Warehouse sorting real-time control system based on edge calculation

Abstract

The invention relates to the technical field of real-time control, in particular to a warehouse sorting real-time control system based on edge computing, which comprises a warehouse-in data processing module, an intelligent sorting control module, a dynamic fault-tolerant response module, an inventory management module and a data analysis and optimization module. According to the method, the change of a real-time order can be quickly adapted by analyzing the declaration batch number and the commodity code in real time, the sorting error can be immediately corrected by adjusting the self-adaptive sorting path and speed, optimizing the resource use, improving the operation efficiency and carrying out real-time monitoring and error detection on the performance of the sorting mechanical arm through the edge computing node, and the sorting efficiency is improved. According to the method, the stability is enhanced, the downtime is reduced, real-time synchronization and accuracy verification of inventory management are realized, real-time updating of inventory data is ensured, more effective inventory scheduling and accurate operation decision are supported, and data-based optimization suggestions are provided for a management layer and the data driving force of decision is enhanced through deep analysis of inventory dynamics.

Description

Technical Field

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

[0002] The field of real-time control technology involves the use of computing technology to monitor, control or adjust operations or processes performed in a real-time environment. This technology is often 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 accurately execute control instructions within strict time constraints to ensure system stability and performance. These systems often rely on embedded systems for edge computing to reduce latency, increase processing speed and system efficiency. The key to real-time control is its ability to continuously and instantly process input data and adjust control outputs in a timely manner to adapt to real-time changes in the environment or process status.

[0003] Among them, the warehouse sorting real-time control system based on edge computing is a system that applies real-time control technology to logistics and warehouse management. It uses the power of edge computing to process data instantly at the location where the data is generated, thereby achieving rapid decision-making and control. Its main purpose is to optimize the warehouse sorting process, reduce delays, and improve the efficiency and accuracy of warehouse operations by real-time monitoring and control of sorting operations. It usually integrates sensors, automated robotic arms, and advanced data processing software, and can respond to inventory changes and order requirements in real time to ensure smooth and accurate logistics operations.

[0004] Although existing technologies are widely used in the field of real-time control, their practical application in warehouse management and logistics operations still shows many limitations. Centralized data processing methods are susceptible to network environment when processing large amounts of data, resulting in operational delays. In the event of emergencies or rapid changes in orders, they are usually unable to adapt quickly and lack the necessary flexibility and adaptability, resulting in inefficiency and resource allocation problems. Existing systems are also significantly deficient in error handling and inventory management, lacking effective real-time monitoring and dynamic adjustment mechanisms. Once an error occurs, the system recovery and error correction time is long, affecting operational continuity. At the same time, there are inconsistencies or outdated inventory information, which directly affects the effectiveness of inventory scheduling and the accuracy of operational decisions. This is particularly prominent in the modern warehouse operating environment with high demand and rapid changes, which severely limits the overall operational efficiency and responsiveness. Summary of the invention

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

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution: The warehouse sorting real-time control system based on edge computing includes: The inbound data processing module receives the arrival information of goods, parses the received information, verifies the data integrity and accuracy, combines the e-commerce enterprise information and the declared batch number, synchronizes the information to prepare for real-time sorting, and generates the arrival data confirmation result; The intelligent sorting control module confirms the result according to the arrival data, performs real-time analysis on the declared batch number and commodity code through edge computing, configures an adaptive sorting path, adjusts the sorting speed to match the real-time order changes, optimizes the task according to the adjustment result combined with the commodity size and weight, adjusts the settings of the sorting robot arm, records the sorting position and batch information of the commodity, and generates sorting completion information; The dynamic fault-tolerant response module continuously monitors the sorting completion information, analyzes the sorting robot arm performance data and operation logs in real time, uses edge computing nodes to detect sorting errors, automatically triggers error responses based on the detection results, redirects and allocates sorting tasks, and instantly feeds back and updates the sorting robot arm operation status to generate fault-tolerant processing results; Based on the fault-tolerant processing results, the inventory management module inputs the updated storage location information, product code and quantity, synchronizes the inventory changes to the database in real time, verifies the consistency of the information, returns the cancelled products, and outputs the inventory update data by synchronously executing inventory and storage location queries; The data analysis and optimization module uses the inventory update data to compare and analyze inventory dynamics and historical data, evaluate inventory liquidity and inventory accuracy, identify processes to be improved and formulate optimization measures, and generate an inventory optimization plan.

[0007] As a further solution of the present invention, the steps for obtaining the arrival data confirmation result are: Receive product arrival information, extract field information, record product name, quantity, arrival time and logistics information, verify completeness and accuracy by comparing the predefined format and requirements of the fields, and generate product arrival information that meets the verification standards; Based on the arrival information of the goods that meet the verification criteria, the unique identifiers in the logistics information are parsed one by one, the e-commerce enterprise information and the declared batch number are associated with the logistics identifier, and the parsed product information is matched with the corresponding e-commerce enterprise information and the declared batch to generate the associated product and declared batch matching data; Using the data of matching the commodity with the declared batch, the timestamp of the commodity quantity and logistics identification is calculated using the formula: ; Calculate the compatibility score between the current arrival information and the real-time sorting requirements , generate arrival data confirmation results, where, is the total quantity of goods, is the difference between the timestamps, It is the sum of the verification results of the logistics identification.

