Logistics scheduling management method and logistics scheduling management system based on WMS

Abstract

The invention relates to the technical field of logistics scheduling data management, in particular to a logistics scheduling management method and a logistics scheduling management system based on a WMS, and the method comprises the steps: receiving an integrated task request, and recognizing task parameters in the integrated task request, the integrated task request being sent by an integrated system having a business cooperation relationship with the WMS; determining logistics scheduling based on the task parameters, and generating a task allocation instruction based on the logistics scheduling so as to control related execution equipment to perform corresponding logistics operation according to the task allocation instruction; real-time logistics parameters generated in the logistics operation process are recorded, and when the real-time logistics parameters contain preset abnormal logistics characteristics, logistics scheduling is optimized in real time based on the preset abnormal logistics characteristics. According to the invention, the efficiency and stability in the logistics scheduling management process can be improved.

Description

Technical Field

[0001] This application relates to the field of logistics scheduling data management technology, and in particular to a logistics scheduling management method and a logistics scheduling management system based on WMS. Background Technology

[0002] With the rapid development of e-commerce retail, intelligent manufacturing and other fields, the business complexity and operational intensity of warehousing and logistics systems continue to rise. As the core hub of warehousing operations, the Warehouse Management System (WMS) needs to achieve deep business collaboration with multiple integrated systems such as the Order Management System (OMS), Enterprise Resource Planning (ERP), and Transportation Management System (TMS) to support the efficient operation of the entire logistics scheduling process.

[0003] However, in existing logistics scheduling data management technologies, the data flow between the WMS system and various integrated systems is not smooth, making it difficult to achieve collaborative management of the entire process. In addition, since logistics scheduling work mainly relies on human experience and lacks intelligent decision support based on data analysis, this model not only leads to uneven utilization of logistics equipment, but also easily causes problems such as path redundancy and task backlog. Ultimately, it may result in low efficiency of logistics scheduling operations, making it difficult to meet the current high-concurrency and high-timeliness warehouse operation needs. Summary of the Invention

[0004] To improve the efficiency and stability of logistics scheduling management, this application provides a logistics scheduling management method and a logistics scheduling management system based on WMS.

[0005] Firstly, this application provides a logistics scheduling and management method based on WMS, employing the following technical solution: A logistics scheduling and management method based on WMS includes: Receive an integration task request and identify the task parameters in the integration task request, wherein the integration task request is issued by an integration system that has a business collaboration relationship with the WMS system; Based on the task parameters, a logistics schedule is determined, and a task allocation instruction is generated based on the logistics schedule to control the relevant execution equipment to perform corresponding logistics operations according to the task allocation instruction. Record real-time logistics parameters generated during logistics operations. When the real-time logistics parameters include preset abnormal logistics characteristics, optimize the logistics scheduling in real time based on the preset abnormal logistics characteristics.

[0006] By adopting the above technical solution, and by actively receiving integration task requests from other integrated systems and accurately identifying the task parameters in the integration task requests, it is easier to solve the scheduling lag problem caused by information silos between various integrated systems, thereby facilitating full-process collaborative management. Through data analysis, logistics scheduling schemes are automatically determined and task allocation instructions are generated, facilitating accurate matching between relevant logistics scheduling tasks and corresponding scheduling resources. This reduces resource waste or deviations caused by manual allocation or scheduling, thereby improving the accuracy of logistics operations. In addition, by recording the actual logistics parameters generated during logistics operations and dynamically optimizing logistics scheduling in a timely manner when preset abnormal logistics characteristics are included in the real-time logistics parameters, it is easier to avoid problems such as production line stoppages and order defaults caused by abnormal logistics conditions, thereby ensuring the stability of logistics scheduling under the demand for high-efficiency warehousing operations.

[0007] In one possible implementation, the method further includes: Real-time logistics features are identified from the real-time logistics parameters, and a corresponding three-dimensional logistics heat map is determined based on the real-time logistics features. The three-dimensional logistics heat map contains multiple logistics heat zones, and each logistics heat zone corresponds to a regional heat value. Based on a preset thermal threshold, it is determined whether the three-dimensional logistics heat map contains abnormal thermal zones. If so, the corresponding regional logistics scheduling is optimized based on the abnormal regional logistics characteristics corresponding to the abnormal thermal zone. The abnormal thermal zone is a logistics thermal zone where the abnormal regional thermal value is higher than the preset thermal threshold.

[0008] By adopting the above technical solution and constructing a three-dimensional logistics heat map containing multiple logistics heat zones, it is easy to transform abstract real-time logistics data into intuitive spatial visualization information. This allows relevant managers to intuitively grasp the actual logistics situation of each logistics heat zone. By setting preset heat thresholds to monitor the three-dimensional logistics heat map in real time, it is easy to accurately identify high-load and high-risk areas in logistics operations. This facilitates timely and targeted optimization of regional logistics scheduling in areas with abnormal situations, thereby reducing the risks of production line stoppages and order delays caused by the spread of abnormal situations. Furthermore, it helps to ensure the accuracy and stability of logistics scheduling under the demand for high-efficiency warehousing operations.

[0009] In one possible implementation, determining the three-dimensional logistics heatmap corresponding to the real-time logistics parameters based on the real-time logistics characteristics includes: The observation area covered by the real-time logistics features is divided into multiple logistics heat zones based on the scheduling location parameters in the real-time logistics features. The planar range of each logistics heat zone is determined based on the heat zone edge information of each logistics heat zone. The regional logistics characteristics corresponding to each logistics heat zone are identified from the real-time logistics characteristics. The regional operation density parameters and regional logistics path parameters in each regional logistics characteristic are identified. Based on the planar range, regional operation density parameters and regional logistics path parameters of each logistics heat zone, the three-dimensional heat height corresponding to each logistics heat zone is determined. Based on the planar range and three-dimensional thermal height of each logistics thermal zone, determine the three-dimensional display cone corresponding to each logistics thermal zone and the regional thermal value corresponding to each logistics thermal zone; Multiple 3D display cones and their corresponding regional thermal values ​​are superimposed onto a region layer containing the region to be observed to obtain a 3D logistics heat map corresponding to the real-time logistics parameters.

