Self-adaptive scheduling method for intelligent logistics sorting equipment

Abstract

The invention discloses a self-adaptive scheduling method for intelligent logistics sorting equipment, and relates to the technical field of equipment scheduling. The method comprises the steps that a sorting task set and a logistics sorting equipment set are obtained, attribute classification marking is conducted on the sorting task set, and a sorting task attribute parameter set is obtained; performing priority analysis sorting on the sorting task set to obtain a sorting task priority sequence; acquiring an equipment working state data stream set; carrying out resource constraint condition analysis on the logistics sorting equipment set to obtain a sorting equipment resource constraint condition set; and scheduling analysis is conducted on the sorting task priority sequence, target equipment scheduling parameters are determined, and self-adaptive sorting scheduling is conducted on the logistics sorting equipment set through the target equipment scheduling parameters. The technical problems that in the prior art, logistics sorting equipment is low in scheduling efficiency and insufficient in equipment utilization rate are solved, and the technical effects that the logistics sorting efficiency is improved, the equipment resource utilization rate is optimized, and self-adaptive efficient scheduling of sorting tasks is achieved are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment scheduling, and in particular to an adaptive scheduling method for intelligent logistics sorting equipment. Background Art

[0002] Existing logistics sorting equipment is widely used in express delivery, warehousing, supply chain and other fields to efficiently handle the growing logistics needs. However, with the rapid development of the logistics industry, the complexity and diversity of sorting tasks are constantly increasing, which puts higher demands on the intelligent scheduling capabilities of sorting equipment. Current sorting equipment scheduling methods are usually based on fixed rules or preset parameters, and lack the ability to adapt to dynamic changes in tasks. In particular, when task priorities change frequently, equipment resources are limited or fail, it is impossible to efficiently coordinate equipment resources, resulting in low sorting efficiency and insufficient equipment utilization. In addition, when dealing with multi-task and multi-device parallel scheduling, traditional methods often face the challenges of complex resource constraints and difficult multi-objective optimization, and cannot meet the efficient and flexible operation requirements of modern logistics systems. Summary of the Invention

[0003] The present application provides an adaptive scheduling method for intelligent logistics sorting equipment, which solves the technical problems of low scheduling efficiency and insufficient equipment utilization of logistics sorting equipment in the prior art.

[0004] In view of the above problems, the present application provides an adaptive scheduling method for intelligent logistics sorting equipment.

[0005] The present application provides an adaptive scheduling method for intelligent logistics sorting equipment, the method comprising: A sorting task set and a logistics sorting equipment set are obtained through a logistics management system, and the sorting task set is classified and marked with attributes to obtain a sorting task attribute parameter set; the sorting task set is prioritized and sorted based on the sorting task attribute parameter set to obtain a sorting task priority sequence; an information perception module is installed on each sorting device in the logistics sorting equipment set, and an equipment working status data stream set of the logistics sorting equipment set is obtained through the information perception module; a resource constraint condition analysis is performed on the logistics sorting equipment set based on the equipment working status data stream set to obtain a sorting equipment resource constraint condition set; a scheduling analysis is performed on the sorting task priority sequence based on the sorting equipment resource constraint condition set to determine the target equipment scheduling parameters, and the logistics sorting equipment set is adaptively sorted and scheduled based on the target equipment scheduling parameters.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, a logistics management system acquires a set of sorting tasks and a set of logistics sorting equipment. The sorting task set is then attribute-classified and labeled to obtain a set of sorting task attribute parameters. Next, the sorting task set is prioritized and sorted based on the set of sorting task attribute parameters to obtain a sorting task priority sequence. Simultaneously, an information perception module is installed on each sorting device in the logistics sorting equipment set. The information perception module then acquires a set of device operating status data streams for the logistics sorting equipment set. Then, resource constraint analysis is performed on the logistics sorting equipment set based on the set of device operating status data streams to obtain a set of sorting device resource constraints. Finally, scheduling analysis is performed on the sorting task priority sequence based on the set of sorting device resource constraints to determine target device scheduling parameters. Adaptive sorting scheduling is then performed on the logistics sorting equipment set using the target device scheduling parameters. This solves the technical problems of low scheduling efficiency and insufficient equipment utilization in the prior art for logistics sorting equipment, achieving the technical effect of improving logistics sorting efficiency, optimizing equipment resource utilization, and realizing adaptive and efficient scheduling of sorting tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A flow chart of an adaptive scheduling method for intelligent logistics sorting equipment provided in an embodiment of the present application; Figure 2 A schematic diagram of a process for obtaining a sorting task priority sequence in an adaptive scheduling method for intelligent logistics sorting equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] This application solves the technical problems of low scheduling efficiency and insufficient equipment utilization of logistics sorting equipment in the prior art by providing an adaptive scheduling method for intelligent logistics sorting equipment.

