Logistics dual-network vehicle line management method, apparatus and device, and storage medium

Abstract

The invention relates to the technical field of logistics, and discloses a logistics dual-network vehicle line management method, which comprises the following steps: acquiring planning data and operation data, acquiring planning data and actual operation data of an express network at a preset time frequency, and processing the acquired data; planning data and operation data are displayed, and a data display interface is provided; business indexes are calculated, and key business indexes are calculated according to the actual operation data and predefined business rules; operation abnormity judgment: comparing the key business indexes with preset abnormity judgment thresholds, and judging that operation abnormity occurs if the key business indexes exceed the corresponding abnormity judgment thresholds; and performing early warning and decision making, and identifying operation abnormity according to an operation abnormity judgment result. According to the invention, the operation abnormity can be actively and timely identified and the early warning is generated, so that the management action is converted from passive remedy to early warning in advance and intervention in the event, and the refinement level, the operation efficiency and the risk management and control capability of the lane management are improved.

Description

Technical Field

[0001] This invention relates to the field of logistics technology, and in particular to a method, apparatus, equipment and storage medium for managing dual-network vehicle lines in logistics. Background Technology

[0002] With the rapid development of the logistics industry, the integration of express delivery networks and courier networks has become an important means to improve transportation efficiency. "Dual-network integration" refers to the integration of the routes of express delivery and courier networks to achieve resource sharing and efficiency improvement. However, the existing logistics management system has the following problems in the management of routes in dual-network integration: (1) The data display is singular. The existing system usually only displays actual operating data and lacks comparative analysis with planning data, so it cannot fully evaluate the effect of dual-network integration; (2) The analysis dimensions are insufficient. There is a lack of in-depth analysis of multiple dimensions such as loading rate, transportation timeliness, and transportation cost, making it difficult to find problems in operation; (3) Predictive analysis cannot be carried out. The existing system cannot predict future operating conditions and cannot allocate transportation resources in advance; (4) There is a lack of intelligent decision support. The existing system only provides data display and cannot give optimization suggestions based on historical data analysis.

[0003] Therefore, it is necessary to invent a logistics dual-network vehicle line management method, device, equipment, and storage medium that can improve the operational efficiency of dual-network converged vehicle lines, reduce operating costs, enhance the decision-making quality of express delivery companies, improve the practicality of the system, and enable predictive analysis. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and storage medium for managing dual-network vehicle routes in logistics, which can improve the operational efficiency of dual-network integrated vehicle routes, reduce operating costs, enhance the decision-making quality of express delivery companies, improve the practicality of the system, and enable predictive analysis.

[0005] Optionally, in a fourth implementation of the first aspect of the present invention, the operational anomaly determination involves comparing the key business indicator with a preset anomaly determination threshold. If the key business indicator exceeds its corresponding anomaly determination threshold, an operational anomaly is determined to have occurred, including: Preset anomaly detection threshold; The calculated key business indicators are compared with the anomaly judgment threshold in real time. When any key business indicator exceeds its corresponding anomaly judgment threshold, the system automatically determines that an operational anomaly has occurred. When any key business indicator is less than or equal to its corresponding anomaly detection threshold, the system automatically determines that normal operation has occurred.

[0006] Optionally, in a fifth implementation of the first aspect of the present invention, the early warning and decision-making process, based on the operational anomaly determination result, identifies operational anomalies, generates early warning information, and provides decision support, including: If the operation is determined to be normal based on the results of the operation anomaly assessment, then the operation is identified as normal. Based on the results of the operational anomaly assessment, if an operational anomaly is determined, then the operational anomaly is identified. The system automatically generates early warning information; The warning information includes the type of abnormality, the identifier of the abnormal line, and the level of abnormality, and is pushed to the corresponding preset user role account through a message channel.

[0007] Optionally, in a sixth implementation of the first aspect of the present invention, the system automatically generates warning information, including: Analyze historical operational data and external environment data based on AI models; Predict the trends of key business indicators within a specific future time period; If the forecast indicates an operational risk, the system will generate a predictive warning and corresponding decision-making suggestions in advance.

