Real-time Control System for Warehouse Sorting Based on Edge Computing

Abstract

The present invention relates to the field of real-time control technology, specifically a real-time control system for warehouse sorting based on edge computing. The system includes an inbound data processing module, an intelligent sorting control module, a dynamic fault tolerance response module, an inventory management module, and a data analysis and optimization module. In the present invention, by analyzing the declaration batch number and product code in real time, it can quickly adapt to the changes of real-time orders. Through the adaptive sorting path and speed adjustment, it optimizes resource utilization and improves operation efficiency. By using edge computing nodes to monitor the performance of the sorting robotic arm in real time and detect errors, sorting errors can be corrected immediately, enhancing stability and reducing downtime. The real-time synchronization and accuracy verification of inventory management ensure the real-time update of inventory data, support more effective inventory scheduling and precise operation decisions. By deeply analyzing inventory dynamics, it provides data-based optimization suggestions for management, strengthening the data-driven force of decision-making.

Description

Technical Field

[0001] The present invention relates to the technical field of real-time control, and particularly to a real-time control system for warehouse sorting based on edge computing. Background Art

[0002] The technical field of real-time control involves using computing technologies to monitor, control, or adjust operations or processes carried out in a real-time environment. Such technologies are typically applied in scenarios that require rapid response to external events or data changes, such as industrial automation, robotics, vehicle systems, and energy management. Real-time control systems must be able to execute control instructions precisely within strict time constraints to ensure system stability and performance. These systems typically rely on embedded systems for edge computing to reduce latency, increase processing speed, and improve system efficiency. The key to real-time control lies in its ability to continuously and instantaneously process input data and promptly adjust control outputs to adapt to real-time changes in the environment or process state.

[0003] Among them, the real-time control system for warehouse sorting based on edge computing is a system that applies real-time control technology to logistics and warehouse management. Utilizing the capabilities of edge computing, it processes data instantaneously at the location where the data is generated, thereby enabling rapid decision-making and control. Its main purpose is to optimize the sorting process in the warehouse. By real-time monitoring and controlling the sorting operations, it reduces latency and improves the efficiency and accuracy of warehouse operations. It usually integrates sensors, automated robotic arms, and advanced data processing software, and can respond in real-time to inventory changes and order demands to ensure the smoothness and precision of logistics operations.

[0004] Although existing technologies are widely applied in the field of real-time control, their practical applications in warehouse management and logistics operations still exhibit several limitations. The centralized data processing method is vulnerable to network environment impacts when processing a large amount of data, resulting in operation delays. In the case of emergencies or rapid order changes, it usually cannot adapt quickly and lacks the necessary flexibility and adaptability, leading to inefficiencies and resource allocation problems. Existing systems also have significant deficiencies in error handling and inventory management, lacking effective real-time monitoring and dynamic adjustment mechanisms. Once an error occurs, the system's recovery and error correction time is long, affecting operational continuity. At the same time, there are problems of inconsistency or obsolescence of inventory information, directly affecting the effectiveness of inventory scheduling and the accuracy of operational decisions, which is particularly prominent in the modern warehouse operation environment with high demand and rapid changes, severely restricting the overall operational efficiency and response capabilities. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose a real-time control system for warehouse sorting based on edge computing.

[0006] To achieve the above object, the present invention adopts the following technical solution: The real-time control system for warehouse sorting based on edge computing includes:

[0007] The inbound data processing module receives the goods arrival information, parses the received information, verifies the integrity and accuracy of the data, combines the e-commerce enterprise information and the declaration batch number, synchronizes the information to prepare for real-time sorting, and generates the arrival data confirmation result;

[0008] The intelligent sorting control module, according to the arrival data confirmation result, performs real-time analysis on the declaration batch number and the commodity code through edge computing, configures the adaptive sorting path, adjusts the sorting speed to match the real-time order changes, optimizes the tasks according to the adjustment result in combination with the commodity size and weight, adjusts the settings of the sorting robotic arm, records the sorting position and batch information of the commodity, and generates the sorting completion information;

[0009] The dynamic fault tolerance response module continuously monitors the sorting completion information, analyzes the performance data and operation logs of the sorting robotic arm in real time, uses the edge computing node to detect sorting errors, automatically triggers an error response according to the detection result, redirects and allocates the sorting tasks, immediately feeds back and updates the operation status of the sorting robotic arm, and generates the fault tolerance processing result;

[0010] The inventory management module, based on the fault tolerance processing result, inputs the updated storage location information, commodity code and quantity, synchronizes the inventory changes to the database in real time, verifies the information consistency, performs the return operation on the cancelled orders, and outputs the inventory update data by synchronously executing the inventory and storage location queries;

[0011] The data analysis and optimization module uses the inventory update data, evaluates the inventory liquidity and inventory accuracy by comparing and analyzing the inventory dynamics and historical data, identifies the processes to be improved and formulates optimization measures, and generates the inventory optimization plan.

[0012] As a further solution of the present invention, the steps for obtaining the arrival data confirmation result are as follows:

[0013] Receive the goods arrival information, extract the field information, record the commodity name, quantity, arrival time and logistics information, verify the integrity and accuracy by comparing with the predefined format and requirements of the fields, and generate the goods arrival information that meets the verification standards;

[0014] Based on the goods arrival information that meets the verification standards, parse the unique identifier in the logistics information item by item, combine the logistics identifier to associate the e-commerce enterprise information and the declaration batch number, pair the parsed commodity information with the corresponding e-commerce enterprise information and declaration batch, and generate the associated commodity and declaration batch matching data;

[0015] Using the commodity and declaration batch matching data, perform operations on the commodity quantity and the time stamp of the logistics identifier, using the formula:

[0016] ;

[0017] Calculate the adaptability score of the current arrival information and the real-time sorting requirements , and generate the arrival data confirmation result, where is the total sum of the quantity of goods, is the difference between the timestamps, is the sum of the verification results of the logistics identifiers.

