Intelligent order sorting and delivery system based on cloud platform

Abstract

The invention belongs to the technical field of order sorting and delivery management, and discloses an intelligent order sorting and delivery system based on a cloud platform, which comprises an order information acquisition module, a warehouse matching analysis module, a specified warehouse identification module, an order sorting management module, an order delivery management module, an abnormal condition identification module and a data storage library. According to the invention, the delivery matching condition of each specified warehouse is analyzed based on the order information of the target order, and then whether each specified warehouse meets the delivery condition is judged, so that whether the warehouse meets the delivery condition can be accurately judged, the scientificity and accuracy of delivery decision are improved, and the delivery efficiency is improved. And the problems of resource waste, low efficiency and the like caused by blind delivery are avoided. According to the method, when whether the specified warehouses have the abnormal management condition or not is judged, the specific abnormal direction and the abnormal specified warehouses are further recognized, the essence of the abnormal management can be deeply mined, the problem can be accurately positioned, and fuzziness caused by general abnormal judgment is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of order sorting and delivery management, and relates to an intelligent order sorting and delivery system based on a cloud platform. Background Art

[0002] Order sorting and delivery refers to a series of operations and processes in logistics or e-commerce business processes, including selecting, classifying and arranging goods (sorting) according to customer order information, packaging and marking the goods, and sending the goods to customers (delivery) according to a certain process and method. In the traditional order sorting process, manual operation is prone to fatigue, mistakes, and relatively low efficiency. Therefore, the research on intelligent order sorting and delivery based on cloud platform is of great significance.

[0003] The existing order management process usually only performs simple abnormal situation identification and processing during sorting management and delivery management, lacking further abnormal direction identification and abnormal designated warehouse identification. The lack of further abnormal direction identification makes it impossible to accurately locate the specific source and type of the abnormality, making it difficult to solve the problem in a targeted manner. The lack of abnormal designated warehouse identification will lead to inefficiency in handling abnormalities. When there are abnormalities in multiple warehouses, it is impossible to quickly determine which warehouse has the problem, resulting in resource waste and time delays, which may affect the smooth progress of the entire order process, reduce customer satisfaction, and increase management costs and operational risks. Summary of the invention

[0004] In view of this, in order to solve the problems raised in the above background technology, an intelligent order sorting and delivery system based on a cloud platform is proposed.

[0005] The purpose of the present invention can be achieved through the following technical solutions: an intelligent order sorting and delivery system based on a cloud platform, including: an order information acquisition module, used to obtain order information of a target order, wherein the order information includes order category, order time, delivery address and quantity of items.

[0006] The warehouse matching analysis module is used to obtain each designated warehouse based on the order information of the target order, analyze the delivery matching situation of each designated warehouse, and then determine whether each designated warehouse meets the delivery conditions.

[0007] The designated warehouse identification module is used to record the designated warehouses that are judged to meet the delivery conditions as qualified designated warehouses, and identify the target designated warehouses based on the remaining inventory of each qualified designated warehouse.

[0008] The order sorting management module is used to arrange the target designated warehouse to perform sorting operations on the target order, obtain the sorting quality data of the target order, analyze the sorting quality of the target order, and feed back to the data repository.

[0009] The order delivery management module is used to arrange the target designated warehouse to carry out delivery operations on the sorted orders, obtain the delivery quality data of the target orders, analyze the delivery quality of the target orders, and feed back to the data repository.

[0010] The abnormal situation identification module is used to determine whether there are management abnormalities in each designated warehouse based on historical sorting records and historical delivery records. If so, it further identifies the specific abnormal direction and identifies the abnormal designated warehouse.

[0011] Data repository, used to store historical picking records and historical shipping records.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention analyzes the shipment matching situation of each designated warehouse based on the order information of the target order, and then determines whether each designated warehouse meets the shipment conditions. This analysis method can comprehensively and systematically consider the multi-faceted relationship between the target order and each designated warehouse, and accurately determine whether the warehouse meets the shipment conditions by analyzing the shipment matching situation. This method improves the scientificity and accuracy of shipment decisions, and avoids problems such as waste of resources and inefficiency caused by blind shipments. At the same time, it helps to optimize warehouse resource allocation, so that goods can be shipped from the most suitable warehouse, improve the overall logistics operation efficiency, and ensure the smooth progress of the shipment process.