[0008] A second aspect of the present invention provides a logistics dual-network vehicle line management device, comprising: The module for acquiring planning data and operational data is used to collect planning data and actual operational data of the express delivery network at a preset time frequency, and to process the collected data. The module for acquiring planning data and operational data includes: The first preset unit is used to preset the time frequency; The data acquisition unit is used to collect planning data and actual operation data of the express delivery network from multiple sources at a preset time frequency. The processing unit is used to clean, integrate, and standardize the collected planning data and actual operation data according to preset rules. The planning data includes route planning data, vehicle configuration data, and cargo sorting planning data; the actual operation data includes vehicle location data, cargo status data, transportation timeliness data, and environmental variable data.

[0009] The module displays planning data and operational data, providing a data display interface that shows the planning data and actual operational data by time dimension. The module displaying planning and operational data includes: The display unit provides a data summary interface and a data detail interface, showing the processed planning data and actual operation data by time dimension. The switching unit is used to display the summary indicators in a hierarchical structure on the data summary interface, and to view data and forecast data in different time dimensions by switching charts. The sorting unit is used to sort the detailed data displayed on the data detail page as a single line according to preset rules.

[0010] The business metrics calculation module is used to calculate key business metrics according to the actual operational data and predefined business rules, and to set the data range that users can access in the data display interface based on their roles. The module for calculating business metrics includes: Predefined units are used to predefine business rules; The calculation unit is used to calculate key business indicators based on the collected actual operational data and according to predefined business rules. The settings unit is used to set the range of data that a user can access in the data display interface based on their role. The business metrics include completion rate, average loading rate, average delivery time, unit price loss, and warehouse space loss; the user roles include headquarters account, regional account, provincial account, and distribution account.

[0011] The operation anomaly determination module is used to compare the key business indicators with preset anomaly determination thresholds. If the key business indicators exceed their corresponding anomaly determination thresholds, an operation anomaly is determined to have occurred. The operational anomaly detection module includes: The second preset unit is used to preset the anomaly detection threshold; The comparison unit is used to compare the calculated key business indicators with the anomaly judgment threshold in real time. When any key business indicator exceeds its corresponding anomaly judgment threshold, the system automatically determines that an operational anomaly has occurred. The judgment unit is used to automatically determine that normal operation has occurred when any key business indicator is less than or equal to its corresponding anomaly judgment threshold.

[0012] The early warning and decision-making module is used to identify operational anomalies based on the results of the operational anomaly assessment, generate early warning information, and provide decision support.

[0013] The early warning and decision-making module includes: The identification unit is used to determine the operational anomaly based on the result. If the operation is determined to be normal, then the operation is identified as normal. Based on the results of the operational anomaly assessment, if an operational anomaly is determined, then the operational anomaly is identified. The early warning unit is used by the system to automatically generate early warning information; The warning information includes the type of abnormality, the identifier of the abnormal line, and the level of abnormality, and is pushed to the corresponding preset user role account through a message channel; The system automatically generates early warning information, including: Analyze historical operational data and external environment data based on AI models; Predict the trends of key business indicators within a specific future time period; If the forecast indicates an operational risk, the system will generate a predictive warning and corresponding decision-making suggestions in advance.

[0014] A third aspect of the present invention provides a logistics dual-network vehicle line management device, including a memory and at least one processor, wherein the memory stores computer-readable instructions; The at least one processor invokes the computer-readable instructions in the memory to perform the various steps of the logistics dual-network vehicle line management method as described above.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the various steps of the logistics dual-network vehicle line management method described above.

