Network knowledge construction system based on big data

Abstract

The invention discloses a network knowledge construction system based on big data, which comprises a data acquisition module, a product analysis module and an output module, and is characterized in that the data acquisition module is used for acquiring related information of a part supply manufacturer acquired by sampling inspection personnel; the data acquisition module is used for acquiring reference values of shops, the product analysis module is used for constructing a virtual sampling inspection model, and the data acquisition module, the product analysis module and the output module are in communication connection with one another. The selection priorities of the shops are further divided, so that the selection level of the spot check shops is effectively improved, and the selection stability of spot check manufacturer schemes is improved; and meanwhile, a scheme for purchasing parts is formulated from the perspective of the purchaser, and the target shop which is most necessary to carry out product quality sampling inspection is screened according to the shop to-be-inspected parameters obtained by the system, so that the sampling inspection accuracy is effectively improved, and the method has the characteristics of low sampling inspection cost and high sampling inspection benefit reliability.

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] Knowledge network is a social network among knowledge participants, which can realize the creation and transfer of knowledge between individuals, organizations and outside organizations. People cooperate and exchange information through knowledge network. The goal is to connect technology with people and realize the effective combination of intellectual capital, structural capital and customer capital. It can be divided into internal knowledge network and external knowledge network. The former emphasizes the knowledge exchange between employees within the organization and between organizations, while the latter emphasizes the 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 progress of science and technology, the perfect purchasing system on some online parts purchasing software provides manufacturers with more choices. In the existing technology, market product quality researchers conduct sampling inspections on different basic parts selling manufacturers, but because the sampled industrial parts involve a wide range of fields and the number of parts selling manufacturers is huge, it is too subjective to screen the supply targets of sampled parts by parameters such as praise rate and sales volume. In particular, many old manufacturers with good reputations, although they have many orders and high praise rates, often need to pay more attention to random inspections of product quality. Therefore, it is necessary to design a network knowledge construction system based on big data with low sampling cost and high reliability of sampling efficiency. 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 obtained by sampling personnel; the product analysis module is used to build 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 target parts information sampled by sampling personnel in the industrial field; the retrieval information acquisition module is used to obtain relevant information about manufacturers selling parts in online stores.

[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 stores corresponding to each combination scheme in the branch equipment composition schemes provided by different online equipment suppliers.

[0008] According to the above technical scheme, 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 in the field of industrial parts to be sampled. 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 sampling;

[0011] Step S2: According to the search results of the search information collection module, several store business plan entries corresponding to the parts search results are tested to detect the device assembly variability of the devices sold by the store business;

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

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

[0014] Step S5: The system outputs the recommended sampling store names to the sampling 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 for supply solutions

[0017] Step S22: searching the parts supply composition scheme information provided by different supply stores in sequence according to the extraction method in step S21, and summarizing the total S1 basic schemes 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 device of step S1, and add the number of times the basic solutions are obtained in turn 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 the parts supply store information with the acquisition times equal to 1 and recording it into the necessity analysis module;

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

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

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

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

[0025] Step S42: in the remaining solution library, two random solutions are compared in turn. When the overlap between the keywords and the part names in the solution is less than μ1%, the two solutions are added to the alternative solution pool; when the overlap is higher than μ2%, the solution involving fewer part names is added to the alternative solution pool, where μ1 and μ2 are respectively the minimum limit ratio and the maximum limit ratio of the overlap between the part names in the solution design;

[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, and the number of occurrences of non-repeated basic parts is obtained to be K2. The remaining basic parts that still need to be configured are (K-K2). The system obtains the sampling target stores for 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 selecting the target store at the first priority, the solution entries entering 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 redundant parts 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<K-K2, according to the overall planning method of step S51, select the target store in the alternative solution pool 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 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, formulates the plan for purchasing parts from the perspective of the buyer, and screens the target stores that are most necessary for 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 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 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 not spot-checking stores with high risk of quality problems. 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[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 sampling personnel; the product analysis module is used to build a virtual sampling model when sampling 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 connected to each other in communication.

[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 random inspections based on the parameters required for inspection of the stores obtained by the system, thereby effectively improving the accuracy of random inspections and promoting the development of random inspections 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 of 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 random inspection stores, improving the stability of selection of random inspection manufacturer plans, and greatly reducing the possibility of not conducting random inspections on stores with high risk of quality problems.

[0037] The data collection module includes a parts information collection module and a retrieval information collection module. The parts information collection module is used to obtain the target parts information sampled by sampling personnel in the industrial field; the retrieval information collection module is used to obtain relevant information about the parts sold by the manufacturer in the online store.

[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 scheme provided by the online equipment supplier meets the sampling requirements of the sampling personnel; the necessity analysis module is used to analyze the necessity of sampling inspections of the corresponding stores for each combination scheme in the branch equipment composition schemes 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 in the field of industrial parts to be sampled. 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 sampling;

[0042] Step S2: According to the search results of the search information collection module, several store business plan entries corresponding to the parts search results are tested to detect the device assembly variability of the devices sold by the store business;

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

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

[0045] Step S5: The system outputs the recommended sampling store names to the sampling 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 for supply solutions

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

[0049] Step S22: retrieve the parts supply composition plan information provided by different supply stores in turn according to the extraction method in step S21, and summarize 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 device of step S1, and add the number of times the basic solutions are obtained in turn 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 the parts supply store information with the acquisition times equal to 1 and recording it into the necessity analysis module;

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

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

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

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

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

[0058] Step S42: in the remaining solution library, two random solutions are compared in turn. When the overlap between the keywords and the part names in the solution is less than μ1%, the two solutions are added to the alternative solution pool; when the overlap is higher than μ2%, the solution involving fewer part names is added to the alternative solution pool, where μ1 and μ2 are respectively the minimum limit ratio and the maximum limit ratio of the overlap between the part names in the solution design;

[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, and the number of occurrences of non-repeated basic parts is obtained to be K2. The remaining basic parts that still need to be configured are (K-K2). The system obtains the sampling target stores for 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 are not obtained by other stores, and there is a high necessity to conduct random inspections on the stores corresponding to these parts.

