Logistics work order management method and system based on big data adaptation analysis, and medium

Abstract

The invention provides a logistics work order management method and system based on big data adaptation analysis, and a medium, and relates to the technical field of big data processing, and the method comprises the steps: executing multi-modal data collection, and obtaining a plurality of pieces of meal characteristic information and a plurality of pieces of logistics demand information of a plurality of meal distribution tasks; performing temperature-sensitive demand grouping to obtain P groups of meal delivery tasks; carrying out space-time path merging, and outputting K initial task sequences; the volume characteristics of the logistics box are called, loading conflict analysis is carried out, and K pieces of loading conflict information are output; carrying out cross-box collaborative analysis to obtain W target task sequences; and constructing a logistics work order to schedule the temperature-sensitive logistics box to distribute a plurality of food distribution tasks. The technical problems that logistics work order management in the prior art mainly focuses on static task allocation and path optimization, meal characteristic information is not fully utilized for dynamic adaptation, and consequently the distribution efficiency is low and the product quality cannot be guaranteed in a high-sensitivity distribution scene are solved.

Description

Technical Field

[0001] The present invention relates to the field of big data processing technology, and in particular to a logistics work order management method, system and medium for big data adaptation and analysis. Background Art

[0002] With the growing demand for the delivery of highly sensitive items such as fresh produce and pharmaceuticals, ensuring that food or pharmaceutical products remain in optimal condition under appropriate storage conditions during transportation has become a major challenge. This is particularly true for the delivery of temperature-sensitive items, where mismatches in storage conditions can cause product spoilage, impacting both quality and safety. Existing logistics work order management technologies primarily focus on static task allocation and route optimization, failing to fully utilize food characteristic information (such as storage temperature and temperature sensitivity) for dynamic adaptation. This results in an inability to effectively prevent food spoilage during transportation due to mismatches in storage conditions. Consequently, problems such as low delivery efficiency and inability to guarantee product quality often arise in highly sensitive delivery scenarios. Summary of the Invention

[0003] This application provides a logistics work order management method, system and medium for big data adaptation analysis, aiming to solve the technical problems that the logistics work order management of existing technologies mainly focuses on static task allocation and route optimization, fails to fully utilize food characteristic information for dynamic adaptation, and leads to low delivery efficiency and unguaranteed product quality in highly sensitive delivery scenarios.

[0004] The first aspect disclosed in the present application provides a logistics work order management method based on big data adaptive analysis, the method comprising: obtaining multiple meal characteristic information and multiple logistics demand information of multiple meal delivery tasks by performing multimodal data collection in a meal task center; grouping the multiple meal delivery tasks according to the multiple meal characteristic information into temperature-sensitive groups to obtain P groups of meal delivery tasks; merging spatiotemporal paths based on the multiple logistics demand information with the P groups of meal delivery tasks as constraints, and outputting K initial task sequences; locally calling the logistics box volume characteristics, and performing loading conflict analysis on the K initial task sequences according to the logistics box volume characteristics and multiple meal characteristic information, and outputting K loading conflict information; performing cross-box collaborative analysis according to the K loading conflict information, and updating the K initial task sequences according to the analysis results to obtain W target task sequences, where W is a positive integer greater than K; and constructing a logistics work order based on the W target task sequences to schedule the temperature-sensitive logistics boxes to deliver the multiple meal delivery tasks.

[0005] The second aspect disclosed in the present application provides a logistics work order management system for big data adaptive analysis, the system is used for the logistics work order management method for the above-mentioned big data adaptive analysis, the system includes: a data acquisition module for obtaining multiple meal characteristic information and multiple logistics demand information of multiple meal delivery tasks by performing multimodal data acquisition in the meal task center; a demand grouping module for grouping the multiple meal delivery tasks into temperature-sensitive demand groups according to the multiple meal characteristic information to obtain P groups of meal delivery tasks; a spatiotemporal path merging module for grouping the P groups of meal delivery tasks into time-space paths according to the multiple logistics demand information. Empty paths are merged to output K initial task sequences; a loading conflict analysis module is used to locally call the logistics box volume characteristics, and perform loading conflict analysis on the K initial task sequences according to the logistics box volume characteristics and multiple meal characteristic information, and output K loading conflict information; an initial task update module is used to perform cross-box collaborative analysis based on the K loading conflict information, and update the K initial task sequences according to the analysis results to obtain W target task sequences, where W is a positive integer greater than K; a delivery module is used to construct a logistics work order based on the W target task sequences to schedule temperature-sensitive logistics boxes to deliver the multiple meal delivery tasks.

[0006] The third aspect disclosed in the present application provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the logistics work order management method of big data adaptation analysis in the first aspect.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects:

[0008] Through multimodal data collection, it is possible to collect food characteristic information and logistics demand information from multiple sources. After big data processing, this information can realize comprehensive analysis and accurate grouping of complex delivery tasks. Multimodal data collection enables the system to process data from different sources and effectively integrate and apply them. Based on the temperature control characteristics of food, multiple delivery tasks are grouped according to temperature-sensitive requirements through big data analysis, so as to ensure that food with similar temperature control requirements can be reasonably allocated to the same group. This grouping method makes delivery route planning and resource allocation more targeted and reduces unnecessary waste of resources. According to the results of temperature-sensitive demand grouping, multiple logistics demand information are combined to merge time and space paths. This process The delivery route was optimized, and the temperature control and timeliness requirements were combined to generate an initial task sequence, providing effective path planning for subsequent task scheduling. Through big data processing technology, the matching of meals and logistics boxes was analyzed, and potential loading conflicts were identified. This analysis result provided data support for subsequent cross-box collaborative analysis, thereby further optimizing the task sequence and resource allocation, and improving the utilization rate of logistics resources. Based on the optimization results of the target task sequence, logistics work orders were intelligently constructed, and distribution was carried out in combination with the scheduling requirements of temperature-sensitive logistics boxes. Big data processing makes the generation of logistics work orders more efficient, and can dynamically adjust the scheduling plan according to the characteristics of the meals and distribution needs, thereby improving the execution efficiency of distribution tasks.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of the logistics work order management method based on big data adaptation analysis provided in an embodiment of the present application.

[0011] Figure 2 Schematic diagram of the logistics work order management system structure for big data adaptation analysis provided in an embodiment of the present application.

[0012] Explanation of the reference numerals: data collection module 10 , demand grouping module 20 , spatiotemporal path merging module 30 , loading conflict analysis module 40 , initial task updating module 50 , distribution module 60 . DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a logistics work order management method, system and medium for big data adaptation analysis, thereby solving the technical problems that the logistics work order management in the prior art mainly focuses on static task allocation and route optimization, fails to fully utilize food characteristic information for dynamic adaptation, and leads to low delivery efficiency and unguaranteed product quality in highly sensitive delivery scenarios.

[0014] After introducing the basic principles of this application, various non-limiting embodiments of this application will be specifically described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0015] Example 1, as Figure 1 As shown, the embodiment of the present application provides a logistics work order management method based on big data adaptation analysis, the method comprising:

[0016] By performing multimodal data collection in the food task center, multiple food characteristic information and multiple logistics demand information of multiple food delivery tasks are obtained.