[0016] This invention achieves a leap from static planning to dynamic management by dynamically collecting and integrating planning data and actual operation data of the express delivery network at a preset frequency. By intuitively displaying data differences along the time dimension, automatically calculating key business indicators and comparing them with preset thresholds, it can proactively and promptly identify operational anomalies and generate early warnings. This transforms management actions from passive remediation to pre-warning and in-process intervention, improving the level of precision in vehicle and route management, operational efficiency, and risk control capabilities, while enhancing decision-making efficiency and ensuring information security. Attached Figure Description

[0017] Figure 1 This is a first flowchart of a logistics dual-network vehicle line management method provided in an embodiment of the present invention; Figure 2 This is a second flowchart of the logistics dual-network vehicle line management method provided in an embodiment of the present invention; Figure 3 This is a third flowchart of the logistics dual-network vehicle line management method provided in an embodiment of the present invention; Figure 4 This is a fourth flowchart of the logistics dual-network vehicle line management method provided in this embodiment of the invention; Figure 5 The fifth flowchart of the logistics dual-network vehicle line management method provided in the embodiments of the present invention; Figure 6 The sixth flowchart of the logistics dual-network vehicle line management method provided in the embodiments of the present invention; Figure 7 This is a schematic diagram of the structure of the logistics dual-network vehicle line management device provided in an embodiment of the present invention; Figure 8This is a schematic diagram of the structure of the logistics dual-network vehicle line management device provided in an embodiment of the present invention. Detailed Implementation

[0018] This invention provides a logistics dual-network vehicle line management method, apparatus, equipment, and storage medium. The method is used to establish dynamic customer profiles and manage customers based on these dynamic customer profiles.

[0019] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 The first embodiment of a logistics dual-network vehicle route management method according to the present invention includes: S101. Obtain planning data and operational data. Collect planning data and actual operational data of the express delivery network at a preset time frequency, and process the collected data. S102. Display planning data and operational data, provide a data display interface, and display the planning data and the actual operational data according to the time dimension; S103. Calculate business indicators. Based on the actual operating data, calculate key business indicators according to predefined business rules, and set the data range that the user can access in the data display interface based on the user role. S104. Operational anomaly determination: The key business indicators are compared with preset anomaly determination thresholds. If the key business indicators exceed their corresponding anomaly determination thresholds, an operational anomaly is determined to have occurred. S105. Early Warning and Decision Making: Based on the results of the operational anomaly assessment, identify operational anomalies, generate early warning information, and provide decision support.

[0021] The logistics dual-network vehicle route management method provided in this invention achieves a leap from static planning to dynamic management by dynamically collecting and integrating the planning data and actual operation data of the express delivery network at a preset frequency. By intuitively displaying data differences in a time dimension, automatically calculating key business indicators and comparing them with preset thresholds, it can proactively and promptly identify operational anomalies and generate early warnings. This transforms management actions from passive remediation to pre-warning and in-process intervention, improving the precision of vehicle route management, operational efficiency, and risk control capabilities, while enhancing decision-making efficiency and ensuring information security.

[0022] Please see Figure 2 The second embodiment of the logistics dual-network vehicle route management method in this invention includes acquiring planning data and operational data, collecting planning data and actual operational data of the express delivery network at a preset time frequency, and processing the collected data, including: S201, Preset time frequency; S202. Collect planning data and actual operation data of the express delivery network through multi-source data at a preset time frequency; S203. According to preset rules, clean, integrate and standardize the collected planning data and actual operation data; The planning data includes route planning data, vehicle configuration data, and cargo sorting planning data; the actual operation data includes vehicle location data, cargo status data, transportation timeliness data, and environmental variable data.

[0023] The embodiments of the present invention ensure the diversity and real-time nature of data sources, enabling the system to comprehensively and dynamically reflect the overall operation from key dimensions such as route planning, vehicle configuration, on-the-way location, cargo status, transportation timeliness, and even environmental variables. At the same time, by cleaning, integrating, and standardizing multi-source heterogeneous data, data noise and format differences are effectively eliminated, providing a high-quality and highly consistent data foundation for subsequent data comparison, indicator calculation, and anomaly detection.

[0024] Please see Figure 3 The third embodiment of a logistics dual-network vehicle line management method in this invention includes displaying planning data and operational data, providing a data display interface, and displaying the planning data and actual operational data by time dimension, including: S301 provides a data summary interface and a data detail interface, displaying the processed planning data and actual operation data according to the time dimension; S302. The data summary interface displays summary indicators in a hierarchical structure, and users can switch charts to view data and forecast data in different time dimensions. S303. The data details page displays detailed data for a single vehicle line, and sorts the detailed data according to preset rules.