[0010] By adopting the above technical solution, the area to be observed is divided into multiple logistics heat zones by scheduling location parameters, and the planar range of each logistics heat zone is determined. This facilitates the division of the broad area to be observed into precise spatial units, thereby improving the accuracy of logistics scheduling management. By transforming abstract data such as operational busyness and route congestion risk into concrete height-dimensional indicators, it is easy to intuitively distinguish the differences in logistics scheduling pressure faced by different logistics heat zones. This allows relevant managers to quickly determine the logistics load level of each logistics heat zone through height changes. By determining the three-dimensional display cone and regional heat value based on the planar range and three-dimensional heat height, and superimposing it onto the regional layer of the area to be observed, a three-dimensional logistics heat map is obtained. This facilitates the three-in-one visualization of spatial location, load height, and heat value, thereby improving the readability of the three-dimensional logistics heat map.

[0011] In one possible implementation, after determining the three-dimensional display cone corresponding to the logistics thermal zone based on the planar range and the three-dimensional thermal height of the logistics thermal zone, the method further includes: Identify the planar diameter of the logistics heat zone and the initial cone vertex of the corresponding three-dimensional display cone, and determine the offset distance corresponding to the initial cone vertex based on the regional operation density parameter and the planar diameter; Based on the regional operation density parameters and regional logistics path parameters corresponding to the logistics heat zone, the offset direction of the initial cone apex is determined; Based on the offset distance and offset direction corresponding to the initial cone vertex, the three-dimensional display cone corresponding to the logistics heat zone is optimized.

[0012] By adopting the above technical solution, the offset distance of the initial cone apex is determined by identifying the plane diameter of the logistics heat zone and the initial display cone apex, and then combining the regional operation density parameters. This facilitates the improvement of the fit between the offset distance and the actual logistics load in the region. By determining the offset direction of the initial cone apex based on the regional operation density parameters and regional logistics path parameters, it is easier to improve the directionality between the offset direction and the core location of dense operations or congested paths. This makes it easier to intuitively display the key logistics status through the three-dimensional display cone, and further improves the accuracy of the three-dimensional display cone in reflecting the regional logistics characteristics.

[0013] In one possible implementation, when the three-dimensional logistics heatmap contains multiple anomalous heat zones, the method of optimizing the corresponding regional logistics scheduling based on the anomalous regional logistics characteristics of the anomalous heat zones includes: Based on the abnormal positions of each of the abnormal thermal zones in the three-dimensional logistics heat map, a joint abnormal thermal zone corresponding to the multiple abnormal thermal zones is determined. Based on the regional characteristic parameters of the multiple abnormal thermal zones corresponding to the joint abnormal thermal zone, the abnormal logistics location type corresponding to each abnormal thermal zone is determined. Based on the regional characteristic parameters of the multiple abnormal thermal zones corresponding to the joint abnormal thermal zone and the location type of each abnormal logistics, the joint abnormal parameters corresponding to the joint abnormal thermal zone are determined. The joint abnormal parameters include time urgency and task backlog. Optimize regional logistics scheduling corresponding to the joint anomaly thermal zone based on the joint anomaly parameters.

[0014] By adopting the above technical solution and combining the regional characteristic parameters of multiple abnormal areas within the joint abnormal heat zone, joint abnormal parameters such as time urgency and task backlog can be extracted. This facilitates the transformation of complex multi-regional abnormal information into quantifiable and comparable core indicators. Then, targeted optimization of joint regional scheduling allows for precise matching of scheduling resource investment with the severity of the abnormality. This helps avoid the problems of blindly adding resources or insufficient resource allocation, further ensuring the stability of logistics scheduling and the overall efficiency of logistics scheduling in high-efficiency warehousing scenarios.

[0015] In one possible implementation, the method further includes: Obtain user access and operation information within a preset operation time period, including clicks, dwell time, and current page size; The predicted user access path is determined based on the click operation and dwell time in the user access operation information, and the content to be displayed is determined based on the predicted user access path. The final content to be displayed is determined based on the content to be displayed and the current page size in the user access operation information; When the deviation between the user's actual access path and the user's predicted access path is less than a preset deviation threshold, the final display content is displayed on the current display page.

[0016] By adopting the above technical solution, and by acquiring user access operation information such as click operations, dwell time, and current page size within a preset operation time period, it is convenient to comprehensively capture users' actual interactive behavior and page adaptation needs. By analyzing user access operation information, the predicted user access path is determined, and the content to be displayed is determined based on this path, so that the content to be displayed can match the user's potential browsing intentions in advance. By determining the final display content based on the content to be displayed and the current page size, it is convenient to adapt the content presentation to different devices and improve the user browsing experience. When the deviation between the user's actual access path and the predicted path is less than a preset threshold, the final content is displayed, which facilitates the push of content while ensuring the accuracy of prediction, avoiding the situation where the pushed content does not meet the user's needs due to large prediction deviations. At the same time, it is also convenient to reduce user churn caused by incompatible display content or push deviations, and effectively improve page interaction efficiency and user experience.

[0017] Secondly, this application provides a logistics dispatch and management system, which adopts the following technical solution: A logistics dispatch and management system, comprising: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the above-described WMS-based logistics scheduling management method.

[0018] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program stored thereon that can be loaded by a processor and execute the above-described WMS-based logistics scheduling and management method.

[0019] Fourthly, this application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program that, when executed by a processor, implements the aforementioned WMS-based logistics scheduling and management method.

