An intelligent matching method and system for warehouse receipt in logistics receiving

Abstract

The present invention relates to the technical field of intelligent logistics management, and particularly relates to an intelligent matching method and system for warehouse entry documents in logistics receiving. An intelligent matching method for warehouse entry documents in logistics receiving includes the following steps: S1: Obtain the goods-related data of the target goods, where the goods-related data includes the transportation stacking data of the goods, the shipping code data of the goods, and the cold chain interruption data. At the same time, obtain the goods-related data of the target goods and use a variation calculation formula to obtain the maximum quality variation value of the goods sub-code data; S2: Perform intelligent matching on the goods according to the shipping code data of the target goods and the maximum quality variation value of the goods sub-code data. The present invention dynamically selects a central or edge device to execute the matching task by fusing the customized goods ratio, coding complexity, and real-time network quality, and accurately and quickly completes the intelligent matching of warehouse entry documents in logistics receiving.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent logistics management, and particularly to an intelligent matching method and system for incoming inventory slips in logistics receiving. Background Art

[0002] Currently, the logistics industry is in a stage of rapid development of informatization and intelligence. With the continuous expansion of e-commerce, supply chain management, and logistics distribution scales, the demands for information collection, data processing, and real-time monitoring in the logistics link are increasing day by day. Especially in the links of goods warehousing, receiving, and inspection, the traditional manual inspection method and static data comparison are difficult to meet the requirements of modern logistics for efficient, accurate, and automated processing. The existing technologies mainly have the following deficiencies: When collecting goods data in traditional logistics management systems, most only focus on static information such as goods codes, weights, and volumes, and fail to fully obtain and utilize the dynamic change data that occurs during the transportation of goods; in traditional systems, central devices and edge devices often operate independently, lacking an effective task allocation and resource scheduling mechanism, and not dynamically adjusting the matching strategy in combination with customer historical data, resulting in unreasonable resource allocation and slow incoming inventory slip matching speed. Summary of the Invention

[0003] In order to overcome the shortcoming of low matching efficiency of incoming inventory slips in logistics receiving, the present invention provides an intelligent matching method and system for incoming inventory slips in logistics receiving.

[0004] The technical solution is as follows: An intelligent matching method for incoming inventory slips in logistics receiving includes the following steps:

[0005] S1: Obtain the goods-related data of the target goods, where the goods-related data includes the transportation stacking data of the goods, the shipping code data of the goods, and the cold chain interruption data. At the same time, obtain the goods-related data of the target goods and use a variation calculation formula to obtain the maximum quality variation value of the goods sub-code data;

[0006] S2: Perform intelligent matching on the goods according to the shipping code data of the target goods and the maximum quality variation value of the goods sub-code data;

[0007] S3: Obtain the historical goods data of the customer, use a matching decision formula to obtain a matching decision value, and adjust the intelligent matching position of the goods according to the matching decision value.

[0008] Preferably, the obtaining of the goods-related data of the target goods, where the goods-related data includes the transportation stacking data of the goods, the shipping code data of the goods, and the cold chain interruption data. At the same time, obtain the goods-related data of the target goods and use a variation calculation formula to obtain the maximum quality variation value of the goods sub-code data, including: obtaining the goods-related data of the target goods, where the goods-related data includes the transportation stacking data of the goods, the shipping code data of the goods, and the cold chain interruption data; inputting the goods-related data into the variation calculation formula to obtain the maximum quality variation value of the goods sub-code data, where the shipping code data of the goods includes the goods fixed code data and the goods sub-code data, and the goods fixed code data is the unchangeable attribute data of the goods; the goods sub-code data is the changeable attribute data of the goods; the transportation stacking data of the goods includes the weight of the goods stacked above the target goods and the duration of the goods stacked above the target goods; the cold chain interruption data includes the cold chain interruption duration and the number of cold chain interruptions.

[0009] Preferably, the goods sub-code data is the changeable attribute data of the goods, including: the sub-code data of the goods includes the initial goods quality, the goods customized graphic data, and the goods customized structure data.

[0010] Preferably, the inputting of the goods-related data into the variation calculation formula to obtain the maximum quality variation value of the goods sub-code data includes: after preprocessing the goods-related data by cleaning, normalizing, noise filtering, and filling in missing values, inputting it into the variation calculation formula to obtain the maximum quality variation value of the goods sub-code data, where the variation calculation formula is:

[0011] ;

[0012] In the formula, is the maximum quality variation value of the goods sub-code data; is the duration of the goods stacked above the target goods for the th time; is the weight of the goods stacked above the target goods for the th time; is the cold chain interruption duration for the th time; , are the corresponding weight adjustment factors; is the initial goods quality in the goods sub-code data; is the maximum allowable quality variation value.