[0008] As a further solution of the present invention, the steps of adjusting the sorting speed are: According to the declared batch number and commodity code in the confirmation result of the arrival data, the commodity codes are classified and counted according to the declared batch number, the quantity ratio and total quantity of the commodity codes in each declared batch are counted, the batch priority is evaluated in combination with the quantity ratio of each batch, and the statistical results of the association between the commodity code and the declared batch number are generated; Based on the statistical results of the association between the commodity code and the declared batch number, combined with the demand change of the commodity code in the real-time order, by analyzing the correlation between the proportion of the quantity of each batch of commodities and the demand change of the real-time order, the batches and routes for priority sorting are selected, and the importance level of the commodity code is marked to generate the sorting route selection result of real-time analysis; Using the sorting path selection results of the real-time analysis and combining the demand changes of real-time orders, the formula is adopted: ; Calculate the adjusted sorting speed , generate the adaptive sorting path configuration result, where is the total quantity of goods in the current priority sorting path, is the dynamic change rate of real-time order demand, is the current load of the sorting equipment.

[0009] As a further solution of the present invention, the steps for obtaining the sorting completion information are: Calling the adaptive sorting path configuration result, combining the size and weight information of the goods, normalizing the size and weight data of the goods, and calculating the proportion of each commodity occupied by the sorting task, and generating a task-optimized commodity characteristic evaluation result; Based on the evaluation results of the commodity characteristics optimized by the task, the characteristic combination with the highest commodity occupancy ratio is extracted, and the grasping range and carrying capacity parameters of the sorting robot are calibrated using the formula: ; Grasp force adjustment ratio , synchronously adjust the operating parameters of the sorting robot arm, and generate the adjusted sorting robot arm configuration parameters, where: Indicates the normalized volume value of the product. Indicates the normalized weight of the product. Indicates the maximum carrying capacity of the sorting robot arm; According to the configuration parameters of the sorting robot arm, the grabbing time, sorting position and corresponding batch number of each commodity during the sorting operation are recorded, the grabbing record and batch information are synchronously sorted, and sorting completion information is generated.

[0010] As a further solution of the present invention, the step of detecting the sorting error is: According to the sorting completion information, the sorting position, batch number and grabbing time of each commodity are extracted, and the sorting position and the target position are matched in combination with the performance data of the sorting robot arm, and the displacement deviation value of each operation is calculated, and the batch number and grabbing time are recorded to generate a sorting position deviation record; Based on the displacement deviation value and the grabbing time in the sorting position deviation record, the cumulative error calculation is performed on all records using the formula: ; Calculate the cumulative error value , the overall error level of the sorting robot arm, generating the error analysis results of the sorting robot arm, where, is the displacement deviation value of each operation, is the total number of sorting times, Indicates the number of deviations; Based on the cumulative error value in the error analysis result of the sorting robot arm, a judgment threshold is set, and records exceeding the threshold are screened and determined as high-error records. By analyzing the batch number and sorting position in the high-error record, it is marked as an error record to obtain a sorting error detection result.

[0011] As a further solution of the present invention, the step of obtaining the fault-tolerant processing result is: Call the sorting error detection result, extract high error records, identify the corresponding batch number, sorting location and commodity code, analyze the sorting task distribution of high error records in combination with real-time sorting task allocation, and generate high error task redirection analysis results; Based on the high-error task redirection analysis result, select a backup path in the sorting task distribution, reallocate the high-error tasks according to the capacity limit of the backup path and the current load, adjust the task allocation order and sorting position, and generate a sorting task redirection result; According to the sorting task redirection result, the operating parameters of the sorting robot arm are updated, the grasping path, force and angle settings are adjusted, and the completed high-error task status is marked as corrected, the updated operating parameters and task completion information are recorded, and the fault-tolerant processing result is generated.

[0012] As a further solution of the present invention, the steps for acquiring the inventory update data are: Based on the fault-tolerant processing result, the change type of the product is extracted one by one, the product code is matched with the corresponding storage location information according to the change type, the occupancy information of the target storage location is updated, and the increase and decrease of the product are recorded. For the order cancellation operation, the original storage location of the canceled product is extracted, the product return operation is associated with the target storage location, and the product change association record is integrated to generate; According to the commodity change association records, the consistency of the commodity code and the storage location information is verified one by one to determine whether the changed quantity of the commodity matches the capacity limit of the corresponding storage location, the impact of the change operation on the inventory occupancy is analyzed, and the commodities exceeding the capacity limit are reallocated to other storage locations to generate the storage location capacity verification result; Based on the storage location capacity verification result, the inventory synchronization operation is performed for the verified goods and storage location information, and the changes in the quantity of goods are updated to the inventory table in real time. At the same time, the updated return record is generated for the return operation of the cancelled goods, and the operation type, timestamp and processing status of all goods are counted, and the inventory update data is output.

