Wholesale data updating method and apparatus

Abstract

This invention relates to the field of data update technology, specifically disclosing a method and apparatus for updating wholesale data. The method includes: acquiring inventory data from wholesale business points in a data integration platform; processing this data to obtain an inventory turnover rate assessment value; monitoring product transportation data to obtain a product flow rate assessment value; obtaining a wholesale data update demand assessment value based on the inventory turnover rate assessment value and the product flow rate assessment value; and matching this with a preset update time period; acquiring the volume of wholesale data stored in the data integration platform to obtain a wholesale data update reference index; and updating the wholesale data at the wholesale business points according to the wholesale data update reference index. This invention comprehensively analyzes and matches preset time periods and optimizes resource allocation, achieving efficient and accurate data updates, providing real-time data support for wholesale data updates, promptly avoiding congestion during the data update process, and improving the efficiency of the data update process.

Description

Technical Field

[0001] This invention relates to the field of data update technology, specifically to a wholesale data update method and apparatus. Background Technology

[0002] Wholesale data refers to the data generated during the process of purchasing goods from producers or suppliers and then reselling them to retailers or other enterprises. The rapid development of modern information technologies such as the Internet, big data, cloud computing, and artificial intelligence has promoted the automation and intelligence of data collection, processing, storage, and analysis, making data updates faster, more accurate, and more efficient.

[0003] For example, the invention patent with announcement number CN108701005B describes a data update technology. A storage system (100) using this technology includes a management node (110) and multiple storage nodes (112, 114, 116, 118). These multiple storage nodes (112, 114, 116, 118) constitute a Redundant Array of Independent Disks (RAID). In this storage system (100), when the management node (110) determines that a received write request has not updated all data in the entire stripe, the management node (110) directly sends the updated data slices obtained based on the data to be written to the corresponding storage nodes (112, 114, 116, 118). The storage nodes (112, 114, 116, 118) that receive the updated data slices do not directly update the data blocks stored in their external storage devices according to the received updated data slices. Instead, they use a log chain to store the updated data slices in their non-volatile memory (NVM).

[0004] For example, the invention patent with announcement number CN105302587B is a data update method and apparatus. A data update method includes: a user terminal obtaining the version number of the most recently updated first differential data packet and obtaining the version number of the highest version of a second differential data packet from a server; determining whether the version number of the first differential data packet and the version number of the second differential data packet are the same; if they are not the same, calculating the difference between the version number of the first differential data packet and the version number of the second differential data packet; when the difference is less than a preset value, sending the version number of the first differential data packet to the server, so that the server determines a first target differential data packet based on the difference; receiving the first target differential data packet returned by the server, and updating the resource data in the target program according to the first target differential data packet.

[0005] However, in the process of implementing the technical solution of the present application, it was found that the above technology has at least the following technical problems: At present, the amount of data transmission is reduced by using log chains and differential data packets, but in the actual network transmission process, especially when the amount of data is large, there will be problems of transmission delay and bandwidth limitation, which will affect the timeliness and efficiency of data update. Simply reducing the amount of data transmission cannot shorten the update time or improve the efficiency of data update. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a wholesale data update method and apparatus, which can effectively solve the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a wholesale data update method, comprising: acquiring inventory data of wholesale business points in a data integration platform, processing it to obtain an inventory turnover rate assessment value for the wholesale business points, and monitoring product transportation data of the wholesale business points, processing it to obtain a product flow rate assessment value for the wholesale business points; based on the inventory turnover rate assessment value and the product flow rate assessment value of the wholesale business points, comprehensively analyzing to obtain an assessment value of the wholesale data update demand level of the wholesale business points, and matching a preset update time period for the wholesale business points; acquiring the volume of wholesale data stored in the data integration platform, comprehensively analyzing to obtain a wholesale data update reference index for the wholesale business points, and updating the wholesale data of the wholesale business points according to the wholesale data update reference index.

[0008] As a further method, the processing obtains the inventory turnover rate assessment value of the wholesale business point. The specific process is as follows: divide the time period according to the preset time period and mark it as each historical time period. Obtain the outbound volume, inbound volume and inventory change frequency of the wholesale business point in each historical time period, and comprehensively analyze to obtain the inventory turnover rate assessment value of the wholesale business point.