[0013] Preferably, the intelligent matching of goods according to the maximum change value of the shipping code data and the sub-code data of the target goods includes: when there is an inconsistency between the target goods and the fixed goods code data, an alarm reminder is given; when the target goods are consistent with the fixed goods code data and there is an inconsistency between the target goods and the sub-code data of the goods, the maximum change value of the target goods is obtained and judged according to the actual quality of the target goods. When the absolute difference between the actual quality of the target goods and the initial goods quality in the sub-code data of the goods is less than or equal to the maximum change value, the target goods are successfully matched; when the absolute difference between the actual quality of the target goods and the initial goods quality in the sub-code data of the goods is greater than the maximum change value, an alarm reminder is given.

[0014] Preferably, obtaining the historical goods data of the customer, obtaining a matching decision value using a matching decision formula, and adjusting the intelligent matching position of the goods according to the matching decision value includes: obtaining the historical goods data of the customer, where the historical goods data includes the standardized goods quantity of the target merchant within a preset time period, the customized goods quantity of the target merchant within a preset time period, the quantity of fixed goods codes, and the quantity of sub-codes of the goods. The quantity of fixed goods codes is the number of coding entries of immovable attribute data; the quantity of sub-codes of the goods is the number of coding entries of movable attribute data; inputting the historical goods data into the matching decision formula to obtain a matching decision value, and adjusting the intelligent matching position of the goods according to the matching decision value.

[0015] Preferably, inputting the historical goods data into the matching decision formula to obtain a matching decision value includes: where the matching decision formula is:

[0016] ;

[0017] In the formula, is the matching decision value; is the quantity of sub-codes of the goods; is the quantity of fixed goods codes; is the customized goods quantity of the target merchant within a preset time period; is the standardized goods quantity of the target merchant within a preset time period; is the real-time network quality score of the edge device; is the standard network quality score of the edge device; 、 、 are weight adjustment factors.

[0018] Preferably, inputting the historical goods data into the matching decision formula to obtain a matching decision value, and adjusting the intelligent matching position of the goods according to the matching decision value includes: when the matching decision value is greater than or equal to a first preset threshold, using a central device to perform intelligent matching on the goods; when the matching decision value is less than the first preset threshold, using an edge device to perform intelligent matching on the goods.

[0019] Preferably, when the matching decision value is less than the first preset threshold, using an edge device to perform intelligent matching on the goods includes: using the edge device to match the fixed coding data of the goods, and when the fixed coding data is successfully matched with the goods, matching the sub-coding data of the goods, and actually recording the changes in the sub-coding data of the goods during the goods transportation process.

[0020] Preferably, an intelligent matching system for the warehousing receipt in logistics receiving includes:

[0021] A data acquisition module, configured to obtain the goods-related data of the target goods and the historical goods data of the customer;

[0022] A variation calculation module, configured to input the goods-related data into a variation calculation formula to obtain the maximum quality variation value of the goods sub-coding data;

[0023] An intelligent matching module, configured to perform intelligent matching on the goods according to the fixed coding data of the goods, the sub-coding data of the goods, and the maximum quality variation value of the goods sub-coding data;

[0024] A decision value acquisition module, configured to obtain the historical goods data of the customer and obtain a matching decision value using a matching decision formula;

[0025] A decision adjustment module, configured to adjust the intelligent matching position of the goods according to the matching decision value;

[0026] An alarm and exception handling module, configured to trigger an alarm in a timely manner for the abnormal situations occurring during the matching process.

[0027] Beneficial effects

[0028] 1. By collecting and analyzing the dynamic change data in the transportation process in real time, the present invention can timely reflect the changes in the goods status, avoid matching errors caused by insufficient static data, thereby greatly improving the matching speed and accuracy of the warehousing receipt, and using the dual verification mechanism of the fixed coding data and the goods sub-coding data, while ensuring the accurate identity of the goods, making a reasonable tolerance determination for the actual changes in the goods quality, further shortening the matching response time, and realizing efficient intelligent matching;

[0029] 2. The system designs a task allocation and resource scheduling mechanism between the central device and the edge device, integrates the customized goods ratio, coding complexity, and real-time network quality, and uses a matching decision formula to dynamically select the central or edge device to execute the matching task, accurately and quickly completing the intelligent matching of the incoming order for logistics receipt.