[0013] As a further solution of the present invention, the steps of obtaining the inventory optimization solution are: According to the inventory update data, the inventory change information within the change period is extracted according to the commodity code classification, the inventory change amount of each commodity is compared and analyzed with its historical inventory change record, the inventory change rate within the period and the difference in historical inventory levels are statistically analyzed, and the inventory dynamic comparison result is generated; Based on the inventory dynamic comparison results, the formula is adopted: ; Calculating Inventory Liquidity Score , evaluate the inventory accuracy according to the error rate in the historical inventory records, identify the key improvement points of inventory management based on the results, and generate inventory liquidity and accuracy evaluation results, among which, 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; 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.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, through the deep integration of intelligence, automation and adaptability, efficient and accurate operation of warehouse management and sorting operations is achieved. The intelligent sorting control module adopts the optimal sorting path selection and dynamic speed adjustment strategy based on real-time order demand and commodity batch priority evaluation to ensure efficient sorting, improve order fulfillment speed, and reduce warehouse operating costs. At the same time, the task scheduling is optimized in combination with the size and weight of the goods, and the intelligent adjustment of the sorting robot arm is realized to improve the operation accuracy and efficiency. The dynamic fault-tolerant response module monitors the errors in the sorting process in real time, draws the error trajectory based on the operation data of the sorting robot arm, and calculates the cumulative error value. It promptly identifies high error records and predicts their impact on the overall sorting task, thereby automatically triggering error response, optimizing task allocation, reducing order mismatch or product damage, and improving the stability and automation level of the system. The inventory management module realizes real-time synchronous update of inventory information based on the fault-tolerant processing results to ensure data consistency. At the same time, it supports the return management of canceled orders and the adjustment of storage locations, reduces inventory confusion and loss risks, improves warehouse management efficiency, enhances the flexibility of inventory operations, and makes warehouse management more accurate and intelligent. Overall, the use of edge computing technology to realize real-time data processing, intelligent optimization of sorting, dynamic fault-tolerant response and efficient inventory management has significantly improved the warehouse operation efficiency and intelligence level, and provides an accurate, efficient and automated warehousing and sorting solution for the e-commerce logistics industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart for obtaining the arrival data confirmation result of the present invention; Figure 3 This is a flow chart of adjusting the sorting speed of the present invention; Figure 4 This is a flow chart for obtaining sorting completion information of the present invention; Figure 5 It is a flow chart of sorting error detection of the present invention; Figure 6 A flowchart for obtaining the fault-tolerant processing result of the present invention; Figure 7 A flowchart for obtaining inventory update data of the present invention; Figure 8 This is a flow chart for obtaining the inventory optimization solution of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are 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 cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 , the warehouse sorting real-time control system based on edge computing includes: The inbound data processing module receives the arrival information of goods, parses the received information, verifies the data integrity and accuracy, combines the e-commerce enterprise information and the declared batch number, synchronizes the information to prepare for real-time sorting, and generates the arrival data confirmation result; The intelligent sorting control module confirms the results based on the arrival data, performs real-time analysis on the declared batch number and product code through edge computing, configures an adaptive sorting path, adjusts the sorting speed to match the real-time order changes, optimizes the task based on the adjustment results combined with the product size and weight, adjusts the settings of the sorting robot arm, records the sorting position and batch information of the product, and generates sorting completion information; The dynamic fault-tolerant response module continuously monitors the sorting completion information, analyzes the sorting robot arm performance data and operation logs in real time, uses edge computing nodes to detect sorting errors, automatically triggers error responses based on the detection results, redirects and allocates sorting tasks, and instantly updates the sorting robot arm operation status to generate fault-tolerant processing results. Based on the fault-tolerant processing results, the inventory management module inputs the updated storage location information, product code and quantity, synchronizes inventory changes to the database in real time, verifies information consistency, returns the cancelled products, and outputs inventory update data by synchronously executing inventory and storage location queries; The data analysis and optimization module uses inventory update data to compare and analyze inventory dynamics and historical data, evaluate inventory liquidity and inventory accuracy, identify processes for improvement and develop optimization measures, and generate inventory optimization plans.

[0019] The arrival data confirmation results include the e-commerce enterprise identification, batch integrity verification results, and data synchronization status records; the sorting completion information includes the sorting path configuration records, sorting speed adjustment records, and robotic arm setting information; the fault-tolerant processing results include error detection logs, task redirection status records, and robotic arm operation update results; the inventory update data includes the storage location information update records, product code synchronization records, and inventory change records; the inventory optimization plan includes liquidity assessment results, accuracy comparison analysis results, and improvement measures recommendations.

[0020] See also Figure 2 , the steps to obtain the arrival data confirmation results are: Receive product arrival information, extract field information, record product name, quantity, arrival time and logistics information, verify completeness and accuracy by comparing the predefined format and requirements of the fields, and generate product arrival information that meets the verification standards; After receiving the arrival information of the goods, the information is parsed one by one. First, key fields such as product name, product quantity, arrival time and logistics information are extracted according to the field definition. Each field is verified separately to check whether the field content meets the predefined format requirements. For example, the product 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 meets the time standard, such as whether the format of year, month, day, hour, minute and second is correct. In addition, it is necessary to check whether the time range is within a reasonable date range. 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 has a complete structure. If the field content is missing, the format is wrong or the value is unreasonable in each record, these records will be eliminated. Finally, the records that have passed all field verifications are collected into the verified product arrival information set to generate product arrival information that meets the verification standards.

[0021] Based on the arrival information of goods that meet the verification standards, the unique identifiers in the logistics information are parsed one by one, and the e-commerce enterprise information and the declared batch number are associated with the logistics identifier. The parsed product information is matched with the corresponding e-commerce enterprise information and the declared batch to generate the associated product and declared batch matching data; For the set of commodity arrival information that meets the verification criteria, the unique identification code in the logistics information is extracted record by record, each identification code is checked with the logistics identification database, the database is queried to see whether the identification code exists and the corresponding e-commerce enterprise information and declared batch number are returned. The specific operation includes ensuring the uniqueness of the match by accurately matching the identification code string, and obtaining the name of the corresponding e-commerce enterprise and the declared batch number information. Then, based on the matching results, the commodity information is further integrated, and the commodity name and quantity in each record are associated with the logistics unique identification, e-commerce enterprise information, and declared batch number. The association results are saved one by one. For records with matching failures or non-unique identification, they are tracked and the error type is marked through record logs. Finally, all records that have successfully matched and completed information integration generate associated commodity and declared batch matching data.

[0022] Using the data of matching the goods and the declared batches, the timestamps of the goods quantity and logistics identification are calculated using the formula: ; Calculate the compatibility score between the current arrival information and the real-time sorting requirements , generate arrival data confirmation results, where, is the total quantity of goods, is the difference between the timestamps, It is the sum of the verification results of the logistics identification; : The number of verified goods. This is obtained by statistically analyzing the number of goods extracted from the goods arrival information. Assuming that the total number of goods in the currently calculated data set is Piece.