[0009] As a further method, the processing obtains the product flow rate assessment value of the wholesale business point. The specific process is as follows: deploy several time monitoring points, collect the transportation rate of each product at each time monitoring point of the wholesale business point, and at the same time obtain the actual distance between the wholesale business point and the transportation destination of each product, mark it as the transportation distance of each product, and comprehensively analyze to obtain the product flow rate assessment value of the wholesale business point.

[0010] As a further method, the comprehensive analysis obtains the assessment value of the wholesale data update demand of the wholesale business points. The specific analysis process is as follows: based on the inventory turnover rate assessment value and product flow rate assessment value of the wholesale business points, the assessment value of the wholesale data update demand of the wholesale business points is obtained.

[0011] As a further method, the process of matching the preset update time period of the wholesale business point is as follows: the wholesale data update demand assessment value of the wholesale business point is compared with the preset update time period corresponding to the interval of each wholesale data update demand assessment value stored in the data integration platform, and the preset update time period of the wholesale business point is obtained.

[0012] As a further method, the comprehensive analysis obtains the wholesale data update reference index for wholesale business points. The specific analysis process is as follows: based on the current time point, the previous adjacent time point that matches the preset update time period is marked as the adjacent update time point, and the data generated before the adjacent update time point is marked as historical wholesale data, and the data generated after the adjacent update time point is marked as active wholesale data; the volume of historical wholesale data and active wholesale data stored in the data integration platform is collected, and the allowed data storage volume of the data integration platform is obtained. The comprehensive analysis yields the wholesale data update reference index for wholesale business points.

[0013] As a further method, the process of updating the wholesale data of the wholesale business point according to the wholesale data update reference index is as follows: the wholesale data update reference index of the wholesale business point is compared with the wholesale data update reference index threshold stored in the data integration platform. If the wholesale data update reference index of the wholesale business point is greater than or equal to the wholesale data update reference index threshold, then the wholesale data of the wholesale business point is updated.

[0014] As a further method, the specific numerical expression for the inventory turnover rate assessment value of the wholesale business point is as follows:

[0015] ;

[0016] In the formula, These are the numbers used to designate each historical period. , The total number for each historical period. This represents the assessed value of inventory turnover rate at wholesale business points. Indicates the first Outbound volume for a historical period Indicates the first Inbound volume for a historical period Indicates the first Number of inventory changes over a historical period This represents the inventory turnover rate impact factor corresponding to the set outgoing quantity. This indicates the inventory turnover rate impact factor corresponding to the set inbound quantity. This represents the inventory turnover rate impact factor corresponding to the set number of inventory changes.

[0017] As a further method, the assessment value of the wholesale data update demand at the wholesale business point is specifically expressed as follows:

[0018] ;

[0019] In the formula, This indicates the assessment value of the need for updating wholesale data at wholesale business points. This represents the assessed value of product flow rate at wholesale business points. This represents the assessed value of inventory turnover rate at wholesale business points. This indicates the factor influencing the degree of demand for wholesale data updates corresponding to the set product flow rate assessment value. This indicates the impact factor on the degree of demand for wholesale data updates corresponding to the set inventory turnover rate assessment value.

[0020] A second aspect of the present invention provides a wholesale data update method and apparatus, comprising: a processor and memory and a network interface connected to the processor; the network interface is connected to non-volatile memory in a server; the processor, during operation, retrieves a computer program from the non-volatile memory through the network interface and runs the computer program through the memory to execute the method described in any one of the above embodiments.

[0021] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0022] (1) By providing a wholesale data update method and apparatus, this invention achieves efficient, accurate and orderly data update by comprehensively analyzing and matching preset time periods and optimizing resource allocation based on real-time acquisition and processing of data, providing real-time data support for wholesale data update, and timely avoiding the blockage phenomenon that occurs during the data update process, thereby improving the efficiency of the data update process.

