Network knowledge construction system based on big data

Abstract

The present invention discloses a network knowledge construction system based on big data, including a data acquisition module, a product analysis module and an output module. The data acquisition module is used to obtain relevant information of parts suppliers obtained by sampling personnel; the product analysis module is used to build a virtual sampling model. The data acquisition module, the product analysis module and the output module are communicated with each other. This technical solution further divides the selection priority of the store by screening the reference value of the store, screening the range of basic parts covered in the basic plan within the store and the ratio of basic parts that are difficult to obtain, thereby effectively improving the level of selection of sampling stores and improving the stability of the selection of sampling manufacturer plans; at the same time, from the perspective of the purchaser, a plan for purchasing parts is formulated, and the target stores that are most necessary for product quality sampling are screened according to the parameters to be inspected of the store obtained by the system, thereby effectively improving the accuracy of the sampling inspection. The present invention has the characteristics of low sampling inspection cost and high reliability of sampling inspection efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of market product quality research, and in particular to a network knowledge construction system based on big data. Background Art

[0002] A knowledge network is a social network among knowledge participants that enables knowledge creation and transfer between individuals, organizations, and external parties. People collaborate and exchange information through knowledge networks. The goal is to connect technology with people and achieve an effective combination of intellectual capital, structural capital, and customer capital. Knowledge networks can be divided into internal knowledge networks and external knowledge networks. The former emphasizes knowledge exchange between employees within an organization and between organizations, while the latter emphasizes knowledge sources outside the organization, including communities, national social relations, and competitors. As an important part of manufacturers' production, market research on the quality of industrial basic parts plays a vital role in the construction of online knowledge networks.

[0003] With the development and advancement of technology, the comprehensive purchasing systems of some online parts purchasing software have provided manufacturers with more options. In existing technologies, market product quality researchers conduct random inspections of various basic parts vendors. However, due to the wide range of industrial parts sampled and the large number of parts vendors, screening the sampled parts supply targets based on parameters such as positive review rate and sales volume is too subjective. This is especially true for many established manufacturers with good reputations. Although they have many orders and high positive review rates, they often require more attention to random inspections of product quality. Therefore, it is necessary to design a big data-based network knowledge construction system that has low random inspection costs and high reliability of random inspection benefits. Summary of the Invention

[0004] The purpose of the present invention is to provide a network knowledge construction system based on big data to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a network knowledge construction system based on big data, including a data acquisition module, a product analysis module and an output module. The data acquisition module is used to obtain relevant information of parts suppliers from sampling personnel; the product analysis module is used to construct a virtual sampling model when sampling industrial parts; the output module is used to output the analysis results of the product analysis module, and the data acquisition module, the product analysis module and the output module are communicatively connected to each other.

[0006] According to the above technical solution, the data acquisition module includes a parts information acquisition module and a retrieval information acquisition module. The parts information acquisition module is used to obtain the target parts information sampled by sampling personnel in the industrial field; the retrieval information acquisition module is used to obtain relevant information about the parts sold by the manufacturer in the online store.

[0007] According to the above technical solution, the product analysis module includes a selection rationality analysis module and a necessity analysis module. The difficulty prediction module and the necessity analysis module, the selection rationality analysis module is used to detect and analyze whether the equipment composition scheme provided by the online equipment supplier meets the sampling inspection requirements of the sampling inspection personnel; the necessity analysis module is used to analyze the necessity of sampling inspection of the corresponding stores for each combination scheme in the branch equipment composition schemes provided by different online equipment suppliers.

[0008] According to the above technical solution, the necessity analysis module further includes a necessity analysis module and a coordination sub-module. The necessity analysis module is used to further plan the rationality and necessity of the sampling targets of the sampling personnel through the analysis results of the selection rationality analysis module; the coordination sub-module is used to obtain a complete plan that takes into account both the sampling costs and the sampling benefits.