[0030] 3. The transportation stacking data and cold chain interruption data are fused with the shipping coding data of the goods, enabling the system to automatically correct errors and ensure the high efficiency and accuracy of the matching results when dealing with various dynamic interferences during transportation. Description of the Drawings

[0031] Figure 1 It is a flowchart of the intelligent matching method for the incoming order used in the logistics receipt of the present invention;

[0032] Figure 2 It is a flowchart of the intelligent matching system for the incoming order used in the logistics receipt of the present invention. Detailed Embodiments

[0033] The above solution will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are for illustrating the present application and not for limiting the scope of the present application. The implementation conditions adopted in the embodiments can be further adjusted according to the conditions of specific manufacturers, and the implementation conditions not specified are usually those in conventional experiments.

[0034] Embodiment 1: An intelligent matching method for the incoming order used in logistics receipt, as Figure 1 shown, includes the following steps:

[0035] S1: Obtain the goods-related data of the target goods. The goods-related data includes the transportation stacking data, shipping coding data, and cold chain interruption data of the goods. At the same time, obtain the goods-related data of the target goods and use a variation calculation formula to obtain the maximum quality variation value of the goods sub-coding data;

[0036] Obtain the goods-related data of the target goods. The goods-related data includes the transportation stacking data, shipping coding data, and cold chain interruption data of the goods; input the goods-related data into the variation calculation formula to obtain the maximum quality variation value of the goods sub-coding data. The shipping coding data of the goods includes the goods fixed coding data and the goods sub-coding data. The goods fixed coding data is the unchangeable attribute data of the goods; the goods sub-coding data is the changeable attribute data of the goods; the transportation stacking data of the goods includes the weight of the goods stacked above the target goods and the existence duration of the goods stacked above the target goods; the cold chain interruption data includes the cold chain interruption duration and the cold chain interruption times.

[0037] It should be noted that for the transportation stacking data of goods, this data details the weight of the stacked goods above the target goods during transportation and the duration of the existence of these stacked goods above the target goods. For example, during logistics transportation, sensors installed in the goods container are used to collect the weight information and stacking duration of the stacked goods in real time and transmit them to the data center; the shipping coding data of goods: this part of the data is further divided into fixed goods coding data and goods sub-coding data; the fixed goods coding data represents the unchangeable attributes of the target goods, such as the factory code and product type; while the goods sub-coding data represents the changeable attributes of the target goods, attributes that change due to external factors during transportation or warehousing, such as the weight of the goods and the appearance of the goods; cold chain interruption data: this data records the interruption situations that occur during cold chain transportation, including the interruption duration and the number of cold chain interruptions. Through the monitoring of the temperature control environment by monitoring equipment, various parameters of cold chain interruption are fed back in real time to ensure the accuracy and timeliness of the data; the transportation stacking data, shipping coding data, and cold chain interruption data of the target goods are sequentially input into a pre-designed change calculation formula. In the calculation process here, the fixed coding data in the shipping coding data of the goods is used as a comparison benchmark to ensure that further quality change judgments are made on the goods sub-coding data of the changeable attributes only when the fixed coding data is consistent.

[0038] The goods sub-coding data includes the initial goods quality, goods customized graphic data, and goods customized structure data.

[0039] It should be noted that the goods sub-coding data includes the initial goods quality, which represents the initial quality value of the goods after passing standard inspections before leaving the factory or being loaded. It is an important benchmark for subsequent judgments on the difference between the actual quality and the initial quality of the goods and is the actual content of the sub-coding; at the same time, the goods sub-coding data also includes goods customized graphic data, which involves customized feature information of the goods appearance and labels and is used to assist in confirming the identity of the goods in visual recognition or automated inspection; goods customized structure data, which records the customized attributes of the goods in terms of structural design, such as special protective structures and packaging forms. These data all reflect the unique information of the target goods during the manufacturing or subsequent packaging process.

[0040] After preprocessing the goods-related data by cleaning, normalizing, filtering noise, and filling missing values, it is input into the change calculation formula to obtain the maximum quality change value of the goods sub-coding data, where the change calculation formula is:

[0041] ;

[0042] In the formula, is the maximum quality change value of the goods sub-coding data; is the The duration of the stacked goods above the secondary target goods; For the The weight of the stacked goods above the secondary target goods; For the The duration of the secondary cold chain interruption; 、 Is the corresponding weight adjustment factor; Is the initial goods quality in the goods sub - coding data; Is the maximum allowable mass change value.