[0018] As a further solution of the present invention, the steps for adjusting the sorting speed are as follows:

[0019] According to the declared batch number and product code in the arrival data confirmation result, classify and count the product codes according to the declared batch number, count the quantity ratio and total quantity of the product codes in each declared batch, evaluate the batch priority in combination with the quantity ratio of goods in each batch, and generate the correlation statistical result of the product code and the declared batch number;

[0020] Based on the correlation statistical result of the product code and the declared batch number, combined with the demand change amount of the product code in the real-time order, by analyzing the correlation between the quantity ratio of goods in each batch and the demand change amount of the real-time order, select the batches and paths for priority sorting, and at the same time mark the importance level of the product code, and generate the sorting path selection result of real-time analysis;

[0021] Utilize the sorting path selection result of the real-time analysis, combined with the demand change of the real-time order, and adopt the formula:

[0022] ;

[0023] Calculate the adjusted sorting speed , and generate the adaptive sorting path configuration result, where is the total quantity of goods on the current priority sorting path, is the dynamic change rate of the real-time order demand, is the current load of the sorting equipment.

[0024] As a further solution of the present invention, the steps for obtaining the sorting completion information are as follows:

[0025] Call the adaptive sorting path configuration result, combined with the size and weight information of the goods, normalize the size and weight data of the goods, and calculate the occupation ratio of each good for the sorting task, and generate the product characteristic evaluation result of task optimization;

[0026] Based on the product characteristic evaluation result of task optimization, extract the characteristic combination with the highest occupation ratio of the goods, calibrate the grasping range and load-bearing capacity parameters of the sorting robotic arm, and adopt the formula:

[0027] ;

[0028] Grasping force adjustment ratio , synchronously adjust the operating parameters of the sorting robotic arm, and generate adjusted sorting robotic arm configuration parameters, where represents the volume normalization value of the commodity, represents the weight normalization value of the commodity, represents the maximum load capacity of the sorting robotic arm;

[0029] According to the configuration parameters of the sorting robotic arm, record the grasping time, sorting position and corresponding batch number of each commodity during the sorting operation, synchronously sort and organize the grasping records and batch information, and generate sorting completion information.

[0030] As a further solution of the present invention, the detection step of sorting errors is as follows:

[0031] According to the sorting completion information, extract the sorting position, batch number and grasping time of each commodity, combine with the performance data of the sorting robotic arm, match the sorting position with the target position, calculate the displacement deviation value of each operation, and record the batch number and grasping time to generate a sorting position deviation record;

[0032] Based on the displacement deviation value and grasping time in the sorting position deviation record, calculate the cumulative error for all records, using the formula:

[0033] ;

[0034] Calculate the cumulative error value , the overall error level of the sorting robotic arm, and generate a sorting robotic arm error analysis result, where is the displacement deviation value of each operation, is the total number of sorting operations, represents the number of deviation times;

[0035] Based on the cumulative error value in the sorting robotic arm error analysis result, set a judgment threshold, screen the records exceeding the threshold and determine them as high-error records, and mark them as error records by analyzing the batch number and sorting position in the high-error records to obtain the sorting error detection result.

[0036] As a further solution of the present invention, the step of obtaining the fault tolerance processing result is as follows:

[0037] Call the sorting error detection result, extract the high-error records, identify the corresponding batch number, sorting position and commodity code, combine with the real-time sorting task allocation to analyze the sorting task distribution of the high-error records, and generate a high-error task redirection analysis result;

[0038] Based on the above high-error task redirection analysis results, select the alternative path in the sorting task distribution, reallocate the high-error tasks according to the capacity limit and current load of the alternative path, adjust the allocation order and sorting position of the tasks, and generate the sorting task redirection result;

[0039] According to the sorting task redirection result, update the operation parameters of the sorting robotic arm, adjust the grasping path, force, and angle settings. At the same time, mark the status of the completed high-error tasks as corrected, record the updated operation parameters and task completion information, and generate the fault tolerance processing result.

[0040] As a further solution of the present invention, the steps for obtaining the inventory update data are as follows:

[0041] Based on the fault tolerance processing result, extract the change types of the commodities item by item, match the commodity codes with the corresponding warehouse location information according to the change types, update the occupancy information of the target warehouse location, record the increase and decrease change amounts of the commodities, extract the original warehouse location of the cancelled order commodities for the cancellation operation, associate the commodity return operation with the target warehouse location, and integrate and generate the commodity change association record;

[0042] According to the commodity change association record, verify the consistency between the commodity code and the warehouse location information item by item, determine whether the change quantity of the commodity matches the capacity limit of the corresponding warehouse location, analyze the impact of the change operation on the inventory occupancy situation, and reallocate the commodities exceeding the capacity limit to other warehouse locations to generate the warehouse location capacity verification result;

[0043] Based on the warehouse location capacity verification result, perform the inventory synchronization operation for the commodities and warehouse location information that pass the verification, update the change of the commodity quantity to the inventory table in real time, generate the updated return record for the return operation of the cancelled order commodities at the same time, count the operation types, timestamps, and processing status of all commodities, and output the inventory update data.

[0044] As a further solution of the present invention, the steps for obtaining the inventory optimization plan are as follows:

[0045] According to the inventory update data, extract the inventory change information within the change cycle by classifying according to the commodity codes, compare and analyze the inventory change amount of each commodity with its historical inventory change record, count the inventory change rate and the difference in historical inventory levels within the cycle, and generate the inventory dynamic comparison result;

[0046] Based on the inventory dynamic comparison result, use the formula:

[0047] ;

[0048] Calculate the inventory liquidity score , the inventory accuracy is evaluated according to the error rate in the historical inbound and outbound records, and the key improvement points of inventory management are identified in combination with the results to generate the evaluation results of inventory liquidity and accuracy. Among them, is the change amount of a single inventory change, is the change time interval, represents the total number of inventory changes, represents the total quantity of inventory items;

[0049] According to the evaluation results of inventory liquidity and accuracy, the identified key improvement points are analyzed, and targeted optimization measures are formulated according to the process problems in inventory management to generate an inventory optimization plan.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0051] In the present invention, through the deep integration of intelligence, automation and self - adaptability, the efficient and precise operation of warehousing management and sorting operations is realized. The intelligent sorting control module, based on real - time order requirements and the evaluation of the priority of commodity batches, adopts the optimal sorting path selection and dynamic speed adjustment strategies to ensure efficient sorting, improve the order fulfillment speed, and reduce the warehouse operation cost. At the same time, the task scheduling is optimized in combination with the size and weight of the commodity to realize the intelligent adjustment of the sorting robotic arm, improve the operation accuracy and efficiency. The dynamic fault - tolerance response module monitors the errors in the sorting process in real time, draws the error trajectory based on the operation data of the sorting robotic arm, and calculates the cumulative error value, timely identifies the high - error records and predicts their impact on the overall sorting task, and then automatically triggers an error response, optimizes the task allocation, reduces order mismatches or commodity damage, improves the stability and automation level of the system. The inventory management module, based on the fault - tolerance processing results, realizes the real - time synchronous update of inventory information to ensure data consistency, and at the same time supports the management of the return of cancelled orders and the adjustment of storage locations, reduces the risk of inventory chaos and loss, improves the warehouse management efficiency, enhances the flexibility of inventory operations, makes the warehousing management more precise and intelligent. Generally speaking, by using edge - computing technology, the real - time processing of data, intelligent optimization of sorting, dynamic fault - tolerance response and efficient inventory management are realized, significantly improving the warehouse operation efficiency and the level of intelligence, and providing a precise, efficient and automated warehousing sorting solution for the e - commerce logistics industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is the system flow chart of the present invention;