[0009] According to the above technical solution, the operation method of the network knowledge construction system mainly includes the following steps:

[0010] Step S1: The sampling management personnel randomly select K different basic parts as sampling target parts within the field of industrial parts to be inspected. The sampling target parts include basic part A, basic part B, ... basic part K, where K is the number of sampling target parts that the system expects to obtain through random inspection;

[0011] Step S2: Based on the search results of the search information collection module, the solution entries of several stores and merchants corresponding to the parts search results are tested to detect the device assembly variability of the devices sold by the merchants in the stores;

[0012] Step S3: After obtaining the device assembly variability of several test results stores, the stores are divided according to the threshold values ​​into random inspection stores, first priority selection target stores and second priority selection stores;

[0013] Step S4: The system evaluates the ratio of excess parts for the equipment obtained after virtual assembly based on the test results, plans the assembly plan for the equipment parts on the production line, and obtains the target stores for random inspection of the assembly needs;

[0014] Step S5: The system outputs the recommended inspection store names to the inspection staff through the output module.

[0015] According to the above technical solution, step S2 further includes:

[0016] Step S21: In the equipment supply stores provided by the sampled target manufacturer, extract the X supply solution information provided by the store and the Y basic solutions provided by the store according to the supply solution entry information provided by the store, store the corresponding Y basic solutions, and the number of solutions that can be composed of the basic solutions of the store is The shop provides equipment assembly variability of supply solutions

[0017] Step S22: searching for parts supply composition plan information provided by different supply stores in sequence according to the extraction method in step S21, and summarizing a total of S1 basic plans obtained;

[0018] Step S23: Arrange the supply solutions provided by all stores in descending order according to their equipment assembly variability η, and accumulate the basic solutions provided by each store in descending order from top to bottom, and count them into the information storage of step S1, and add the number of times the basic solutions are obtained in sequence until the number of times all K parts are obtained is greater than or equal to 1, then stop accumulating.

[0019] According to the above technical solution, step S3 further includes:

[0020] Step S31: extracting parts supply store information with a number of acquisitions equal to 1 and recording it in the necessity analysis module;

[0021] Step S32: Arrange the basic solutions in descending order according to the number of times they have been acquired, and mark them as "1", "2", ... "KQ" in the order of arrangement, where Q is the number of parts that have been acquired 1 times;

[0022] Step S33: Linearly superimpose the corresponding store basic plan on the arranged marks to obtain the reference value of each store, and set the store with a reference value higher than the limit value as the first priority target store; set the store with a reference value lower than the limit value as the second priority target store.

[0023] According to the above technical solution, step S4 further includes:

[0024] Step S41: The necessity analysis module removes the supply plans provided by all stores in step S31, and the supply plans that contain parts that are not included in the basic demand parts;

[0025] Step S42: Randomly compare two solutions in the remaining solution library. When the overlap between keywords and part names in a solution is less than μ1%, the two solutions are added to the alternative solution pool. When the overlap is greater than μ2%, the solution with fewer part names is added to the alternative solution pool. μ1 and μ2 are the minimum and maximum limit ratios of the overlap between the part names in the solution design, respectively.

[0026] Step S43: In the alternative solution pool, the solution entries entering the pool are split according to the names of the basic parts, and the number of times the basic parts are obtained is accumulated in sequence. The number of occurrences of non-repeated basic parts is K2, and the remaining basic parts that still need to be configured are (K-K2). The system obtains the target stores for random inspection of assembly requirements through the priority store screening method.

[0027] According to the above technical solution, in step S42, the priority store screening method includes:

[0028] Step S421: When K2 < K, according to the overall planning method of step S4, in the pool of alternative solutions obtained by the target store at the first priority, the solution entries in the pool are split according to the names of the basic parts, and the total number of splits is K3. The number of times the basic parts are obtained is accumulated in sequence, and the number of occurrences of non-repeated basic parts is K4. The complete purchase rate of parts of the optimal solution is Optimal solution for parts redundancy Output the five stores with the lowest ratio of (optimal solution parts full purchase rate - optimal solution parts surplus rate);

[0029] Step S422: When K4 is less than K-K2, according to the coordination method of step S51, the target store is selected from the pool of alternative solutions obtained by selecting the target store at the second priority level, and the target store is selected to replenish the remaining target parts, so that the complete purchase rate of parts of the optimal solution is ≥100% and the surplus rate of parts of the optimal solution is the lowest, and the remaining selected stores are output.