[0020] In summary, this application includes at least one of the following beneficial technical effects: By proactively receiving integration task requests from other integrated systems and accurately identifying the task parameters in these requests, the system effectively addresses scheduling lags caused by information silos between systems, facilitating end-to-end collaborative management. Through data analysis, it automatically determines logistics scheduling plans and generates task allocation instructions, enabling precise matching between relevant logistics scheduling tasks and corresponding scheduling resources. This reduces resource waste or deviations caused by manual allocation or scheduling, improving the accuracy of logistics operations. Furthermore, by recording actual logistics parameters generated during operations and dynamically optimizing logistics scheduling when preset abnormal logistics characteristics are present in real-time parameters, it helps avoid production stoppages and order defaults caused by abnormal logistics conditions, ensuring the stability of logistics scheduling under high-efficiency warehousing operation requirements.

[0021] By identifying the planar diameter of the logistics heat zone and the initial display cone apex, and then combining the regional operation density parameters to determine the offset distance of the initial cone apex, it is easier to improve the fit between the offset distance and the actual logistics load in the region. By determining the offset direction of the initial cone apex based on the regional operation density parameters and regional logistics path parameters, it is easier to improve the directionality between the offset direction and the core location of dense operations or congested paths. This makes it easier to intuitively display the key logistics status through the 3D display cone, and further improve the accuracy of the 3D display cone in reflecting the regional logistics characteristics. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a logistics scheduling and management method based on WMS in an embodiment of this application. Figure 2 This is a flowchart illustrating an optimized three-dimensional display cone according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a logistics scheduling and management system according to an embodiment of this application. Detailed Implementation

[0023] The following is in conjunction with the appendix Figures 1 to 3 This application will be described in further detail.

[0024] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0027] Specifically, this application provides a WMS-based logistics scheduling and management method, executed by a logistics scheduling and management system. This system can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not impose any limitations on this connection.

[0028] refer to Figure 1 , Figure 1 This is a flowchart illustrating a logistics scheduling and management method based on WMS in an embodiment of this application. The method includes steps S110-S130, wherein: Step S110: Receive an integration task request and identify the task parameters in the integration task request. The integration task request is issued by an integration system that has a business collaboration relationship with the WMS system.

[0029] Specifically, since the business needs of the entire warehousing and logistics chain may involve multiple stages such as order initiation, resource planning, production coordination, and transportation delivery, a single WMS system may not be able to cover all the business needs of the entire warehousing and logistics chain, and the core requirements of each stage may also be different. Therefore, multiple professional systems are needed to perform their respective functions and achieve efficient operation of the entire chain through collaboration. The integrated systems that have business collaboration relationships with the WMS system can include, but are not limited to, OMS, ERP, TMS, etc. In order to ensure communication between various integrated systems, a unified standardized data interaction interface can be provided for the integrated systems that have business collaboration with the WMS. The interface request format, field definition, data transmission protocol, and authentication method should be clearly defined. For example, the standardized data interaction interface can be a RESTful API or WebService, and the interface request format can be JSON, XML, etc. The specific standardized data interaction interface, the clear interface request format, field definition, data transmission protocol, and authentication method are not specifically limited in this application embodiment, as long as it can be ensured that each integrated system can initiate integration task requests according to a unified specification and avoid data transmission failure due to interface incompatibility and other issues.

[0030] The specific content of the integration task request is strongly related to the initiator, i.e., different integration systems. Essentially, it is a carrier of business instructions transmitted from the integration system to the WMS system. Its core function is to clearly inform the WMS system what tasks need to be performed, according to what standards, and what key constraints exist. Based on a preset feature recognition algorithm, the corresponding task parameters can be identified from the received integration task request. These task parameters may include, but are not limited to, parameters related to the raw material warehousing, processing, and storage of the transported items, such as the items to be transported, raw materials, processing lines, transportation locations, and transportation specification constraints. Specific preset feature recognition algorithms can be feature recognition algorithms based on field matching rules, feature recognition algorithms based on regular expressions, etc., and are not specifically limited in this embodiment.

[0031] Step S120: Determine the logistics schedule based on the task parameters, and generate task allocation instructions based on the logistics schedule to control the relevant execution equipment to perform corresponding logistics operations according to the task allocation instructions.

[0032] Specifically, the core of logistics scheduling is to accurately match task parameters with resources within the warehouse. This process requires combining the priority, timeliness requirements, operation type, and real-time resource status of each scheduling stage to construct a complete logic for parameter analysis, resource matching, and scheduling plan generation, ensuring efficient and orderly execution of logistics scheduling operations. First, parameter features in the task parameters can be identified based on a preset semantic feature recognition algorithm. Then, the scheduling type corresponding to the parameter features can be determined according to a preset scheduling type mapping relationship. Scheduling types include, but are not limited to, material allocation scheduling, outbound transportation scheduling, production material requisition scheduling, return warehousing scheduling, manpower collaborative scheduling, and equipment-specific scheduling. The preset scheduling type mapping relationship is the correspondence between parameter features and scheduling types. The specific content of the preset scheduling type mapping relationship and the preset semantic feature recognition algorithm are not specifically limited in this embodiment; they can be determined by relevant personnel based on historical experimental data and uploaded to the logistics scheduling management system. After determining the scheduling type, the trained logistics scheduling output model corresponding to that scheduling type can be directly called. The task parameters are input into the trained logistics scheduling output model to obtain the logistics scheduling corresponding to the task parameters. By constructing corresponding logistics scheduling output models for different scheduling types, classified scheduling management can be achieved, while also improving management accuracy. The training process of the scheduling output model for each scheduling type is determined based on the sample data of the corresponding scheduling type. The output scheme is made to approximate the optimal solution through iterative adjustment. The specific training process is not specifically limited in the embodiments of this application.

[0033] Each logistics schedule corresponds to at least one execution device. First, the corresponding execution device can be determined based on the logistics schedule. Then, the instruction form can be determined based on the device parameters of the execution device. Finally, the logistics schedule is processed into an instruction structure based on the instruction form, converting the logistics schedule into content that the execution device can understand.