[0023] (2) By analyzing the outbound volume, inbound volume and inventory change frequency of wholesale business points in various historical time periods, this invention quantifies the inventory turnover rate assessment value of wholesale business points, which can more accurately analyze the inventory turnover rate of wholesale business points in actual applications, ensuring the timeliness and orderliness of the method, and can more accurately assess the update of wholesale data.

[0024] (3) By analyzing the volume of wholesale data stored in the data integration platform, this invention can more realistically simulate the performance of products at wholesale business points in actual updates, and more comprehensively consider the product status of wholesale business points. By comparing the volume of wholesale data stored in the data integration platform with the reference indicators for wholesale data updates, it provides a more reliable basis for practical applications. Attached Figure Description

[0025] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0027] Figure 2 This is a schematic diagram illustrating the functional relationship between the outbound volume and the inventory turnover rate assessment value in an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0029] Reference Figure 1 As shown, the first aspect of the present invention provides a wholesale data update method, comprising: acquiring inventory data of wholesale business points in a data integration platform, processing it to obtain an inventory turnover rate assessment value of the wholesale business points, and monitoring product transportation data of the wholesale business points to obtain a product flow rate assessment value of the wholesale business points.

[0030] Specifically, the time period is divided according to the preset time period and marked as each historical time period. The outbound volume, inbound volume and inventory change frequency of the wholesale business point in each historical time period are obtained. The inventory turnover rate assessment value of the wholesale business point is obtained by comprehensive analysis.

[0031] It should be understood that, in this embodiment, the number of inventory changes refers to a period of time within a wholesale business point, where outbound shipments are counted as one instance and inbound shipments as one instance.

[0032] In a specific embodiment, the inventory turnover rate assessment value for wholesale business points is specifically expressed as follows:

[0033] ;

[0034] In the formula, These are the numbers used to designate each historical period. , The total number for each historical period. This represents the assessed value of inventory turnover rate at wholesale business points. Indicates the first Outbound volume for a historical period Indicates the first Inbound volume for a historical period Indicates the first Number of inventory changes over a historical period This represents the inventory turnover rate impact factor corresponding to the set outgoing quantity. This indicates the inventory turnover rate impact factor corresponding to the set inbound quantity. This represents the inventory turnover rate impact factor corresponding to the set number of inventory changes.

[0035] In this embodiment, the value range of the preset inventory turnover rate influence factors corresponding to the outbound quantity, the inbound quantity, and the number of inventory changes is [0,1], and a mapping table is established between the outbound quantity, inbound quantity, and number of inventory changes and the corresponding inventory turnover rate influence factors. Based on the outbound quantity, inbound quantity, and number of inventory changes, the inventory turnover rate influence factors corresponding to the outbound quantity, inbound quantity, and number of inventory changes are obtained through the mapping table.

[0036] like Figure 2 As shown, the horizontal axis represents the outbound volume for each historical time period, in units of pieces, and the vertical axis represents the inventory turnover rate assessment value of the wholesale business points for each historical time period. Among these, when... =750 units. The functional relationship between the outbound quantity and the inventory turnover rate assessment value is shown as curve a. =1000 units, the functional relationship between outbound quantity and inventory turnover rate is shown as curve b. =1200 pieces. The functional relationship between the outbound quantity and the inventory turnover rate assessment value is shown in curve c.

[0037] Table 1. Example of Inventory Turnover Rate Assessment Values ​​for Wholesale Business Points

[0038]

[0039] As shown in Table 1, the inventory turnover rate assessment value is jointly determined by the outbound volume, inbound volume, and inventory change frequency for each historical period. The higher the outbound volume, inbound volume, and inventory change frequency, the higher the corresponding inventory turnover rate assessment value, indicating a faster inventory turnover rate at the wholesale business points for each historical period. It should be understood that, for simplified calculation, it is assumed that there are five sets of data, and the summation and average of each set of data are taken to have the same value. Let... =1100, =900, =6, calculate the inventory turnover rate of wholesale business points for each historical time period.