[0017] Multimodal data collection is performed to gather information on food characteristics and logistics requirements for multiple food delivery tasks. This information includes temperature sensitivity levels, three-dimensional volume models, and transportation time thresholds. The temperature sensitivity level represents the temperature requirement for the food, for example, a temperature range of 0-5°C ± ΔT, where ΔT is a preset temperature fluctuation range. This means that the food must remain within a specified temperature range during delivery to ensure quality and safety. The three-dimensional volume model provides volumetric data for the food, which assists in subsequent logistics planning, particularly regarding space utilization during packaging and transportation. The transportation time threshold specifies the maximum time each food must remain within during delivery, ensuring that it is delivered within a certain timeframe to prevent expiration or quality degradation. Logistics requirement information includes start and end point coordinates and route priorities. The start and end point coordinates represent the geographic coordinates of each delivery task's starting and ending points, helping to define delivery routes. Among multiple delivery tasks, certain items may require priority delivery due to special requirements (such as temperature sensitivity). Route priorities prioritize these tasks, allowing these requirements to be prioritized when planning delivery routes.

[0018] The plurality of meal delivery tasks are grouped according to temperature-sensitive requirements based on the plurality of meal characteristic information to obtain P groups of meal delivery tasks.

[0019] Based on the collected food characteristic information, the food delivery tasks are grouped according to temperature sensitivity requirements. Specifically, according to the temperature sensitivity level of each food, the delivery tasks with the same temperature requirements are grouped together. For example, all meals that are required to be delivered within the temperature range of 0-5℃±ΔT are grouped together. This grouping is to ensure that during the delivery process, the meals in the same group can be kept under similar temperature conditions to avoid quality problems caused by inconsistent delivery temperatures. After the grouping is completed, P groups of food delivery tasks are obtained.

[0020] Taking the P groups of meal delivery tasks as constraints, the spatiotemporal paths are merged according to the multiple logistics demand information, and K initial task sequences are output.

[0021] Spatiotemporal path merging involves integrating the paths of each delivery task based on known start and end point coordinates, transportation deadlines, route priorities, and other requirements. The goal is to optimize delivery routes, reduce delivery time and costs, and improve overall efficiency. Constraints in the merging process include temperature control requirements, transportation deadlines, and route priorities. Based on the results of spatiotemporal path merging, K initial task sequences are generated. These sequences serve as preliminary results of task allocation and require further optimization in subsequent steps.

[0022] The logistics box volume feature is locally called, and the K initial task sequences are subjected to loading conflict analysis according to the logistics box volume feature and multiple meal characteristic information, and K loading conflict information is output.

[0023] After generating K initial task sequences, they are subjected to loading conflict analysis. This step is based on the volume characteristics of the logistics box and multiple food characteristic information to analyze whether there is a conflict in the loading of food in each task sequence. Specifically, the volume characteristics of the logistics box refer to the capacity and shape characteristics of the logistics box (or delivery vehicle) used to transport food, such as the volume, shape and size of each partition area of the box. Different logistics boxes have different loading restrictions, which affect the storage and handling efficiency of food. Each food has its own specific volume and shape information (represented by a three-dimensional volume model). Therefore, it is necessary to combine the volume and shape information of the food with the volume characteristics of the logistics box to calculate whether the food in each delivery task can be reasonably loaded in the specified logistics box. If the volume and shape of multiple meals do not match the volume of the box, loading conflicts may occur.

[0024] Loading conflict analysis is used to determine whether there are any meals in the task sequence that cannot be successfully loaded into the delivery box due to volume, weight, etc. Specifically, if the number or volume of meals in a logistics box exceeds the volume limit, or the shape of the meals does not fit the structure of the current box (such as there are multiple partitions in the box), a loading conflict will occur. In order to achieve accurate loading conflict analysis, each meal and logistics box is modeled using three-dimensional modeling technology, and the conflicts during the loading process are analyzed using simulation methods. These three-dimensional models can reflect the volume and shape of the meals and help calculate the space utilization during loading. Based on the above analysis, K loading conflict information is output, which indicates the possible loading conflicts in each initial task sequence. For example, some meals require the use of logistics boxes with larger volumes, or some meals require a change in loading order. The loading conflict information provides a basis for subsequent optimization.

[0025] A cross-box collaborative analysis is performed based on the K loading conflict information, and the K initial task sequences are updated based on the analysis results to obtain W target task sequences, where W is a positive integer greater than K.

[0026] Cross-box collaborative analysis is to analyze the collaborative relationship between multiple logistics boxes (or delivery vehicles) to ensure that the loading problem of food in the overall delivery process is effectively optimized. At this time, a single logistics box may not be able to perfectly solve all loading conflicts, so it is necessary to consider the coordination of multiple logistics boxes. Through cross-box collaborative analysis, food can be redistributed to make the loading of each delivery box more efficient. If the space of a box is not fully utilized, the excess food in other boxes can be transferred to ensure that the space of each box is fully utilized. For conflicting task sequences, cross-box collaborative analysis can also reduce loading conflicts in each logistics box by adjusting the loading order of food, avoiding space waste. Through collaborative analysis, the delivery path, timeliness requirements and priority of each box can be coordinated during the cross-box delivery process.

[0027] After cross-box collaborative analysis, the K initial task sequences are adjusted and updated based on the analysis results. The key to this process is to avoid loading conflicts and space waste through effective loading and path planning, ensuring that each delivery box can carry tasks more efficiently. Ultimately, W target task sequences are generated. Since cross-box collaborative analysis introduces the collaboration of multiple logistics boxes, W is greater than K, indicating that more target task sequences are generated after optimization and the task distribution is more balanced.

[0028] A logistics work order is constructed based on the W target task sequences to schedule temperature-sensitive logistics boxes to deliver the multiple meal delivery tasks.

[0029] Based on the optimized W target task sequences, a logistics work order is constructed to dispatch temperature-sensitive logistics boxes to deliver multiple food delivery tasks. The logistics work order contains detailed information about the delivery task, including the task sequence, logistics box configuration, delivery timeliness, and route information. The logistics work order is an important basis for the scheduling system. Dispatchers will use the work order to arrange delivery vehicles, determine the delivery order, and allocate appropriate temperature-sensitive logistics boxes. With the detailed information in the work order, dispatchers can ensure that each delivery task receives reasonable resource allocation and ensures the timeliness and temperature control requirements of delivery.

[0030] Furthermore, by performing multimodal data collection in the food task center, a plurality of food characteristic information and a plurality of logistics demand information of a plurality of food delivery tasks are obtained. The method includes:

[0031] In the meal task center, multi-dimensional temperature-sensitive parameter mapping is performed according to the task type of the first meal delivery task to obtain a first storage temperature range, a first temperature sensitivity weight, a first meal volume model and a first time tolerance, wherein the first storage temperature range, the first temperature sensitivity weight and the first meal volume model constitute the first meal characteristic information; the first task order of the first meal delivery task is parsed to obtain a first benchmark transportation time limit and first starting and ending point coordinates; based on the first time tolerance and the first benchmark transportation time limit, the first transportation time limit threshold is calculated, wherein the first transportation time limit threshold and the first starting and ending point coordinates constitute the first logistics demand information.