[0034] Step S130: Record the real-time logistics parameters generated during the logistics operation. When the real-time logistics parameters contain preset abnormal logistics characteristics, optimize the logistics scheduling in real time based on the preset abnormal logistics characteristics.

[0035] Specifically, after the generated task allocation instructions are distributed to the corresponding execution devices, the execution devices will perform relevant logistics operations according to the logistics schedule. During the execution process, real-time logistics parameters generated during each logistics operation are recorded. These real-time logistics parameters are related to the corresponding scheduling type and include, but are not limited to, equipment operating status parameters; goods transportation safety parameters, including but not limited to goods shape, appearance, and location; transportation progress parameters, including but not limited to the current task stage, stage duration, and task completion rate; environmental and path parameters, including but not limited to path congestion information and path obstacle information; and energy parameters, including but not limited to remaining power and remaining fuel.

[0036] The real-time logistics parameters are identified using a preset feature recognition algorithm. When the real-time logistics parameters contain preset abnormal logistics features, it indicates that there may be risks such as equipment malfunction, goods safety risks, operation delays, blocked routes, and insufficient energy supply during the current logistics operation. Different risk types correspond to different preset abnormal logistics features. The risk level can be determined based on the risk type, preset abnormal logistics features, and corresponding abnormal feature values. The optimization content is determined based on the preset optimization content mapping relationship, risk type, and risk level. The preset optimization content mapping relationship is the correspondence between the combination of risk type and risk level and the optimization content. The specific content is not specifically limited in this application embodiment and can be determined by relevant personnel based on historical experimental data and then uploaded to the logistics scheduling association system. For example, if the risk type is equipment malfunction (AGV failure and no backup equipment), the risk level is 1. The corresponding optimization could be to split the current transportation task, dispatching two idle forklifts to handle the "pick-up-transfer" and "transfer-unloading" stages respectively, simultaneously extending the downstream task's delivery time by 15 minutes. If the risk type is insufficient energy supply (AGV has 15% remaining power, enough to support the nearest charging station), the risk level is 2. The corresponding optimization could be to adjust the AGV's transportation route, prioritizing its journey to the charging station 300 meters away to replenish its power to 50%, and then continuing the original task without changing other resources. Finally, based on the optimization, the initial logistics scheduling should be optimized in a timely manner to eliminate any abnormal situations in logistics operations.

[0037] In this embodiment of the application, by actively receiving integration task requests from other integrated systems and accurately identifying the task parameters in the integration task requests, it is easier to solve the scheduling lag problem caused by information silos between various integrated systems, thereby facilitating the realization of full-process collaborative management. Through data analysis, logistics scheduling schemes are automatically determined and task allocation instructions are generated, which facilitates the accurate matching between relevant logistics scheduling tasks and corresponding scheduling resources, reducing resource waste or deviations caused by manual allocation or scheduling, thereby improving the accuracy of logistics operations. In addition, by recording the actual logistics parameters generated during logistics operations and dynamically optimizing logistics scheduling in a timely manner when preset abnormal logistics characteristics are included in the real-time logistics parameters, it is easier to avoid problems such as production line stoppages and order defaults caused by abnormal logistics conditions, thereby ensuring the stability of logistics scheduling under the demand for high-efficiency warehousing operations.

[0038] Furthermore, to facilitate ensuring the accuracy and stability of logistics scheduling under the demand for high-efficiency warehousing operations, the method provided in this application embodiment also includes: Real-time logistics characteristics are identified from real-time logistics parameters, and corresponding 3D logistics heatmaps are determined based on these characteristics. Each 3D logistics heatmap contains multiple logistics heat zones, and each heat zone corresponds to a regional heat value. Based on a preset heat threshold, it is determined whether the 3D logistics heatmap contains abnormal heat zones. If so, the corresponding regional logistics scheduling is optimized based on the abnormal regional logistics characteristics corresponding to the abnormal heat zone. An abnormal heat zone is a logistics heat zone where the abnormal regional heat value is higher than the preset heat threshold.

[0039] Specifically, real-time logistics features can be identified from real-time logistics parameters based on a preset feature recognition algorithm. These real-time logistics features are characteristics related to the transportation and delivery of goods, including information such as transportation routes and transportation operation density. By constructing a three-dimensional logistics heat map containing multiple logistics heat zones, abstract real-time logistics data can be easily transformed into intuitive spatial visualization information, allowing relevant managers to intuitively grasp the actual logistics situation of each logistics heat zone. Real-time monitoring of the three-dimensional logistics heat map using preset heat thresholds facilitates precise identification of high-load and high-risk areas in logistics operations, enabling timely and targeted optimization of regional physical scheduling for areas with anomalies. This application embodiment provides a specific process for determining the three-dimensional logistics heat map, which involves determining the three-dimensional logistics heat map corresponding to real-time logistics parameters based on real-time logistics features, including: Based on the scheduling location parameters in the real-time logistics features, the area to be observed covered by the real-time logistics features is divided into multiple logistics heat maps. The planar range of each logistics heat map is determined based on the edge information of each heat map. The regional logistics features corresponding to each logistics heat map are identified from the real-time logistics features. The regional operation density parameters and regional logistics path parameters in each regional logistics feature are identified. Based on the planar range, regional operation density parameters, and regional logistics path parameters of each logistics heat map, the three-dimensional heat map height corresponding to each logistics heat map is determined. Based on the planar range and three-dimensional heat map height of each logistics heat map, the three-dimensional display cone and the regional heat value corresponding to each logistics heat map are determined. The multiple three-dimensional display cones and the corresponding regional heat values ​​are superimposed on the region layer containing the area to be observed to obtain the three-dimensional logistics heat map corresponding to the real-time logistics parameters.