[0010] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0011] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0012] Examples, such as Figure 1 As shown, the embodiment of the present application provides an adaptive scheduling method for intelligent logistics sorting equipment, wherein the method includes: A sorting task set and a logistics sorting equipment set are acquired through a logistics management system, and attribute classification and marking are performed on the sorting task set to obtain a sorting task attribute parameter set.

[0013] By interacting with the logistics management system, the sorting task set and logistics sorting equipment set are obtained. The sorting task set includes various types of task data, such as task urgency, cargo volume, cargo weight, destination information, etc.; the logistics sorting equipment set contains information such as the type of sorting equipment, performance parameters, current working status, etc.

[0014] After obtaining a collection of sorting tasks, the task collection is classified and labeled according to its specific attributes (such as urgency, cargo type, and delivery route), thereby generating a parameter set containing task classification information (i.e., a sorting task attribute parameter set). For example, task T001 is labeled "high priority, fragile goods, lightweight, and delivered to area A," and task T002 is labeled "medium priority, general cargo, medium weight, and delivered to area B." The urgency of the task determines the priority, the cargo type (such as fragile goods and cold chain cargo) requires matching specific sorting equipment, the cargo weight (lightweight, medium weight, and heavy weight) is used to select equipment with appropriate load-bearing capacity, and the delivery area is used to optimize task allocation and route scheduling.

[0015] The sorting task set is analyzed and sorted according to priority based on the sorting task attribute parameter set to obtain a sorting task priority sequence.

[0016] Based on the sorting task attribute parameter set, the task set is prioritized and sorted. Specifically, a comprehensive assessment is conducted based on the task's urgency, cargo type, delivery area, and other key attributes to generate a sorting task priority sequence. For example, tasks with a "high" urgency have the highest priority, followed by tasks with a "medium" or "low" urgency. Fragile goods and cold chain goods have higher priority due to their higher handling requirements. At the same time, tasks of the same priority are further sorted based on the distance to the delivery area and the complexity of the route.

[0017] Furthermore, if Figure 2 As shown, obtaining the sorting task priority sequence includes: Acquire task priority evaluation factors, and construct a task priority star diagram based on the task priority evaluation factors, wherein each ray coordinate axis of the task priority star diagram corresponds one-to-one to each factor information in the task priority evaluation factors; sequentially map the sorting task attribute parameter set to the task priority star diagram to generate a sorting task priority star diagram set; perform quantitative evaluation of the enclosed area of ​​the sorting task priority star diagram set to obtain a quantitative area set of the sorting task star diagram; perform priority comparison and sorting on the sorting task set based on the quantitative area set of the sorting task star diagram to obtain the sorting task priority sequence.

[0018] Specifically, key factors for task priority evaluation are extracted from the sorting task attribute parameter set, such as urgency, cargo type, delivery distance, cargo weight, etc. Each evaluation factor is used to measure the importance or priority of the task; based on the above evaluation factors, a task priority star diagram is constructed, and the ray coordinate axes of the task priority star diagram correspond one-to-one to the task priority evaluation factors, such as the "urgency" ray, "cargo type" ray, "delivery distance" ray, etc. The length of each ray represents the weight or quantitative value of the corresponding factor; the tasks in the sorting task attribute parameter set are mapped one by one to the task priority star diagram. For example, for a certain task, according to its specific attributes such as urgency, cargo type, and delivery distance, these attribute values ​​are filled in the corresponding ray coordinate axes respectively, thereby generating a priority star diagram (sorting task priority star diagram) for the task; the above mapping process is repeated for all sorting tasks to form a set of sorting task priority star diagrams, with each task corresponding to a star diagram; each task star diagram is quantitatively evaluated, and the area enclosed by the star diagram is calculated. The enclosed area of ​​the star diagram reflects the overall priority of the task. For example, tasks with high urgency and more special cargo handling requirements will have a larger enclosed area of ​​their star diagram; based on the quantified area set of the star diagram, the sorting tasks are prioritized and sorted. The larger the area, the higher the overall priority of the task, thus obtaining the final sorting task priority sequence.