[0043] It should be noted that in the data cleaning stage, duplicate data are deleted, records with format errors are removed, and outliers are corrected; in the normalization process, min - max scaling or Z - score standardization is adopted to ensure that the relative contribution degrees of the variable data are in line with the actual situation; in the noise filtering stage, the sliding window mean filtering method is used to smooth the data to eliminate the interference caused by short - term abnormal fluctuations and improve data stability; in the stage of filling missing values, interpolation methods based on historical data or machine learning prediction algorithms are used to fill the missing values to ensure the integrity of the data during the calculation process.

[0044] S2: Perform intelligent matching on the goods according to the maximum quality change value of the shipping coding data of the target goods and the goods sub - coding data;

[0045] When there is an inconsistency between the target goods and the goods fixed coding data, an alarm reminder is given; when the target goods are consistent with the goods fixed coding data and there is an inconsistency between the target goods and the goods sub - coding data, obtain the maximum quality change value of the target goods and make a judgment according to the actual quality of the target goods. When the absolute difference between the actual quality of the target goods and the initial goods quality in the goods sub - coding data is less than or equal to the maximum quality change value, the target goods are successfully matched; when the absolute difference between the actual quality of the target goods and the initial goods quality in the goods sub - coding data is greater than the maximum quality change value, an alarm reminder is given.

[0046] It should be noted that for the matching of the fixed coding data of goods, the fixed coding data of the target goods is compared. If the target goods are inconsistent with the fixed coding data of the goods, it indicates that the identity of the goods is incorrect or abnormal. At this time, the system triggers an alarm to notify the operator to conduct a verification. For example, if the fixed coding data of the goods indicates that the goods are cups, but the target goods are bowls, the entry of the goods is blocked and a warning is sent to the management personnel; when the fixed coding data of the target goods is consistent, the sub-coding data of the goods is further compared. Since the sub-coding data of the goods is variable attribute data, it will not directly determine whether it is completely consistent with the data in the incoming order form. Instead, the difference between the actual quality of the goods and the initial goods quality in the sub-coding data of the goods is calculated, and the maximum quality change value calculated previously is combined for matching; if the absolute difference between the actual quality of the target goods and the initial goods quality in the sub-coding data of the goods, that is, the absolute value of the difference between the two, is less than or equal to the maximum quality change value, it is considered that the target goods are successfully matched, and the entry of the goods is automatically confirmed; if the absolute difference between the actual quality of the target goods and the initial goods quality in the sub-coding data of the goods is greater than the maximum quality change value, an alarm is triggered to prompt that there is an abnormality in the quality of the goods, such as damaged packaging, damaged goods, or overweight, and it is necessary to manually verify before deciding whether to allow the entry.

[0047] S3: Obtain the historical goods data of the customer, obtain the matching decision value using the matching decision formula, and adjust the intelligent matching position of the goods according to the matching decision value.

[0048] Obtain the historical goods data of the customer. The historical goods data includes the standardized goods quantity of the target merchant within a preset time period, the customized goods quantity of the target merchant within a preset time period, the quantity of fixed coding of goods, and the quantity of sub-coding of goods. The quantity of fixed coding of goods is the number of coding entries of non-variable attribute data; the quantity of sub-coding of goods is the number of coding entries of variable attribute data; input the historical goods data into the matching decision formula to obtain the matching decision value, and adjust the intelligent matching position of the goods according to the matching decision value.

[0049] It should be noted that the standardized goods quantity of the target merchant within the preset time period. The standardized goods of the target merchant are the ordinary sold goods of the target merchant, and the quantity of the ordinary sold goods sold within the preset time period, such as in the past 30 days, is obtained; the customized goods quantity of the target merchant within the preset time period. The customized goods of the target merchant are the sold goods that require specific temperature control, special packaging or special labels, and the quantity of the customized goods sold within the preset time period, such as in the past 30 days, is obtained; the quantity of fixed goods codes is the number of coding entries of immovable attribute data, such as the SKU code and batch number of the commodity, so the quantity of fixed goods codes is two; the quantity of sub-codes of goods is the number of coding entries of variable attribute data, such as the weight, storage conditions and packaging size of the goods, so the quantity of sub-codes of goods is three.