[0013] (2) When judging whether there are management anomalies in each designated warehouse, the present invention further identifies specific anomaly directions and abnormal designated warehouses. This analysis method can deeply explore the essence of management anomalies, accurately locate the problem, and avoid the ambiguity and uncertainty caused by general judgment of anomalies. By clarifying the anomaly directions, it can provide accurate directions for targeted problem solving and improve the efficiency and quality of problem solving. At the same time, accurate identification of abnormal designated warehouses helps to concentrate resources to accurately manage and improve specific warehouses, reduce the impact of anomalies on overall operations, improve the level of refinement of warehouse management, ensure the smooth operation of the entire logistics supply chain, and enhance the operational stability and risk resistance of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0015] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 As shown, the present invention provides an intelligent order sorting and delivery system based on a cloud platform, which includes an order information acquisition module, a warehouse matching analysis module, a designated warehouse identification module, an order sorting management module, an order delivery management module, an abnormal situation identification module and a data storage library, wherein the order information acquisition module is connected to the warehouse matching analysis module, the warehouse matching analysis module is connected to the designated warehouse identification module, the designated warehouse identification module is respectively connected to the order sorting management module and the order delivery management module, the order sorting management module and the order delivery management module are both connected to the data storage library, and the data storage library is connected to the abnormal situation identification module.

[0018] The order information acquisition module is used to acquire order information of a target order, wherein the order information includes order category, order time, delivery address and item quantity.

[0019] The warehouse matching analysis module is used to obtain each designated warehouse based on the order information of the target order, analyze the delivery matching situation of each designated warehouse, and then determine whether each designated warehouse meets the delivery conditions.

[0020] It should be added that the specific method of obtaining each designated warehouse is: extracting the order category of the target order, and then comparing the order category of the target order with each warehouse. If a warehouse has the order category of the target order, the warehouse is recorded as the designated warehouse.

[0021] In a preferred embodiment of the present invention, the analysis of the delivery matching of each designated warehouse requires constructing a delivery matching index of each designated warehouse, and the specific method is as follows: extract the delivery address of the target order, and obtain the address of each designated warehouse, and then obtain the delivery distance corresponding to each designated warehouse, which is recorded as ,in Indicates the number of the specified warehouse. , Indicates the quantity of the specified warehouse.

[0022] Using the formula Analyze and obtain the shipment matching index of each designated warehouse ,in Indicates the preset reference distance.

[0023] In a preferred embodiment of the present invention, the specific method of judging whether each designated warehouse meets the shipping conditions is as follows: extracting the shipping matching index of each designated warehouse, and then comparing them with the preset shipping matching index thresholds; if the shipping matching index of a designated warehouse is greater than or equal to the shipping matching index threshold, it is judged that the designated warehouse meets the shipping conditions; if the shipping matching index of a designated warehouse is less than the shipping matching index threshold, it is judged that the designated warehouse does not meet the shipping conditions.

[0024] For example, the shipment matching index threshold is .

[0025] It should be noted that the present invention analyzes the shipment matching situation of each designated warehouse based on the order information of the target order, and then determines whether each designated warehouse meets the shipment conditions. This analysis method can comprehensively and systematically consider the multi-faceted relationship between the target order and each designated warehouse, and accurately judge whether the warehouse meets the shipment conditions by analyzing the shipment matching situation. This method improves the scientificity and accuracy of shipment decisions, and avoids problems such as waste of resources and inefficiency caused by blind shipments. At the same time, it helps to optimize warehouse resource allocation, so that goods can be shipped from the most suitable warehouse, improve the overall logistics operation efficiency, and ensure the smooth progress of the shipment process.

[0026] The designated warehouse identification module is used to record the designated warehouses that are judged to meet the delivery conditions as qualified designated warehouses, and identify the target designated warehouses based on the remaining inventory of each qualified designated warehouse.

[0027] In a preferred embodiment of the present invention, the specific method of identifying the target designated warehouse is as follows: extracting the delivery matching index of each qualified designated warehouse, and then arranging them in order from large to small to obtain a list of qualified designated warehouses.