[0032] The delivery requirements of the meals are identified based on the task type of the first meal delivery task. Task types include, for example, cold chain delivery, normal temperature delivery, hot chain delivery, etc., which will affect the temperature control requirements and timeliness requirements. For different task types, mapping and calculation are performed from the following aspects to obtain the temperature-sensitive characteristics of the meals, including: the first preservation temperature range, based on the task type, determines the temperature range that the meals need to maintain during the delivery process. For example, for cold chain delivery tasks, the temperature range may be 0-5°C to ensure that the meals remain fresh. If the task type is hot chain delivery, a higher temperature is required (such as 60-70°C) to avoid cooling of the meals; the first temperature sensitivity weight, the temperature sensitivity weight reflects the sensitivity of the meals to temperature changes, and is calculated based on the characteristics of the meals (such as whether they are perishable or require specific temperature control). Calculate the temperature sensitivity weight, which means that some meals may be very sensitive to temperature changes and therefore require higher priority and more stringent temperature control requirements; the first meal volume model, generates a three-dimensional volume model based on the shape, size, weight and other characteristics of the meal. This volume model helps with subsequent loading analysis, path planning and space optimization; the first time tolerance, the time tolerance reflects the flexibility of the meal delivery time limit. Some meals may have very strict time requirements, while some meals can be delivered within a certain time limit. By analyzing the task type and meal characteristics, the specific value of the time tolerance is determined. Through the above mapping process, the first storage temperature range, the first temperature sensitivity weight, the first meal volume model and the first time tolerance are obtained. These parameters constitute the first meal characteristic information.

[0033] The first task order contains all information related to the delivery task and automatically extracts key data from the task order, including the first benchmark transportation time limit and the first starting and ending point coordinates. The benchmark transportation time limit refers to the standard transportation time from the starting point to the end point of the task. In the absence of other influencing factors (such as traffic conditions, weather, etc.), the benchmark transportation time limit defines the ideal time required for delivery; the starting and ending point coordinates are the geographic coordinates of the starting point and target point of the delivery task. These coordinates are used for route planning to ensure that the delivery task can be completed smoothly.

[0034] The first transport time limit threshold is calculated based on the first time tolerance and the first benchmark transport time limit, and the first logistics demand information is obtained in combination with the first starting and ending point coordinates. Specifically, the transport time limit threshold is a flexible time window, indicating how long the task must be delivered. It is calculated based on the sum of the benchmark transport time limit and the time tolerance. The calculation of this threshold takes into account the time tolerance of the food, so a certain amount of time fluctuation can be tolerated. For example, if the time tolerance is large, the delivery task can be completed in a longer time. Otherwise, the time requirement is more stringent and the delivery time needs to be shortened as much as possible. Based on the calculated first transport time limit threshold and the starting and ending point coordinates, the first logistics demand information is formed, which provides key data support for subsequent delivery route planning, temperature-sensitive logistics box scheduling, etc., to ensure that the food can be delivered to the destination within a reasonable time and under appropriate temperature conditions.

[0035] Furthermore, with the P groups of meal delivery tasks as constraints, spatiotemporal paths are merged according to the multiple logistics demand information to output K initial task sequences. The method includes:

[0036] A first group of logistics demand information corresponding to a first group of meal delivery tasks is extracted from the multiple logistics demand information, wherein the first group of meal delivery tasks includes T meal delivery tasks; a highly sensitive task feature is preset, and the highly sensitive task feature is used to traverse the first group of meal characteristic information of the first group of meal delivery tasks to locate N seed delivery tasks, wherein the highly sensitive task feature includes a temperature range scale and a temperature sensitivity weight scale; the first logistics demand information is decomposed to obtain N logistics demand information of the N seed delivery tasks and TN logistics demand information of the remaining TN meal delivery tasks; N starting and ending point coordinates are extracted from the N logistics demand information to construct N benchmark delivery paths; with T transportation time limit thresholds as constraints, the TN logistics demand information are path-filled and fitted on the N benchmark delivery paths to obtain a first group of initial task sequences; and so on, after obtaining P groups of initial task sequences, the group division of the P groups of initial task sequences is removed to obtain the K initial task sequences.

[0037] A first group of logistics demand information corresponding to a first group of food delivery tasks is extracted from the plurality of logistics demand information. The first group of food delivery tasks is a specific group of food delivery tasks used as a current analysis object, and includes T food delivery tasks.

[0038] High-sensitivity task features are used to measure the sensitivity of food delivery tasks to environmental changes (especially temperature changes). Specific features include temperature range scale and temperature sensitivity weight scale. The temperature range scale refers to the range of temperature ranges that food needs to maintain. For example, some food may require the temperature to be maintained between 0°C and 5°C. This range is relatively narrow and belongs to highly sensitive tasks, while food with a wider temperature control range (such as 5°C to 10°C) is less sensitive. The temperature sensitivity weight scale is used to measure the sensitivity of food to temperature changes. For example, some food is prone to quality changes when the temperature changes, so these foods have a higher temperature sensitivity weight.

[0039] Analyze each meal in the first group of meal delivery tasks, check their temperature range scales and temperature sensitivity weight scales, and determine whether these meals meet the criteria for high-sensitivity task characteristics. Specifically, if the temperature control range of a meal is narrow and the temperature sensitivity weight is high, the meal is considered a high-sensitivity task. Based on the temperature requirements and temperature sensitivity standards, N seed delivery tasks are selected. These tasks require special attention to temperature control to ensure that they maintain an appropriate temperature during the delivery process.

[0040] The first logistics demand information is decomposed into two main parts: N logistics demand information for N seed delivery tasks, and TN logistics demand information for the remaining TN meal delivery tasks. Specifically, N logistics demand information is determined by locating the N seed delivery tasks. These tasks have highly sensitive temperature control and timeliness requirements, so they need to be processed separately to ensure that their delivery routes and times are prioritized. TN logistics demand information is determined by the remaining TN meal delivery tasks. Although these tasks also have temperature control and timeliness requirements, they are less sensitive to temperature and timeliness than the seed delivery tasks. Therefore, their logistics demand information will be placed in the second group, and the processing order is more flexible.

[0041] Each seed delivery mission has clear starting and ending point coordinates. These coordinates will be used for path planning. These coordinates are extracted from the logistics demand information of the seed mission. The baseline delivery path is the shortest path calculated based on the starting and ending point coordinates using a path planning algorithm (for example, Dijkstra or A* algorithm). Since the temperature control and timeliness of seed delivery missions are relatively strict, their paths will be planned as priority paths to ensure that they can reach the destination within the specified time and according to the temperature control requirements.

[0042] The transportation time limit threshold is the maximum time limit of each delivery task calculated based on the time tolerance of each delivery task and the benchmark transportation time limit. These time limit values limit how long the delivery task must be completed. For the remaining TN delivery tasks, according to their logistics demand information (including the starting and ending point coordinates and the transportation time limit threshold), combined with the existing N benchmark delivery paths, path filling and fitting are performed. Specifically, according to the starting and ending point coordinates and transportation time limits of the remaining tasks, the paths are filled on the basis of the benchmark paths to ensure that these tasks are delivered within the specified time limit. Through path filling and fitting, the first set of initial task sequences are generated. These task sequences include the path planning and time requirements of the seed delivery task and other tasks.