[0030] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention divides the manufacturer types according to the production plans provided by the manufacturers, and formulates the plan for purchasing parts from the perspective of the buyer, and screens the target stores that are most necessary to conduct product quality spot checks based on the store inspection parameters obtained by the system, thereby effectively improving the accuracy of spot checks and promoting the development of spot checks on market product quality research in a positive direction to the greatest extent; at the same time, by screening the reference values ​​of the stores, screening the range of basic parts covered in the basic plans within the stores and the ratio of basic parts that are more difficult to obtain, the selection priority of the stores is further divided, effectively improving the level of selection of spot-checked stores, improving the stability of the selection of spot-checked manufacturer plans, and greatly reducing the possibility of stores with high risk of quality problems not being spot-checked. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0032] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0034] See also Figure 1 The invention provides a technical solution: a network knowledge construction system based on big data, including:

[0035] Data acquisition module, product analysis module and output module. The data acquisition module is used to obtain relevant information of parts suppliers from inspection personnel; the product analysis module is used to build a virtual inspection model when inspecting industrial parts; the output module is used to output the analysis results of the product analysis module. The data acquisition module, product analysis module and output module are communicated with each other.

[0036] The present invention divides the manufacturer types according to the production plans provided by the manufacturers, formulates the plan for purchasing parts from the perspective of the buyer, and screens the target stores that are most necessary to conduct product quality spot checks based on the store inspection parameters obtained by the system, thereby effectively improving the accuracy of spot checks and promoting the development of market product quality research in a positive direction through spot checks to the greatest extent; at the same time, by screening the reference values ​​of the stores, screening the range of basic parts covered in the basic plans within the stores and the ratio of basic parts that are difficult to obtain, the selection priority of the stores is further divided, effectively improving the level of selection of spot-checked stores, improving the stability of the selection of spot-checked manufacturer plans, and greatly reducing the possibility that stores with high risk of quality problems are not spot-checked.

[0037] The data acquisition module includes a parts information acquisition module and a retrieval information acquisition module. The parts information acquisition module is used to obtain the target parts information inspected by sampling personnel in the industrial field; the retrieval information acquisition module is used to obtain relevant information about parts sold by manufacturers in online stores.

[0038] The product analysis module includes a selection rationality analysis module and a necessity analysis module, a difficulty prediction module and a necessity analysis module. The selection rationality analysis module is used to detect and analyze whether the equipment composition plan provided by the online equipment supplier meets the sampling inspection requirements of the sampling inspection personnel; the necessity analysis module is used to analyze the necessity of sampling inspection of the corresponding stores for each combination plan in the branch equipment composition plans provided by different online equipment suppliers.

[0039] The necessity analysis module further includes a necessity analysis module and a coordination sub-module. The necessity analysis module is used to further plan the rationality and necessity of the sampling targets of the sampling personnel by selecting the analysis results of the rationality analysis module; the coordination sub-module is used to obtain a complete plan that takes into account both the sampling costs and the sampling benefits.

[0040] In a preferred embodiment, the operation method of the network knowledge construction system mainly includes the following steps:

[0041] Step S1: The sampling management personnel randomly select K different basic parts as sampling target parts within the field of industrial parts to be inspected. The sampling target parts include basic part A, basic part B, ... basic part K, where K is the number of sampling target parts that the system expects to obtain through random inspection;

[0042] Step S2: Based on the search results of the search information collection module, the solution entries of several stores and merchants corresponding to the parts search results are tested to detect the device assembly variability of the devices sold by the merchants in the stores;

[0043] Step S3: After obtaining the device assembly variability of several test results stores, the stores are divided according to the threshold values ​​into random inspection stores, first priority selection target stores and second priority selection stores;

[0044] Step S4: The system evaluates the ratio of excess parts for the equipment obtained after virtual assembly based on the test results, plans the assembly plan for the equipment parts on the production line, and obtains the target stores for random inspection of the assembly needs;

[0045] Step S5: The system outputs the recommended inspection store names to the inspection staff through the output module.