[0023] : 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 Hour.

[0024] : The sum of the logistics identification verification results. It is obtained by accumulating the verification scores of the logistics identification in the records. Assuming that the score of each record is 10 points and there are 12 records in total, then .

[0025] According to the formula, calculate : calculate : Will , and Substituting into the formula: Calculate the value between the brackets: Calculate the final result: The results show that the compatibility score between the current arrival information and the real-time sorting requirements is This score reflects the comprehensive adaptability of the product quantity, logistics timestamp difference and logistics identification verification results. The higher the value, the more the arrival information meets the requirements of real-time sorting. The high score in this example shows that the arrival data of the current batch of goods is highly adaptable and suitable for direct entry into the sorting process, further providing data guarantee for the efficiency and accuracy of sorting.

[0026] See also Figure 3 , the steps to adjust the sorting speed are: According to the declared batch number and commodity code in the arrival data confirmation result, the commodity codes are classified and counted according to the declared batch number, and the quantity ratio and total quantity of the commodity codes in each declared batch are counted. The batch priority is evaluated in combination with the quantity ratio of each batch, and the statistical results of the association between the commodity code and the declared batch number are generated; Call the declared batch number and commodity code in the arrival data confirmation result, extract the declared batch number one by one and classify and count the corresponding commodity codes, and divide and count the distribution of commodity codes according to each declared batch number, including calculating the number of commodity codes in each batch and the total number of commodity codes to generate the proportion of commodity codes in each batch. At the same time, judge whether the distribution of commodity quantity is uniform through statistical results, and record the priority mark in the batch for commodity codes with a high proportion of quantity. On this basis, construct a batch commodity distribution table, which includes the name of the commodity code, the proportion of quantity and its total number. In addition, cross-compare the distribution information of each batch of commodities with the proportion of commodity quantity to generate a strength matrix of the association between commodities and batches. The data in the matrix is ​​used to further analyze the importance ranking of commodities and the priority of batches, and form the statistical results of the association between commodity codes and declared batch numbers.

[0027] Based on the statistical results of the association between the commodity code and the declared batch number, combined with the demand change of the commodity code in the real-time order, by analyzing the correlation between the proportion of the quantity of each batch of commodities and the demand change of the real-time order, the batches and routes for priority sorting are selected, and the importance level of the commodity code is marked to generate the sorting route selection results for real-time analysis; Based on the statistical results of the association between the commodity code and the declared batch number, the commodity code demand information in the order is obtained in real time, and the order demand is compared with the statistical results. First, the commodity code demand quantity in the real-time order is extracted, and the demand change and trend of the order commodity are calculated according to its commodity distribution in different batches. Then, according to the batch priority in the associated statistical results, the commodity demand of the real-time order is matched one by one, and the change of the demand of each commodity code in the batch is recorded to generate a commodity demand change table. In the process of generating the demand change table, the high-priority batches that meet the order demand change are screened out by cross-mapping the demand trend of the real-time order with the commodity distribution data in each batch, and the batch importance is dynamically updated according to the demand trend. Finally, the updated batch priority information is organized into a comprehensive real-time sorting path selection table, which marks the batches with the priority selection path and their associated commodity information, and generates the sorting path selection results for real-time analysis.

[0028] Using the sorting path selection results of real-time analysis and the changes in demand for real-time orders, the formula is adopted: ; Calculate the adjusted sorting speed , generate the adaptive sorting path configuration result, where is the total quantity of goods in the current priority sorting path, is the dynamic change rate of real-time order demand, is the current load of the sorting equipment; : The total number of goods in the current priority sorting path, obtained by counting the batches of goods in the sorting path. The current number is ; :The dynamic change rate of real-time order demand, calculated by comparing the current order demand quantity with the order demand quantity of the previous cycle. The change rate is ; :The current load of the sorting equipment is obtained by real-time monitoring of the equipment's task occupancy rate. The current load is .

[0029] Calculate the numerator, i.e. : Calculate the denominator, that is : Calculate the ratio of the numerator to the denominator: Calculate the final sorting speed : Calculated sorting speed Indicates 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 demand changes of real-time orders, thereby completing the configuration of the dynamic adaptive sorting path. The equipment operating speed of the current priority sorting path has been adjusted comprehensively based on the number of items, the change rate of real-time order demand and the equipment load. , providing a basis for the efficient operation of the equipment and ensuring rapid response to real-time order requirements.

[0030] See also Figure 4 , the steps to obtain the sorting completion information are: 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 proportion of each product in the sorting task, and generate the task-optimized product characteristic evaluation result; The batch number and product code information of the goods recorded in the adaptive sorting path configuration result are called to extract the size (length, width, height) and weight data of the goods in each batch, and these data are read one by one and stored in the product characteristic table. For each record, the volume of the goods is first calculated. The volume is obtained by multiplying the length, width and height, and then normalized. The volume of each item is divided by the maximum volume value in its batch to obtain the relative volume ratio. Subsequently, the weight information of the goods is read and normalized by batch. The weight value of each item is divided by the maximum weight value in the batch to obtain the relative weight ratio. The normalized volume and weight data are linearly weighted to calculate the task occupancy ratio of each item. The weight values ​​of volume and weight are determined according to the type of goods. The weight of goods with larger volume is usually greater than the weight to reflect the occupancy of sorting resources. After completing the calculation of all records, the normalized data and the occupancy ratio results are summarized in the characteristic table, sorted by batch and product type, and the product characteristic evaluation results of task optimization are generated.