[0050] Among them, the matching decision formula is:

[0051] ;

[0052] In the formula, is the matching decision value; is the quantity of sub-codes of goods; is the quantity of fixed goods codes; is the quantity of customized goods of the target merchant within the preset time period; is the quantity of standardized goods of the target merchant within the preset time period; is the real-time network quality score of the edge device; is the standard network quality score of the edge device; , , are the weight adjustment factors.

[0053] It should be noted that the signal strength, link quality index, transmission rate, packet loss rate and retransmission rate in the edge computing device are obtained, and after being scored respectively and then weighted and summed, the real-time network quality score of the edge device is obtained; when is much greater than , it indicates that the goods attributes of the target merchant have great variability and a more accurate matching algorithm is required; when is much greater than , it indicates that within the preset time period, the target merchant mainly processes customized goods, and the customized goods have stronger inaccuracy and a more accurate matching algorithm is required.

[0054] When the matching decision value is greater than or equal to the first preset threshold, the central device is used to perform intelligent matching on the goods; when the matching decision value is less than the first preset threshold, the edge device is used to perform intelligent matching on the goods.

[0055] It should be noted that when the matching decision value is greater than or equal to the first preset threshold, the central device is used to perform intelligent matching on the goods. The central device usually has stronger computing power and is suitable for complex matching scenarios, such as goods matching tasks that require precise comparison of multiple data sources, and matching processing tasks for more refined customized goods; when the matching decision value is less than the first preset threshold, the edge device is used to perform intelligent matching on the goods. Since a low matching decision value means that the goods matching process is simple or restricted by the network environment, it is suitable to use the nearby edge device to complete the matching task to reduce computing latency and consumption of central device resources.

[0056] Use the edge device to match the fixed coding data of the goods. When the fixed coding data matches the goods successfully, match the sub-coding data of the goods, and actually record the changes in the sub-coding data of the goods during the goods transportation process.

[0057] Embodiment 2: On the basis of Embodiment 1, an intelligent matching system for warehouse entry forms for logistics receipt, as Figure 2 shown, includes:

[0058] A data acquisition module for obtaining goods-related data of target goods and historical goods data of customers;

[0059] A change calculation module for inputting the goods-related data into a change calculation formula to obtain the maximum change value of the quality of the sub-coding data of the goods;

[0060] An intelligent matching module for performing intelligent matching on the goods according to the fixed coding data, sub-coding data of the goods, and the maximum change value of the quality of the sub-coding data of the goods;

[0061] A decision value acquisition module for obtaining historical goods data of customers and obtaining a matching decision value using a matching decision formula;

[0062] A decision adjustment module for adjusting the intelligent matching position of the goods according to the matching decision value;

[0063] An alarm and exception handling module for promptly triggering an alarm for abnormal situations occurring during the matching process.

[0064] Although the present invention has been described with reference to exemplary embodiments, it should be understood that the present invention is not limited to the disclosed exemplary embodiments. The scope of the following claims should be given the broadest interpretation so as to cover all modifications and equivalent structures and functions.

Claims

1. An intelligent matching method for warehouse receipt in logistics receiving, characterized in that It includes the following steps: S1: Obtain the goods-related data of the target goods. The goods-related data includes the transportation stacking data of the goods, the shipping code data of the goods, and the cold chain interruption data. At the same time, obtain the goods-related data of the target goods and use the variation calculation formula to obtain the maximum quality variation value of the goods sub-code data; S2: Perform intelligent matching on the goods according to the shipping code data of the target goods and the maximum quality variation value of the goods sub-code data; S3: Obtain the historical goods data of the customer, use the matching decision formula to obtain the matching decision value, and adjust the intelligent matching position of the goods according to the matching decision value; The obtaining of the goods-related data of the target goods, the goods-related data includes the transportation stacking data of the goods, the shipping code data of the goods, and the cold chain interruption data. At the same time, obtain the goods-related data of the target goods and use the variation calculation formula to obtain the maximum quality variation value of the goods sub-code data, includes: Input the goods-related data into the variation calculation formula to obtain the maximum quality variation value of the goods sub-code data. The shipping code data of the goods includes the goods fixed code data and the goods sub-code data. The goods fixed code data is the unchangeable attribute data of the goods; the goods sub-code data is the changeable attribute data of the goods; the transportation stacking data of the goods includes the weight of the goods stacked above the target goods and the existence duration of the goods stacked above the target goods; the cold chain interruption data includes the cold chain interruption duration and the cold chain interruption times; The performing of intelligent matching on the goods according to the shipping code data of the target goods and the maximum quality variation value of the goods sub-code data, includes: When there is an inconsistency between the target goods and the goods fixed code data, an alarm reminder is given; When the target goods are consistent with the goods fixed code data and there is an inconsistency between the target goods and the goods sub-code data, obtain the maximum quality variation value of the target goods and make a judgment according to the actual quality of the target goods. When the absolute difference between the actual quality of the target goods and the initial goods quality in the goods sub-code data is less than or equal to the maximum quality variation value, the target goods are successfully matched; When the absolute difference between the actual quality of the target goods and the initial goods quality in the goods sub-code data is greater than the maximum quality variation value, an alarm reminder is given; The obtaining of the historical goods data of the customer, using the matching decision formula to obtain the matching decision value, and adjusting the intelligent matching position of the goods according to the matching decision value, includes: Obtain the historical goods data of the customer. The historical goods data includes the standardized goods quantity of the target merchant within a preset time period, the customized goods quantity of the target merchant within a preset time period, the goods fixed code quantity and the goods sub-code quantity. The goods fixed code quantity is the number of coding entries of the unchangeable attribute data; the goods sub-code quantity is the number of coding entries of the changeable attribute data; Input the historical goods data into the matching decision formula to obtain the matching decision value, and adjust the intelligent matching position of the goods according to the matching decision value.