[0019] Furthermore, obtaining the quantized area set of the sorting task star graph includes: A quantitative evaluation of the enclosed areas of the sorting task priority star diagram sets is performed respectively to obtain an enclosed area set of the sorting task star diagrams; a criticality evaluation is performed on the information of each factor in the task priority evaluation factors according to the logistics sorting demand target to obtain a priority factor criticality factor set; a ray axis correction coefficient set is generated based on the priority factor criticality factor set; and a quantitative correction is performed on the enclosed area set of the sorting task star diagram based on the ray axis correction coefficient set to obtain the quantized area set of the sorting task star diagram.

[0020] Specifically, for each star graph in the sorting task priority star graph set, the enclosed area of ​​the star graph is calculated according to the task priority evaluation factor values ​​corresponding to its each ray coordinate axis. For example, the star graph is quantified using a polygon area formula (such as based on a geometric algorithm or vector calculation) to obtain the enclosed area set of the star graph corresponding to each sorting task, and the size of each enclosed area reflects the basic priority level of the task; according to the demand goals of logistics sorting (such as priority for urgent tasks, priority for special goods, etc.), a criticality analysis is performed on the information of each factor in the task priority evaluation factor, and the importance weights of different evaluation factors in the current sorting demand are determined through statistical analysis or expert evaluation. For example, "urgency" may be given a higher weight to form a priority factor criticality factor set. For example, the criticality factor of urgency is 0.4, the criticality factor of cargo type is 0.3, the criticality factor of delivery distance is 0.2, and the criticality factor of cargo weight is 0.1. Based on the priority factor and criticality factor set, a corresponding set of ray axis correction coefficients is generated. The correction coefficients are used to adjust the weight of each ray axis in the star diagram so that the impact of different evaluation factors on the enclosed area better meets the logistics sorting demand objectives. Each ray axis correction coefficient = original ray axis value × criticality factor. For example, for a task's star diagram, assuming the original ray axis value for urgency is 5, the corresponding corrected value is 5 × 0.4 = 2.0. Based on the ray axis correction coefficient set, the enclosed area of ​​each sorting task's star diagram is quantitatively corrected to generate a set of quantified areas for the sorting task star diagram. The corrected area better reflects the actual priority of each task in the current logistics demand scenario. The corrected area = original area × weighted average of each ray correction coefficient. For example, if the original area of ​​task T001 is 45 and the average correction coefficient is 0.8, the corrected quantified area is 45 × 0.8 = 36.

[0021] An information perception module is installed on each sorting device in the logistics sorting device set, and a device working status data stream set of the logistics sorting device set is obtained through the information perception module.

[0022] An information perception module is installed on each sorting device in the logistics sorting equipment collection. The module includes various sensors and data acquisition devices to monitor and collect real-time information about the sorting device's operating status. This information may include the device's operating status (e.g., idle, working, faulty), the type and number of tasks it processes, its workload (e.g., processing speed, throughput), energy consumption, and its health (e.g., temperature, vibration, noise). This information perception module generates a data stream of device operating status, integrating each device's status data into a single, real-time, streaming data set. For example, for device E001, its status data may include the current number of tasks being 10, the operating status being "working," a processing speed of 20 pieces / minute, and a temperature of 40°C. Similarly, device E002 may be in the "idle" state, with zero tasks and minimal energy consumption. The real-time and comprehensive nature of the device operating status data stream provides accurate data support for subsequent resource constraint analysis and scheduling optimization. This allows the system to dynamically perceive changes in the status of sorting devices and promptly adjust sorting task allocation strategies, thereby improving equipment utilization and sorting efficiency.