[0040] Specifically, the area to be observed is the area where transportation and delivery tasks need to be performed. For example, the area to be observed could be town A, district B, etc. The scope of the area to be observed is not specifically limited in this embodiment. First, the scheduling location parameters can be identified from real-time logistics features using a preset feature recognition algorithm. The scheduling location parameters at least include the transportation start point and transportation destination in each transportation and delivery task. Then, the area to be observed is divided based on the scheduling location parameters. The distance between any two transportation start points and transportation destinations in the same logistics heat zone is not less than a preset interval threshold. The specific preset interval threshold is not specifically limited in this embodiment. The area to be observed can contain multiple logistics heat zones. The heat zone edge information of each logistics heat zone can be identified from the regional image containing the area to be observed using a preset edge feature recognition algorithm. The heat zone edge information includes the coordinates of each edge point. The planar range of each logistics heat zone can be determined by calculating based on the edge information of each heat zone. The specific calculation process is not described in this embodiment.

[0041] The system identifies the regional characteristics of each logistics heat zone, and divides the real-time logistics characteristics based on these regional characteristics to obtain the corresponding regional logistics characteristics for each heat zone. These regional characteristics can include regional location and regional number, etc., and the specific content is not specifically limited in this embodiment. For any logistics heat zone, a preset feature recognition algorithm can be used to identify the corresponding regional operation density parameters and regional logistics path parameters from the regional logistics characteristics. The regional operation density parameters include the number of transportation tasks, the number of transportation equipment, the number of transportation products, and the transportation frequency. A preset density value mapping relationship can be used to determine the regional operation density value corresponding to the regional operation density parameters. The preset feature recognition algorithm identifies each regional logistics path from the regional logistics path parameters, and compares these paths to determine the number of intersections and the path overlap rate. A preset path value mapping relationship is used to determine the regional logistics path value corresponding to the number of intersections and the path overlap rate. The preset path value mapping relationship is the correspondence between the combination of the number of intersections and the path overlap rate and the regional logistics path value. Finally, the three-dimensional thermal height of the logistics heat zone is determined by combining the planar range, regional operation density value, and regional logistics path value. Different combinations of these values ​​correspond to different three-dimensional thermal heights. Since the planar range, regional operation density value, and regional logistics path value can all describe the current situation faced by the logistics heat zone from one perspective, a larger planar range, a higher regional operation density value, and a higher regional logistics path value indicate greater logistics transportation pressure on the corresponding logistics heat zone, thus resulting in a higher three-dimensional thermal height. This allows relevant managers to promptly identify potential hazards or anomalies in a logistics heat zone. Based on the above method, the planar range and three-dimensional thermal height corresponding to each logistics heat zone can be obtained.

[0042] The bottom circle of the 3D display cone can be determined based on the planar range, and the cone height can be determined based on the 3D thermal height. The specific process of determining the 3D display cone based on the planar range and 3D thermal height is not specifically limited in this embodiment. By visually displaying the logistics situation of each logistics thermal zone in the 3D thermal map as a cone shape, the characteristics of the cone's bottom circle corresponding to the logistics area range, the 3D thermal height corresponding to the logistics thermal intensity, and the core-focusing shape can be utilized to more accurately and intuitively convey the spatial distribution and load status of each logistics thermal zone. The regional thermal value corresponding to each logistics thermal zone can be determined based on a preset regional thermal value mapping relationship. This preset regional thermal value mapping relationship is the correspondence between the combination of planar range and 3D thermal height and the regional thermal value. The specific content can be determined by relevant personnel based on historical experimental data and then uploaded to the logistics scheduling management system. After obtaining the 3D display cones and regional heat values ​​of each logistics heat zone, multiple 3D display cones and their corresponding regional heat values ​​can be superimposed onto a region layer containing the area to be observed by layer overlay, resulting in a 3D logistics heat map corresponding to the real-time logistics parameters. This facilitates the integrated visualization of spatial location, load height, and heat values, thereby improving the readability of the 3D logistics heat map.

[0043] Furthermore, to improve the accuracy of the three-dimensional display cone in reflecting regional logistics characteristics, this embodiment of the application, after determining the three-dimensional display cone corresponding to the logistics thermal zone based on the planar range and three-dimensional thermal height of the logistics thermal zone, may further include steps S210-S230, such as... Figure 2 As shown, where: Step S210: Identify the planar diameter of the logistics heat zone and the initial cone vertex of the corresponding 3D display cone, and determine the offset distance corresponding to the initial cone vertex based on the regional operation density parameters and the planar diameter.

[0044] Specifically, the planar diameter and initial cone vertex of the logistics heat zone can be identified from the planar range or the three-dimensional display cone using a preset feature recognition algorithm. Besides defaulting to a standard cone, the shape of the three-dimensional display cone can be optimized and adjusted according to the actual logistics situation in the heat zone to improve its adaptability to the actual logistics conditions. In this embodiment, optimization of the three-dimensional display cone is mainly achieved by adjusting the offset distance and offset direction of the initial cone vertex. The logistics heat zone can be divided into high-density and low-density zones based on preset density division values ​​and the corresponding regional operation density parameters. High-density zones are sub-regions with sub-operation density values ​​higher than the preset density division values. The method for determining the sub-operation density value corresponding to each sub-region can refer to the method for determining the regional operation density value in the above embodiments, and will not be elaborated here. The high-density ratio and low-density ratio are determined based on the number of high-density and low-density zones. Finally, the offset distance of the initial cone vertex is determined based on the offset distance calculation formula, whereby: .

[0045] Step S220: Based on the regional operation density parameters and regional logistics path parameters corresponding to the logistics heat zone, determine the offset direction of the initial cone apex.