[0028] Obtain the remaining inventory of each designated warehouse that meets the standards, extract the quantity of items in the target order, and compare the remaining inventory of each designated warehouse that meets the standards with the quantity of items in the target order in the order of the designated warehouse list to determine the target designated warehouses for the target order and the corresponding shipment quantity.

[0029] It needs to be explained that if the target order contains a large number of items, and the remaining inventory of a single designated warehouse cannot meet the needs of the target order, multiple designated warehouses are required to ship the items simultaneously.

[0030] What needs to be further explained is the specific steps of determining the target designated warehouses and the corresponding shipment quantity of the target order: extracting the quantity of items of the target order and the list of qualified designated warehouses, obtaining the remaining inventory of each designated warehouse in the list of qualified designated warehouses, and then determining the target designated warehouses and the corresponding shipment quantity of the target order according to the order of the list of qualified designated warehouses.

[0031] For example, the order of the list of qualified designated warehouses is: designated warehouse 1, designated warehouse 4, designated warehouse 3, and the corresponding remaining inventory quantities are 4, 2, and 6 respectively. If the number of items in the target order is 7 at this time, the target designated warehouses obtained at this time are: designated warehouse 1, designated warehouse 4, designated warehouse 3, and the corresponding shipment quantities are: 4, 2, and 1 respectively.

[0032] The order sorting management module is used to arrange the target designated warehouse to perform sorting operations on the target order, obtain the sorting quality data of the target order, analyze the sorting quality of the target order, and feed back to the data storage repository.

[0033] In a preferred embodiment of the present invention, the sorting quality data includes sorting efficiency, item packaging integrity index and single order integrity index.

[0034] It should be noted that the reasons for selecting sorting efficiency, item packaging integrity index and one-time order integrity index as sorting quality data are: 1. Sorting efficiency directly affects the cost of the entire logistics or warehousing operation. Efficient sorting can process more orders per unit time and reduce the time occupied by manpower, equipment and other resources. 2. The item packaging integrity index is the key to ensuring that the goods are not damaged during sorting and subsequent transportation. During the sorting process, the goods may undergo multiple handling, scanning and other operations. If the packaging is incomplete, the goods are easily damaged by collision, extrusion and other damages. 3. The one-time order integrity index is one of the core elements of customer satisfaction. Customers expect to receive all the ordered goods in one order, and the lack of any item will lead to customer dissatisfaction.

[0035] In a preferred embodiment of the present invention, the analysis of the sorting quality of the target order requires constructing a sorting quality index of the target order, and the specific method is as follows: extracting the sorting efficiency, item packaging integrity index and one-time order integrity index of the target order, respectively denoted as , , .

[0036] It should be added that the calculation method of the sorting efficiency of the target order is as follows: the order placement time of the target order and the sorting completion time of each target designated warehouse are extracted, the sorting completion time of each target designated warehouse is compared, the latest sorting completion time is selected as the sorting completion time of the target order, the sorting completion time of the target order is calculated by difference between the sorting completion time of the target order and the order placement time to obtain the sorting time of the target order, and then the ratio is calculated with the preset sorting time threshold and the inverse is taken to obtain the sorting efficiency of the target order.

[0037] It should be noted that the analysis method of the item packaging integrity index of the target order is as follows: a high-definition camera is used to obtain images of packages in each target designated warehouse corresponding to the target order, and it is determined whether the packages in each target designated warehouse corresponding to the target order are completely packaged, and the number of packages with complete packaging corresponding to the target order and the total number of packages are obtained by counting, and then the number of packages with complete packaging corresponding to the target order and the total number of packages are calculated as a proportion to obtain the item packaging integrity index of the target order.

[0038] It should be supplemented that the specific method for judging whether the packages of the target order corresponding to each target designated warehouse are completely packaged is as follows: extracting the package images of the target order corresponding to each target designated warehouse, using image processing software to obtain the uncovered area of ​​the packages of the target order corresponding to each target designated warehouse, and comparing the uncovered area with a preset uncovered area threshold; if the uncovered area of ​​the packages of the target order corresponding to a certain target designated warehouse is greater than the uncovered area threshold, it is judged that the packages of the target designated warehouse are not completely packaged; otherwise, it is judged that the packages of the target designated warehouse are completely packaged.