[0023] A resource constraint condition analysis is performed on the logistics sorting equipment set based on the equipment working status data stream set to obtain a sorting equipment resource constraint condition set.

[0024] Real-time working status data of each logistics sorting device is extracted from the device working status data stream. For example, the device's current operating status (such as idle, busy, faulty, etc.), task load (number of processed tasks or task queue depth), device processing speed, energy consumption level, health status (such as temperature, vibration, etc.), etc. These parameters directly reflect the device's resource usage and working capacity.

[0025] Key resource constraint indicators are defined based on the operating status parameters of logistics sorting equipment. These may include: task load limit (the maximum number of tasks the equipment can currently handle), processing capacity limit (the maximum number of tasks the equipment can handle per unit time), equipment health limit (such as limits imposed when equipment temperature, vibration, or operating time reach safety thresholds), energy consumption limit (whether the equipment's energy consumption level is acceptable under high load), and equipment priority (dynamically assigning equipment priority based on its health and current workload). Based on the equipment's real-time data stream and in conjunction with preset operating thresholds or constraints, the resource status of each device is quantitatively analyzed. For example, if device E001 currently has 10 tasks and a processing capacity of 20 pieces / minute, and the task load limit is the device's maximum processing capacity (e.g., 100 tasks), E001's load status is "mildly busy." If the temperature of device E002 exceeds a safe operating threshold (e.g., 50°C), its health status is marked as "limited." If the task queue depth of device E003 is 0, its status is marked as "idle." The resource status of each device is summarized to form a sorting device resource constraint condition set. The sorting device resource constraint condition set refers to the resource constraint parameters that can be used by the current logistics sorting device within the sorting device attribute constraint boundary set based on the sorting device working status parameter set.

[0026] Furthermore, obtaining a set of sorting equipment resource constraints includes: Obtaining sorting equipment constraint factor information, the sorting equipment constraint factor information includes equipment performance constraint, material sorting space constraint, and energy consumption constraint; obtaining the working attribute application threshold set of the logistics sorting equipment set through the logistics management system, performing correlation analysis on the working attribute application threshold set based on the sorting equipment constraint factor information, and determining the sorting equipment attribute constraint boundary set; performing equipment working status analysis on the equipment working status data stream set respectively to determine the sorting equipment working status parameter set; performing available resource constraint condition analysis on the sorting equipment working status parameter set based on the sorting equipment attribute constraint boundary set to obtain the sorting equipment resource constraint condition set.

[0027] Specifically, information on sorting equipment constraints that affect the operation of the sorting equipment is obtained. The information on sorting equipment constraints includes equipment performance constraints, material sorting space constraints, and energy consumption constraints. Among them, equipment performance constraints refer to the equipment's maximum processing capacity (such as the number of tasks processed per minute), response speed, failure rate, etc.; material sorting space constraints refer to the size of goods that the equipment can handle, weight range, and adaptability of material types; energy consumption constraints refer to the energy consumption level of the equipment under different workloads, and its corresponding operating costs.

[0028] The logistics management system obtains the working attribute application threshold set of the logistics sorting equipment set. This threshold set defines the operating limits of the equipment under various constraints, such as the maximum processing capacity of the equipment performance (such as processing 50 tasks per minute), the cargo size range (such as length 30cm to 100cm, weight 1kg to 10kg), and the energy consumption threshold (such as maximum power 200W). Based on the sorting equipment constraint factor information, the working attribute application threshold set is associated and analyzed to determine the attribute constraint boundary set of the sorting equipment. For example, the attribute constraint boundary of equipment E001 may include: maximum number of tasks is 50, cargo size range is 30cm to 100cm, and energy consumption does not exceed 200W; the attribute constraint boundary of equipment E002 may include: maximum number of tasks is 30, cargo weight range is 1kg to 5kg, and energy consumption does not exceed 150W.

[0029] Combining the sorting device attribute constraint boundary set and the sorting device operating status parameter set, a comprehensive analysis of each device's available resources is conducted to determine whether the device is currently available and its available resource capacity. For example, if the device's current number of tasks does not exceed the maximum number of tasks and its energy consumption level does not exceed the threshold, it is marked as "available." If a parameter of the device (such as processing capacity) approaches or exceeds the threshold, it is marked as "restricted" or "unavailable." The analysis results are integrated to output the resource constraints of each device, including the device number (uniquely identifying the sorting device), the current status (marking the device as "available," "restricted," or "unavailable"), the remaining processing capacity (the remaining available resources of the current device within the constraint boundary), the energy consumption status (comparison of the device's actual energy consumption during operation with the energy consumption threshold), and other constraint information (such as whether the current cargo type or size exceeds the device's support range).

