Intelligent industrial cluster management system based on big data analysis

Abstract

The present invention discloses an intelligent industrial cluster management system based on big data analysis, which belongs to the field of big data analysis and intelligent management technology. It specifically includes: collecting and preprocessing research, project and material data of similar companies in the supply chain to generate a target company data set; loading and training a regression analysis prediction model, and analyzing material requirements based on the prediction results to determine the type and quantity of parts. At the same time, it optimizes procurement plans through association rule mining; constructing a potential customer model to match the required parts and recommend potential partners or customers; collecting feedback data to optimize the potential customer model, part determination and potential customer matching to form a closed-loop optimization system, thereby improving supply chain efficiency and matching accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of big data analysis and intelligent management, and specifically to an intelligent industrial cluster management system based on big data analysis. Background Art

[0002] An industrial cluster is an industrial ecosystem formed by the aggregation of numerous related enterprises, institutions, and organizations within a specific region. These enterprises and institutions interact with each other in complex relationships, involving supply chain management, production management, marketing, technological research and development, and other aspects. Traditional industrial cluster management suffers from numerous shortcomings. For example, internal information exchange channels may be insufficient, resulting in delayed responses to external environmental changes and weak risk resilience. Furthermore, unhealthy competition among enterprises within an industrial cluster can hinder the cluster's upgrading and development.

[0003] For example, the Chinese patent publication number CN113902599A discloses an intelligent navigation system for an industrial cluster park, comprising: a client terminal, an enterprise terminal, a management terminal and a server. The management terminal is used to register and review the client terminal and the enterprise terminal, and to authorize the operation of the client terminal and the enterprise terminal. This technical solution enhances the standardization of enterprise information by reviewing the customer information and enterprise information, and uniformly uploads the enterprise information to the park display page of the client terminal for display, further enhancing the standardized management of enterprises in the park. By uploading the enterprise information to the corresponding display column after it is reviewed and approved, the enterprise is promoted, which also facilitates customer retrieval and understanding, and marks the display column where the approved enterprise is located, thereby enhancing customer trust in the enterprise and further improving the promotion of the enterprise.

[0004] For example, Chinese patent application publication number CN101908165A discloses a GIS-based industrial cluster information integration service system and method, comprising a spatial information management subsystem, an attribute information management subsystem, a data element entry subsystem, a spatial analysis and decision-making subsystem, and a system management and maintenance subsystem. This technical solution incorporates advanced GIS technology to achieve the mapping and correspondence between spatial information and various attribute information, meeting system users' requirements for advanced, accurate, and timely geographic and technical information. By utilizing content-based information processing technology and conducting intelligent analysis over the network, this system provides public service support for geographic location, equipment, technology, processes, products, and other resources to enterprises within and across clusters in a visual and convenient manner.

[0005] The above existing technologies all have the following problems: lack of prediction and optimization capabilities; although they provide corporate information display and customer retrieval functions, they lack in-depth exploration of customer needs and personalized recommendations; insufficient data analysis depth and prediction capabilities; and at the same time, lack of comprehensive insights into supply chains and market demands. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention proposes an intelligent industrial cluster management system based on big data analysis, which collects and preprocesses the research, project and material data of similar companies in the supply chain to generate a target company data set; loads and trains the regression analysis prediction model, and analyzes material requirements based on the prediction results to determine the type and quantity of parts. At the same time, it optimizes the procurement plan through association rule mining; builds a potential customer model to match the required parts and recommend potential partners or customers; collects feedback data, optimizes the potential customer model, part determination and potential customer matching, forming a closed-loop optimization system, and improving supply chain efficiency and matching accuracy.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] Intelligent industrial cluster management system based on big data analysis, including: data processing module, model prediction module, demand analysis module, matching and recommendation module, and optimization module;

[0009] The data processing module is used to collect research directions, project data, and material usage of similar companies from the supply chain, and perform preprocessing to generate a target company data set;

[0010] The model prediction module is used to load a pre-built regression analysis prediction model and train it using the target company data set to obtain a trained regression analysis prediction model;

[0011] The demand analysis module is used to analyze the materials required for the company's subsequent research based on the forecast results, determine the required part types and quantities, and optimize the parts procurement plan through association rule mining strategies;

[0012] The matching and recommendation module is used to build a potential customer model using customer profiling and market segmentation methods, match the required parts with the potential customer model, identify potential companies or customers, and recommend potential partners or customers through an improved collaborative filtering algorithm;

[0013] The optimization module is used to collect feedback data and optimize the regression analysis prediction model, part determination and potential customer matching process based on the feedback data to form a closed-loop optimization system.