[0039] It should be supplemented that the analysis method of the one-time order completeness index of the target order is as follows: the shipment quantity and one-time sorting quantity of each target designated warehouse corresponding to the target order are obtained, and the one-time sorting quantity and the shipment quantity of each target designated warehouse corresponding to the target order are calculated by ratio, so as to obtain the one-time order completeness index of the target order corresponding to each target designated warehouse, and then the one-time order completeness index of the target order is obtained by average calculation.

[0040] Using the formula Analyze and obtain the sorting quality index of the target order ,in Indicates the preset reference sorting efficiency, Indicates the preset threshold value of the item packaging integrity index. Indicates the preset order completeness index threshold. They respectively represent the weight factors corresponding to the sorting efficiency of the target order, the item packaging integrity index and the single order integrity index.

[0041] It should be noted that the weight factors corresponding to the sorting efficiency, item packaging integrity index and one-time order integrity index of the target order are set based on the following: the sorting efficiency weight factor is set based on business priorities, operating costs and competitive advantages. For example, the weight factor for fresh food e-commerce may be set high due to timeliness requirements; the item packaging integrity index weight factor is related to product characteristics, brand image maintenance and customer feedback, and high-value and fragile products require higher weights; the one-time order integrity index weight factor is based on key factors of customer satisfaction, operational risk reduction, and industry standards and regulations to ensure that customers receive complete orders and that the company's operations are stable. Reasonable settings of each weight factor help improve the company's overall operational quality and customer satisfaction.

[0042] For example, .

[0043] The order delivery management module is used to arrange the target designated warehouse to deliver the sorted orders, obtain the delivery quality data of the target orders, analyze the delivery quality of the target orders, and feed back to the data storage repository.

[0044] In a preferred embodiment of the present invention, the delivery quality data includes historical delivery time compliance and required delivery time compliance.

[0045] It should be noted that the reasons for choosing historical delivery time compliance and required delivery time compliance as delivery quality data are: from the perspective of customer satisfaction, they are related to customer shopping experience and willingness to repurchase, and affect the relationship between the company and its customers. In supply chain management, they can reflect the stability and flexibility of the supply chain process, helping companies to discover and solve problems and optimize processes. For corporate reputation and brand image, good delivery time performance can establish a reliable image and enhance competitiveness, while delays will have a negative impact. At the same time, these data provide companies with a quantitative evaluation basis, support data-driven decision-making, and promote companies to continuously improve delivery quality.

[0046] It should be added that the analysis method of the historical delivery time compliance is as follows: extract the historical delivery records stored in the data storage library, obtain the delivery time corresponding to each historical delivery record, and then perform mean calculation to obtain the historical average delivery time, obtain the actual delivery time of each target designated warehouse corresponding to the target order, calculate the ratio of the actual delivery time of each target designated warehouse corresponding to the target order to the historical average delivery time, and then take the inverse to obtain the historical delivery time compliance of the target order corresponding to each target designated warehouse, and then compare and select the minimum historical delivery time compliance as the historical delivery time compliance of the target order.

[0047] It should be supplemented that the analysis method of the required delivery time compliance is: the ratio of the actual delivery time of the target order corresponding to each target designated warehouse is calculated with the pre-set delivery time threshold, and then the inverse is taken to obtain the required delivery time compliance of the target order corresponding to each target designated warehouse, and then the minimum required delivery time compliance is selected as the required delivery time compliance of the target order.

[0048] In a preferred embodiment of the present invention, the analysis of the delivery quality of the target order requires the construction of a delivery quality index of the target order, which is specifically performed as follows: extracting the historical delivery time compliance and the required delivery time compliance of the target order, which are respectively recorded as , .

[0049] Using the formula Analyze and obtain the delivery quality index of the target order ,in They respectively represent the weight factors corresponding to the preset target order historical delivery time compliance and the required delivery time compliance.