[0030] The sorting task priority sequence is scheduled and parsed based on the sorting equipment resource constraint condition set, target equipment scheduling parameters are determined, and adaptive sorting scheduling is performed on the logistics sorting equipment set using the target equipment scheduling parameters.

[0031] The compatibility between tasks and equipment is analyzed one by one based on the attributes of the tasks in the sorting task priority sequence (such as urgency, cargo type, size, weight, etc.) and the equipment capabilities (such as processing capacity, supported cargo types, current status, etc.) in the sorting equipment resource constraint set. For example, suppose task T001 requires the processing of fragile items weighing 2kg and has a high priority; equipment E001 supports fragile item processing, has a remaining available processing capacity of 20 tasks, and is currently in the "available" state. Therefore, task T001 and equipment E001 are a good match and are marked as "compatible."

[0032] Based on the task priority sequence, starting with the highest priority tasks, tasks are assigned to the most compatible devices one by one. The assignment rules include: Prioritizing devices with a Priority Available status; if multiple devices meet the requirements, the device with the lowest task load or the best energy consumption is selected; if no device is compatible, the task enters the pending assignment queue, awaiting device resource release. After each task is assigned to a device, target device scheduling parameters are generated, describing the specific scheduling relationship between the task and the device. Based on the target device scheduling parameters, the device is controlled to execute the specific sorting task.

[0033] Furthermore, determining the target device scheduling parameters includes: Based on the sorting equipment resource constraint condition set, the sorting task priority sequence is sequentially associated with scheduling equipment to obtain a sequence task matching sorting equipment set; a sorting equipment scheduling effect target is defined, evaluation indicators are extracted and fitted for the sorting equipment scheduling effect target, and a scheduling effect evaluation fitness function is constructed; the effect of the sequence task matching sorting equipment set is evaluated using the scheduling effect evaluation fitness function to obtain a sequence task matching equipment fitness set; based on the sequence task matching equipment fitness set, equipment scheduling analysis is performed on the sequence task matching sorting equipment set to determine target equipment scheduling parameters.

[0034] Specifically, an adaptation analysis is performed based on the attributes of the sorting tasks (such as urgency, cargo type, weight, etc.) and the resource capabilities of the sorting equipment (such as processing capacity, space constraints, current load, etc.) to obtain a set of sorting equipment that matches the sequence tasks; then, the scheduling effect objectives of the sorting equipment are defined, such as minimizing task completion time, equipment load balancing, and energy consumption optimization, and the corresponding evaluation indicators are extracted. A scheduling effect evaluation fitness function is constructed through fitting, which comprehensively calculates completion time, load balancing factor, and energy consumption factor. For example, the fitness function = w1 completion time factor + w2 load balancing factor + w3 energy consumption optimization factor, where w1, w2, and w3 are weight coefficients, and the scheduling effect is optimized according to the scheduling effect. The importance of the target is assigned a weight, the completion time factor represents the time required for the device to complete the task (the shorter the better, the smaller the value), the load balancing factor represents the proportion of the current load of the device to the total load (the closer to the mean the better, the smaller the value), and the energy consumption optimization factor represents the energy consumption level of the device (the lower the better, the smaller the value). The fitness function is used to evaluate the set of sequence task matching sorting devices one by one to obtain the fitness value set of each task and device, that is, the sequence task matching device fitness set. Finally, the matching devices are scheduled and analyzed based on the fitness value, and the device with the best fitness value is selected as the target sorting device. The target device scheduling parameters are generated, including task number, assigned device number, scheduling time and priority.