[0043] Following the generation rules for the first set of initial task sequences, similar path planning and filling are performed for other groups of delivery tasks (such as the second and third groups). By gradually optimizing the path and timeliness requirements for each task, P groups of initial task sequences are ultimately generated. Then, the group divisions are removed, meaning the criteria for dividing different groups are no longer considered. All initial task sequences are merged together to form the final K initial task sequences. In this way, all delivery tasks are no longer processed by group, but rather by the optimized task sequences. These task sequences, taking into account path filling, timeliness optimization, and temperature control requirements, can efficiently complete delivery tasks while ensuring that temperature control and timeliness are strictly guaranteed for each task.

[0044] Furthermore, with T transport time limit thresholds as constraints, path filling and fitting is performed on the N benchmark delivery paths for the TN logistics demand information to obtain a first set of initial task sequences. The method includes:

[0045] Extract TN starting and ending point coordinates from the TN logistics demand information to perform feasible path fitting, and output TN groups of accompanying delivery paths; with path overlap expansion as a constraint, merge the TN groups of accompanying delivery paths into the N benchmark delivery paths, and output N groups of expanded delivery paths; combine and enumerate the N groups of expanded delivery paths to obtain multiple expanded delivery path group sets; based on the T meal delivery tasks, perform delivery task check on the multiple expanded delivery path group sets to screen out O expanded delivery path group sets; perform delivery time efficiency calculation on the O expanded delivery path group sets, and use the T transportation time limit thresholds to perform time efficiency adaptability judgment to screen out the target delivery path group set; perform task extraction on the N target delivery paths in the target delivery path group set to obtain N initial task sequences, which constitute the first group of initial task sequences.

[0046] Extract TN starting and ending point coordinates from TN logistics demand information, perform path fitting on these coordinates, and output TN groups of accompanying delivery paths. Specifically, TN logistics demand information corresponds to the remaining TN delivery tasks. These tasks were not included in the seed delivery task in the previous steps, so separate path planning is required. The geographic coordinates of the starting and ending points of each delivery task are extracted from the logistics demand information of these delivery tasks. These coordinates will be used for subsequent path fitting and planning.

[0047] After obtaining the coordinates of the starting and ending points, a path planning algorithm (such as Dijkstra or A* algorithm) is used to calculate the feasible delivery paths for these delivery tasks. These paths are calculated and fitted based on the coordinates of the starting and ending points and other logistics requirements (such as transportation time limit thresholds, traffic conditions, etc.). Through path fitting, an accompanying delivery path is generated for each delivery task. This path is a possible route for the delivery task, ensuring that the task can be completed from the starting point to the end point within the specified time limit and conditions.

[0048] In actual distribution, the distribution paths of different tasks may have overlapping parts. For example, the starting points of two distribution tasks may be the same, or their end points may coincide. Path overlapping expansion is to reduce distribution time and resource waste by making full use of these overlapping parts. For each group of accompanying distribution paths, it is overlapped and expanded with the corresponding benchmark distribution path. Specifically, TN groups of accompanying distribution paths are combined with N benchmark distribution paths according to the overlapping parts of the paths to generate expanded distribution paths. These expanded paths take into account the order of tasks and maximize the efficiency of path utilization. According to the rules of path overlapping expansion, N groups of expanded distribution paths are generated. Each group of expanded paths corresponds to a new distribution path, which may include multiple distribution tasks, but not all tasks must be included in it. The specific task selection will be based on the path optimization strategy and resource scheduling.

[0049] The N groups of generated extended delivery paths are combined and enumerated. Since different path combinations may produce different delivery task sequences and paths, these extended path combinations are enumerated to generate multiple extended delivery path group sets. Each extended path group set contains some specific delivery paths. Each group of paths can represent a different delivery plan. These extended delivery path group sets represent different delivery task arrangement methods. Through these enumeration combinations, the optimal delivery path plan can be analyzed and selected.

[0050] Traverse all expanded delivery path groups to ensure that each expanded delivery path group can cover every task in the T food delivery tasks. Specifically, each path in the path group should include at least a part of the delivery tasks to ensure that all T tasks are taken into account. By checking the entire process, select O expanded delivery path groups that can fully cover all delivery tasks. This means excluding those path groups that fail to fully cover all delivery tasks and only retaining the path groups that can fully meet the delivery tasks, thus obtaining O expanded delivery path groups.

[0051] For each expanded delivery path set, the total delivery time is calculated. This calculation takes into account not only the path length and transportation time limit, but also the timeliness requirements of each delivery task to ensure that the path meets the timeliness requirements. Each delivery task has a corresponding transportation time limit threshold, indicating that the task should be completed within the specified time range. These time limit thresholds are compared with the actual timeliness of each path set to determine whether it meets the task requirements. During the timeliness compatibility judgment process, path sets that meet the timeliness requirements are selected, and those that do not meet the timeliness requirements are eliminated. Ultimately, the target delivery path set that meets the timeliness requirements is obtained.

[0052] From the target delivery path set, N target delivery paths are extracted. These paths are the optimal delivery routes that both meet the timeliness requirements of the tasks and cover all delivery tasks. Based on these N extracted target delivery paths, an initial task sequence is generated for each path. Each task sequence represents a set of optimized delivery tasks, ensuring that the temperature control and timeliness requirements of each task are met. By extracting tasks from the target path set, the first set of initial task sequences is generated. These task sequences represent the final optimized delivery task schedule, ready for the subsequent scheduling and execution phases.

[0053] Furthermore, the volume feature of the logistics box is locally called, and loading conflict analysis is performed on the K initial task sequences based on the volume feature of the logistics box and multiple meal characteristic information, and K loading conflict information is output. The method includes:

[0054] Based on the K initial task sequences, K meal volume model sequences are extracted from the multiple meal characteristic information; a logistics box volume model is constructed according to the logistics box volume characteristics; based on the task parallel relationship of the K initial task sequences, the K meal volume model sequences are dynamically superimposed on the logistics box volume model, and K loading conflict node sequences are located to constitute the K loading conflict information.

[0055] The food characteristic information includes multi-dimensional characteristics such as temperature control requirements, volume, and weight of each food. Among them, the volume information of the food is represented by a three-dimensional volume model, which reflects the space occupied by the food. K food volume model sequences are extracted. Each volume model sequence represents the food volume information of a group of tasks, which facilitates subsequent loading optimization.

[0056] The volume characteristics of a logistics box include the external dimensions of the box, available internal space, different types of partitions, carrying capacity, etc. Different box designs can affect loading efficiency. Therefore, the volume of the logistics box needs to be modeled. The logistics box volume model reflects the three-dimensional spatial configuration of each logistics box, including the total volume of the box, the effective volume, the number and size of the partitions, and whether it is suitable for transporting specific types of food. Based on these characteristics, a logistics box volume model is constructed for subsequent loading calculations and conflict detection.