[0040] The formula comprehensively considers three key factors: outbound volume, inbound volume, and inventory change frequency, thus enabling a more comprehensive and accurate assessment of inventory turnover rate at wholesale business points by introducing influencing factors. , and The impact of various factors on the inventory turnover rate assessment can be dynamically adjusted according to actual conditions, making the assessment results more in line with actual needs. , and Dividing each data point by its historical average standardizes the data, ensuring a consistent basis for comparison across different time periods and improving the accuracy of the evaluation results. This is achieved using a hyperbolic cosine function. The introduction of this factor allows the assessment value to reflect the non-linear changes in inventory turnover rate. When the values ​​of each factor are large, the changes in the assessment value will be more significant, thus more accurately reflecting the actual situation of inventory turnover rate.

[0041] It should be understood that the inventory turnover rate assessment value of a wholesale business point is used to evaluate the efficiency and effectiveness of the business point's inventory management, that is, the speed of inventory turnover and the effectiveness of the inventory management strategy.

[0042] It should be understood that the greater the volume of outbound shipments, inbound shipments, and the number of inventory changes, the faster the inventory turnover rate, and the higher the assessed value of the inventory turnover rate.

[0043] In one specific embodiment, the present invention analyzes the outbound volume, inbound volume, and inventory change frequency of wholesale business points in various historical time periods to quantify the inventory turnover rate assessment value of wholesale business points. This can more accurately analyze the inventory turnover rate of wholesale business points in actual applications, ensuring the timeliness and orderliness of the method, and enabling more accurate assessment of wholesale data updates.

[0044] Based on the inventory turnover rate assessment value and product flow rate assessment value of the wholesale business points, a comprehensive analysis is conducted to obtain the wholesale data update demand assessment value of the wholesale business points, and a preset update time cycle is matched to the wholesale business points.

[0045] Specifically, several time monitoring points are deployed to collect the transportation rate of each product at each time monitoring point of the wholesale business point. At the same time, the actual distance between the wholesale business point and the destination of each product transportation is obtained and marked as the transportation distance of each product. The comprehensive analysis yields the product flow rate assessment value of the wholesale business point.

[0046] It should be understood that the comprehensive analysis yields the product flow rate assessment value for wholesale business points. The specific analysis process is as follows: the average product transportation rate is obtained from the data integration platform, and the average product transportation rate, the product transportation rate at each time monitoring point, and the actual distance between the wholesale business point and each product transportation destination are quantitatively analyzed to obtain the product flow rate assessment value for the wholesale business points.

[0047] In one specific embodiment, the evaluation value of the product flow rate at wholesale business points is specifically expressed as follows:

[0048] ;

[0049] In the formula, The numbering of each monitoring point at each time point. , This represents the total number of time monitoring points. , For each product number, The total number of products, This represents the assessed value of product flow rate at wholesale business points. Indicates the first The first time monitoring point Product transportation rate Indicates the average product transport rate. Indicates the first The product transportation distance for each product This represents the product flow rate influence factor corresponding to the set product transportation rate. This indicates the product flow rate influence factor corresponding to the set product transportation distance.

[0050] It should be understood that, It can be obtained directly from the data integration platform.

[0051] In this embodiment, the product flow rate influence factor corresponding to the preset product transportation rate and the product flow rate influence factor corresponding to the preset product transportation distance are set in the data integration platform with a value range of [0,1]. A mapping table is established between the product transportation rate and the product transportation distance and the corresponding product flow rate influence factor. Based on the monitored product transportation rate and product transportation distance, the corresponding product flow rate influence factor is obtained through the mapping table.

[0052] The formula comprehensively considers two key factors: product transport rate and product transport distance. It measures the product flow rate at wholesale points of business, combining the transport rate and distance at different points in time to more comprehensively reflect the actual efficiency of product flow. Considering the product's transportation rate at different points in time helps analyze the relative magnitude of the transportation rate at those points, thus allowing for targeted optimization of transportation strategies. The distance traveled to transport the product plays a crucial role in the formula. Longer distances mean higher transportation costs and longer transit times. Therefore, by considering transportation distance, the formula can more accurately assess the efficiency of product flow. This is further enhanced by introducing influencing factors. and This makes the formula highly flexible, allowing for customized assessments based on different business needs and market environments. Through mathematical calculations, the product flow rate assessment value at wholesale business points can be obtained, thereby providing support for business decisions.