[0046] Specifically, the target high-density area and the intersection point of logistics paths can be determined based on a preset feature recognition algorithm and regional operation density parameters. The target high-density area is the high-density area with the highest sub-operation density value, and the intersection point is the intersection location with the highest number of logistics path intersections. The offset direction is determined based on the target high-density area and the intersection point. For example, if "the target high-density area is located in the east and the intersection point is concentrated in the northeast corner", the offset direction can be determined to be northeast. In this embodiment, it is not necessary to determine the specific offset angle based on the regional operation density parameters and regional logistics path parameters when determining the offset direction. It is only necessary to determine the offset direction based on the target high-density area and the intersection point.

[0047] Step S230: Based on the offset distance and offset direction corresponding to the initial cone apex, optimize the three-dimensional display cone corresponding to the logistics thermal zone.

[0048] Finally, based on the offset distance and offset direction, the initial cone apex of the 3D display cone corresponding to the logistics heat zone is optimized and adjusted. The optimized 3D display cone may be a non-standard cone. By determining the offset direction of the initial cone apex based on the regional operation density parameters and regional logistics path parameters, it is easier to improve the directionality between the offset direction and the core location of dense operation or congested path, so as to make it easier to intuitively display the key logistics status through the 3D display cone.

[0049] After the three-dimensional logistics heat map is determined, it can be judged whether there are abnormal heat areas in the three-dimensional logistics heat map based on the preset heat threshold. When the regional heat value of the logistics heat area is higher than the preset heat threshold, the corresponding logistics heat area can be determined as an abnormal heat area. The specific preset heat threshold is not specifically limited in this application embodiment, but can be determined by relevant personnel based on historical experimental data and then uploaded to the logistics scheduling management system. The abnormal area logistics characteristics are the regional logistics characteristics corresponding to the abnormal heat zone. They can be obtained from real-time logistics characteristics based on a preset feature recognition algorithm. The regional logistics characteristics include regional operation density parameters and regional logistics path parameters. The preset feature recognition algorithm can identify the characteristics to be optimized from the regional logistics characteristics, and optimize regional logistics scheduling for the characteristics to be optimized. The characteristics to be optimized can be the number of transportation tasks exceeding a preset task threshold, the number of transportation equipment exceeding a preset equipment threshold, the number of transportation products exceeding a preset product threshold, the transportation frequency exceeding a preset frequency threshold, the number of regional logistics path intersections exceeding a preset intersection threshold, and the path overlap rate exceeding a preset overlap rate threshold. The preset task threshold, preset equipment threshold, preset product threshold, preset frequency threshold, preset intersection threshold, and preset overlap rate threshold are not specifically limited in this application embodiment and can be set according to actual needs. Optimized regional logistics scheduling based on features to be optimized can alleviate anomalies or potential risks caused by these features at any given time. By timely and targeted optimization of regional logistics scheduling in areas with anomalies, it is easier to reduce the risks of production line stoppages and order delays caused by the spread of anomalies, and further facilitate the accuracy and stability of logistics scheduling under the demand for high-efficiency warehousing operations.

[0050] In addition, the system can identify the vertex coordinates of each 3D display cone in the optimized 3D logistics heatmap. By comparing the vertex coordinates of each 3D display cone in real time and determining whether all the vertex vertices contain a group of hazard vertex vertices based on a preset interval threshold, a group of hazard vertex vertices must contain at least two vertex vertices, and the vertex interval between at least two vertex vertices must be less than the preset interval threshold. When a group of hazard vertex vertices exists, at least two 3D display cones corresponding to the group of hazard vertex vertices can be identified as abnormal display cones and marked with color to facilitate timely detection and handling of abnormal logistics situations within the logistics heatmap corresponding to the abnormal display cones by relevant management personnel. Since the method provided in this embodiment requires optimization of the offset direction and offset distance of the 3D display cones based on regional operation density parameters and regional logistics path parameters, the system determines whether there are abnormal logistics situations in the corresponding logistics heatmap by observing and analyzing the interval between the vertex vertices of the 3D display cones.

[0051] Furthermore, when a 3D logistics heatmap contains multiple anomalous heat zones, optimizing the corresponding regional logistics scheduling based on the anomalous region logistics characteristics of these heat zones can specifically include: Based on the abnormal locations of each abnormal heat zone in the 3D logistics heat map, a joint abnormal heat zone corresponding to multiple abnormal heat zones is determined. Based on the regional characteristic parameters of the multiple abnormal heat zones corresponding to the joint abnormal heat zone, the abnormal logistics location type corresponding to each abnormal heat zone is determined. Based on the regional characteristic parameters of the multiple abnormal heat zones corresponding to the joint abnormal heat zone and the abnormal logistics location type, the joint abnormal parameters corresponding to the joint abnormal heat zone are determined. The joint abnormal parameters include time urgency and task backlog. Based on the joint abnormal parameters, the regional logistics scheduling corresponding to the joint abnormal heat zone is optimized.

[0052] Specifically, when a 3D logistics heatmap contains multiple abnormal heatmaps, the method of optimizing the logistics scheduling of corresponding areas based on the features to be optimized, as disclosed in the above embodiments, can be used to analyze each abnormal heatmap, determine the features to be optimized for each abnormal heatmap, and then optimize the regional logistics scheduling for each feature to be optimized. In addition, joint anomaly analysis can be performed on multiple abnormal heatmaps. This joint anomaly analysis helps to overcome the limitations of isolated optimization of a single area, solves problems of anomaly diffusion, resource conflicts, and inefficient collaboration from a global warehousing perspective, and avoids chain anomalies in other logistics heatmaps caused by optimizing the logistics scheduling of a single area, ultimately achieving optimal overall logistics efficiency. When performing joint anomaly analysis on multiple abnormal heatmaps, the abnormal locations of each abnormal heatmap can first be identified in the 3D logistics heatmap using a preset feature recognition algorithm, and then the joint abnormal heatmap can be determined by connecting the various abnormal locations.