[0050] It should be noted that the weight factors corresponding to the historical delivery time compliance and the required delivery time compliance of the target order are set based on: the business type of the enterprise and the characteristics of the customer group are important considerations. For example, in industries that are sensitive to timeliness or scenarios where customers have high requirements, the weight of the required delivery time compliance may be higher; the company's own operating conditions and supply chain stability also have an impact. If the historical delivery fluctuates greatly, it is necessary to pay attention to the historical compliance weight for improvement; under the market competition situation, in order to enhance competitiveness, the required compliance weight may be emphasized; it is also necessary to combine industry standards and regulatory requirements, as well as past data performance and customer feedback to make a comprehensive determination to ensure that the delivery quality assessment is reasonable and effective, and promote enterprise operation optimization and customer satisfaction.

[0051] For example, .

[0052] The abnormal situation identification module is used to determine whether there are management abnormalities in each designated warehouse based on historical sorting records and historical delivery records. If so, it further identifies the specific abnormal direction and identifies the abnormal designated warehouse.

[0053] In a preferred embodiment of the present invention, the specific method of judging whether each designated warehouse has management abnormality is as follows: extracting historical sorting records stored in the data storage library, obtaining sorting quality indexes of each designated warehouse corresponding to each historical sorting record, and then comparing them with preset sorting quality index thresholds respectively; if the sorting quality index of a designated warehouse corresponding to a historical sorting record is greater than or equal to the sorting quality index threshold, it is judged that the historical sorting record corresponding to the designated warehouse is a normal sorting record; otherwise, it is judged that the historical sorting record corresponding to the designated warehouse is an abnormal sorting record; the number of abnormal sorting records corresponding to each designated warehouse and the total number of historical sorting records are obtained by statistics, and then a ratio calculation is performed to obtain the historical sorting abnormality index corresponding to each designated warehouse.

[0054] It should be noted that the analysis method of the sorting quality index of each designated warehouse corresponding to each historical sorting record refers to the analysis method of the sorting quality of the target order.

[0055] Exemplarily, the sorting quality index threshold is .

[0056] The historical shipment records saved in the data repository are extracted, and the shipment quality index of each designated warehouse corresponding to each historical shipment record is obtained, and then compared with the preset shipment quality index thresholds respectively. If the shipment quality index of a designated warehouse corresponding to a historical shipment record is greater than or equal to the shipment quality index threshold, the historical shipment record corresponding to the designated warehouse is judged to be a normal shipment record. Otherwise, the historical shipment record corresponding to the designated warehouse is judged to be an abnormal shipment record. The number of abnormal shipment records corresponding to each designated warehouse and the total number of historical shipment records are obtained by statistics, and then the ratio is calculated to obtain the historical shipment abnormality index corresponding to each designated warehouse.

[0057] It should be noted that the analysis method of the delivery quality index of each designated warehouse corresponding to each historical delivery record refers to the analysis method of the delivery quality of the target order.

[0058] Exemplarily, the sorting quality index threshold is .

[0059] The historical sorting anomaly index and historical shipping anomaly index corresponding to each designated warehouse are compared with the preset historical sorting anomaly index threshold and historical shipping anomaly index threshold respectively. When the historical sorting anomaly index corresponding to a designated warehouse is less than the historical sorting anomaly index threshold and the historical shipping anomaly index is less than the historical shipping anomaly index threshold, it is judged that there is no management anomaly in the designated warehouse; otherwise, it is judged that there is a management anomaly in the designated warehouse.

[0060] In a preferred embodiment of the present invention, the method for further identifying specific abnormality directions is as follows: extract the historical sorting abnormality index and the historical delivery abnormality index of each designated warehouse, and then perform mean calculations to obtain the monitoring sorting abnormality index and the monitoring delivery abnormality index, and then perform comparisons.

[0061] If the monitoring sorting anomaly index of a designated warehouse is greater than the monitoring delivery anomaly index, it is determined that the specific anomaly of the designated warehouse is a sorting anomaly.

[0062] If the monitoring sorting anomaly index of a designated warehouse is less than the monitoring delivery anomaly index, it is determined that the specific anomaly of the designated warehouse is a delivery anomaly.

[0063] In a preferred embodiment of the present invention, the specific method of identifying abnormal designated warehouses is as follows: the historical sorting anomaly index and the historical delivery anomaly index of each designated warehouse are summed up according to the weight to obtain the management anomaly index of each designated warehouse, and then arranged in order from large to small, and the designated warehouse corresponding to the largest management anomaly index is selected as the abnormal designated warehouse.