[0053] Figure 2 is the flow chart for obtaining the confirmation result of the arrival data of the present invention;

[0054] Figure 3 is the flow chart for adjusting the sorting speed of the present invention;

[0055] Figure 4Flow chart for obtaining the sorting completion information of the present invention;

[0056] Figure 5 Flow chart for detecting sorting errors of the present invention;

[0057] Figure 6 Flow chart for obtaining the fault tolerance processing result of the present invention;

[0058] Figure 7 Flow chart for obtaining the inventory update data of the present invention;

[0059] Figure 8 Flow chart for obtaining the inventory optimization plan of the present invention. Detailed implementation manners

[0060] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0062] Please refer to Figure 1 , the real-time control system for warehouse sorting based on edge computing includes:

[0063] The inbound data processing module receives the commodity arrival information, parses the received information, verifies the integrity and accuracy of the data, combines the e-commerce enterprise information and the declared batch number, synchronizes the information for real-time sorting preparation, and generates the arrival data confirmation result;

[0064] The intelligent sorting control module performs real-time analysis on the declared batch number and commodity code through edge computing according to the arrival data confirmation result, configures the adaptive sorting path, adjusts the sorting speed to match the real-time order changes, optimizes the task according to the adjustment result in combination with the commodity size and weight, adjusts the settings of the sorting robotic arm, records the sorting position and batch information of the commodity, and generates the sorting completion information;

[0065] The dynamic fault-tolerant response module continuously monitors the sorting completion information, analyzes the performance data and operation logs of the sorting robotic arm in real time, uses the edge computing node to detect sorting errors, automatically triggers an error response according to the detection results, redirects and allocates sorting tasks, immediately feeds back and updates the operation status of the sorting robotic arm, and generates a fault-tolerant processing result;

[0066] Based on the fault-tolerant processing result, the inventory management module inputs the updated location information, product code and quantity, synchronizes the inventory changes to the database in real time, verifies the information consistency, performs the return operation on the cancelled orders, and outputs the inventory update data by synchronously executing the inventory and location queries;

[0067] The data analysis and optimization module uses the inventory update data, evaluates the inventory liquidity and inventory accuracy by comparing and analyzing the inventory dynamics and historical data, identifies the processes to be improved and formulates optimization measures, and generates an inventory optimization plan.

[0068] The arrival data confirmation result includes the e-commerce enterprise identifier, the batch integrity verification result, and the data synchronization status record; the sorting completion information includes the sorting path configuration record, the sorting speed adjustment record, and the robotic arm setting information; the fault-tolerant processing result includes the error detection log, the task redirection status record, and the robotic arm operation update result; the inventory update data includes the location information update record, the product code synchronization record, and the inventory change record; the inventory optimization plan includes the liquidity evaluation result, the accuracy comparison and analysis result, and the improvement measure suggestions.

[0069] Please refer to Figure 2 , the steps for obtaining the arrival data confirmation result are as follows:

[0070] Receive the product arrival information, extract the field information, record the product name, quantity, arrival time and logistics information, verify the integrity and accuracy by comparing with the predefined format and requirements of the fields, and generate the product arrival information that meets the verification standards;

[0071] After receiving the commodity arrival information, parse the information item by item. First, extract the keyword fields such as commodity name, commodity quantity, arrival time, and logistics information according to the field definition, and perform verification processing on each field separately to check whether the field content meets the predefined format requirements. For example, the commodity quantity field needs to be a non-negative integer and cannot be zero. For the arrival time field, it is necessary to confirm that its format conforms to the time standard, such as whether the form of year, month, day, hour, minute, and second is correct. In addition, it is also necessary to check whether the time range is within a reasonable date interval. The logistics information field needs to be verified using the preset regular expression format to ensure that the logistics identifier is a unique number and the structure is complete. If there are missing, incorrect format, or unreasonable numerical situations in the field content of each record, these records will be excluded. Finally, collect the records that have passed all field verifications into the set of commodity arrival information that has passed the verification, and generate commodity arrival information that meets the verification standards.

[0072] Based on the commodity arrival information that meets the verification standards, parse the unique identifier in the logistics information item by item, and combine the logistics identifier to associate the e-commerce enterprise information and the declaration batch number. Match the parsed commodity information with the corresponding e-commerce enterprise information and declaration batch to generate associated commodity and declaration batch matching data;

[0073] For the set of commodity arrival information that meets the verification standards, extract the unique identification code in the logistics information from each record, and check each identification code against the logistics identification database to query whether the identification code exists in the database and return the corresponding e-commerce enterprise information and declaration batch number. The specific operations include accurately matching the identification code string to ensure the uniqueness of the match, and at the same time obtaining the name of the corresponding e-commerce enterprise and the information of the declaration batch number. Then, according to the matching results, further integrate the commodity information, associate the commodity name, commodity quantity in each record with the logistics unique identifier, as well as the e-commerce enterprise information and the declaration batch number, and save the association results item by item. For records with matching failures or non-unique identifiers, track them through logging and mark the error types. Finally, generate associated commodity and declaration batch matching data for all records that have successfully matched and completed information integration.

[0074] Use the commodity and declaration batch matching data to perform operations on the commodity quantity and the timestamp of the logistics identifier, using the formula:

[0075] ;

[0076] Calculate the adaptability score of the current arrival information and the real-time sorting requirements , and generate the arrival data confirmation result, where is the total sum of the commodity quantity, is the difference between the timestamps, is the sum of the verification results of the logistics identifier;

[0077] : The number of verified goods. It is obtained by counting the goods quantity field extracted from the goods arrival information. Assuming that in the current data set for calculation, the total number of goods is pieces.