[0061] Through this technical solution, the problem of providing similar parts composition solutions in different stores, causing chaos in the sampling method, and being unable to eliminate the problem of increasing sampling costs due to selecting stores with low sampling necessity is solved. The cost of sampling is effectively reduced through further store parts screening.

[0062] In step S42 of this implementation, 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 entering 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 redundant parts 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<K-K2, according to the overall planning method of step S51, select the target store in the alternative solution pool 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 the remaining selected stores are output.

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

[0066] The optimal solution parts redundancy rate is the ratio of parts overlap in the solution when the buyer purchases parts from an online parts supply store and completes the required purchases in the same store and different stores. The higher the ratio, the greater the mixing rate of the solution provided by the store, 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 purchaser in the store, and the greater the positive review of the quality after purchasing the 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 favorable reviews of online manufacturers’ product sales are subjective and the 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 favorable reviews of manufacturers’ product purchases, sales volume and other parameters;

[0069] This technical solution divides manufacturers into types according to the production plans provided by manufacturers, formulates a plan for purchasing parts from the perspective of the buyer, and selects the target stores that are most necessary for product quality random inspections based on the store inspection parameters obtained by the system. It effectively improves the accuracy of random inspections and promotes the development of random inspections on market product quality research in a positive direction to the greatest extent.

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

[0071] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is 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 can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope 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 from the parts supplier to the sampling personnel; the product analysis module is used to build a virtual sampling model when sampling 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 for communication.

2. The network knowledge construction system based on big data according to claim 1 is characterized by: The data collection module includes a parts information collection module and a retrieval information collection module. The parts information collection module is used to obtain target parts information sampled by sampling personnel in the industrial field; the retrieval information collection module is used to obtain relevant information about the manufacturer selling parts in the online store.

3. The network knowledge construction system based on big data according to claim 2 is characterized in that: 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.

4. The network knowledge construction system based on big data according to claim 3 is characterized in that: 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.

5. The network knowledge construction system based on big data according to claim 4 is characterized in that: The operation method of the network knowledge construction system mainly includes the following steps: Step S1: The sampling management personnel randomly select K different basic parts as sampling target parts in the field of industrial parts to be sampled. 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 sampling; Step S2: According to the search results of the search information collection module, several store business plan entries corresponding to the parts search results are tested to detect the device assembly variability of the devices sold by the store business; Step S3: After obtaining the device assembly variability of several test result stores, the stores are divided according to the limit values ​​into sampling stores, first priority selection target stores and second priority selection stores; Step S4: The system evaluates the ratio of excess parts sold by the equipment after virtual assembly based on the test results, plans the assembly plan for the equipment parts of the production line, and obtains the target store for random inspection of the assembly needs; Step S5: The system outputs the recommended sampling store names to the sampling staff through the output module.

6. The network knowledge construction system based on big data according to claim 5 is characterized in that: The step S2 further comprises: 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 for supply solutions Step S22: searching the parts supply composition scheme information provided by different supply stores in sequence according to the extraction method in step S21, and summarizing the total S1 basic schemes 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 count them into the information storage device of step S1, and add the number of times the basic solutions are obtained in turn until the number of times all K parts are obtained is greater than or equal to 1, then stop accumulating.

7. The network knowledge construction system based on big data according to claim 6 is characterized in that: The step S3 further comprises: Step S31: extracting the parts supply store information with the acquisition times equal to 1 and recording it into the necessity analysis module; Step S32: Arrange the basic solutions in descending order according to the number of times the basic solutions are obtained, and mark them as "1", "2", ... "KQ" in the order of arrangement, where Q is the number of parts that are obtained the same number of times as 1; Step S33: Linearly superimpose the corresponding store basic plan on the arranged marks against the reference marks to obtain the reference value of each store, and set the stores with reference values ​​higher than the limit value as the first priority target stores; set the stores with reference values ​​lower than the limit value as the second priority target stores.

8. The network knowledge construction system based on big data according to claim 7 is characterized in that: The step S4 further comprises: Step S41: The necessity analysis module removes the supply solutions provided by all stores in step S31, and the supply solutions that contain parts that are not included in the demand base; Step S42: in the remaining solution library, two random solutions are compared in turn. When the overlap between the keywords and the part names in the solution is less than μ1%, the two solutions are added to the alternative solution pool; when the overlap is higher than μ2%, the solution involving fewer part names is added to the alternative solution pool, where μ1 and μ2 are respectively the minimum limit ratio and the maximum limit ratio of the overlap between the part names in the solution design; 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, and the number of occurrences of non-repeated basic parts is obtained to be K2. The remaining basic parts that still need to be configured are (K-K2). The system obtains the sampling target stores for assembly requirements through the priority store screening method.

9. The network knowledge construction system based on big data according to claim 8 is characterized in that: 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 selecting the target store at the first priority, the solution entries entering 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 redundant parts Output the five stores with the lowest ratio of (optimal solution parts full purchase rate - optimal solution parts surplus rate); Step S422: When K4<K-K2, according to the overall planning method of step S51, select the target store in the alternative solution pool 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 the remaining selected stores are output.

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