[0046] In this embodiment, step S2 further includes:

[0047] Step S21: In the equipment supply stores provided by the sampled target manufacturer, extract the X supply solution information provided by the store and the Y basic solutions provided by the store according to the supply solution entry information provided by the store, store the corresponding Y basic solutions, and the number of solutions that can be composed of the basic solutions of the store is The shop provides equipment assembly variability of supply solutions

[0048] The higher the variability of equipment assembly in a store's supply plan, the more purchasing plans that can be selected in the store, the lower the possibility of overlapping and redundant parts with equipment from other stores when purchasing parts, and the higher the priority for store spot checks; conversely, the lower the variability of equipment assembly in a store's supply plan, the fewer purchasing plans that can be selected in the store, the greater the possibility of overlapping and redundant parts with equipment from other stores when purchasing parts, and the lower the priority for store spot checks.

[0049] Step S22: searching for parts supply composition plan information provided by different supply stores in sequence according to the extraction method in step S21, and summarizing the total S1 basic plans obtained;

[0050] Step S23: Arrange the supply solutions provided by all stores in descending order according to their equipment assembly variability η, and accumulate the basic solutions provided by each store in descending order from top to bottom, and count them into the information storage of step S1, and add the number of times the basic solutions are obtained in sequence until the number of times all K parts are obtained is greater than or equal to 1, then stop accumulating.

[0051] In this embodiment, step S3 further includes:

[0052] Step S31: extracting parts supply store information with a number of acquisitions equal to 1 and recording it in the necessity analysis module;

[0053] Step S32: Arrange the basic solutions in descending order according to the number of times they have been acquired, and mark them as "1", "2", ... "KQ" in the order of arrangement, where Q is the number of parts that have been acquired 1 times;

[0054] Step S33: Linearly superimpose the corresponding store basic plan on the arranged marks to obtain the reference value of each store, and set the store with a reference value higher than the limit value as the first priority target store; set the store with a reference value lower than the limit value as the second priority target store.

[0055] By filtering the reference values ​​of the stores, we can filter the scope of basic parts covered in the basic plans of the stores and the ratio of basic parts that are difficult to obtain, and further divide the selection priorities of the stores, effectively improving the level of selection of random inspection stores, improving the stability of random inspection manufacturer plan selection, and greatly reducing the possibility of stores with high risk of quality problems not being inspected.

[0056] In this embodiment, step S4 further includes:

[0057] Step S41: The necessity analysis module removes the supply plans provided by all stores in step S31, and the plans that contain parts that are not included in the demand base;

[0058] Step S42: Randomly compare two solutions in the remaining solution library. When the overlap between keywords and part names in a solution is less than μ1%, the two solutions are added to the alternative solution pool. When the overlap is greater than μ2%, the solution with fewer part names is added to the alternative solution pool. μ1 and μ2 are the minimum and maximum limit ratios of the overlap between the part names in the solution design, respectively.

[0059] Step S43: In the alternative solution pool, the solution entries entering the pool are split according to the names of the basic parts, and the number of times the basic parts are obtained is accumulated in sequence. The number of occurrences of non-repeated basic parts is K2, and the remaining basic parts that still need to be configured are (K-K2). The system obtains the target stores for random inspection of assembly requirements through the priority store screening method.

[0060] Since this type of target store is a included store, the published basic plan contains basic parts that other stores have not obtained, and there is a high necessity to conduct random inspections of the stores corresponding to these parts.

[0061] This technical solution solves the problem of providing similar parts components in different stores, causing confusion in the sampling inspection method, and being unable to eliminate the problem of increasing the sampling inspection cost due to selecting stores with low sampling inspection necessity. The cost of sampling inspection is effectively reduced through further store parts screening.