[0078] : The difference between the logistics identification timestamps. It is calculated by comparing the difference between the arrival time field in the record and the target time range (such as the time window required for real-time sorting). Assuming the time difference is hours.

[0079] : The sum of the logistics identification verification results. It is obtained by accumulating the verification scores of the logistics identifications in the record. Assuming that the score of each record is 10 points and there are 12 records in total, then .

[0080] According to the formula, calculate :

[0081]

[0082] Calculate :

[0083]

[0084] Substitute , and into the formula:

[0085]

[0086] Calculate the value inside the parentheses:

[0087]

[0088] Calculate the final result:

[0089]

[0090] The result shows that the adaptability score of the current arrival information to the real-time sorting requirement is . This score reflects the comprehensive adaptability degree of the goods quantity, the logistics timestamp difference, and the logistics identification verification result. The higher the value, the more the arrival information meets the requirement standard of real-time sorting. The high score value in this example indicates that the goods arrival data of the current batch has a high adaptability and is suitable for directly entering the sorting process, further providing data guarantee for the efficiency and accuracy of sorting.

[0091] Please refer to Figure 3 , the adjustment steps of the sorting speed are:

[0092] According to the declared batch number and product code in the arrival data confirmation result, classify and count the product codes based on the declared batch number, calculate the quantity proportion and total quantity of product codes in each declared batch, evaluate the batch priority in combination with the quantity proportion of each batch of products, and generate the associated statistical result of product code and declared batch number;

[0093] Call the declared batch number and product code in the arrival data confirmation result, extract the declared batch number item by item and classify and count the corresponding product codes, divide and count the distribution of product codes according to each declared batch number, specifically including calculating the quantity of product codes within each batch and the total number of product codes, so as to generate the proportion of product codes in each batch. At the same time, judge whether the distribution of product quantity is uniform through the statistical result. For the product codes with a relatively high quantity proportion, record their priority marks in the batch. On this basis, construct a batch product distribution table, which includes the name, quantity proportion and total number of product codes. In addition, cross-compare the commodity distribution information of each batch with the proportion of commodity quantity to generate a strong and weak matrix of the association between commodities and batches. The data in the matrix is used to further analyze the importance ranking of commodities and batch priorities, and form the associated statistical result of product code and declared batch number.

[0094] Based on the associated statistical result of product code and declared batch number, combined with the demand change amount of product codes in real-time orders, by analyzing the correlation between the quantity proportion of each batch of products and the demand change amount of real-time orders, select the batches and paths for priority sorting, and at the same time mark the importance level of product codes, and generate the sorting path selection result of real-time analysis;

[0095] Based on the associated statistical result of product code and declared batch number, obtain the product code demand information in the order in real-time and compare the order demand with the statistical result. First, extract the demand quantity of product codes in the real-time order, and calculate the demand change amount and trend of order products according to their distribution in different batches. Then, according to the batch priority in the associated statistical result, match the demand quantity of real-time orders one by one, record the change of the demand quantity of each product code in the batch, and generate a product demand change table. In the process of generating the demand change table, by cross-mapping the demand trend of the real-time order with the product distribution data in each batch, screen out the high-priority batches that meet the order demand change amount, and dynamically update the batch importance according to the demand trend. Finally, organize this updated batch priority information into a comprehensive real-time sorting path selection table, mark the batches and their associated commodity information for the preferred path in the table, and generate the sorting path selection result of real-time analysis.

[0096] Utilize the sorting path selection result of real-time analysis, combined with the demand change of real-time orders, and adopt the formula:

[0097] ;

[0098] Calculate the adjusted sorting speed , and generate an adaptive sorting path configuration result, where is the total number of items on the current priority sorting path, is the dynamic change rate of the real-time order demand, is the current load of the sorting equipment;

[0099] : The total number of items on the current priority sorting path is obtained by counting the batches of items in the sorting path. Currently, it is ;

[0100] : The dynamic change rate of the real-time order demand is calculated by comparing the current order demand quantity with the order demand quantity in the previous cycle. The change rate is ;

[0101] : The current load of the sorting equipment is obtained by real-time monitoring of the task occupancy rate of the equipment. Currently, it is .

[0102] Calculate the numerator part, that is :

[0103]

[0104] Calculate the denominator part, that is :

[0105]

[0106] Calculate the ratio of the numerator to the denominator:

[0107]

[0108] Calculate the final sorting speed :

[0109]

[0110] The sorting speed obtained through calculation represents the optimized operating speed of the current equipment, in units of the number of items sorted per second. This speed value will be used to actually adjust the operating parameters of the sorting equipment to match the change in the real-time order demand, thereby completing the configuration of the dynamic adaptive sorting path. The operating speed of the equipment on the current priority sorting path has been comprehensively adjusted according to the number of items, the dynamic change rate of the real-time order demand, and the equipment load to , which provides a basis for the efficient operation of the equipment and ensures the ability to quickly respond to real-time order demands.

[0111] Please refer to Figure 4 , and the steps for obtaining the sorting completion information are as follows:

[0112] Call the adaptive sorting path configuration result, combine the size and weight information of the goods, normalize the size and weight data of the goods, calculate the occupancy ratio of each good for the sorting task, and generate an evaluation result of the goods characteristics optimized for the task;

[0113] Call the batch number and product code information of the goods recorded in the adaptive sorting path configuration result, extract the size (length, width, height) and weight data of the goods in each batch, read and store these data one by one into the product characteristics table. For each record, first calculate the volume of the product, which is obtained by multiplying the length, width, and height. Then perform normalization processing by dividing the volume of each product by the maximum volume value in its batch to obtain the relative volume ratio. Subsequently, read the weight information of the product and also perform normalization processing by batch, dividing the weight value of each product by the maximum weight value in the batch to obtain the relative weight ratio. The normalized volume and weight data are used to calculate the task occupancy ratio of each product through linear weighted combination, where the weight values of volume and weight are determined according to the product type. Generally, for products with larger volumes, the weight of volume is greater than that of weight to reflect the occupancy of sorting resources. After completing the calculations for all records, summarize the normalized data and occupancy ratio results into the characteristics table, classify and organize them according to batches and product types, and generate an evaluation result of the goods characteristics optimized for the task.