[0025] This invention provides a data aggregation interface that displays aggregated indicators and forecasted data through hierarchical charts, enabling managers to quickly grasp the overall operational trends and future directions across different time dimensions, supporting strategic decision-making. Meanwhile, the data detail interface uses individual train lines as the granularity and combines preset sorting rules to clearly present specific operational details, providing a direct and efficient operational entry point for operations personnel to accurately locate anomalies and trace the root causes of problems.

[0026] Please see Figure 4 The fourth embodiment of a logistics dual-network vehicle line management method in this invention includes calculating business indicators based on the actual operational data and according to predefined business rules, and setting the data range accessible to users in the data display interface based on their roles, including: S401, Predefined business rules; S402. Calculate key business indicators based on the collected actual operational data and according to predefined business rules; S403. Based on user roles, set the range of data that each user can access in the data display interface; The business metrics include completion rate, average loading rate, average delivery time, unit price loss, and warehouse space loss; the user roles include headquarters account, regional account, provincial account, and distribution account.

[0027] This invention transforms massive amounts of actual operational data into key quantitative indicators with scientific and clear business guidance significance, such as completion rate, average loading rate, average timeliness, unit price loss, and warehouse space loss, enabling precise measurement and evaluation of operational status. At the same time, by strictly binding data access permissions to user roles at different levels, such as headquarters, regional offices, provincial offices, and distribution centers, it ensures that managers at all levels can obtain the necessary, granular data views within their scope of responsibility to support effective decision-making, while also achieving secure control over core sensitive data.

[0028] Please see Figure 5 The fifth embodiment of a logistics dual-network vehicle line management method in this invention includes an operational anomaly determination by comparing the key business indicators with a preset anomaly determination threshold. If the key business indicator exceeds its corresponding anomaly determination threshold, an operational anomaly is determined to have occurred, including: S501, Preset anomaly detection threshold; S502. The calculated key business indicators are compared with the anomaly judgment threshold in real time. When any key business indicator exceeds its corresponding anomaly judgment threshold, the system automatically determines that an operational anomaly has occurred. S503. When any key business indicator is less than or equal to its corresponding anomaly judgment threshold, the system automatically determines that normal operation has occurred.

[0029] This invention transforms operation management from a subjective and lagging model that relies on human experience to an automated and real-time intelligent monitoring model based on objective data and clear rules. The system can continuously compare key business indicators such as completion rate and loading rate with preset thresholds and output normal or abnormal judgment results in real time and accurately. This not only greatly improves the speed and accuracy of discovering potential risks and efficiency bottlenecks in train line operation, but also avoids the expansion of losses caused by human negligence or delay.

[0030] Please see Figure 6 The sixth embodiment of a logistics dual-network vehicle line management method in this invention includes an early warning and decision-making process that identifies operational anomalies based on the operational anomaly determination results, generates early warning information, and provides decision support, comprising: S601. Based on the results of the operational anomaly determination, if the operation is determined to be normal, then the operation is identified as normal. S602. Based on the results of the operational anomaly determination, if an operational anomaly is determined, then identify the operational anomaly. S603, The system automatically generates early warning information; The warning information includes the type of abnormality, the identifier of the abnormal line, and the level of abnormality, and is pushed to the corresponding preset user role account through a message channel; In some embodiments, the system automatically generates early warning information, including: Analyze historical operational data and external environment data based on AI models; Predict the trends of key business indicators within a specific future time period; If the forecast indicates an operational risk, the system will generate a predictive warning and corresponding decision-making suggestions in advance.

[0031] This invention not only automatically generates accurate early warning information containing the anomaly type, line identifier, and level when an operational anomaly is detected, and pushes it to the corresponding user roles, achieving rapid response and clear closed-loop processing of the problem; it also introduces AI models to analyze historical and external data to predict future risks. The system significantly shifts management actions from post-event remediation to pre-event prevention, and can issue predictive warnings and provide decision suggestions before potential anomalies actually occur.