[0031] Based on the evaluation results of commodity characteristics of task optimization, the characteristic combination with the highest commodity occupancy ratio is extracted, and the grasping range and carrying capacity parameters of the sorting robot arm are calibrated using the formula: ; Grasp force adjustment ratio , synchronously adjust the operating parameters of the sorting robot arm, and generate the adjusted sorting robot arm configuration parameters, where: Indicates the normalized volume value of the product. Indicates the normalized weight of the product. Indicates the maximum carrying capacity of the sorting robot arm; : The normalized volume value of the product, calculated by dividing the volume of the product by the maximum volume value of its batch, currently ; : The normalized weight value of the product, calculated by dividing the weight of the product by the maximum weight value of its batch, currently ; : The maximum load capacity of the sorting robot arm, obtained from the technical specifications of the equipment, is currently .

[0032] Calculate volume by adding weight directly: Calculate the square root of the carrying capacity: Calculating grip strength : Calculated Indicates the current gripping force adjustment ratio that the robot arm needs to adjust. It is mainly used to set the gripping parameters of the robot arm, including force and angle adjustment, to ensure that the volume and weight requirements of the goods can be met during the gripping process without exceeding the carrying capacity of the equipment. The optimized gripping force parameters comprehensively consider the volume and weight characteristics of the goods, and use the maximum carrying capacity of the sorting robot arm for nonlinear calibration.

[0033] According to the configuration parameters of the sorting robot arm, the grabbing time, sorting position and corresponding batch number of each item in the sorting operation are recorded, the grabbing record and batch information are synchronized and sorting completion information is generated; According to the adjusted sorting robot configuration parameters, the product information is recorded in real time during the sorting operation, and the batch number, size, weight and sorting position data of the product are obtained each time the product is grabbed. At the beginning of the grabbing, the initial position of the robot and the start time of the grabbing are recorded. Then, during the grabbing process, the robot action information is collected in real time, including the grabbing angle, force and moving distance. These parameters are synchronously associated with the product information, and the moving path and final sorting position of the product are recorded. For the sorting operation of each product, its specific placement position in the sorting area is recorded, and a mapping relationship is established between the sorting position and the batch number of the product to ensure that each batch of products can be accurately classified. After each sorting is completed, the sorting log table is updated. The log table contains the grabbing time, completion time, sorting position and batch information of the product. At the same time, a grabbing action trajectory diagram is generated in chronological order, and the completed sorting information is sorted and filed in the task completion record. Finally, the complete sorting completion information is generated and saved for subsequent use.

[0034] See also Figure 5 ,The detection steps of sorting errors are: According to the sorting completion information, the sorting position, batch number and grabbing time of each product are extracted, and the sorting position and target position are matched in combination with the sorting robot arm performance data. The displacement deviation value of each operation is calculated, and the batch number and grabbing time are recorded to generate the sorting position deviation record; The commodity sorting position, batch number and grabbing time in the sorting completion information are called, and these data are extracted and organized into a sorting record table one by one, which contains the correspondence between the target position and the actual sorting position of the commodity. When analyzing the records one by one, the difference between the actual sorting position and the target position is calculated to obtain the displacement deviation value of each record, and the displacement deviation value is stored synchronously with the batch number and the grabbing time. In order to accurately characterize the time and space characteristics of the sorting action, the grabbing time field is refined, and the start time and end time of the grabbing are divided into separate columns, which correspond to the displacement deviation data one by one. Then, each data in the record is arranged 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, combined with the performance data of the sorting robot arm, the displacement deviation data and the grabbing time of each sorting action are linked and analyzed to determine whether the operation meets the established efficiency range. All these analysis results are integrated into the sorting position deviation record table, and finally a complete sorting position deviation record is generated for subsequent processing.

[0035] Based on the displacement deviation value and grasping time in the sorting position deviation record, the cumulative error of all records is calculated using the formula: ; Calculate the cumulative error value , the overall error level of the sorting robot arm, generating the error analysis results of the sorting robot arm, where, is the displacement deviation value of each operation, is the total number of sorting times, Indicates the number of deviations; : The displacement deviation value of a single sorting is calculated by the difference between the actual sorting position and the target position. The current value is , , , ; : Total sorting times, calculated through the record table, currently .

[0036] Compute the square of each deviation value: Compute the sum of squared deviations: Divide the sum by the total number of sorts: Calculate the square root to get the cumulative error value : Calculation result cumulative error value It means that in the current sorting task, the average amplitude of the sorting position deviation is 3.67 units. This result reflects whether the operation accuracy of the sorting robot arm is up to standard when performing the task, and provides a key basis for the subsequent screening of high error records. Cumulative error value As an error evaluation indicator for the overall sorting task, it is directly used to determine whether the current robot arm operation needs to be adjusted or corrective measures are triggered. A higher error value indicates that the sorting task accuracy is insufficient, which may affect the quality of task completion, while a low error value indicates that the operation accuracy meets the standard and no further intervention is required.

[0037] Based on the cumulative error value in the error analysis result of the sorting robot arm, a judgment threshold is set, and records exceeding the threshold are screened and determined as high-error records. By analyzing the batch number and sorting position in the high-error record, it is marked as an error record to obtain the sorting error detection result; According to the cumulative error value in the error analysis results of the sorting robot arm, the error value is compared with the set judgment threshold one by one to filter out the records whose errors exceed the threshold. For the records exceeding the threshold, the corresponding batch number and sorting position are extracted, and the deviation pattern in the high-error records is analyzed to gradually confirm the frequency and concentrated area of ​​the error records. Then, the high-error records are associated with the relevant records in the sorting path, and the impact range of the high-error records on the overall sorting task is located by cross-comparing the batch number and sorting position. On the basis of completing the marking of the high-error records, the deviation of the remaining records in the sorting path is further analyzed, the displacement deviation and grasping time of the records are compared, the degree of association with the high-error records is evaluated, and the associated records are synchronously included in the error record range. Finally, all the error sorting records obtained by analysis are organized into an error sorting report, which contains the sorting path, batch number, displacement deviation range and time distribution characteristics of the high-error records, and generates sorting error detection results to support the triggering of subsequent corrective actions.