[0114] Based on the evaluation result of the goods characteristics optimized for the task, extract the characteristic combination with the highest occupancy ratio of the goods, calibrate the grasping range and load-bearing capacity parameters of the sorting robotic arm, and use the formula:

[0115] ;

[0116] Grasping force adjustment ratio , synchronously adjust the operating parameters of the sorting robotic arm, and generate adjusted configuration parameters of the sorting robotic arm, where represents the normalized value of the volume of the product, represents the normalized value of the weight of the product, represents the maximum load-bearing capacity of the sorting robotic arm;

[0117] : The normalized value of the volume of the product is calculated by dividing the volume of the product by the maximum volume value of its batch, and the current value is ;

[0118] : The weight normalization value of the commodity, calculated by dividing the weight of the commodity by the maximum weight value of its batch. Currently, it is ;

[0119] : The maximum load capacity of the sorting robotic arm, obtained from the technical specifications of the equipment. Currently, it is .

[0120] Calculate the direct addition of volume and weight:

[0121]

[0122] Calculate the square root of the load capacity:

[0123]

[0124] Calculate the grasping force :

[0125]

[0126] Obtained through calculation Indicates the adjustment ratio of the grasping force that the current robotic arm needs to adjust, mainly used to set the grasping parameters of the robotic arm, including force and angle adjustment, to ensure that during the grasping process, it can meet the volume and weight requirements of the commodity without exceeding the load capacity of the equipment. The optimized grasping force parameters comprehensively consider the volume and weight characteristics of the commodity, and at the same time perform non-linear calibration using the maximum load capacity of the sorting robotic arm.

[0127] According to the configuration parameters of the sorting robotic arm, record the grasping time, sorting position, and corresponding batch number of each commodity during the sorting operation, synchronize and organize the grasping records with the batch information to generate sorting completion information;

[0128] According to the adjusted configuration parameters of the sorting robotic arm, record the commodity information in real time during the sorting operation. Each time a commodity is grasped, obtain the batch number, size, weight, and sorting position data of the commodity. At the start of grasping, record the initial position of the robotic arm and the grasping start time. Subsequently, during the grasping process, collect the robotic arm motion information in real time, including the grasping angle, force, and moving distance, synchronize and associate these parameters with the commodity information, record the moving path and the final sorting position of the commodity. For the sorting operation of each commodity, record its specific placement position in the sorting area, establish a mapping relationship between the sorting position and the batch number of the commodity to ensure that the commodities of each batch can be accurately classified. After each sorting is completed, update the sorting log table. The log table contains the grasping time, completion time, sorting position, and batch information of the commodity. At the same time, generate a grasping action trajectory diagram in chronological order, organize the completed sorting information and archive it in the task completion record, and finally generate complete sorting completion information and save it for subsequent use.

[0129] Please refer to Figure 5 , and the detection steps for sorting errors are as follows:

[0130] According to the sorting completion information, extract the sorting position, batch number, and grasping time of each commodity. Combine with the performance data of the sorting robotic arm, match the sorting position with the target position, calculate the displacement deviation value of each operation, record the batch number and grasping time, and generate a sorting position deviation record;

[0131] Call the sorting position, batch number, and grasping time of the commodities in the sorting completion information, extract and organize these data item by item into a sorting record table, which contains the corresponding relationship between the target position and the actual sorting position of the commodities. When analyzing the records item by item, calculate the difference between the actual sorting position and the target position to obtain the displacement deviation value of each record, and synchronously store the displacement deviation value with the batch number and grasping time. In order to accurately depict the time and space characteristics of the sorting action, refine the grasping time field, divide the start time and end time of the grasping into separate columns, and correspond one by one with the displacement deviation data. Then, arrange each piece of data in the record in chronological order to generate a time series trajectory diagram of the sorting deviation, which is used to show the dynamic change trend of the sorting operation. In addition, combine with the performance data of the sorting robotic arm, conduct a linkage analysis of the displacement deviation data and the grasping time of each sorting action, judge whether the operation meets the established efficiency range, integrate all these analysis results into the sorting position deviation record table, and finally generate a complete sorting position deviation record for subsequent processing.

[0132] Based on the displacement deviation value and grasping time in the sorting position deviation record, calculate the cumulative error for all records, using the formula:

[0133] ;

[0134] Calculate the cumulative error value , the overall error level of the sorting robotic arm, and generate the error analysis result of the sorting robotic arm, where is the displacement deviation value of each operation, is the total number of sorting times, represents the number of deviation times;

[0135] : The displacement deviation value of a single sorting, calculated from the difference between the actual sorting position and the target position, currently is , , , ;

[0136] : The total number of sorting times, obtained by counting in the record table, currently is 。

[0137] Calculate the square of each deviation value:

[0138]

[0139]

[0140]

[0141]

[0142] Calculate the sum of the squared deviations:

[0143]

[0144] Divide the sum by the total number of sorting operations:

[0145]

[0146] Calculate the square root to obtain the cumulative error value :

[0147]

[0148] The calculated cumulative error value indicates that in the current sorting task, the average magnitude of the sorting position deviation is 3.67 units. This result reflects whether the operating accuracy of the sorting robotic arm meets the standard during task execution and provides a key basis for screening subsequent high-error records. The cumulative error value As an error evaluation indicator for the overall sorting task, it is directly used to determine whether the current robotic arm operation needs to be adjusted or corrective measures need to be triggered. A higher error value indicates insufficient accuracy in the sorting task, which may affect the quality of task completion, while a lower error value indicates that the operating accuracy meets the standard and no further intervention is required.

[0149] Based on the cumulative error value in the error analysis result of the sorting robotic arm, set a judgment threshold, screen the records exceeding the threshold and determine them as high-error records, and mark them as error records by analyzing the batch numbers and sorting positions in the high-error records to obtain the sorting error detection result;

[0150] According to the cumulative error values in the error analysis results of the sorting robot arm, compare the error values with the set judgment thresholds one by one, and screen out the records with errors exceeding the thresholds. For the records exceeding the thresholds, extract their corresponding batch numbers and sorting positions, and analyze the deviation rules in the high-error records to gradually confirm the frequency and concentrated area of the error records. Then, associate the high-error records with the relevant records in the sorting path, and locate the impact range of the high-error records on the overall sorting task by cross-comparing the batch numbers and sorting positions. On the basis of marking the high-error records, further analyze the deviation conditions of the remaining records in the sorting path, compare the displacement deviation and grasping time of the records, evaluate their correlation with the high-error records, and synchronously include the associated records in the error record range. Finally, organize all the analyzed mis-sorting records into a mis-sorting report, which includes the sorting path, batch number, displacement deviation range, and time distribution characteristics of the high-error records, and generate the sorting error detection results to support the triggering of subsequent corrective actions.