[0035] Furthermore, determining the target device scheduling parameters includes: According to the fitness set of the sequence task matching device, a first sorting task and a first task matching device fitness set are determined; based on the first task matching device fitness set, the first sorting task is compared and optimized to determine a first sorting device with the greatest fitness; based on the first sorting device, an equipment scheduling taboo table is constructed; based on the equipment scheduling taboo table and the fitness set of the sequence task matching device, equipment scheduling analysis is performed on the sequence task matching sorting device set in turn to determine target equipment scheduling parameters.

[0036] First, based on the fitness set of sequential task matching devices, the sorting tasks are processed one by one to determine the first sorting task and its corresponding first task matching device fitness set. The fitness set contains all available sorting devices for the first task and their corresponding fitness values. Next, based on the fitness set of the first task matching devices, all available sorting devices are compared and optimized. The sorting device with the highest fitness value is selected as the first sorting device, i.e., the currently optimal device. This device will be prioritized for the current task allocation. Then, based on the determined first sorting device, a device scheduling taboo table is constructed, recording the current device's resource status, load, and time periods occupied during the scheduling process, thereby limiting its simultaneous use by other tasks in the next phase. Next, based on the device scheduling taboo table and the sequential task matching device fitness set, the matching sorting device sets of the remaining tasks are analyzed one by one for device scheduling, dynamically adjusting the task allocation strategy. Specifically, for each subsequent task, after removing the devices restricted in the taboo table, the fitness values ​​of the remaining devices are re-evaluated, and the device with the highest fitness value is selected as the target device for the corresponding task. Finally, after completing the equipment allocation for all tasks, the target equipment scheduling parameters are output. The parameters include information such as task number, equipment number, scheduling start and end time, and equipment status, which are used to guide the task execution of the actual sorting equipment, thereby realizing an efficient and dynamic adaptive scheduling process.

[0037] Furthermore, determining the target device scheduling parameters includes: Based on the sequence task matching device fitness set, the sequence task matching sorting device set is compared and sorted in sequence to obtain a sequence task matching priority device set; the device scheduling taboo table is used to traverse the sequence task matching priority device set to perform scheduling device optimization to obtain a sequence task scheduling device parameter set and remaining sorting sequence tasks; based on the sequence task scheduling device parameter set, the remaining sorting sequence tasks are optimized for device scheduling to obtain remaining sequence task device scheduling parameters; based on the sequence task scheduling device parameter set and the remaining sequence task device scheduling parameters, the target device scheduling parameters are determined.

[0038] First, based on the fitness set of sequence task matching devices, the tasks in the sequence task matching sorting device set are compared and sorted one by one according to their fitness values ​​from high to low. The priority device for each task is determined, resulting in the sequence task matching priority device set. Subsequently, the sequence task matching priority device set is traversed and analyzed in conjunction with the device scheduling taboo table. Unavailable devices listed in the taboo table are filtered out, and the device with the highest fitness is selected from the remaining devices as the scheduling device for the current task. This generates the sequence task scheduling device parameter set and simultaneously identifies the remaining sorting sequence tasks that failed to be assigned due to taboo restrictions. Next, for the remaining sorting sequence tasks, the availability and fitness of the remaining devices are recalculated based on the existing sequence task scheduling device parameter set by dynamically updating the device scheduling taboo table. The device scheduling optimization is performed for the unassigned tasks. By again comparing the fitness values ​​of the remaining devices, the device with the best fitness is selected and assigned to the remaining tasks, generating the device scheduling parameters for the remaining sequence tasks. Finally, the sequence task scheduling device parameter set and the remaining sequence task device scheduling parameters are combined to form the target device scheduling parameters, which include information such as task number, device number, scheduling start and end times, and fitness value. The target equipment scheduling parameters are used to guide the equipment execution of all sorting tasks, ensuring the optimal match between task priority and equipment utilization, thereby achieving efficient dynamic scheduling of sorting equipment.

[0039] Furthermore, obtaining the remaining sequence task equipment scheduling parameters includes: Based on the sequence task scheduling equipment parameter set, task sorting prediction is performed to obtain scheduling equipment sorting task prediction progress information; according to the scheduling equipment sorting task prediction progress information, the taboo period is set for the equipment scheduling taboo table to obtain an equipment scheduling dynamic taboo table; based on the equipment scheduling dynamic taboo table, the remaining sorting sequence tasks are iteratively scheduled and optimized to obtain the remaining sequence task equipment scheduling parameters.