[0038] See also Figure 6 , the steps to obtain the fault-tolerant processing results are: Call the sorting error detection results, extract high-error records, identify the corresponding batch number, sorting location and commodity code, analyze the sorting task distribution of high-error records in combination with real-time sorting task allocation, and generate high-error task redirection analysis results; The records marked as high errors in the sorting error detection results are called, and these records are extracted one by one, including batch number, sorting position, commodity code and error accumulation value. The batch numbers are grouped, and the number and distribution of high error records in each batch are counted, and they are organized into a batch error statistics table. According to the sorting position information, combined with the commodity code, the area where the errors occur is analyzed, and the error distribution map is drawn by summarizing the number of repetitions and error amplitudes of the sorting positions to intuitively display the spatial distribution characteristics of high error records. Subsequently, the error accumulation value is extracted and cross-matched with the sorting task allocation table. By comparing the recorded task execution time, path capacity and load conditions, the paths that may have redundant tasks or insufficient capacity are screened out. Finally, a mapping relationship is established between the high error records and the backup paths suitable for redirection, and the backup path recommendation table for high error tasks is sorted out to generate the high error task redirection analysis results.

[0039] Based on the high-error task redirection analysis results, select the backup path in the sorting task distribution, redistribute the high-error tasks according to the capacity limit of the backup path and the current load, adjust the task allocation order and sorting position, and generate the sorting task redirection results; Based on the high-error task redirection analysis results, the task execution time, sorting path and corresponding error accumulation value of each high-error record are extracted, and compared with the capacity limit and current load of the backup path one by one. For each record, the priority of the task is calculated, and the allocation order of the high-error records is reordered according to the priority, and matched to the backup 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 capacity close to full load, low-priority tasks are preferentially selected for reallocation to ensure the effective use of path resources. After completing the redirection allocation of all high-error records, the adjustment results are organized into a real-time sorting task update table, which contains the time schedule of the assigned tasks, path adjustments and the sorting priority of the goods, and generates the sorting task redirection results.

[0040] According to the sorting task redirection result, the operating parameters of the sorting robot arm are updated, the grasping path, force and angle settings are adjusted, and the completed high-error task status is marked as corrected, the updated operating parameters and task completion information are recorded, and the fault-tolerant processing results are generated; According to the redirection results of the sorting tasks, the adjusted grasping paths, force parameters and angle settings are extracted for the reallocated sorting tasks one by one, and these parameters are directly applied to the configuration update of the sorting robot arm to ensure that the range of motion and execution force of the robot arm are adapted to the task requirements. For each updated parameter, its setting value is recorded in real time, including the displacement of the grasping path, the force range and the angle change, and the record is synchronously updated to the sorting log, corresponding to the completion time and status of the sorting task. In the process of path update, the sorting tasks are reordered according to the adjusted path to form a sorting path update table, in which the adjustment details of each task are clearly marked. The records of completed tasks are sorted, the status is marked as corrected, the adjustment information of completed tasks is archived, and the fault-tolerant processing results are generated, while providing updated path and operation parameter records for subsequent sorting tasks.

[0041] See also Figure 7 , the steps to obtain inventory update data are: Based on the fault-tolerant processing results, the change types of the products are extracted one by one, the product codes are matched with the corresponding storage location information according to the change types, the occupancy information of the target storage location is updated, and the addition and reduction of the products are recorded. For the order cancellation operation, the original storage location of the cancelled product is extracted, and the product return operation is associated with the target storage location, and the product change association record is integrated to generate; Call the updated storage location information and product code data in the fault-tolerant processing result, classify all product records according to the change type, and use the product code as the primary key to match the target storage location information. For newly added types of products, extract the available capacity of the target storage location and compare it with the product quantity to ensure that the target storage location can accommodate the newly added products. If the match is successful, add the newly added product quantity to the current inventory of the target storage location, and record the completion time and allocation path of the operation. For reduced types of products, extract the current storage location of the product, deduct the corresponding quantity, and update the remaining available capacity of the storage location at the same time, and record the storage location status after the deduction is completed. For canceled products, query the original storage location and corresponding quantity of the product, confirm that the canceled product can be returned to the original storage location, generate the return path record, and update the storage location occupancy after the return operation is completed. Finally, the detailed records of all product operations are integrated into the classification processing results of product changes.

[0042] According to the commodity change association records, check the consistency of commodity codes and storage location information one by one, determine whether the changed quantity of commodities matches the capacity limit of the corresponding storage location, analyze the impact of the change operation on inventory occupancy, and reallocate commodities that exceed the capacity limit to other storage locations to generate storage location capacity verification results; Based on the classification processing results of commodity changes, verify the matching of commodity codes and target warehouse information one by one, including the comparison of the changed quantity of commodities with the total capacity of the target warehouse and the currently occupied capacity. For new commodity operations, check whether the quantity of commodities exceeds the available capacity of the target warehouse. If there is an excess, generate a reallocation record and allocate the excess commodities to other spare warehouses that meet the capacity requirements. For reduction operations, verify whether the changed warehouse status meets the safety stock requirements. If it is lower than the safety stock threshold, mark it as an abnormal state and generate a replenishment plan. For the return operation of canceled goods, check the integrity of the return path and the correctness of the original warehouse information to ensure that the commodity return operation can be executed correctly, and record all verified and adjusted data as warehouse status and allocation adjustment results to form a final verification report.