[0151] Please refer to Figure 6 , and the steps to obtain the fault tolerance processing result are as follows:

[0152] Call the sorting error detection results, extract the high-error records, identify the corresponding batch numbers, sorting positions, and product codes, and analyze the sorting task distribution of the high-error records in combination with the real-time sorting task allocation to generate the redirection analysis results for high-error tasks;

[0153] Call the records marked as high-error in the sorting error detection results, extract these records one by one, including batch numbers, sorting positions, product codes, and cumulative error values. Group the batch numbers, count the number and distribution of high-error records in each batch, and organize them into a batch error statistical table. For the sorting position information, combined with the product code, analyze the area where the error occurs concentratedly, and draw an error distribution map by summarizing the repetition times and error amplitudes of the sorting positions to visually display the spatial distribution characteristics of the high-error records. Subsequently, extract the cumulative error values and cross-match them with the sorting task allocation table, and screen out the paths that may have redundant tasks or insufficient capacity by comparing the task execution time, path capacity, and load conditions of the records. Finally, establish a mapping relationship between the high-error records and the alternative paths suitable for redirection, organize a recommended list of alternative paths for high-error tasks, and generate the redirection analysis results for high-error tasks.

[0154] Based on the redirection analysis results of high-error tasks, select the alternative paths in the sorting task distribution, reallocate the high-error tasks according to the capacity limit of the alternative paths and the current load, adjust the allocation order and sorting positions of the tasks, and generate the redirection results for sorting tasks;

[0155] Based on the results of high-error task redirection analysis, extract the task execution time, sorting path, and corresponding error accumulation value for each high-error record, and compare them one by one with the capacity limit and current load of the alternative path. For each record, calculate the priority of the task, reorder the allocation order of high-error records according to the priority, and match them to the alternative path that meets the capacity and load requirements. During the task redirection process, check whether the idle time period and capacity of the sorting path match the task execution time and demand. For paths with a capacity close to full load, preferentially select low-priority tasks for reallocation to ensure the effective utilization of path resources. After completing the redirection allocation of all high-error records, organize the adjustment results into a real-time sorting task update table, which includes the time arrangement of the allocated tasks, path adjustment, and sorting priority of the goods, and generate the sorting task redirection result.

[0156] According to the sorting task redirection result, update the operation parameters of the sorting robotic arm, adjust the grasping path, force, and angle settings. At the same time, mark the status of the completed high-error tasks as corrected, record the updated operation parameters and task completion information, and generate the fault tolerance processing result.

[0157] According to the sorting task redirection result, extract the adjusted grasping path, force parameters, and angle settings for each reallocated sorting task one by one, and directly apply these parameters to the configuration update of the sorting robotic arm to ensure that the movement range and execution force of the robotic arm are adapted to the task requirements. For each updated parameter, record its setting value in real time, including the displacement of the grasping path, force range, and angle change, and synchronize the record to the sorting log, corresponding one by one to the completion time and status of the sorting task. During the path update process, reorder the sorting tasks according to the adjusted path to form a sorting path update table, which clearly marks the adjustment details of each task. Organize the records of completed tasks, mark the status as corrected, archive the task adjustment information that has been completed, generate the fault tolerance processing result, and at the same time provide updated path and operation parameter records for subsequent sorting tasks.

[0158] Please refer to Figure 7 , the steps for obtaining inventory update data are as follows:

[0159] Based on the fault tolerance processing result, extract the change type of each commodity one by one, match the commodity code with the corresponding warehouse location information according to the change type, update the occupancy information of the target warehouse location, record the increase and decrease changes of the commodity, extract the original warehouse location of the cancelled order commodity for the cancellation operation, associate the commodity return operation with the target warehouse location, and integrate and generate a commodity change association record.

[0160] Call the updated bin location information and product code data in the fault tolerance processing result, classify all product records according to the change type, and match the product code as the primary key with the target bin location information. For newly added products, extract the available capacity of the target bin location and compare it with the product quantity to ensure that the target bin location can accommodate the newly added products. If the match is successful, add the quantity of the newly added products to the current inventory of the target bin location, and record the completion time and allocation path of this operation. For products of the reduction type, extract the current bin location where the product is located, deduct the corresponding quantity, and at the same time update the remaining available capacity of the bin location, and record the bin location status after the deduction is completed. For cancelled orders, query the original bin location and the corresponding quantity of the product. After confirming that the cancelled order products can be returned to the original bin location, generate a return path record, and update the occupancy of the bin location after the return operation is completed. Finally, integrate the detailed records of all product operations into the classified processing result of product changes.

[0161] According to the product change association records, verify the consistency of the product code and the bin location information item by item, determine whether the change quantity of the product matches the capacity limit of the corresponding bin location, analyze the impact of the change operation on the inventory occupancy, and reallocate the products that exceed the capacity limit to other bin locations to generate the bin location capacity verification result;

[0162] Based on the classified processing result of product changes, verify the matching of the product code and the target bin location information item by item, including the comparison of the change quantity of the product with the total capacity and the current occupied capacity of the target bin location. For the operation of newly added products, check whether the product quantity exceeds the available capacity of the target bin location. If there is an excess, generate a reallocation record and allocate the excess products to other spare bin locations that meet the capacity requirements. For the operation of products of the reduction type, verify whether the bin location status after the change meets the safety stock requirements. If it is lower than the safety stock threshold, mark it as an abnormal status and generate a replenishment plan. For the return operation of cancelled order products, check the integrity of the return path and the correctness of the original bin location information to ensure that the product return operation can be executed without error, and record all the verified and adjusted data as the bin location status and the allocation adjustment result to form the final verification report.