[0064] It should be noted that the present invention further identifies specific abnormal directions and abnormal designated warehouses when judging whether there are management anomalies in each designated warehouse. This analysis method can deeply explore the essence of management anomalies, accurately locate the problem, and avoid the ambiguity and uncertainty caused by general judgment of anomalies. By clarifying the abnormal direction, it can provide accurate direction for targeted problem solving and improve the efficiency and quality of problem solving. At the same time, accurate identification of abnormal designated warehouses helps to concentrate resources to accurately manage and improve specific warehouses, reduce the impact of anomalies on overall operations, improve the level of refinement of warehouse management, ensure the smooth operation of the entire logistics supply chain, and enhance the operational stability and risk resistance of the enterprise.

[0065] The data repository is used to store historical sorting records and historical shipping records.

[0066] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. Intelligent order sorting and delivery system based on cloud platform, characterized by: include: An order information acquisition module is used to acquire order information of a target order, wherein the order information includes order category, order time, delivery address and item quantity; The warehouse matching analysis module is used to obtain each designated warehouse based on the order information of the target order, analyze the shipment matching of each designated warehouse, and then determine whether each designated warehouse meets the shipment conditions; The designated warehouse identification module is used to record the designated warehouses that meet the delivery conditions as qualified designated warehouses, and identify the target designated warehouses based on the remaining inventory of each qualified designated warehouse; The order sorting management module is used to arrange the target designated warehouse to sort the target order, obtain the sorting quality data of the target order, analyze the sorting quality of the target order, and feed back to the data repository; The order delivery management module is used to arrange the target designated warehouse to deliver the sorted orders, obtain the delivery quality data of the target orders, analyze the delivery quality of the target orders, and feed back to the data repository; The abnormal situation identification module is used to determine whether there are management abnormalities in each designated warehouse based on historical sorting records and historical delivery records. If so, it further identifies the specific abnormal direction and the abnormal designated warehouse; Data repository, used to store historical picking records and historical shipping records.

2. The intelligent order sorting and delivery system based on the cloud platform as claimed in claim 1, characterized in that: The analysis of the shipment matching of each designated warehouse requires the construction of a shipment matching index of each designated warehouse, and the specific method is as follows: Extract the delivery address of the target order, obtain the address of each designated warehouse, and then obtain the corresponding delivery distance of each designated warehouse, recorded as ,in Indicates the number of the specified warehouse. , Indicates the number of specified warehouses; Using the formula Analyze and obtain the shipment matching index of each designated warehouse ,in Indicates the preset reference distance.

3. The intelligent order sorting and delivery system based on the cloud platform as claimed in claim 2, characterized in that: The specific method for determining whether each designated warehouse meets the delivery conditions is as follows: The shipment matching index of each designated warehouse is extracted, and then compared with the preset shipment matching index threshold. If the shipment matching index of a designated warehouse is greater than or equal to the shipment matching index threshold, it is judged that the designated warehouse meets the shipment conditions. If the shipment matching index of a designated warehouse is less than the shipment matching index threshold, it is judged that the designated warehouse does not meet the shipment conditions.

4. The intelligent order sorting and delivery system based on the cloud platform as claimed in claim 3, characterized in that: The specific method of identifying the target designated warehouse is as follows: Extract the delivery matching index of each qualified designated warehouse, and then arrange them in descending order to obtain a list of qualified designated warehouses; Obtain the remaining inventory of each designated warehouse that meets the standards, extract the quantity of items in the target order, and compare the remaining inventory of each designated warehouse that meets the standards with the quantity of items in the target order in the order of the designated warehouse list to determine the target designated warehouses for the target order and the corresponding shipment quantity.

5. The intelligent order sorting and delivery system based on the cloud platform as claimed in claim 1, characterized in that: The sorting quality data includes sorting efficiency, item packaging integrity and one-time order integrity, and the delivery quality data includes historical delivery time compliance and required delivery time compliance.