[0062] In step S42 of this embodiment, the priority store screening method includes:

[0063] Step S421: When K2 < K, according to the overall planning method of step S4, in the pool of alternative solutions obtained by the target store at the first priority, the solution entries in the pool are split according to the names of the basic parts, and the total number of splits is K3. The number of times the basic parts are obtained is accumulated in sequence, and the number of occurrences of non-repeated basic parts is K4. The complete purchase rate of parts of the optimal solution is Optimal solution for parts redundancy Output the five stores with the lowest ratio of (optimal solution parts full purchase rate - optimal solution parts surplus rate);

[0064] Step S422: When K4 is less than K-K2, according to the coordination method of step S51, the target store is selected from the pool of alternative solutions obtained by selecting the target store at the second priority level, and the target store is selected to replenish the remaining target parts, so that the complete purchase rate of parts of the optimal solution is ≥100% and the surplus rate of parts of the optimal solution is the lowest, and the remaining selected stores are output.

[0065] The optimal solution for the complete parts acquisition rate is the ratio of the required purchases to be completed in the online parts supply store when the buyer purchases parts. The higher the ratio, the higher the store's completeness rate, which is more beneficial to the buyer.

[0066] The optimal solution's parts redundancy rate is the ratio of parts overlap when the buyer purchases parts from an online parts supplier, both within the same store and across different stores. The higher the ratio, the greater the complexity of the store's solutions, which is more disadvantageous to the buyer.

[0067] The parameter ratio of (optimal solution parts complete purchase rate - optimal solution parts surplus rate) can effectively reflect the completeness rate of parts provided in the store. The higher the completeness rate, the greater the purchase volume of the buyer in the store, and the greater the effect of the good reviews of the quality of the purchased parts. Conversely, the higher the risk of quality problems, the more necessary it is for researchers to conduct quality surveys on it.

[0068] Since the positive reviews of online manufacturers' product sales are subjective and sales volume is related to the scale of the manufacturer, it is difficult for market product quality researchers to objectively select target manufacturers for random inspection based on the positive reviews and sales volume of the manufacturer's products.

[0069] This technical solution divides the manufacturer types according to the production plans provided by the manufacturers, and formulates a plan for purchasing parts from the perspective of the buyer. It also selects the target stores that are most necessary to conduct product quality spot checks based on the store inspection parameters obtained by the system, effectively improving the accuracy of spot checks and promoting the positive development of spot checks on market product quality research to the greatest extent.