[0043] Based on the results of the storage capacity verification, perform inventory synchronization operations for the verified products and storage location information, update the changes in product quantity to the inventory table in real time, and generate updated return records for the return operations of canceled products. Count the operation types, timestamps, and processing status of all products, and output inventory update data; According to the location status and allocation adjustment results, extract the change records of the goods one by one, and update the product code and the changed quantity to the inventory table synchronously. At the same time, update the occupancy information of the location to reflect the actual impact of the product change on the location status. For the operation of adding and reducing goods, 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 goods, integrate the return record into the inventory table and return log, and review the inventory status of the original location to ensure the accuracy of the return operation. Organize the detailed information of all update operations into an inventory change confirmation table, which includes the product code, target location information, change type, change quantity, operation completion time and status mark, and finally output the complete inventory update data and archive it.

[0044] See also Figure 8 , the steps to obtain the inventory optimization plan are: Based on the inventory update data, extract the inventory change information within the change cycle according to the commodity code classification, compare and analyze the inventory change of each commodity with its historical inventory change record, and calculate the inventory change rate within the cycle and the difference in historical inventory levels to generate dynamic inventory comparison results; When analyzing inventory update data, we first need to identify the specific information items in the update data, including the unique code of the product, the current inventory quantity, the type of update (such as addition, reduction or correction) and the corresponding timestamp. After parsing the inventory update data and extracting the above key information, we need to perform a logical check on the extracted results to ensure that the correlation between the information items is complete and accurate. For example, whether the product code exists in the predefined product list, whether the update type complies with the established rules, and whether the timestamp is within the expected time range. By confirming these checks, we can generate a set of valid inventory update records. On this basis, we need to combine historical inventory dynamic data to conduct a comparative analysis of the current inventory change trend, including calculating the inventory fluctuation range, cumulative change and other key indicators of each product in unit time. By calculating these indicators and matching them with historical trend data, we can identify abnormal situations, such as excessive inventory fluctuations or abnormal update frequency. Through this process, we can obtain verified inventory dynamic analysis results.

[0045] Based on the inventory dynamic comparison results, the formula is used: ; Calculating Inventory Liquidity Score , evaluate the inventory accuracy according to the error rate in the historical inventory records, identify the key improvement points of inventory management based on the results, and generate inventory liquidity and accuracy evaluation results, among which, 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; There are the following data: , , ,but: 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.

[0046] 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; 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.

[0047] 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. Warehouse sorting real-time control system based on edge computing, characterized by: The system comprises: The inbound data processing module receives the arrival information of goods, parses the received information, verifies the data integrity and accuracy, combines the e-commerce enterprise information and the declared batch number, synchronizes the information to prepare for real-time sorting, and generates the arrival data confirmation result; The intelligent sorting control module confirms the result according to the arrival data, evaluates the batch priority in combination with the proportion of the number of goods in each batch, analyzes the correlation between the proportion of the number of goods in each batch and the change in real-time order demand, selects the batches and paths for priority sorting, and calculates the adjusted sorting speed to match the real-time order change. According to the adjustment result and the size and weight of the goods, the task is optimized, the setting of the sorting robot arm is adjusted, the sorting position and batch information of the goods are recorded, and the sorting completion information is generated; The dynamic fault-tolerant response module continuously monitors the sorting completion information, analyzes the sorting robot arm performance data and operation log in real time, matches the sorting position with the target position, calculates the displacement deviation value of each operation, draws a sorting deviation time series trajectory diagram, analyzes the dynamic change trend of the sorting operation, and calculates the cumulative error value, the overall error level of the sorting robot arm, determines the high error record in combination with the judgment threshold, analyzes the impact range of the high error record on the overall sorting task, obtains the sorting error detection result, automatically triggers the error response according to the detection result, redirects the sorting task, and instantly feeds back to update the sorting robot arm operation status to generate a fault-tolerant processing result; Based on the fault-tolerant processing results, the inventory management module inputs the updated storage location information, product code and quantity, synchronizes inventory changes to the database in real time, verifies information consistency, returns the cancelled products, and outputs inventory update data by synchronously executing inventory and storage location queries.

2. The warehouse sorting real-time control system based on edge computing according to claim 1 is characterized in that: The steps for obtaining the arrival data confirmation result are: Receive product arrival information, extract field information, record product name, quantity, arrival time and logistics information, verify each field separately, check whether the field content meets the predefined format requirements, determine whether the logistics identifier is a unique number and has a complete structure, and generate product arrival information that meets the verification standards; Based on the arrival information of the goods that meet the verification criteria, the unique identifiers in the logistics information are parsed one by one, the e-commerce enterprise information and the declared batch number are associated with the logistics identifier, and the parsed product information is matched with the corresponding e-commerce enterprise information and the declared batch to generate the associated product and declared batch matching data; Using the data of matching the commodity with the declared batch, the timestamp of the commodity quantity and logistics identification is calculated using the formula: ； Calculate the compatibility score between the current arrival information and the real-time sorting requirements , generate arrival data confirmation results, where, is the total quantity of goods, is the difference between the timestamps, It is the sum of the verification results of the logistics identification.