6. The intelligent order sorting and delivery system based on the cloud platform as claimed in claim 5, characterized in that: The analysis of the sorting quality of the target order requires the construction of a sorting quality index of the target order, and the specific method is as follows: Extract the sorting efficiency, item packaging integrity and one-time order integrity of the target order, which are recorded as , , ; Using the formula Analyze and obtain the sorting quality index of the target order ,in Indicates the preset reference sorting efficiency, Indicates the preset item packaging integrity threshold. Indicates the pre-set order completeness threshold. They represent the weight factors corresponding to the sorting efficiency, item packaging integrity and one-time order integrity of the target order respectively.

7. The intelligent order sorting and delivery system based on the cloud platform as claimed in claim 6, characterized in that: The analysis of the delivery quality of the target order requires the construction of a delivery quality index of the target order, and the specific method is as follows: Extract the historical delivery time compliance and required delivery time compliance of the target order, which are recorded as , ; Using the formula Analyze and obtain the delivery quality index of the target order ,in They respectively represent the weight factors corresponding to the preset target order historical delivery time compliance and the required delivery time compliance.

8. The intelligent order sorting and delivery system based on the cloud platform as claimed in claim 7, characterized in that: The specific method for determining whether each designated warehouse has management abnormality is as follows: Extract the historical sorting records stored in the data storage library, obtain the sorting quality index of each designated warehouse corresponding to each historical sorting record, and then compare them with the preset sorting quality index threshold. If the sorting quality index of a designated warehouse corresponding to a historical sorting record is greater than or equal to the sorting quality index threshold, the historical sorting record corresponding to the designated warehouse is judged to be a normal sorting record. Otherwise, the historical sorting record corresponding to the designated warehouse is judged to be an abnormal sorting record. The number of abnormal sorting records corresponding to each designated warehouse and the total number of historical sorting records are obtained by statistics, and then the ratio is calculated to obtain the historical sorting abnormality index corresponding to each designated warehouse; Extract the historical shipment records stored in the data repository, obtain the shipment quality index of each designated warehouse corresponding to each historical shipment record, and then compare them with the preset shipment quality index thresholds respectively. If the shipment quality index of a designated warehouse corresponding to a historical shipment record is greater than or equal to the shipment quality index threshold, the historical shipment record corresponding to the designated warehouse is judged to be a normal shipment record. Otherwise, the historical shipment record corresponding to the designated warehouse is judged to be an abnormal shipment record. The number of abnormal shipment records corresponding to each designated warehouse and the total number of historical shipment records are obtained by statistics, and then the ratio is calculated to obtain the historical shipment abnormality index corresponding to each designated warehouse. The historical sorting anomaly index and historical shipping anomaly index corresponding to each designated warehouse are compared with the preset historical sorting anomaly index threshold and historical shipping anomaly index threshold respectively. When the historical sorting anomaly index corresponding to a designated warehouse is less than the historical sorting anomaly index threshold and the historical shipping anomaly index is less than the historical shipping anomaly index threshold, it is judged that there is no management anomaly in the designated warehouse; otherwise, it is judged that there is a management anomaly in the designated warehouse.

9. The intelligent order sorting and delivery system based on the cloud platform as claimed in claim 8, characterized in that: The method of further identifying the specific abnormal direction is as follows: Extract the historical sorting anomaly index and historical delivery anomaly index of each designated warehouse, and then calculate the mean to obtain the monitoring sorting anomaly index and monitoring delivery anomaly index, and then compare them; If the monitoring sorting anomaly index of a designated warehouse is greater than the monitoring delivery anomaly index, the specific anomaly of the designated warehouse is judged to be a sorting anomaly; If the monitoring sorting anomaly index of a designated warehouse is less than the monitoring delivery anomaly index, it is determined that the specific anomaly of the designated warehouse is a delivery anomaly.

10. The intelligent order sorting and delivery system based on cloud platform as claimed in claim 9, characterized in that: The specific method of identifying abnormal designated warehouses is as follows: The historical sorting anomaly index and historical delivery anomaly index of each designated warehouse are summed up according to the weight to obtain the management anomaly index of each designated warehouse, and then arranged in order from large to small, and the designated warehouse corresponding to the largest management anomaly index is selected as the abnormal designated warehouse.

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