[0032] The above describes the logistics dual-network vehicle line management method in the embodiments of the present invention. The following describes the apparatus in the embodiments of the present invention. Please refer to [link / reference]. Figure 7 The implementation methods of the logistics dual-network vehicle line management device in this invention include: The planning data and operation data acquisition module 701 is used to collect planning data and actual operation data of the express delivery network at a preset time frequency, and process the collected data. The module 702 displays planning data and operational data, providing a data display interface to show the planning data and the actual operational data by time dimension. The business indicator calculation module 703 is used to calculate key business indicators according to the actual operating data and predefined business rules, and to set the data range that users can access in the data display interface based on their roles. The operation anomaly determination module 704 is used to compare the key business indicators with preset anomaly determination thresholds. If the key business indicators exceed their corresponding anomaly determination thresholds, an operation anomaly is determined to have occurred. The early warning and decision-making module 705 is used to identify operational anomalies based on the results of the operational anomaly determination, generate early warning information, and provide decision support.

[0033] The planning data and operation data acquisition module 701 is used to collect planning data and actual operation data of the express delivery network at a preset time frequency, and process the collected data. In some embodiments, the module 701 for acquiring planning data and operational data includes: The first preset unit 7011 is used to preset the time frequency; The acquisition unit 7012 is used to collect planning data and actual operation data of the express delivery network through multi-source data at a preset time frequency; The processing unit 7013 is used to clean, integrate, and standardize the collected planning data and actual operation data according to preset rules. The planning data includes route planning data, vehicle configuration data, and cargo sorting planning data; the actual operation data includes vehicle location data, cargo status data, transportation timeliness data, and environmental variable data.

[0034] The embodiments of the present invention ensure the diversity and real-time nature of data sources, enabling the system to comprehensively and dynamically reflect the overall operation from key dimensions such as route planning, vehicle configuration, on-the-way location, cargo status, transportation timeliness, and even environmental variables. At the same time, by cleaning, integrating, and standardizing multi-source heterogeneous data, data noise and format differences are effectively eliminated, providing a high-quality and highly consistent data foundation for subsequent data comparison, indicator calculation, and anomaly detection.

[0035] The module 702 displays planning data and operational data, providing a data display interface to show the planning data and the actual operational data by time dimension. In some embodiments, the module 702 for displaying planning data and operational data includes: Display unit 7021 is used to provide a data summary interface and a data detail interface, displaying the processed planning data and actual operation data by time dimension; The switching unit 7022 is used to display the summary indicators in a hierarchical structure on the data summary interface, and to view data and forecast data in different time dimensions by switching charts. The sorting unit 7023 is used to sort the detailed data displayed on the data detail page as a single line according to preset rules.

[0036] This invention provides a data aggregation interface that displays aggregated indicators and forecasted data through hierarchical charts, enabling managers to quickly grasp the overall operational trends and future directions across different time dimensions, supporting strategic decision-making. Meanwhile, the data detail interface uses individual train lines as the granularity and combines preset sorting rules to clearly present specific operational details, providing a direct and efficient operational entry point for operations personnel to accurately locate anomalies and trace the root causes of problems.

[0037] The business indicator calculation module 703 is used to calculate key business indicators according to the actual operating data and predefined business rules, and to set the data range that users can access in the data display interface based on their roles. In some embodiments, the business metrics calculation module 703 includes: Predefined unit 7031 is used to predefine business rules; The calculation unit 7032 is used to calculate key business indicators based on the collected actual operational data and according to predefined business rules. Setting unit 7033 is used to set the range of data that a user can access in the data display interface based on the user's role; The business metrics include completion rate, average loading rate, average delivery time, unit price loss, and warehouse space loss; the user roles include headquarters account, regional account, provincial account, and distribution account.

[0038] This invention transforms massive amounts of actual operational data into key quantitative indicators with scientific and clear business guidance significance, such as completion rate, average loading rate, average timeliness, unit price loss, and warehouse space loss, enabling precise measurement and evaluation of operational status. At the same time, by strictly binding data access permissions to user roles at different levels, such as headquarters, regional offices, provincial offices, and distribution centers, it ensures that managers at all levels can obtain the necessary, granular data views within their scope of responsibility to support effective decision-making, while also achieving secure control over core sensitive data.