[0070] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0071] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A network knowledge construction system based on big data, characterized by: It includes a data acquisition module, a product analysis module and an output module. The data acquisition module is used to obtain relevant information about the parts supplier obtained by the inspection personnel; the product analysis module is used to build a virtual inspection model when inspecting industrial parts; the output module is used to output the analysis results of the product analysis module. The data acquisition module, the product analysis module and the output module are connected to each other in communication; The operation method of the network knowledge construction system mainly includes the following steps: The data acquisition module includes a retrieval information acquisition module, which is used to obtain relevant information about parts sold by manufacturers in online stores; The product analysis module includes a necessity analysis module, which is used to analyze the necessity of random inspections of stores corresponding to branch equipment combination solutions provided by different online equipment suppliers; Step S1: The sampling management personnel randomly select K different basic parts as sampling target parts within the field of industrial parts to be inspected. The sampling target parts include basic part A, basic part B, ... basic part K, where K is the number of sampling target parts that the system expects to obtain through random inspection; Step S2: Based on the search results of the search information collection module, the solution entries of several stores and merchants corresponding to the parts search results are tested to detect the device assembly variability of the devices sold by the merchants in the stores; Step S21: In the equipment supply stores provided by the sampled target manufacturer, extract the X supply solution information provided by the store and the Y basic solutions provided by the store according to the supply solution entry information provided by the store, store the corresponding Y basic solutions, and the number of solutions that can be composed of the basic solutions of the store is , the shop provides equipment assembly variability of supply solutions ; Step S22: searching for parts supply composition plan information provided by different supply stores in sequence according to the extraction method in step S21, and summarizing a total of S1 basic plans obtained; Step S23: Arrange the supply solutions provided by all stores in descending order according to their equipment assembly variability η, and accumulate the basic solutions provided by each store in descending order from top to bottom, and add them to the information storage in step S1. The number of times the basic solutions are obtained is accumulated in sequence until the number of times all K parts are obtained is greater than or equal to 1, then stop accumulating; Step S3: After obtaining the device assembly variability of several test results stores, the stores are divided according to the threshold values ​​into random inspection stores, first priority selection target stores and second priority selection stores; Step S31: extracting parts supply store information with a number of acquisitions equal to 1 and recording it in the necessity analysis module; Step S32: Arrange the basic solutions in descending order according to the number of times they have been acquired, and mark them as "1", "2", ... "KQ" in the order of arrangement, where Q is the number of parts that have been acquired 1 times; Step S33: Linearly superimpose the marks of the corresponding store basic plan on the arrangement of the control marks to obtain the reference value of each store, and set the store with the reference value higher than the threshold value as the first priority target store; The stores with reference values ​​lower than the threshold value are set as the second priority selection stores; Step S4: The system evaluates the parts redundancy rate of the optimal solution for the equipment obtained by virtual assembly based on the test results, plans the assembly plan for the production line equipment parts, and obtains the target shops for random inspection of the assembly requirements; Step S41: The necessity analysis module removes the supply plans provided by all stores in step S31, and the supply plans that contain parts that are not included in the basic demand parts; Step S42: Randomly compare two solutions in the remaining solution library. When the overlap between keywords and part names in a solution is less than μ1%, the two solutions are added to the alternative solution pool. When the overlap is greater than μ2%, the solution with fewer part names is added to the alternative solution pool. μ1 and μ2 are the minimum and maximum limit ratios of the overlap between the part names in the solution design, respectively. In step S42, the priority store screening method includes: Step S421: When K2 < K, according to the overall planning method of step S4, in the pool of alternative solutions obtained by the target store at the first priority, the solution entries in the pool are split according to the names of the basic parts, and the total number of splits is K3. The number of times the basic parts are obtained is accumulated in sequence, and the number of occurrences of non-repeated basic parts is K4. The complete purchase rate of parts of the optimal solution is , the optimal solution of parts redundancy rate , output the five stores with the lowest ratio of (optimal solution parts full purchase rate - optimal solution parts surplus rate); Step S5: The system outputs the recommended inspection store names to the inspection staff through the output module.

2. The network knowledge construction system based on big data according to claim 1, characterized in that: The data acquisition module further includes a parts information acquisition module, which is used to obtain target parts information that are sampled by inspection personnel in the industrial field.

3. The network knowledge construction system based on big data according to claim 2, characterized in that: The product analysis module also includes a selection rationality analysis module, which is used to detect and analyze whether the equipment composition plan provided by the online equipment supplier meets the sampling inspection requirements of the sampling inspection personnel.

4. The network knowledge construction system based on big data according to claim 3 is characterized by: The necessity analysis module further includes a screening analysis submodule and a coordination submodule. The screening analysis submodule is used to further plan the rationality and necessity of the sampling targets of the sampling personnel through the analysis results of the selection rationality analysis module; the coordination submodule is used to obtain a complete plan that takes into account both the sampling costs and the sampling benefits.

5. The network knowledge construction system based on big data according to claim 4 is characterized in that: The step S4 further comprises: Step S43: In the alternative solution pool, the solution entries entering the pool are split according to the names of the basic parts, and the number of times the basic parts are obtained is accumulated in sequence. The number of occurrences of non-repeated basic parts is obtained to be K2, and the remaining basic parts that still need to be configured are (K-K2). The system obtains the target stores for random inspection of assembly requirements through the priority store screening method.

6. The network knowledge construction system based on big data according to claim 5 is characterized in that: In step S42, the priority store screening method further includes: Step S422: When K4 is less than K-K2, according to the coordination method of step S51, select the target store in the pool of alternative solutions obtained by selecting the target store at the second priority level to replenish the remaining target parts, so that the complete purchase rate of parts of the optimal solution is ≥100% and the surplus rate of parts of the optimal solution is the lowest, and output the remaining selected stores.

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