[0053] Based on the regional logistics path parameters in the regional characteristic parameters corresponding to each abnormal heat zone, the abnormal logistics location type of each abnormal heat zone in the joint abnormal heat zone is determined. The abnormal logistics location type includes upstream logistics, logistics hub, and downstream logistics. If an abnormal heat zone contains many transportation origin points, its abnormal logistics location type can be determined as upstream logistics; if it contains many transportation destinations, its abnormal logistics location type can be determined as downstream logistics; if it contains many intersecting logistics paths, its abnormal logistics location type can be determined as logistics hub. Different abnormal logistics location types correspond to different abnormal weights. The abnormal weight corresponding to each abnormal logistics location type can be determined according to a preset weight mapping relationship, which is the correspondence between abnormal logistics location types and abnormal weights. Specific details are not limited in this embodiment. Based on timeliness and task backlog dimensions, the regional feature parameters of all abnormal heat zones within a joint abnormal heat zone can be integrated into a global joint abnormal parameter, avoiding the local limitations of individual parameters. Specifically, this can be based on the feature vectors corresponding to timeliness and task backlog, as well as the basic weight ratio of these feature vectors. The feature vectors and basic weight ratios corresponding to timeliness can be set by relevant management personnel according to the actual logistics scheduling management needs for transportation timeliness, and the feature vectors and basic weight ratios corresponding to task backlog can be set by relevant management personnel according to the actual logistics scheduling management needs for task backlog. From the regional features of each abnormal heat zone, the urgency parameters and task backlog parameters corresponding to each abnormal heat zone can be identified. The urgency parameters include task timeliness constraints, task type priority, and already delayed duration; the task backlog parameters include task inventory and growth rate. Based on the urgency parameters and their corresponding basic weight ratios, the corresponding regional time urgency can be determined; based on the task backlog parameters and their corresponding basic weight ratios, the corresponding regional task backlog can be determined.

[0054] Finally, based on the regional time urgency and anomaly weight of each abnormal heat zone, the time urgency of the joint abnormal heat zone is obtained after weighted summation. Similarly, based on the regional task backlog and anomaly weight of each abnormal heat zone, the task backlog of the joint abnormal heat zone is obtained after weighted summation. After accurately determining the time urgency and task backlog, the optimization content corresponding to the joint anomaly parameters can be determined based on a preset optimized scheduling mapping relationship. Finally, based on the optimization content, targeted optimization of the regional logistics scheduling corresponding to the joint abnormal heat zone is performed. The preset optimized scheduling mapping relationship is the correspondence between the joint anomaly parameters and the optimization content; the specific content can be determined by relevant personnel based on historical experimental data and uploaded to the logistics scheduling management system in advance.

[0055] By combining the regional characteristic parameters of multiple anomalous areas within the joint anomaly hot zone, joint anomaly parameters such as time urgency and task backlog can be extracted. This facilitates the transformation of complex multi-regional anomaly information into quantifiable and comparable core indicators. Targeted optimization of joint regional scheduling allows for precise matching of scheduling resource allocation to the severity of anomalies, thus avoiding the problems of blindly adding resources or insufficient resource allocation. This further ensures the stability of logistics scheduling and the overall efficiency of logistics scheduling in high-time-efficiency warehousing scenarios.

[0056] In addition, by jointly analyzing multiple abnormal hot zones, the priority of the joint abnormal hot zones in the logistics scheduling and allocation process can be improved. For example, the production material requisition tasks of the joint abnormal hot zones will be automatically scheduled before the allocation tasks of ordinary logistics hot zones. If an emergency task is added to the joint abnormal hot zone, the logistics scheduling management system will automatically trigger the queue-jumping rule. Even if there are transportation tasks currently being executed in ordinary logistics hot zones, the scheduling of ordinary transportation tasks will be suspended, and the emergency task scheduling of the joint area will be initiated first.

[0057] Furthermore, the method provided in the embodiments of this application may also include: Obtain user access operation information within a preset operation time period, including click operations, dwell time, and current display page size; determine the user's predicted access path based on the click operations and dwell time in the user access operation information, and determine the content to be displayed based on the user's predicted access path; determine the final display content based on the content to be displayed and the current display page size in the user access operation information; when the access deviation between the user's actual access path and the user's predicted access path is less than a preset deviation threshold, display the final display content on the current display page.

[0058] Specifically, the preset operation time period can be a period of time prior to the current moment. The duration of the preset operation time period can be 10 minutes or 8 minutes. The specific duration is not limited in this embodiment and can be set according to the actual needs of relevant management personnel. All click operations performed by the user within the preset operation time period are collected. Click operations include, but are not limited to, clicking buttons and clicking links. The dwell time of the user after performing a click operation is collected, including the page ID, entry timestamp, and exit timestamp. The dwell time corresponding to each page ID is obtained by calculating the time difference between the exit timestamp and the entry timestamp. The page rendering size of the user's current access device is collected, including the device type and pixel density, to determine the current displayed page size.

[0059] Valid clicks are filtered from multiple clicks based on the dwell time after each click and a preset dwell threshold. Since different access paths correspond to different click order, the user's access intent can be predicted based on the displayed content and click order of each valid click—that is, the user's predicted access path. Based on the user's predicted access path and the user's historical access records within a historical time period, the content the user needs to access in the next operation time period can be predicted. The next operation time period is a period after the user's predicted access path is determined. The final displayed content is determined in advance based on the current display page size and the content to be displayed. To avoid situations where the pushed content does not match the user's needs due to large prediction deviations, the user's actual access path within the operation observation time period can be obtained after the final displayed content is determined. If the access deviation between the actual access path and the user's predicted access path is less than a preset deviation threshold, the final displayed content is displayed on the current display page. The operation observation time period is located before the next operation time period.

[0060] By acquiring user access information such as clicks, dwell time, and current page size within a preset operation period, it is easy to comprehensively capture users' real interactive behaviors and page adaptation needs. By analyzing user access information, the predicted user access path can be determined, and the content to be displayed can be determined based on this path, so that the content to be displayed can match the user's potential browsing intentions in advance. By determining the final display content based on the content to be displayed and the current page size, it is easy to make the content presentation adaptable to different devices and improve the user browsing experience.