[0039] The operation anomaly determination module 704 is used to compare the key business indicators with preset anomaly determination thresholds. If the key business indicators exceed their corresponding anomaly determination thresholds, an operation anomaly is determined to have occurred. In some embodiments, the operation anomaly determination module 704 includes: The second preset unit 7041 is used to preset the anomaly detection threshold; The comparison unit 7042 is used to compare the calculated key business indicators with the anomaly judgment threshold in real time. When any key business indicator exceeds its corresponding anomaly judgment threshold, the system automatically determines that an operational anomaly has occurred. The judgment unit 7043 is used to automatically determine that the operation is normal when any key business indicator is less than or equal to its corresponding abnormal judgment threshold.

[0040] This invention transforms operation management from a subjective and lagging model that relies on human experience to an automated and real-time intelligent monitoring model based on objective data and clear rules. The system can continuously compare key business indicators such as completion rate and loading rate with preset thresholds and output normal or abnormal judgment results in real time and accurately. This not only greatly improves the speed and accuracy of discovering potential risks and efficiency bottlenecks in train line operation, but also avoids the expansion of losses caused by human negligence or delay.

[0041] The early warning and decision-making module 705 is used to identify operational anomalies based on the results of the operational anomaly determination, generate early warning information, and provide decision support.

[0042] In some embodiments, the early warning and decision-making module 705 includes: The identification unit 7051 is used to identify normal operation based on the result of the operation anomaly determination. Based on the results of the operational anomaly assessment, if an operational anomaly is determined, then the operational anomaly is identified. Early warning unit 7052 is used for the system to automatically generate early warning information; The warning information includes the type of abnormality, the identifier of the abnormal line, and the level of abnormality, and is pushed to the corresponding preset user role account through a message channel; In some embodiments, the system automatically generates early warning information, including: Analyze historical operational data and external environment data based on AI models; Predict the trends of key business indicators within a specific future time period; If the forecast indicates an operational risk, the system will generate a predictive warning and corresponding decision-making suggestions in advance.

[0043] This invention not only automatically generates accurate early warning information containing the anomaly type, line identifier, and level when an operational anomaly is detected, and pushes it to the corresponding user roles, achieving rapid response and clear closed-loop processing of the problem; it also introduces AI models to analyze historical and external data to predict future risks. The system significantly shifts management actions from post-event remediation to pre-event prevention, and can issue predictive warnings and provide decision suggestions before potential anomalies actually occur.

[0044] Figure 7 The structure of the logistics dual-network vehicle line management device shown does not constitute a limitation on the logistics dual-network vehicle line management device, and can realize the steps of the logistics dual-network vehicle line management method provided in the above-described method embodiments.

[0045] above Figure 7 The logistics dual-network vehicle line management device in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The logistics dual-network vehicle line management device in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0046] Figure 8 This is a schematic diagram of the structure of a logistics dual-network vehicle line management device provided in an embodiment of the present invention. The device 800 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 810 (e.g., one or more processors) and a memory 820, and one or more storage media 830 (e.g., one or more mass storage devices) for storing application programs 833 or data 832. The memory 820 and storage media 830 can be temporary or persistent storage. The program stored in the storage media 830 may include one or more modules (not shown), each module including a series of instruction operations on the device 800. Furthermore, the processor 810 may be configured to communicate with the storage media 830 and execute the series of instruction operations in the storage media on the device 800.

[0047] Device 800 may also include one or more power supplies 840, one or more wired or wireless network interfaces 850, one or more input / output interfaces 860, and / or one or more operating systems 831, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc.

[0048] This invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the logistics dual-network vehicle line management method.