[0053] It should be understood that the product flow rate assessment value of a wholesale business point reflects the speed and efficiency of product flow from the origin to the destination within a specific time period. The faster the product transportation speed and the shorter the product transportation distance, the faster the product flow rate and the larger the product flow rate assessment value. Conversely, the slower the product transportation speed and the longer the product transportation distance, the slower the product flow rate and the smaller the product flow rate assessment value.

[0054] Specifically, based on the inventory turnover rate assessment value and product flow rate assessment value of the wholesale business points, the wholesale data update demand assessment value of the wholesale business points is obtained.

[0055] Specifically, the assessment value of the wholesale data update demand at the wholesale business point is compared with the preset update time period corresponding to the range of assessment values ​​of the wholesale data update demand stored in the data integration platform to obtain the preset update time period for the wholesale business point.

[0056] In a specific embodiment, the assessment value for the wholesale data update demand at wholesale business points is specifically expressed as follows:

[0057] ;

[0058] In the formula, This indicates the assessment value of the need for updating wholesale data at wholesale business points. This represents the assessed value of product flow rate at wholesale business points. This represents the assessed value of inventory turnover rate at wholesale business points. This indicates the factor influencing the degree of demand for wholesale data updates corresponding to the set product flow rate assessment value. This indicates the impact factor on the degree of demand for wholesale data updates corresponding to the set inventory turnover rate assessment value.

[0059] In this embodiment, the value range of the wholesale data update demand degree influence factor corresponding to the preset product flow rate assessment value and the preset inventory turnover rate assessment value is [0,1]. A mapping table is established between the product flow rate assessment value and the inventory turnover rate assessment value and the corresponding wholesale data update demand degree influence factor. Based on the product flow rate assessment value and the inventory turnover rate assessment value, the wholesale data update demand degree influence factor corresponding to the product flow rate assessment value and the inventory turnover rate assessment value is obtained through the mapping table.

[0060] The formula comprehensively considers two key factors: product flow rate and inventory turnover rate at wholesale business points. This allows for a more comprehensive assessment of the need for updated wholesale data at these business points, by introducing a product flow rate influencing factor. Factors affecting the degree of demand for wholesale data updates The weights of different factors in the assessment can be adjusted according to specific circumstances, making the formula adaptable to different business scenarios and needs, and providing more realistic assessment results. The degree of wholesale data update demand is quantified into a specific value, making the assessment results more intuitive and easier to understand. By comparing the assessment values ​​of the degree of wholesale data update demand at different time points or different business points, the changes and differences in the degree of data update demand can be clearly seen. The assessment value using the inventory turnover rate of historical time periods reflects the past operation of wholesale business points. The assessment results based on historical data are more referential and forward-looking, and can help enterprises predict future data update needs and make corresponding preparations in advance.

[0061] It should be understood that the wholesale data update requirement refers to an assessment based on inventory turnover rate and product flow rate to determine whether the wholesale data needs to be updated. The higher the inventory turnover rate assessment value at a wholesale point, the greater the need for wholesale data updates; conversely, the lower the inventory turnover rate assessment value, the lower the need for updates. Similarly, the higher the product flow rate assessment value, the greater the need for updates; and vice versa.

[0062] Obtain the volume of wholesale data stored in the data integration platform, conduct comprehensive analysis to obtain reference indicators for updating wholesale data at wholesale business points, and update the wholesale data at wholesale business points according to the reference indicators.

[0063] Specifically, based on the current time point, the previous time point that matches the preset update time period is marked as the adjacent update time point, and the data generated before the adjacent update time point is marked as historical wholesale data, and the data generated after the adjacent update time point is marked as active wholesale data.

[0064] The volume of historical wholesale data and active wholesale data stored in the data integration platform is collected, and the allowed data storage volume of the data integration platform is obtained. The comprehensive analysis yields the reference indicators for updating wholesale data at the wholesale business points.

[0065] Specifically, the wholesale data update reference index of the wholesale business point is compared with the wholesale data update reference index threshold stored in the data integration platform. If the wholesale data update reference index of the wholesale business point is greater than or equal to the wholesale data update reference index threshold, then the wholesale data of the wholesale business point is updated.