[0040] Based on the device parameter set for sequential task scheduling, a predictive analysis is performed on the assigned sorting tasks. Taking into account the device's processing capacity, task complexity, and current load, the task completion time and processing progress of each sorting device are estimated, generating predicted progress information for the sorting tasks of the scheduled devices. This information is used to dynamically update the device scheduling taboo table and set a taboo period. The taboo period is defined as the time range during which a device must be unavailable before completing the current task, thus forming a dynamic taboo table for device scheduling. For example, the taboo period for device E001 is "current time + 10 minutes" (expected to be available in 10 minutes), and the taboo period for device E002 is "current time + 20 minutes" (expected to be available in 20 minutes). Based on this, an iterative optimization search is performed on the remaining sorting sequence tasks. After removing unavailable devices from the taboo table, the fitness values ​​of the remaining tasks and available devices are recalculated. The optimal device is selected based on the fitness value and assigned to the remaining tasks. The device's taboo table and status information are also updated. Finally, the equipment scheduling parameters for the remaining sequence tasks are generated, including task number, equipment number, expected start time, expected completion time and fitness value, etc., to ensure that all sorting tasks are efficiently allocated and dynamically scheduled under resource constraints.

[0041] In summary, the embodiments of the present application have at least the following technical effects: First, a logistics management system acquires a set of sorting tasks and a set of logistics sorting equipment. The sorting task set is then attribute-classified and labeled to obtain a set of sorting task attribute parameters. Next, the sorting task set is prioritized and sorted based on the set of sorting task attribute parameters to obtain a sorting task priority sequence. Simultaneously, an information perception module is installed on each sorting device in the logistics sorting equipment set. The information perception module then acquires a set of device operating status data streams for the logistics sorting equipment set. Then, resource constraint analysis is performed on the logistics sorting equipment set based on the set of device operating status data streams to obtain a set of sorting device resource constraints. Finally, scheduling analysis is performed on the sorting task priority sequence based on the set of sorting device resource constraints to determine target device scheduling parameters. Adaptive sorting scheduling is then performed on the logistics sorting equipment set using the target device scheduling parameters. This solves the technical problems of low scheduling efficiency and insufficient equipment utilization in the prior art for logistics sorting equipment, achieving the technical effect of improving logistics sorting efficiency, optimizing equipment resource utilization, and realizing adaptive and efficient scheduling of sorting tasks.

[0042] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0043] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0044] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. An adaptive scheduling method for intelligent logistics sorting equipment, characterized in that: The method comprises: Obtaining a sorting task set and a logistics sorting equipment set through a logistics management system, classifying and marking the sorting task set by attributes, and obtaining a sorting task attribute parameter set; Performing priority analysis and sorting on the sorting task set based on the sorting task attribute parameter set to obtain a sorting task priority sequence; An information sensing module is installed on each sorting device in the logistics sorting device set, and a device working status data stream set of the logistics sorting device set is obtained through the information sensing module; Performing resource constraint analysis on the logistics sorting equipment set based on the equipment working status data stream set to obtain a sorting equipment resource constraint condition set; The sorting task priority sequence is scheduled and parsed based on the sorting equipment resource constraint condition set, target equipment scheduling parameters are determined, and adaptive sorting scheduling is performed on the logistics sorting equipment set using the target equipment scheduling parameters.

2. The adaptive scheduling method for intelligent logistics sorting equipment according to claim 1, characterized in that: The obtaining of the sorting task priority sequence comprises: Acquire task priority evaluation factors, and construct a task priority star diagram based on the task priority evaluation factors, wherein each ray coordinate axis of the task priority star diagram corresponds one-to-one to each factor information in the task priority evaluation factors; Mapping the sorting task attribute parameter set to the task priority star graph in sequence to generate a sorting task priority star graph set; Performing quantitative evaluation of the enclosed areas of the sorting task priority star graph sets respectively to obtain a quantified area set of the sorting task star graphs; The sorting task set is prioritized and sorted based on the quantized area set of the sorting task star graph to obtain the sorting task priority sequence.