[0057] Each task sequence contains multiple tasks, which may need to be loaded in parallel within the same delivery box. Consider the parallel relationship between these tasks, namely, which tasks can be loaded together in the same logistics box and which require separate loading. The parallel relationship of the tasks is related to the volume and weight of the food, as well as whether there are special temperature control requirements. Based on the logistics box volume model, the food volume model for each delivery task is dynamically superimposed. Based on the task parallel relationship, multiple food volume models are sequentially superimposed onto the effective volume of the logistics box. By superimposing the food volume models, it is analyzed whether any food occupied space exceeding the effective volume of the logistics box during loading, or whether multiple food items overlap in the same area. Whenever such a situation occurs, it is marked as a loading conflict node. The loading conflict node represents the conflict area between the food volume and the logistics box volume in the current task sequence. Based on the results of the dynamic superposition, all loading conflict nodes are identified and recorded to generate loading conflict information. Each information includes the specific location of the conflict node and the cause of the conflict (such as insufficient volume, overlapping food, etc.).

[0058] Furthermore, based on the plurality of meal characteristic information, the plurality of meal delivery tasks are grouped according to temperature-sensitive requirements to obtain P groups of meal delivery tasks. The method includes:

[0059] A second storage temperature range and a second temperature sensitivity weight of the second meal delivery task are extracted from the multiple meal characteristic information; by overlapping the first storage temperature range and the second storage temperature range, the first overlapping range length and the first merged range length are solved and output; a first temperature-sensitive weight ratio is calculated according to the first temperature-sensitive weight and the second temperature-sensitive weight; a first temperature-sensitive correlation is calculated and output based on the first overlapping range length, the first merged range length and the first temperature-sensitive weight ratio; and so on, after combining and enumerating the multiple meal delivery tasks, a temperature-sensitive correlation analysis is performed based on the multiple meal characteristic information, and multiple groups of temperature-sensitive correlations of the multiple meal delivery tasks are output; the multiple meal delivery tasks are grouped according to the temperature-sensitive requirements according to the multiple groups of temperature-sensitive correlations to obtain the P groups of meal delivery tasks.

[0060] The temperature control characteristics of the second meal delivery task are extracted from the multiple meal characteristic information, including a second storage temperature range and a second temperature sensitivity weight. The second meal delivery task is a separate task from the first meal delivery task. The second storage temperature range refers to the temperature range that the second meal delivery task must maintain during the delivery process. The temperature control requirements of each meal vary depending on its characteristics and preservation needs. The temperature range ensures that the meal maintains an appropriate storage temperature throughout the delivery process. The second temperature sensitivity weight indicates the sensitivity of the meal to temperature changes and is typically determined based on characteristics such as the type and perishability of the meal.

[0061] Overlap the first preservation temperature interval and the second preservation temperature interval, and calculate the length of the first overlapping interval and the length of the first merged interval, where the length of the first overlapping interval represents the overlapping part of the two temperature intervals, that is, the intersection of the two temperature intervals. For example, the temperature interval of the first meal may be 0-5℃, and the temperature interval of the second meal may be 3-8℃. In this case, their temperature intervals will have an overlapping interval, that is, 3-5℃. By calculating the intersection of the two temperature intervals, the temperature control range shared by the two meals during the delivery process can be determined; the length of the first merged interval represents the union of the two temperature intervals, that is, the merger of the two temperature control ranges, which can help determine how large a temperature control environment is needed to accommodate the two meals at the same time during the delivery process. For example, if the temperature control interval of the first meal is 0-5℃ and the temperature control interval of the second meal is 3-8℃, the merged interval is 0-8℃, and the length of the merged interval is 8℃.

[0062] Calculate the difference between the first temperature sensitivity weight and the second temperature sensitivity weight, and take its absolute value. This difference reflects the difference in temperature control requirements between the two dishes. For example, if the temperature sensitivity weight of the first dish is 0.8 and the temperature sensitivity weight of the second dish is 0.6, the absolute value of the weight difference is |0.8-0.6|=0.2. Then, calculate the mean of the first temperature sensitivity weight and the second temperature sensitivity weight. The mean represents the average temperature sensitivity level of the two dishes. For example, if the weight of the first dish is 0.8 and the weight of the second dish is 0.6, the mean is (0.8+0.6) / 2=0.7. Finally, calculate the ratio of the absolute value of the weight difference to the weight mean to obtain the first temperature sensitivity weight ratio. This ratio reflects the relative difference in temperature control sensitivity between the two dishes. A higher ratio indicates that the two dishes have a larger difference in temperature control requirements, while a lower ratio indicates a smaller difference.

[0063] The first temperature-sensitive correlation is calculated and output based on the length of the first overlapping interval, the length of the first merged interval, and the first temperature sensitivity weight ratio. The specific calculation process is detailed in subsequent steps. The temperature-sensitive correlation reflects the degree of correlation between the temperature control requirements of two dishes. This indicator takes into account the overlapping length of the temperature control intervals, the length of the merged interval, and the difference in the temperature sensitivity of the dishes, so it can evaluate their similarity in temperature control.

[0064] Multiple food delivery tasks are combined and enumerated. This process is to arrange different food delivery tasks in different combinations to form different task combinations. The food in each combination may have similar or different temperature control requirements. For each combination, based on the temperature control requirements of each pair of food (including temperature range, temperature sensitivity weight, etc.), a temperature-sensitive correlation is calculated. By analyzing the overlap and difference in temperature control of each group of food, the temperature-sensitive correlation is calculated for each group of tasks, and finally multiple groups of temperature-sensitive correlations for multiple food delivery tasks are output. Each group of correlations represents the similarity in temperature control requirements of food in a specific task combination. These correlation values provide a basis for subsequent task grouping to ensure that the food in each group of tasks can be delivered under a reasonable temperature control environment.

[0065] Based on multiple groups of temperature-sensitive correlations, meals with similar temperature control requirements are grouped together. The meals in each group will share similar temperature and time requirements during the delivery process, thereby simplifying delivery scheduling and optimizing temperature control management. Specifically, meals with higher temperature sensitivity correlations are first grouped together. This means that the temperature control requirements of these meals are relatively close and are suitable for joint delivery in the same delivery task. Meals with lower temperature sensitivity correlations are grouped together in different groups to ensure that meals in each group can meet specific temperature control requirements. Through temperature-sensitive correlation analysis, all meal delivery tasks are ultimately divided into P groups of tasks. The meals in each group have similar temperature control and time requirements, ensuring that the delivery of each group of tasks can be carried out under the most suitable temperature control conditions, avoiding a decline in delivery quality due to inconsistent temperature control.

[0066] Furthermore, based on the first overlapping interval length, the first merged interval length, and the first temperature-sensitive weight ratio, calculating and outputting a first temperature-sensitive correlation, the method includes:

[0067] Substituting the first overlapping interval length, the first merged interval length, and the first temperature-sensitive weight ratio into the temperature interval similarity formula, calculating and outputting the first temperature similarity; performing cosine similarity calculation on the first temperature sensitivity weight and the second temperature sensitivity weight, and outputting the first temperature-sensitive similarity; and adding the first temperature similarity and the first temperature-sensitive similarity based on a preset weight configuration, and outputting the first temperature-sensitive association.

[0068] Substitute the first overlapping interval length, the first combined interval length, and the first temperature sensitivity weight ratio into the temperature interval similarity formula to calculate the first temperature similarity. The temperature interval similarity formula is as follows: Temperature Similarity = (Overlapping Interval Length / Combined Interval Length) * Temperature Sensitivity Weight. This formula indicates that longer overlapping intervals, shorter combined intervals, and greater temperature sensitivity weights indicate a higher degree of temperature control similarity between two dishes. The calculated first temperature similarity measures the degree of similarity between the two dishes' temperature requirements.