3. The warehouse sorting real-time control system based on edge computing according to claim 2 is characterized in that: The steps for adjusting the sorting speed are as follows: According to the declared batch number and commodity code in the confirmation result of the arrival data, the commodity codes are classified and counted according to the declared batch number, the quantity ratio and total quantity of the commodity codes in each declared batch are counted, the batch priority is evaluated in combination with the quantity ratio of each batch, and the statistical results of the association between the commodity code and the declared batch number are generated; Based on the statistical results of the association between the commodity code and the declared batch number, combined with the demand change of the commodity code in the real-time order, by analyzing the correlation between the proportion of the quantity of each batch of commodities and the demand change of the real-time order, the batches and routes for priority sorting are selected, and the importance level of the commodity code is marked to generate the sorting route selection result of real-time analysis; Using the sorting path selection results of the real-time analysis and combining the demand changes of real-time orders, the formula is adopted: ； Calculate the adjusted sorting speed , generate the adaptive sorting path configuration result, where is the total quantity of goods in 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 warehouse sorting real-time control system based on edge computing according to claim 3 is characterized in that: The steps for obtaining the sorting completion information are as follows: Calling the adaptive sorting path configuration result, combining the size and weight information of the goods, normalizing the size and weight data of the goods, and calculating the proportion of each commodity occupied by the sorting task, and generating a task-optimized commodity characteristic evaluation result; Based on the evaluation results of the commodity characteristics optimized by the task, the characteristic combination with the highest commodity occupancy ratio is extracted, and the grasping range and carrying capacity parameters of the sorting robot are calibrated using the formula: ； Grasp force adjustment ratio , synchronously adjust the operating parameters of the sorting robot arm, and generate the adjusted sorting robot arm configuration parameters, where: Indicates the normalized volume value of the product. Indicates the normalized weight of the product. Indicates the maximum carrying capacity of the sorting robot arm; According to the configuration parameters of the sorting robot arm, the grabbing time, sorting position and corresponding batch number of each commodity during the sorting operation are recorded, the grabbing record and batch information are synchronously sorted, and sorting completion information is generated.

5. The warehouse sorting real-time control system based on edge computing according to claim 4 is characterized in that: The steps for detecting the sorting error are: According to the sorting completion information, the sorting position, batch number and grabbing time of each commodity are extracted, and the sorting position and the target position are matched in combination with the performance data of the sorting robot arm, and the displacement deviation value of each operation is calculated, and a sorting deviation time series trajectory diagram is drawn to show the dynamic change trend of the sorting operation, and the batch number and grabbing time are recorded to generate a sorting position deviation record; Based on the displacement deviation value and the grabbing time in the sorting position deviation record, the cumulative error calculation is performed on all records using the formula: ； Calculate the cumulative error value , the overall error level of the sorting robot arm, determines whether the current robot arm operation needs to be adjusted or triggers corrective measures, and generates the sorting robot arm error analysis results, where, is the displacement deviation value of each operation, is the total number of sorting times, Indicates the number of deviations; Based on the cumulative error value in the error analysis result of the sorting robot arm, a judgment threshold is set, records exceeding the threshold are screened and determined as high-error records, batch numbers and sorting positions corresponding to the high-error records are extracted, and the deviation patterns appearing in the high-error records are analyzed, and the frequency and concentrated areas of the error records are gradually confirmed. The high-error records are associated with related records in the sorting path, and the impact range of the high-error records on the overall sorting task is analyzed by cross-comparing the batch numbers and sorting positions, so as to obtain the sorting error detection results.

6. The warehouse sorting real-time control system based on edge computing according to claim 5 is characterized in that: The steps for obtaining the fault-tolerant processing result are: Call the sorting error detection result, extract high error records, identify the corresponding batch number, sorting location and commodity code, analyze the sorting task distribution of high error records in combination with real-time sorting task allocation, and generate high error task redirection analysis results; Based on the high-error task redirection analysis result, select a backup path in the sorting task distribution, reallocate the high-error tasks according to the capacity limit of the backup path and the current load, adjust the task allocation order and sorting position, and generate a sorting task redirection result; According to the sorting task redirection result, the operating parameters of the sorting robot arm are updated, the grasping path, force and angle settings are adjusted, and the completed high-error task status is marked as corrected, the updated operating parameters and task completion information are recorded, and the fault-tolerant processing result is generated.

7. The warehouse sorting real-time control system based on edge computing according to claim 6 is characterized in that: The steps for obtaining the inventory update data are as follows: Based on the fault-tolerant processing result, the change type of the product is extracted one by one, the product code is matched with the corresponding storage location information according to the change type, the occupancy information of the target storage location is updated, and the increase and decrease of the product are recorded. For the order cancellation operation, the original storage location of the canceled product is extracted, the product return operation is associated with the target storage location, and the product change association record is integrated to generate; According to the commodity change association records, the consistency of the commodity code and the storage location information is verified one by one to determine whether the changed quantity of the commodity matches the capacity limit of the corresponding storage location, the impact of the change operation on the inventory occupancy is analyzed, and the commodities exceeding the capacity limit are reallocated to other storage locations to generate the storage location capacity verification result; Based on the storage location capacity verification result, the inventory synchronization operation is performed for the verified goods and storage location information, and the changes in the quantity of goods are updated to the inventory table in real time. At the same time, the updated return record is generated for the return operation of the cancelled goods, and the operation type, timestamp and processing status of all goods are counted, and the inventory update data is output.

8. The warehouse sorting real-time control system based on edge computing according to claim 7 is characterized in that: It also includes data analysis and optimization modules. According to the inventory update data, the inventory change information within the change period is extracted according to the commodity code classification, the inventory change amount of each commodity is compared and analyzed with its historical inventory change record, the inventory change rate within the period and the difference in historical inventory levels are statistically analyzed, and the inventory dynamic comparison result is generated; Based on the inventory dynamic comparison results, the formula is adopted: ； Calculating Inventory Liquidity Score , evaluate the inventory accuracy according to the error rate in the historical inventory records, identify the key improvement points of inventory management based on the results, and generate inventory liquidity and accuracy evaluation results, among which, 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; 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.

Images