[0061] This application provides a logistics scheduling and management system, such as... Figure 3 As shown, Figure 3 The logistics scheduling and management system 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the logistics scheduling and management system 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one, and the structure of this logistics scheduling and management system 300 does not constitute a limitation on the embodiments of this application.

[0062] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0063] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0064] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0065] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0066] The logistics dispatch and management system includes, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and vehicle-mounted terminals (such as vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. It can also include servers. Figure 3 The logistics scheduling and management system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0067] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0068] This application provides a computer program product including a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0069] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0070] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A logistics scheduling and management method based on WMS, characterized in that, include: Receive an integration task request and identify the task parameters in the integration task request, wherein the integration task request is issued by an integration system that has a business collaboration relationship with the WMS system; Based on the task parameters, a logistics schedule is determined, and a task allocation instruction is generated based on the logistics schedule to control the relevant execution equipment to perform corresponding logistics operations according to the task allocation instruction. Record real-time logistics parameters generated during logistics operations. When the real-time logistics parameters include preset abnormal logistics characteristics, optimize the logistics scheduling in real time based on the preset abnormal logistics characteristics.

2. The logistics scheduling and management method based on WMS according to claim 1, characterized in that, Also includes: Real-time logistics features are identified from the real-time logistics parameters, and a corresponding three-dimensional logistics heat map is determined based on the real-time logistics features. The three-dimensional logistics heat map contains multiple logistics heat zones, and each logistics heat zone corresponds to a regional heat value. Based on a preset thermal threshold, it is determined whether the three-dimensional logistics heat map contains abnormal thermal zones. If so, the corresponding regional logistics scheduling is optimized based on the abnormal regional logistics characteristics corresponding to the abnormal thermal zone. The abnormal thermal zone is a logistics thermal zone where the abnormal regional thermal value is higher than the preset thermal threshold.

3. The logistics scheduling and management method based on WMS according to claim 2, characterized in that, The process of determining the three-dimensional logistics heatmap corresponding to the real-time logistics parameters based on the real-time logistics characteristics includes: The observation area covered by the real-time logistics features is divided into multiple logistics heat zones based on the scheduling location parameters in the real-time logistics features. The planar range of each logistics heat zone is determined based on the heat zone edge information of each logistics heat zone. The regional logistics characteristics corresponding to each logistics heat zone are identified from the real-time logistics characteristics. The regional operation density parameters and regional logistics path parameters in each regional logistics characteristic are identified. Based on the planar range, regional operation density parameters and regional logistics path parameters of each logistics heat zone, the three-dimensional heat height corresponding to each logistics heat zone is determined. Based on the planar range and three-dimensional thermal height of each logistics thermal zone, determine the three-dimensional display cone corresponding to each logistics thermal zone and the regional thermal value corresponding to each logistics thermal zone; Multiple 3D display cones and their corresponding regional thermal values ​​are superimposed onto a region layer containing the region to be observed to obtain a 3D logistics heat map corresponding to the real-time logistics parameters.

4. The logistics scheduling and management method based on WMS according to claim 3, characterized in that, After determining the three-dimensional display cone corresponding to the logistics heat zone based on the planar range and the three-dimensional heat height of the logistics heat zone, the method further includes: Identify the planar diameter of the logistics heat zone and the initial cone vertex of the corresponding three-dimensional display cone, and determine the offset distance corresponding to the initial cone vertex based on the regional operation density parameter and the planar diameter; Based on the regional operation density parameters and regional logistics path parameters corresponding to the logistics heat zone, the offset direction of the initial cone apex is determined; Based on the offset distance and offset direction corresponding to the initial cone vertex, the three-dimensional display cone corresponding to the logistics heat zone is optimized.

5. A logistics scheduling and management method based on WMS according to claim 2, characterized in that, When the three-dimensional logistics heatmap contains multiple abnormal heat zones, the optimization of the corresponding regional logistics scheduling based on the abnormal regional logistics characteristics of the abnormal heat zones includes: Based on the abnormal positions of each of the abnormal thermal zones in the three-dimensional logistics heat map, a joint abnormal thermal zone corresponding to the multiple abnormal thermal zones is determined. Based on the regional characteristic parameters of the multiple abnormal thermal zones corresponding to the joint abnormal thermal zone, the abnormal logistics location type corresponding to each abnormal thermal zone is determined. Based on the regional characteristic parameters of the multiple abnormal thermal zones corresponding to the joint abnormal thermal zone and the location type of each abnormal logistics, the joint abnormal parameters corresponding to the joint abnormal thermal zone are determined. The joint abnormal parameters include time urgency and task backlog. Optimize regional logistics scheduling corresponding to the joint anomaly thermal zone based on the joint anomaly parameters.

6. The logistics scheduling and management method based on WMS according to claim 3, characterized in that, Also includes: Obtain user access and operation information within a preset operation time period, including clicks, dwell time, and current page size; The predicted user access path is determined based on the click operation and dwell time in the user access operation information, and the content to be displayed is determined based on the predicted user access path. The final content to be displayed is determined based on the content to be displayed and the current page size in the user access operation information; When the deviation between the user's actual access path and the user's predicted access path is less than a preset deviation threshold, the final display content is displayed on the current display page.

7. A logistics dispatch and management system, characterized in that, This logistics dispatch and management system includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform a WMS-based logistics scheduling management method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, include: The system stores a computer program capable of being loaded by a processor and executed as described in any one of claims 1-6, which is a WMS-based logistics scheduling and management method.

9. A computer program product, characterized in that, It includes a computer program, which, when executed by a processor, implements the steps of a WMS-based logistics scheduling and management method according to any one of claims 1-6.

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