2. The intelligent matching method for the warehousing receipt used in logistics receiving according to claim 1, wherein The goods sub-code data is the variable attribute data of the goods, including: the sub-code data of the goods contains the initial goods quality, goods customization graphic data, and goods customization structure data.

3. The intelligent matching method for the warehouse receipt in logistics receiving according to claim 1, characterized in that, Inputting the goods-related data into the variation calculation formula to obtain the maximum variation value of the quality of the goods sub-code data includes: after preprocessing the goods-related data by cleaning, normalizing, filtering noise, and filling in missing values, inputting it into the variation calculation formula to obtain the maximum variation value of the quality of the goods sub-code data, where the variation calculation formula is: ; Wherein, is the maximum change value of the quality of the cargo sub - coding data; is the existence duration of the stacked cargo above the target cargo for the th time; is the weight of the stacked cargo above the target cargo for the thtime; are the corresponding weight adjustment factors; is the initial cargo quality in the cargo sub - coding data; is the maximum allowable quality change value.

4. An intelligent matching method for the incoming order in logistics receiving according to claim 1, characterized in that, Inputting the historical goods data into the matching decision formula to obtain a matching decision value, including: The matching decision formula is: ; Wherein, is the matching decision value; is the number of sub-codes of goods; is the number of fixed codes of goods; is the number of customized goods of the target merchant within a preset time period; is the number of standardized goods of the target merchant within a preset time period; is the real-time network quality score of the edge device; is the standard network quality score of the edge device; , , are the weight adjustment factors.

5. A method for intelligent matching of incoming warehouse receipts for logistics receipt, as described in claim 4, wherein The step of inputting the historical goods data into the matching decision formula to obtain a matching decision value and adjusting the intelligent matching position of the goods according to the matching decision value includes: when the matching decision value is greater than or equal to the first preset threshold, using the central device to perform intelligent matching on the goods; when the matching decision value is less than the first preset threshold, using the edge device to perform intelligent matching on the goods.

6. The intelligent matching method for the warehousing receipt used in logistics receipt according to claim 5, characterized in that, The step of using the edge device to perform intelligent matching on the goods when the matching decision value is less than the first preset threshold includes: using the edge device to match the fixed code data of the goods. When the fixed code data matches the goods successfully, matching the goods sub-code data of the goods, and actually recording the changes in the goods sub-code data during the goods transportation process.

7. An intelligent matching system for warehouse entry documents in logistics receiving, which is characterized by the intelligent matching method for warehouse entry documents in logistics receiving according to any one of claims 1-6. Including: A data acquisition module for acquiring goods-related data of the target goods and historical goods data of the customer; A change calculation module for inputting the goods-related data into a change calculation formula to obtain the maximum change value of the quality of the goods sub-code data; An intelligent matching module for performing intelligent matching on the goods according to the fixed code data of the goods, the goods sub-code data, and the maximum change value of the quality of the goods sub-code data of the target goods; A decision value acquisition module for acquiring the historical goods data of the customer and obtaining a matching decision value using a matching decision formula; A decision adjustment module for adjusting the intelligent matching position of the goods according to the matching decision value; An alarm and exception handling module for triggering an alarm in a timely manner for abnormal situations occurring during the matching process.

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