[0069] For the first temperature sensitivity weight and the second temperature sensitivity weight, cosine similarity is calculated. Cosine similarity is used to measure the similarity between two vectors. For the temperature sensitivity weight, the cosine similarity formula is as follows: Among them, S W (i, j) represents the temperature-sensitive similarity, W i Characterizes the weight of the i-th temperature sensitivity, W j The cosine similarity value represents the jth temperature sensitivity weight. It is a value between -1 and 1, indicating the similarity between the temperature sensitivity weights of the two dishes. The closer the value is to 1, the more similar their temperature sensitivities are; the closer the value is to 0, the greater the difference in temperature sensitivity. By calculating the cosine similarity, we can determine the degree of similarity between the temperature sensitivities of two dishes.

[0070] To ensure that temperature similarity and temperature sensitivity similarity play different roles in the calculation of temperature-sensitive association, a weighted sum of the two is calculated based on a preset weighting configuration. This weighting configuration can generally be adjusted based on the actual delivery task requirements. For example, if the delivery task requires higher temperature control precision, a higher weight will be assigned to the first temperature-sensitive similarity. This weighted summation results in the first temperature-sensitive association, which measures the overall correlation between the two dishes in terms of temperature control requirements, including their temperature control similarity and temperature sensitivity similarity.

[0071] Furthermore, the plurality of meal delivery tasks are grouped according to temperature-sensitive requirements based on the plurality of groups of temperature-sensitive correlations to obtain the P groups of meal delivery tasks. The method includes:

[0072] The multiple food delivery tasks are regarded as multiple topological nodes, and the topological connection distances are quantified based on the multiple groups of temperature-sensitive association metrics to construct a temperature-sensitive association topology; a temperature-sensitive association threshold is preset; the temperature-sensitive association threshold is used to traverse the temperature-sensitive association topology to perform topological connection removal judgment to obtain an updated association topology, wherein the updated association topology includes P isolated topologies; the food delivery tasks of the P isolated topologies are extracted to obtain the P groups of food delivery tasks.

[0073] Take multiple food delivery tasks as multiple topological nodes, which represent the food tasks that need to be delivered. By calculating the temperature-sensitive correlation of each pair of food delivery tasks (such as the temperature-sensitive correlation calculated in the previous step), the connection distance between each pair of food can be quantified. Specifically, the connection distance between food with a higher temperature-sensitive correlation (i.e., food with similar temperature ranges and temperature sensitivity) is shorter, while the connection distance between food with a lower correlation (i.e., food with large differences in temperature control and temperature sensitivity) is longer.

[0074] Based on the temperature-sensitive correlation between food delivery tasks, these tasks are organized into a temperature-sensitive correlation topology. In the topology, each food delivery task is a node, and the lines between the nodes represent the strength of their correlation in temperature control and temperature sensitivity (i.e., the weight of the lines). Shorter topological line distances indicate that the temperature control requirements between tasks are more similar, while longer lines indicate larger differences in temperature control.

[0075] Based on the actual delivery needs, a temperature-sensitive correlation threshold is preset to be used in subsequent steps to determine the degree of correlation between the temperature control and temperature sensitivity of two food delivery tasks. If the temperature-sensitive correlation of the two tasks is higher than the threshold, they can be considered similar tasks and can be classified into the same group; conversely, if their temperature-sensitive correlation is lower than the threshold, it means that the two tasks have significant differences in temperature control requirements and should be divided into different groups.

[0076] According to the preset temperature-sensitive association threshold, all nodes in the temperature-sensitive association topology are traversed, and the connection between each pair of nodes, that is, the temperature-sensitive association degree, is compared with the temperature-sensitive association threshold. If the temperature-sensitive association degree between two tasks is lower than the threshold, the connection between the two tasks is removed, which means that they no longer belong to the similar group. In this way, the originally connected nodes will no longer be connected, forming multiple independent topological branches. After the elimination judgment, a new updated association topology is obtained. This structure contains P isolated topologies, each of which represents a group of food delivery tasks with similar temperature-sensitive requirements. They have high similarity in temperature control and temperature sensitivity requirements.

[0077] Extract tasks from P isolated topologies and treat each isolated topology as an independent delivery task group. Finally, P groups of food delivery tasks are obtained, each group containing food tasks with similar temperature control requirements.

[0078] In summary, the logistics work order management method based on big data adaptive analysis provided by the embodiments of the present application has the following technical effects:

[0079] Through multimodal data collection, it is possible to collect food characteristic information and logistics demand information from multiple sources. After big data processing, this information can realize comprehensive analysis and accurate grouping of complex delivery tasks. Multimodal data collection enables the system to process data from different sources and effectively integrate and apply them. Based on the temperature control characteristics of food, multiple delivery tasks are grouped according to temperature-sensitive requirements through big data analysis, so as to ensure that food with similar temperature control requirements can be reasonably allocated to the same group. This grouping method makes delivery route planning and resource allocation more targeted and reduces unnecessary waste of resources. According to the results of temperature-sensitive demand grouping, multiple logistics demand information are combined to merge time and space paths. This process The delivery route was optimized, and the temperature control and timeliness requirements were combined to generate an initial task sequence, providing effective path planning for subsequent task scheduling. Through big data processing technology, the matching of meals and logistics boxes was analyzed, and potential loading conflicts were identified. This analysis result provided data support for subsequent cross-box collaborative analysis, thereby further optimizing the task sequence and resource allocation, and improving the utilization rate of logistics resources. Based on the optimization results of the target task sequence, logistics work orders were intelligently constructed, and distribution was carried out in combination with the scheduling requirements of temperature-sensitive logistics boxes. Big data processing makes the generation of logistics work orders more efficient, and can dynamically adjust the scheduling plan according to the characteristics of the meals and distribution needs, thereby improving the execution efficiency of distribution tasks.

[0080] Example 2, based on the same inventive concept as the logistics work order management method of big data adaptation analysis in the above embodiment, Figure 2 As shown, the embodiment of the present application provides a logistics work order management system for big data adaptation analysis, the system comprising:

[0081] The data collection module 10 is used to obtain multiple meal characteristic information and multiple logistics demand information of multiple meal delivery tasks by performing multimodal data collection in the meal task center.

[0082] The demand grouping module 20 is configured to group the plurality of meal delivery tasks according to temperature-sensitive demand based on the plurality of meal characteristic information to obtain P groups of meal delivery tasks.

[0083] The spatiotemporal path merging module 30 is configured to merge the spatiotemporal paths based on the P groups of meal delivery tasks and the plurality of logistics demand information, and output K initial task sequences.

[0084] The loading conflict analysis module 40 is used to locally call the logistics box volume characteristics, and perform loading conflict analysis on the K initial task sequences based on the logistics box volume characteristics and multiple meal characteristic information, and output K loading conflict information.

[0085] The initial task updating module 50 is configured to perform cross-box collaborative analysis based on the K loading conflict information, and update the K initial task sequences based on the analysis results to obtain W target task sequences, where W is a positive integer greater than K.