[0066] It needs to be explained that the wholesale data update reference index threshold is extracted from the data integration platform. The wholesale data update reference index of the wholesale business point is compared with the wholesale data update reference index threshold stored in the data integration platform. If the wholesale data update reference index of the wholesale business point is greater than or equal to the wholesale data update reference index threshold, the wholesale data of the wholesale business point is updated. If the wholesale data update reference index of the wholesale business point is less than the wholesale data update reference index threshold, the update operation of the wholesale data of the wholesale business point is stopped.

[0067] It should be understood that the allowable data storage volume is obtained directly from the data integration platform.

[0068] In one specific embodiment, the present invention analyzes the volume of wholesale data stored in the data integration platform. Considering the volume of wholesale data, it can more realistically simulate the performance of products at wholesale business points in actual updates, and more comprehensively consider the product status at wholesale business points. By comparing the volume of wholesale data stored in the data integration platform with the reference indicators for wholesale data updates, it provides a more reliable basis for practical applications.

[0069] In a specific embodiment, the wholesale data update reference indicator for wholesale business points has the following specific numerical expression:

[0070] ;

[0071] In the formula, This indicates the reference indicator for updating wholesale data at wholesale business points. This indicates the volume of historical wholesale data stored in the data collection and integration platform. This indicates the volume of active wholesale data stored in the data collection and integration platform. This indicates the allowed data storage volume of the data integration platform. This indicates the impact factor of the wholesale data update reference indicator corresponding to the set historical wholesale data volume. This indicates the impact factor of the wholesale data update reference index corresponding to the set active wholesale data volume.

[0072] In this embodiment, the value range of the wholesale data update reference index influence factor corresponding to the set historical wholesale data volume and the set active wholesale data volume is [0,1]. A mapping table is established between the historical wholesale data volume and the active wholesale data volume and the corresponding wholesale data update reference index influence factor. Based on the historical wholesale data volume and the active wholesale data volume stored in the data integration platform, the wholesale data update reference index influence factor corresponding to the historical wholesale data volume and the active wholesale data volume is obtained through the mapping table.

[0073] It should be understood that the wholesale data update benchmark is a quantitative evaluation indicator that combines two key factors: the volume of historical wholesale data and the volume of active wholesale data, as well as the allowable data storage volume of the data integration platform. It can help enterprises more accurately assess the need for data updates, thereby optimizing data management strategies, ensuring the accuracy and timeliness of data, and providing strong data support for wholesale operations.

[0074] The formula comprehensively considers the impact of historical and active data, helping to balance the retention of historical data and the real-time nature of active data during data updates. The function maps the results to a finite range, making the values ​​of wholesale data update reference indicators easier to interpret and compare. Through this method, even if the volumes of historical and active wholesale data are very large or very small, the values ​​of the wholesale data update reference indicators can be kept within a manageable range. Introducing two influencing factors allows users to adjust the weights of historical and active data in data update decisions based on business needs.

[0075] Reference Figure 2 As shown, a second aspect of the present invention provides a wholesale data update method and apparatus, comprising: a processor and memory and a network interface connected to the processor; the network interface is connected to a non-volatile memory in a server; the processor, during operation, retrieves a computer program from the non-volatile memory through the network interface and runs the computer program through the memory to execute the method described in any one of the above inventions.

[0076] In one specific embodiment, the present invention provides a wholesale data update method and apparatus, which, based on real-time acquisition and processing of data, comprehensively analyzes and matches preset time periods and optimizes resource allocation, achieves efficient, accurate and orderly data updates, provides real-time data support for wholesale data updates, can promptly avoid congestion during the data update process, and improves the efficiency of the data update process.

