation probability relationship of each node in the fault propagation path tree and the equipment features, maintenance information and vehicle operating parameters that match the downstream fault type caused by the features of the abnormal propagation source are selected from the fused association kernel to obtain the initial triplet constrained by fault propagation. The initial triples are bound to the digital identifiers based on the digital identifier index pool to obtain the final triples for each node.

6. The method for full lifecycle management of intelligent sanitation vehicles based on digital twins according to claim 2, characterized in that, The method of concatenating the three-level index chain and the association rules based on the structure tree using the digital identifier to obtain the daily vehicle electronic file includes: A spatial skeleton is constructed using the spatial location in the tree structure as the spatial coordinate reference, the functional dependency relationship as the functional connection network, and the fault propagation path as the fault evolution time axis. The nodes of the three-level index chain are embedded into the spatial skeleton according to the hierarchical correspondence to obtain a three-level mapping chain; the root node index, child node index and leaf node index in the three-level index chain are respectively associated with the root node, child node and target node corresponding to the abnormal device in the spatial skeleton; The rules in the association rules are mapped to the corresponding nodes and edge relationships of the spatial skeleton according to the device nodes involved in the rule conditions and rule conclusions, so as to obtain the mapped rules; Based on the digital identifier, the three-level mapping chain and the mapped rules are linked together to obtain the daily vehicle electronic file.

7. A smart sanitation vehicle lifecycle management system based on digital twins, characterized in that, Applied to the intelligent sanitation vehicle lifecycle management method based on digital twins as described in any one of claims 1 to 6; The intelligent sanitation vehicle lifecycle management system based on digital twins includes: The digital twin construction module is used to construct a vehicle digital twin based on the static information and real-time dynamic information of the smart sanitation vehicle; the vehicle digital twin reflects the real-time status of the smart sanitation vehicle; the real-time dynamic information is collected based on various sensors installed on the smart sanitation vehicle; An anomaly information generation module is used to generate anomaly information of the abnormal equipment if an anomaly is detected in the smart sanitation vehicle based on the vehicle digital twin, and associate the corresponding maintenance parts database based on the vehicle digital twin. The information push module is used to match the corresponding parts information in the maintenance parts database based on the equipment type of the abnormal equipment, and push the abnormal information and the parts information to the maintenance mobile terminal, so that the maintenance user can perform maintenance on the smart sanitation vehicle based on the information in the maintenance mobile terminal; The information association module is used to associate the digital identifier of the vehicle's digital twin with the maintenance information fed back by the maintenance mobile terminal during the maintenance process and the vehicle operation parameters of the smart sanitation vehicle in daily operations to obtain a daily vehicle electronic file. The full lifecycle management module is used to summarize the daily electronic vehicle files of the smart sanitation vehicle throughout its entire lifecycle, from purchase to scrapping, based on the digital identifier, to obtain a full lifecycle electronic file. The maintenance information includes the parts being repaired, the cause of the malfunction, and the repair duration; the steps for obtaining the daily vehicle electronic record include: Based on the physical hierarchy and functional association of devices in the vehicle digital twin, a structure tree is constructed that includes an aggregation tree of devices of the same type, a dependency tree of devices across types, and a fault propagation path tree. The target node in the structure tree is bound to the vehicle digital twin through the digital identifier to generate an association matrix; the target node represents the node corresponding to the abnormal device; the matrix elements in the association matrix represent the mapping strength between the node and the digital identifier, the matrix row index is the structure tree node ID, and the column index is the digital identifier; when there is a unique correspondence between the node and the digital identifier, the element value is the digital identifier itself; when there is a one-to-many association between the node and the digital identifier, the element value is the association identifier. Based on the association matrix, the maintenance information is bound to the tree structure nodes through the digital identifiers to obtain an association graph; each digital identifier in the association graph corresponds to a subgraph, and the subgraph information includes the tree structure nodes involved in the maintenance process, as well as the maintenance parts, the cause of the failure, and the maintenance time; Based on the correlation map and the daily vehicle operation parameters, the daily vehicle electronic file is obtained; The same type of device aggregation tree represents the spatial location of the vehicle digital twin with the device model as the node and the digital identifier as the mapping; the cross-type device dependency tree represents the functional dependency relationship between devices with the device as the node; and the fault propagation path tree represents the probability relationship of fault propagation between abnormal devices with abnormal devices as the node.

8. An electronic device, comprising: Memory, used to store computer software programs; A processor for reading and executing the computer software program, characterized in that, when the processor executes the computer software program, it implements the intelligent sanitation vehicle lifecycle management method based on digital twins as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, wherein a computer software program is stored therein, characterized in that, When the computer software program is executed by the processor, it implements the intelligent sanitation vehicle full life cycle management method based on digital twin as described in any one of claims 1 to 6.

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