[0049] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0050] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0051] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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. Such 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 logistics double-net vehicle line management method, characterized by, The logistics double-net vehicle line management method comprises: Obtaining planning data and operation data, collecting planning data and actual operation data of the express network at a preset time frequency, processing the collected data; Displaying planning data and operation data, providing a data display interface, and displaying the planning data and the actual operation data according to time dimensions; Calculating business indicators, calculating key business indicators according to a predefined business rule based on the actual operation data, and setting the data range accessible in the data display interface based on the user role; Operation exception determination, comparing the key business indicators with preset exception determination thresholds, and determining that an operation exception occurs when the key business indicators exceed the corresponding exception determination thresholds; Warning and decision-making, identifying operation exceptions according to the operation exception determination result, and generating warning information and providing decision support.

2. The logistics double-net vehicle line management method according to claim 1, characterized by, The method comprises: A preset time frequency; Collecting planning data and actual operation data of the express network at a preset time frequency through multi-source data; According to the preset rule, the collected planning data and actual operation data are cleaned, integrated and standardized; The planning data includes path planning data, vehicle configuration data and cargo sorting planning data; the actual operation data includes vehicle location data, cargo status data, transportation time limit data and environmental variable data.

3. The method of claim 1, wherein, The method comprises: Providing a data summary interface and a data detail interface, and displaying the processed planning data and actual operation data according to time dimensions; The data summary interface displays summary indicators according to a hierarchical structure, and displays data and prediction data of different time dimensions through switching charts; The data detail page displays detailed data in a single vehicle line, and sorts the detailed data according to a preset rule.

4. The logistics double-net vehicle line management method according to claim 3, characterized by, The method comprises: Defining a business rule; According to the collected actual operation data, the key business indicators are calculated according to the predefined business rule; Based on the user role, the data range accessible in the data display interface is set; The business indicators include completion rate, average loading rate, average time limit, unit price loss and space loss; the user roles include headquarters account, regional account, provincial headquarters account and distribution account.

5. The method of claim 4, wherein, The method comprises: Presetting exception determination thresholds; Real-time comparison of the calculated key business indicators and the exception determination thresholds, automatic determination of operation exceptions when any key business indicator exceeds the corresponding exception determination threshold; When any key business indicator is less than or equal to its corresponding abnormality determination threshold, the system automatically determines that the operation is normal.

6. The logistics double-net vehicle line management method according to claim 5, wherein, The early warning and decision making identifies operation abnormality according to the operation abnormality determination result, generates early warning information and provides decision support, including: According to the operation abnormality determination result, if it is determined that the operation is normal, the operation is identified as normal; According to the operation abnormality determination result, if it is determined that the operation is abnormal, the operation is identified as abnormal; The system automatically generates early warning information. The early warning information includes abnormal type, abnormal line identification and abnormal level, and the early warning information is pushed to the preset corresponding user role account through the message channel.

7. The method of claim 6, wherein, The system automatically generates early warning information, including: Based on the AI model, the historical operation data and external environment data are analyzed; The trend of key business indicators in a specific future time period is predicted; If the prediction result indicates that there is an operation abnormality risk, the system generates predictive early warning information and corresponding decision suggestions in advance.

8. A logistics double-net vehicle line management device characterized by comprising: It includes: A planning data and operation data acquisition module is used to collect planning data and actual operation data of the express network at a preset time frequency, process the collected data; A planning data and operation data display module is used to provide a data display interface to display the planning data and the actual operation data in time dimension; A business indicator calculation module is used to calculate key business indicators according to the actual operation data and pre-defined business rules, and set the data range accessible in the data display interface based on user roles; An operation abnormality determination module is used to compare the key business indicators with the preset abnormality determination threshold, and determine that the operation is abnormal if the key business indicators exceed the corresponding abnormality determination threshold; An early warning and decision making module is used to identify operation abnormality according to the operation abnormality determination result, generate early warning information and provide decision support.

9. A logistics double-net vehicle line management apparatus characterized by comprising: It includes a memory and at least one processor, and the memory stores computer readable instructions; The at least one processor calls the computer readable instructions in the memory to perform the steps of the logistics double-net vehicle line management method according to any one of claims 1-7.

10. A computer-readable storage medium having stored thereon computer-readable instructions, wherein, The computer readable instructions are executed by the processor to realize the steps of the logistics double-net vehicle line management method according to any one of claims 1-7.

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