3. The adaptive scheduling method for intelligent logistics sorting equipment according to claim 2, characterized in that: The step of obtaining a quantized area set of a star graph of a sorting task comprises: Performing quantitative evaluation on the enclosed areas of the sorting task priority star graph sets respectively to obtain the enclosed area sets of the sorting task star graphs; Performing a criticality assessment on each factor in the task priority assessment factors according to the logistics sorting demand target to obtain a priority factor criticality factor set; generating a ray axis correction coefficient set according to the priority factor critical factor set; The area set enclosed by the sorting task star diagram is quantitatively corrected based on the ray axis correction coefficient set to obtain the quantized area set of the sorting task star diagram.

4. The adaptive scheduling method for intelligent logistics sorting equipment according to claim 3, characterized in that: The obtaining of a set of sorting equipment resource constraints includes: Obtaining sorting equipment constraint factor information, wherein the sorting equipment constraint factor information includes equipment performance constraint, material sorting space constraint, and energy consumption constraint; Obtaining a work attribute application threshold set of the logistics sorting equipment set through a logistics management system, performing correlation analysis on the work attribute application threshold set based on the sorting equipment constraint factor information, and determining a sorting equipment attribute constraint boundary set; Performing equipment working condition analysis on the equipment working status data stream set respectively to determine a sorting equipment working condition parameter set; Based on the sorting equipment attribute constraint boundary set, available resource constraint condition analysis is performed on the sorting equipment working status parameter set to obtain the sorting equipment resource constraint condition set.

5. The adaptive scheduling method for intelligent logistics sorting equipment according to claim 1, characterized in that: Determining the target device scheduling parameters includes: Based on the sorting equipment resource constraint condition set, the sorting task priority sequence is sequentially associated with scheduling equipment to obtain a sorting equipment set matching the sequence task; Defining the sorting equipment scheduling effect target, extracting and fitting the evaluation indicators for the sorting equipment scheduling effect target, and constructing a scheduling effect evaluation fitness function; Using the scheduling effect evaluation fitness function to evaluate the effect of the sequence task matching sorting equipment set, to obtain the sequence task matching equipment fitness set; The equipment scheduling analysis is performed on the sequence task matching sorting equipment set based on the sequence task matching equipment fitness set to determine target equipment scheduling parameters.

6. The adaptive scheduling method for intelligent logistics sorting equipment according to claim 5, characterized in that: Determining the target device scheduling parameters includes: Determining a first sorting task and a first task matching device fitness set according to the sequence task matching device fitness set; Comparing and selecting the first sorting task based on the first task matching device fitness set, and determining the first sorting device with the greatest fitness; Based on the first sorting device, construct an equipment scheduling taboo table; Based on the equipment scheduling taboo table and the sequence task matching equipment fitness set, the equipment scheduling analysis is performed on the sequence task matching sorting equipment set in turn to determine the target equipment scheduling parameters.

7. The adaptive scheduling method for intelligent logistics sorting equipment according to claim 6, characterized in that: Determining the target device scheduling parameters includes: Compare and sort the sequence task matching sorting equipment set in sequence based on the sequence task matching equipment fitness set to obtain a sequence task matching priority equipment set; The equipment scheduling taboo table is used to traverse the sequence task matching priority equipment set to perform scheduling equipment optimization, and obtain the sequence task scheduling equipment parameter set and the remaining sorting sequence tasks; Performing equipment scheduling optimization on the remaining sorting sequence tasks based on the sequence task scheduling equipment parameter set to obtain the remaining sequence task equipment scheduling parameters; The target device scheduling parameters are determined based on the sequence task scheduling device parameter set and the remaining sequence task device scheduling parameters.

8. The adaptive scheduling method for intelligent logistics sorting equipment according to claim 7, characterized in that: The obtaining of the remaining sequence task equipment scheduling parameters includes: Perform task sorting prediction based on the sequence task scheduling device parameter set to obtain scheduling device sorting task prediction progress information; Setting taboo periods for the equipment scheduling taboo table according to the predicted progress information of the scheduling equipment sorting task to obtain a dynamic taboo table for equipment scheduling; Based on the equipment scheduling dynamic taboo table, equipment iterative scheduling optimization is performed on the remaining sorting sequence tasks to obtain the equipment scheduling parameters of the remaining sequence tasks.

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