[0086] The delivery module 60 is used to construct a logistics work order based on the W target task sequences to schedule the temperature-sensitive logistics boxes to deliver the multiple meal delivery tasks.

[0087] Furthermore, the data acquisition module 10 is used to perform the following operation steps:

[0088] In the meal task center, multi-dimensional temperature-sensitive parameter mapping is performed according to the task type of the first meal delivery task to obtain a first storage temperature range, a first temperature sensitivity weight, a first meal volume model and a first time tolerance, wherein the first storage temperature range, the first temperature sensitivity weight and the first meal volume model constitute the first meal characteristic information; the first task order of the first meal delivery task is parsed to obtain a first benchmark transportation time limit and first starting and ending point coordinates; based on the first time tolerance and the first benchmark transportation time limit, the first transportation time limit threshold is calculated, wherein the first transportation time limit threshold and the first starting and ending point coordinates constitute the first logistics demand information.

[0089] Furthermore, the spatiotemporal path merging module 30 is configured to perform the following steps:

[0090] A first group of logistics demand information corresponding to a first group of meal delivery tasks is extracted from the multiple logistics demand information, wherein the first group of meal delivery tasks includes T meal delivery tasks; a highly sensitive task feature is preset, and the highly sensitive task feature is used to traverse the first group of meal characteristic information of the first group of meal delivery tasks to locate N seed delivery tasks, wherein the highly sensitive task feature includes a temperature range scale and a temperature sensitivity weight scale; the first logistics demand information is decomposed to obtain N logistics demand information of the N seed delivery tasks and TN logistics demand information of the remaining TN meal delivery tasks; N starting and ending point coordinates are extracted from the N logistics demand information to construct N benchmark delivery paths; with T transportation time limit thresholds as constraints, the TN logistics demand information are path-filled and fitted on the N benchmark delivery paths to obtain a first group of initial task sequences; and so on, after obtaining P groups of initial task sequences, the group division of the P groups of initial task sequences is removed to obtain the K initial task sequences.

[0091] Furthermore, the spatiotemporal path merging module 30 is configured to perform the following steps:

[0092] Extract TN starting and ending point coordinates from the TN logistics demand information to perform feasible path fitting, and output TN groups of accompanying delivery paths; with path overlap expansion as a constraint, merge the TN groups of accompanying delivery paths into the N benchmark delivery paths, and output N groups of expanded delivery paths; combine and enumerate the N groups of expanded delivery paths to obtain multiple expanded delivery path group sets; based on the T meal delivery tasks, perform delivery task check on the multiple expanded delivery path group sets to screen out O expanded delivery path group sets; perform delivery time efficiency calculation on the O expanded delivery path group sets, and use the T transportation time limit thresholds to perform time efficiency adaptability judgment to screen out the target delivery path group set; perform task extraction on the N target delivery paths in the target delivery path group set to obtain N initial task sequences, which constitute the first group of initial task sequences.

[0093] Furthermore, the loading conflict analysis module 40 is configured to perform the following steps:

[0094] Based on the K initial task sequences, K meal volume model sequences are extracted from the multiple meal characteristic information; a logistics box volume model is constructed according to the logistics box volume characteristics; based on the task parallel relationship of the K initial task sequences, the K meal volume model sequences are dynamically superimposed on the logistics box volume model, and K loading conflict node sequences are located to constitute the K loading conflict information.

[0095] Furthermore, the demand grouping module 20 is configured to perform the following steps:

[0096] A second storage temperature range and a second temperature sensitivity weight of the second meal delivery task are extracted from the multiple meal characteristic information; by overlapping the first storage temperature range and the second storage temperature range, the first overlapping range length and the first merged range length are solved and output; a first temperature-sensitive weight ratio is calculated according to the first temperature-sensitive weight and the second temperature-sensitive weight; a first temperature-sensitive correlation is calculated and output based on the first overlapping range length, the first merged range length and the first temperature-sensitive weight ratio; and so on, after combining and enumerating the multiple meal delivery tasks, a temperature-sensitive correlation analysis is performed based on the multiple meal characteristic information, and multiple groups of temperature-sensitive correlations of the multiple meal delivery tasks are output; the multiple meal delivery tasks are grouped according to the temperature-sensitive requirements according to the multiple groups of temperature-sensitive correlations to obtain the P groups of meal delivery tasks.

[0097] Furthermore, the demand grouping module 20 is configured to perform the following steps:

[0098] Substituting the first overlapping interval length, the first merged interval length, and the first temperature-sensitive weight ratio into the temperature interval similarity formula, calculating and outputting the first temperature similarity; performing cosine similarity calculation on the first temperature sensitivity weight and the second temperature sensitivity weight, and outputting the first temperature-sensitive similarity; and adding the first temperature similarity and the first temperature-sensitive similarity based on a preset weight configuration, and outputting the first temperature-sensitive association.

[0099] Furthermore, the demand grouping module 20 is configured to perform the following steps:

[0100] The multiple food delivery tasks are regarded as multiple topological nodes, and the topological connection distances are quantified based on the multiple groups of temperature-sensitive association metrics to construct a temperature-sensitive association topology; a temperature-sensitive association threshold is preset; the temperature-sensitive association threshold is used to traverse the temperature-sensitive association topology to perform topological connection removal judgment to obtain an updated association topology, wherein the updated association topology includes P isolated topologies; the food delivery tasks of the P isolated topologies are extracted to obtain the P groups of food delivery tasks.

[0101] Through the above detailed description of the logistics work order management method based on big data adaptation analysis in this specification, those skilled in the art can clearly understand the logistics work order management system based on big data adaptation analysis in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant matters, please refer to the description of the method part.

[0102] In a third embodiment, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any step of the first embodiment is implemented.

[0103] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A logistics work order management method based on big data adaptation analysis, characterized by: The method comprises: By performing multimodal data collection in the food task center, multiple food characteristic information and multiple logistics demand information for multiple food delivery tasks are obtained; According to the plurality of meal characteristic information, the plurality of meal delivery tasks are grouped according to temperature-sensitive requirements to obtain P groups of meal delivery tasks; Taking the P groups of meal delivery tasks as constraints, merging spatiotemporal paths according to the multiple logistics demand information is performed to output K initial task sequences; Locally calling the logistics box volume feature, and performing loading conflict analysis on the K initial task sequences based on the logistics box volume feature and multiple meal characteristic information, and outputting K loading conflict information; Perform cross-box collaborative analysis based on the K loading conflict information, and update the K initial task sequences based on the analysis results to obtain W target task sequences, where W is a positive integer greater than K; A logistics work order is constructed based on the W target task sequences to schedule temperature-sensitive logistics boxes to deliver the multiple meal delivery tasks.

2. The logistics work order management method based on big data adaptation analysis according to claim 1 is characterized in that: By performing multimodal data collection in a food task center, a plurality of food characteristic information and a plurality of logistics demand information of a plurality of food delivery tasks are obtained, and the method includes: In the food task center, multi-dimensional temperature-sensitive parameter mapping is performed based on the task type of the first food delivery task to obtain a first storage temperature range, a first temperature sensitivity weight, a first food volume model, and a first aging tolerance, wherein the first storage temperature range, the first temperature sensitivity weight, and the first food volume model constitute first food characteristic information; Parsing the first task order of the first meal delivery task to obtain a first benchmark transportation time limit and first starting and ending point coordinates; A first transportation time limit threshold is calculated based on the first time tolerance and the first benchmark transportation time limit, wherein the first transportation time limit threshold and the first starting and ending point coordinates constitute first logistics demand information.