[0163] Based on the bin location capacity verification result, perform inventory synchronization operations for the products and bin location information that pass the verification, update the change of the product quantity to the inventory table in real time, and at the same time generate an updated return record for the return operation of cancelled order products, count the operation type, timestamp and processing status of all products, and output the inventory update data;

[0164] Extract the change records of each commodity item by item according to the bin status and allocation adjustment results, synchronously update the commodity code and the changed quantity to the inventory table, and at the same time update the occupancy information of the bin to reflect the actual impact of the commodity change on the bin status. For the operations of adding and reducing types of commodities, record the timestamp and completion status of each update record to ensure that all change operations have consistent traceability information in the inventory table. For the return operation of the cancelled order commodities, integrate the return records into the inventory table and the return log, and review the inventory status of the original bin to ensure the accuracy of the return operation. Organize the detailed information of all update operations into an inventory change confirmation form, including commodity code, target bin information, change type, change quantity, operation completion time and status flag, and finally output the complete inventory update data and archive it.

[0165] Please refer to Figure 8 , the steps to obtain the inventory optimization plan are as follows:

[0166] According to the inventory update data, extract the inventory change information within the change cycle by classifying according to the commodity code, compare the inventory change amount of each commodity with its historical inventory change records, statistically analyze the inventory change rate and the difference in historical inventory levels within the cycle, and generate the inventory dynamic comparison result;

[0167] When analyzing the inventory update data, it is first necessary to clarify the specific information items in the update data, including the unique code of the commodity, the current inventory quantity, the type of update (such as addition, reduction or correction), and the corresponding timestamp, etc. After parsing the inventory update data and extracting the above key information, it is necessary to perform a logical check on the extraction results to ensure the integrity and accuracy of the relevance between the information items. For example, whether the commodity code exists in the predefined commodity list, whether the update type conforms to the established rules, and whether the timestamp is within the expected time range. By confirming these check contents, a set of valid inventory update records can be generated; on this basis, it is necessary to combine the historical inventory dynamic data to compare and analyze the current inventory change trend, including calculating key indicators such as the inventory fluctuation range and the cumulative change amount of each commodity per unit time. By matching the calculation of these indicators with the historical trend data, abnormal situations can be identified, such as excessive inventory fluctuations or abnormal update frequencies. Through this process, a verified inventory dynamic analysis result can be obtained.

[0168] Based on the inventory dynamic comparison result, use the formula:

[0169] ;

[0170] Calculate the inventory liquidity score , evaluate the inventory accuracy according to the error rate in the historical inbound and outbound records, combine the results to identify the key improvement points of inventory management, and generate the inventory liquidity and accuracy evaluation results, where, is the change in a single inventory change, is the time interval of change, Indicates the total number of inventory changes, Indicates the total amount of inventory items;

[0171] There are the following data: , , ,but:

[0172]

[0173]

[0174] The results show that the liquidity score of the current inventory is 22, indicating that the inventory update record has a high frequency and scale of dynamic changes, which provides a basis for subsequent optimization analysis.

[0175] Based on the inventory liquidity and accuracy assessment results, analyze and identify key improvement points, formulate targeted optimization measures based on process problems in inventory management, and generate inventory optimization plans;

[0176] When identifying processes to be improved, it is necessary to first extract abnormal records from the inventory dynamic analysis results and further analyze the associated context of these records, including the frequency of abnormal occurrence, the corresponding product category, the specific operation type of inventory update, etc., in order to clarify the root cause of the abnormality. By classifying the abnormal records by type and frequency, a preliminary attribution table for each type of abnormality is generated. Then, based on the attribution results, combined with the processing records of similar situations in the historical inventory data, a correlation comparison analysis is performed to further confirm possible improvement points, such as the key process links in inventory management or the lagging parts in the operation steps. Through this analysis, a targeted process optimization suggestion table can be formed; finally, on the basis of the process optimization suggestion table, it is necessary to combine the constraints and execution environment of the actual inventory operation to convert the suggestions into specific optimization measures, and finally output an operational process improvement plan, which clearly defines the improvement steps, goals and corresponding evaluation indicators.

[0177] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A real-time control system for warehouse sorting based on edge computing, characterized in that, The system includes: The inbound data processing module receives the goods arrival information, parses the received information, verifies the integrity and accuracy of the data, combines the e-commerce enterprise information and the declaration batch number, synchronizes the information for real-time sorting preparation, and generates the arrival data confirmation result; The intelligent sorting control module evaluates the batch priority according to the arrival data confirmation result, combines the proportion of the quantity of each batch of goods, analyzes the correlation between the proportion of the quantity of each batch of goods and the change amount of real-time order demand, selects the batch and path for priority sorting, calculates the adjusted sorting speed to match the real-time order change, optimizes the task according to the adjustment result in combination with the size and weight of the goods, adjusts the settings of the sorting robotic arm, records the sorting position and batch information of the goods, and generates the sorting completion information; The dynamic fault tolerance response module continuously monitors the sorting completion information, analyzes the performance data and operation logs of the sorting robotic arm in real time, matches the sorting position with the target position, calculates the displacement deviation value of each operation, draws the sorting deviation time series trajectory diagram, analyzes the dynamic change trend of the sorting operation, and calculates the cumulative error value and the overall error level of the sorting robotic arm. Combines the determination threshold to determine the high error records, analyzes the influence range of the high error records on the overall sorting task, obtains the sorting error detection result, automatically triggers an error response according to the detection result, redirects and allocates the sorting task, immediately feeds back and updates the operation state of the sorting robotic arm, and generates the fault tolerance processing result; The inventory management module, based on the fault tolerance processing result, inputs the updated warehouse location information, product code and quantity, synchronizes the inventory change to the database in real time, verifies the information consistency, performs the return operation on the cancelled orders, and outputs the inventory update data by synchronously executing the inventory and warehouse location queries; The detection steps of the sorting error are as follows: According to the sorting completion information, extract the sorting position, batch number and grasping time of each piece of goods, combine the performance data of the sorting robotic arm, match the sorting position with the target position, calculate the displacement deviation value of each operation, draw the sorting deviation time series trajectory diagram, display the dynamic change trend of the sorting operation, and record the batch number and grasping time to generate the sorting position deviation record; Based on the displacement deviation value and grasping time in the sorting position deviation record, calculate the cumulative error for all records, the overall error level of the sorting robotic arm, and judge whether the current robotic arm operation needs to be adjusted or a corrective measure is triggered, generating the sorting robotic arm error analysis result; Based on the cumulative error value in the sorting robotic arm error analysis result, set the determination threshold, screen the records exceeding the threshold and determine them as high error records, extract the batch number and sorting position corresponding to the high error records, analyze the deviation law in the high error records, gradually confirm the frequency and concentration area of the error records, associate the high error records with the relevant records in the sorting path, and analyze the influence range of the high error records on the overall sorting task through cross-comparing the batch number and sorting position to obtain the sorting error detection result.