[0077] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A wholesale data update method, characterized in that, include: The system acquires inventory data from wholesale business points in the data integration platform, processes it to obtain the inventory turnover rate assessment value of the wholesale business points, and monitors product transportation data from the wholesale business points, processes it to obtain the product flow rate assessment value of the wholesale business points. Based on the inventory turnover rate assessment value and product flow rate assessment value of the wholesale business points, a comprehensive analysis is conducted to obtain the wholesale data update demand assessment value of the wholesale business points, and a preset update time cycle for the wholesale business points is matched. Obtain the volume of wholesale data stored in the data integration platform, conduct comprehensive analysis to obtain reference indicators for updating wholesale data at wholesale business points, and update the wholesale data at wholesale business points according to the reference indicators. The processing yields the inventory turnover rate assessment value for wholesale business points. The specific process is as follows: The time period is divided according to the preset time period and marked as each historical time period. The outbound volume, inbound volume and inventory change frequency of the wholesale business point in each historical time period are obtained. The inventory turnover rate assessment value of the wholesale business point is obtained by comprehensive analysis. The process yields the product flow rate assessment value for wholesale business points, and the specific process is as follows: Several time monitoring points are deployed to collect the transportation rate of each product at each time monitoring point of the wholesale business point. At the same time, the actual distance between the wholesale business point and the destination of each product transportation is obtained and marked as the transportation distance of each product. The comprehensive analysis yields the product flow rate assessment value of the wholesale business point. The comprehensive analysis yields an assessment value for the degree of need for wholesale data updates at wholesale business points. The specific analysis process is as follows: Based on the inventory turnover rate assessment value and product flow rate assessment value of the wholesale business points, the assessment value of the wholesale data update demand of the wholesale business points is obtained. The specific numerical expression for the inventory turnover rate assessment value of the wholesale business point is as follows: ； In the formula, These are the numbers used to designate each historical period. , The total number for each historical period. This represents the assessed value of inventory turnover rate at wholesale business points. Indicates the first Outbound volume for a historical period Indicates the first Inbound volume for a historical period Indicates the first Number of inventory changes over a historical period This represents the inventory turnover rate impact factor corresponding to the set outgoing quantity. This indicates the inventory turnover rate impact factor corresponding to the set inbound quantity. This indicates the inventory turnover rate impact factor corresponding to the set number of inventory changes; The specific numerical expression for the assessment value of the wholesale data update demand at the wholesale business points is as follows: ; In the formula, This indicates the assessment value of the need for updating wholesale data at wholesale business points. This represents the assessed value of product flow rate at wholesale business points. This represents the assessed value of inventory turnover rate at wholesale business points. This indicates the factor influencing the degree of demand for wholesale data updates corresponding to the set product flow rate assessment value. This indicates the impact factor on the degree of demand for wholesale data updates corresponding to the set inventory turnover rate assessment value.

2. The wholesale data update method according to claim 1, characterized in that: The matching process obtains the preset update time period for the wholesale business points as follows: The wholesale business point's wholesale data update demand assessment value is compared with the preset update time period corresponding to the wholesale data update demand assessment value range stored in the data integration platform to obtain the preset update time period for the wholesale business point.

3. The wholesale data update method according to claim 1, characterized in that: The comprehensive analysis yields reference indicators for updating wholesale data at wholesale business points. The specific analysis process is as follows: Based on the current time point, the previous time point that matches the preset update time period is marked as the adjacent update time point, and the data generated before the adjacent update time point is marked as historical wholesale data, and the data generated after the adjacent update time point is marked as active wholesale data. The volume of historical wholesale data and active wholesale data stored in the data integration platform is collected, and the allowed data storage volume of the data integration platform is obtained. The comprehensive analysis yields the reference indicators for updating wholesale data at the wholesale business points.

4. The wholesale data update method according to claim 3, characterized in that: The process of updating the wholesale data of wholesale business points based on the wholesale data update reference indicators is as follows: The wholesale data update reference index of the wholesale business point is compared with the wholesale data update reference index threshold stored in the data integration platform. If the wholesale data update reference index of the wholesale business point is greater than or equal to the wholesale data update reference index threshold, the wholesale data of the wholesale business point is updated.

5. A wholesale data update device, characterized in that: include: The processor, along with the memory and network interface connected to the processor; The network interface is connected to the non-volatile memory in the server; When the processor is running, it retrieves a computer program from the non-volatile memory through the network interface and runs the computer program through the memory to perform the method described in any one of claims 1-4.

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