3. The logistics work order management method based on big data adaptation analysis as claimed in claim 2, characterized in that: Taking the P groups of meal delivery tasks as constraints, merging spatiotemporal paths according to the multiple logistics demand information and outputting K initial task sequences, the method includes: Extracting a first set of logistics demand information corresponding to a first set of meal delivery tasks from the plurality of logistics demand information, wherein the first set of meal delivery tasks includes T meal delivery tasks; Preset highly sensitive task features, and use the highly sensitive task features to traverse the first group of meal characteristic information of the first group of meal delivery tasks to locate N seed delivery tasks, wherein the highly sensitive task features include a temperature range scale and a temperature sensitivity weight scale; Decomposing the first logistics demand information to obtain N pieces of logistics demand information for the N seed delivery tasks and TN pieces of logistics demand information for the remaining TN meal delivery tasks; Extracting N starting and ending point coordinates from the N logistics demand information to construct N benchmark delivery routes; Taking T transport time limit thresholds as constraints, path filling and fitting is performed on the N benchmark distribution paths for the TN logistics demand information to obtain a first set of initial task sequences; Similarly, after obtaining P groups of initial task sequences, the group divisions of the P groups of initial task sequences are removed to obtain the K initial task sequences.

4. The logistics work order management method based on big data adaptation analysis as claimed in claim 3 is characterized in that: Taking T transport time limit thresholds as constraints, path filling and fitting is performed on the N benchmark delivery paths for the TN logistics demand information to obtain a first set of initial task sequences. The method includes: Extracting TN starting and ending point coordinates from the TN logistics demand information to perform feasible path fitting, and outputting TN groups of accompanying delivery paths; With path overlap and expansion as a constraint, merge the TN groups of accompanying delivery paths into the N benchmark delivery paths, and output N groups of expanded delivery paths; Combining and enumerating the N groups of expanded delivery paths to obtain a plurality of expanded delivery path group sets; Based on the T food delivery tasks, performing a delivery task search on the multiple expanded delivery path groups to screen out O expanded delivery path groups; Calculate the delivery timeliness of the O expanded delivery path groups, and use the T transportation time limit thresholds to determine their suitability, thereby obtaining a target delivery path group. Tasks are extracted from the N target delivery paths in the target delivery path group to obtain N initial task sequences, which constitute the first group of initial task sequences.

5. The logistics work order management method based on big data adaptation analysis according to claim 1 is characterized in that: Locally calling the logistics box volume feature, and performing loading conflict analysis on the K initial task sequences based on the logistics box volume feature and multiple meal characteristic information, and outputting K loading conflict information, the method includes: Extracting K food volume model sequences from the plurality of food characteristic information based on the K initial task sequences; Constructing a logistics box volume model according to the logistics box volume characteristics; According to the task parallel relationship of the K initial task sequences, the K meal volume model sequences are dynamically superimposed on the logistics box volume model, and K loading conflict node sequences are located to form the K loading conflict information.

6. The logistics work order management method based on big data adaptation analysis as claimed in claim 2, characterized in that: The method includes grouping the plurality of meal delivery tasks according to temperature-sensitive requirements based on the plurality of meal characteristic information to obtain P groups of meal delivery tasks. extracting a second storage temperature range and a second temperature sensitivity weight of a second food delivery task from the plurality of food characteristic information; By overlapping the first preservation temperature interval and the second preservation temperature interval, a first overlapping interval length and a first combined interval length are calculated and outputted; Calculating a first temperature sensitivity weight ratio according to the first temperature sensitivity weight and the second temperature sensitivity weight; Calculating and outputting a first temperature-sensitive correlation degree based on the first overlapping interval length, the first merged interval length, and the first temperature-sensitive weight ratio; Similarly, after the plurality of meal delivery tasks are combined and enumerated, temperature-sensitive correlation analysis is performed based on the plurality of meal characteristic information, and a plurality of groups of temperature-sensitive correlation degrees of the plurality of meal delivery tasks are output; The plurality of meal delivery tasks are grouped into temperature-sensitive demand groups according to the plurality of groups of temperature-sensitive correlations to obtain the P groups of meal delivery tasks.

7. The logistics work order management method based on big data adaptation analysis according to claim 6 is characterized in that: Based on the first overlapping interval length, the first merged interval length, and the first temperature-sensitive weight ratio, calculating and outputting a first temperature-sensitive correlation, the method comprising: Substituting the first overlapping interval length, the first merged interval length, and the first temperature-sensitive weight ratio into the temperature interval similarity formula, and calculating and outputting a first temperature similarity; Performing cosine similarity calculation on the first temperature sensitivity weight and the second temperature sensitivity weight, and outputting a first temperature sensitivity similarity; The first temperature similarity and the first temperature-sensitive similarity are added based on a preset weight configuration, and the first temperature-sensitive association is output.

8. The logistics work order management method based on big data adaptation analysis as claimed in claim 6, characterized in that: The plurality of meal delivery tasks are grouped into temperature-sensitive groups according to the plurality of groups of temperature-sensitive correlations to obtain the P groups of meal delivery tasks, the method comprising: Taking the multiple food delivery tasks as multiple topological nodes, quantifying topological connection distances based on the multiple sets of temperature-sensitive association metrics, and constructing a temperature-sensitive association topology; Preset temperature-sensitive correlation threshold; Using the temperature-sensitive correlation threshold to traverse the temperature-sensitive correlation topology and perform topology connection elimination judgment to obtain an updated correlation topology, wherein the updated correlation topology includes P isolated topologies; The P isolated topological meal delivery tasks are extracted to obtain the P groups of meal delivery tasks.

9. Logistics work order management system based on big data adaptation analysis, characterized by: A logistics work order management method for implementing the big data adaptation analysis according to any one of claims 1 to 8, the system comprising: A data collection module is used to obtain multiple meal characteristic information and multiple logistics demand information of multiple meal delivery tasks by performing multimodal data collection in the meal task center; a demand grouping module, configured to group the plurality of meal delivery tasks into temperature-sensitive demand groups according to the plurality of meal characteristic information, to obtain P groups of meal delivery tasks; a spatiotemporal path merging module, configured to merge spatiotemporal paths based on the P groups of meal delivery tasks and the plurality of logistics demand information, and output K initial task sequences; a loading conflict analysis module, configured to locally call the volume characteristics of the logistics box, perform loading conflict analysis on the K initial task sequences based on the volume characteristics of the logistics box and information on multiple meal characteristics, and output K loading conflict information; an initial task updating module, configured to perform cross-tank collaborative analysis based on the K loading conflict information, and update the K initial task sequences based on the analysis results to obtain W target task sequences, where W is a positive integer greater than K; The delivery module is used to build a logistics work order based on the W target task sequences to schedule temperature-sensitive logistics boxes to deliver the multiple meal delivery tasks.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the logistics work order management method for big data adaptation analysis according to any one of claims 1 to 8 are implemented.

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