2. The real-time control system for warehouse sorting based on edge computing according to claim 1, wherein The obtaining steps of the arrival data confirmation result are as follows: Receive the information of the arrival of goods, extract the field information, record the goods name, quantity, arrival time and logistics information, perform verification processing on each field respectively, check whether the field content meets the predefined format requirements, determine that the logistics identifier is a unique number and has a complete structure, and generate the goods arrival information that meets the verification standard; Based on the goods arrival information that meets the verification standard, parse the unique identifier in the logistics information item by item, associate the e-commerce enterprise information and the declared batch number in combination with the logistics identifier, pair the parsed goods information with the corresponding e-commerce enterprise information and declared batch, and generate the matching data of the associated goods and declared batch; Use the matching data of the goods and declared batch to perform operations on the quantity of goods and the timestamp of the logistics identifier, using the formula: ； Calculate the adaptability score of the current arrival information and the real-time sorting requirements , and generate the arrival data confirmation result, where is the total sum of the quantity of goods, is the difference between the timestamps, is the sum of the verification results of the logistics identifiers.

3. The real-time control system for warehouse sorting based on edge computing according to claim 2, characterized in that, The adjustment steps of the sorting speed are: According to the declared batch number and goods code in the arrival data confirmation result, classify and count the goods codes according to the declared batch number, count the quantity proportion and total quantity of the goods codes in each declared batch, evaluate the batch priority in combination with the quantity proportion of each batch of goods, and generate the associated statistical result of the goods code and declared batch number; Based on the associated statistical result of the goods code and declared batch number, combined with the demand change amount of the goods code in the real-time order, by analyzing the relevance between the quantity proportion of each batch of goods and the demand change amount of the real-time order, select the batches and paths for priority sorting, and at the same time mark the importance level of the goods code, and generate the sorting path selection result of real-time analysis; Use the sorting path selection result of real-time analysis, combined with the demand change of the real-time order, using the formula: ； Calculate the adjusted sorting speed , and generate an adaptive sorting path configuration result, where is the total number of items on the current priority sorting path, is the dynamic change rate of real-time order demand, is the current load of the sorting equipment.

4. The real-time control system for warehouse sorting based on edge computing according to claim 3, characterized in that, The steps for obtaining the sorting completion information are: Call the adaptive sorting path configuration result, combined with the size and weight information of the goods, normalize the size and weight data of the goods, and calculate the occupation ratio of each goods to the sorting task, and generate the goods characteristic evaluation result of task optimization; Based on the goods characteristic evaluation result of task optimization, extract the characteristic combination with the highest goods occupation ratio, calibrate the grasping range and load-bearing capacity parameters of the sorting robotic arm, using the formula: ； Grasping force adjustment ratio , synchronously adjust the operating parameters of the sorting robotic arm to generate the adjusted configuration parameters of the sorting robotic arm, where represents the volume normalization value of the commodity, represents the weight normalization value of the commodity, represents the maximum load-bearing capacity of the sorting robotic arm; According to the configuration parameters of the sorting robotic arm, record the grasping time, sorting position and corresponding batch number of each goods during the sorting operation, synchronize and organize the grasping records and batch information, and generate the sorting completion information.

5. The real-time control system for warehouse sorting based on edge computing according to claim 1, wherein, The cumulative error, using the formula: ； Perform calculations, where is the cumulative error value, is the displacement deviation value for each operation, is the total number of sorting times, represents the number of deviation times.

6. The real-time control system for warehouse sorting based on edge computing according to claim 1, characterized in that The steps for obtaining the fault tolerance processing result are: Call the sorting error detection result, extract the high-error records, identify the corresponding batch number, sorting position and goods code, combine the real-time sorting task allocation to analyze the sorting task distribution of the high-error records, and generate the high-error task redirection analysis result; Based on the high-error task redirection analysis result, select the alternative path in the sorting task distribution, re-allocate the high-error tasks according to the capacity limit and current load of the alternative path, adjust the allocation order and sorting position of the tasks, and generate the sorting task redirection result; Update the operating parameters of the sorting robotic arm according to the sorting task redirection result, adjust the grasping path, force, and angle settings, and at the same time mark the status of the completed high-error tasks as corrected, record the updated operating parameters and task completion information, and generate a fault tolerance processing result.

7. The real-time control system for warehouse sorting based on edge computing according to claim 6, wherein The steps for obtaining the inventory update data are as follows: Based on the fault tolerance processing result, extract the change types of the goods item by item, match the product codes with the corresponding warehouse location information according to the change types, update the occupancy information of the target warehouse location, record the increase and decrease changes in the quantity of the goods, extract the original warehouse location of the cancelled order items for the cancellation operation, and associate the goods return operation with the target warehouse location, and integrate to generate a goods change association record; According to the goods change association record, verify the consistency of the product code and the warehouse location information item by item, determine whether the change quantity of the goods matches the capacity limit of the corresponding warehouse location, analyze the impact of the change operation on the inventory occupancy situation, and reallocate the goods exceeding the capacity limit to other warehouse locations to generate a warehouse location capacity verification result; Based on the warehouse location capacity verification result, perform an inventory synchronization operation for the goods and warehouse location information that pass the verification, update the change in the quantity of the goods to the inventory table in real time, and at the same time generate an updated return record for the return operation of the cancelled order items, and count the operation types, timestamps, and processing statuses of all goods to output the inventory update data.

8. The real-time control system for warehouse sorting based on edge computing according to claim 7, wherein It also includes a data analysis and optimization module. According to the inventory update data, extract the inventory change information within the change cycle by classifying according to the product code, compare and analyze the inventory change quantity of each product with its historical inventory change record, and count the inventory change rate and the difference in historical inventory levels within the cycle to generate an inventory dynamic comparison result; Based on the inventory dynamic comparison result, use the formula: ； Calculate the inventory liquidity score , evaluate the inventory accuracy according to the error rate in the historical inbound and outbound records, identify the key improvement points of inventory management in combination with the results, and generate the evaluation results of inventory liquidity and accuracy. Among them, is the change amount of a single inventory change, is the change time interval, represents the total number of inventory changes, represents the total number of inventory items; According to the inventory liquidity and accuracy evaluation results, analyze and identify the key improvement points, and formulate targeted optimization measures according to the process problems in inventory management to generate an inventory optimization plan.

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