[0014] Specifically, the demand analysis module includes a procurement optimization unit, which is configured with an association rule mining strategy. The association rule mining strategy is used to discover the association relationship between parts and optimize the parts procurement plan based on the association relationship. The specific steps of the association rule mining strategy include:

[0015] A1: Obtain the target company dataset ,in, represents the nth target company data, where n represents the number of target company data;

[0016] A2: Set a minimum support threshold based on the company's business needs and data characteristics and the minimum confidence threshold ;

[0017] A3: Based on the target company dataset , using a counter to traverse , count the frequency of occurrence of each target company's data , organize each target company’s data and its corresponding occurrence frequency to generate a frequency table;

[0018] like , then abandon it;

[0019] like , then retain The corresponding target company data forms a frequent itemset;

[0020] A4: Sort the frequent itemsets in descending order and construct an FP tree in the sorted order. At the same time, insert each target company data into the FP tree in the sorted order of the frequent itemsets and update the node count. In the FP tree, the tree node represents the target company data, and the node count represents the target company data in Frequency of occurrence ,The paths of the tree represent the co-occurrence relationships of the target ,company data.

[0021] Specifically, the specific steps of the association rule mining strategy also include:

[0022] A5: Generate all non-empty true subsets of frequent itemsets from the FP tree, and for each non-empty true subset Z, calculate the credibility between it and the complement B ,in, Representing item sets exist The number of times it appears in Represents the item set Z in The number of times it appears in Indicates the implied logical relationship. represents a union;

[0023] A6: Based on the minimum credibility threshold ,like , then retain the association rules corresponding to the frequent item set to form the association rule set R;

[0024] A7: Output the filtered association rule set R to form association rules between parts.

[0025] Specifically, the specific steps of the association rule mining strategy also include:

[0026] A8: Interpret the generated association rules between parts, identify the relationships between parts, analyze purchasing patterns based on parts purchase records, and present the analysis results as an association rule network diagram. Based on the analysis results, formulate targeted inventory management strategies.

[0027] like , then consider bulk purchases or combined orders, where represents the support of the association rule, and , Represents association rules support, represents the number of items containing association rule A and association rule D, Indicates the total number of items;

[0028] like , then optimize the inventory management strategy, where Indicates the credibility of the association rule;

[0029] A9: Implement the optimized procurement plan and monitor the execution of the procurement plan.

[0030] Specifically, the matching and recommendation module includes a model building unit and a recommendation unit. The model building unit is used to build a potential customer model using customer portraits and market segmentation methods. The specific steps of building the potential customer model include:

[0031] B1: Obtain target company dataset , and extract features from the target company dataset, and build customer profiles based on the extracted features;

[0032] B2: Based on customer profile data, use clustering algorithms to segment the market into different customer groups and identify potential customer characteristics from the segmented market groups. At the same time, extract data corresponding to potential customer characteristics from the customer profile data to generate customer feature data.

[0033] B3: Load the pre-built decision tree model and train it using customer feature data to learn the characteristics and patterns of potential customers. This results in a trained decision tree model, which is then saved as a potential customer model.

[0034] B4: Input real-time target company data into the potential customer model and calculate the target company data based on the preset threshold. , convert the identification results of the potential customer model into a potential customer list, classify the potential customers, prioritize the potential customers based on the company's potential needs, and output the classified and prioritized potential customer list to a report;

[0035] B5: Regularly monitor and evaluate the potential customer model, and adjust the potential customer model based on the evaluation results.

[0036] Specifically, the recommendation unit is configured with an improved collaborative filtering algorithm, which is used to recommend potential partners or customers. The specific steps of the improved collaborative filtering algorithm include:

[0037] C1: Obtain target company dataset ,according to , build a company-customer matrix , where rows represent companies, columns represent customers, and matrix elements represent the company's behavior records towards customers;

[0038] C2: Using an improved similarity calculation method, using the company-customer matrix Calculate the similarity between companies and store the similarity between companies in a company similarity matrix Among them, The rows and columns in represent companies, and the matrix elements represent the similarity between companies;

[0039] C3: For the target company, sort the companies from high to low according to the company similarity matrix. Based on the company similarity sorting results, select the K companies that are most similar to it as neighboring companies;

[0040] C4: Analyze the transaction patterns of neighboring companies based on their behavioral records. Combined with the target company's business needs and customer characteristics, a recommendation list is generated for the target company.

[0041] C5: Use accuracy evaluation indicators to evaluate the recommendation results and optimize the recommendation process based on the evaluation results.

[0042] Specifically, the specific formula of the improved similarity calculation method in C2 is:

[0043] ;

[0044] in, express The similarity with V, and V represent two companies of the same type in the company-customer matrix The behavior records in , m represents the number of companies of the same type, represents the weight of the i-th dimension, express The i-th element of represents the i-th element of V, express The offset in the i-th dimension, represents the offset of V in the i-th dimension, represents the nonlinear term coefficient, and express and The nonlinear transformation function of and Represents a positive number.

[0045] Specifically, the specific steps of C4 include:

[0046] C4.1: Collect behavioral records of neighboring companies, as well as the target company's business needs and customer characteristics, and pre-process the collected data;

[0047] C4.2: Extract the material characteristics information required for the neighboring company research based on the neighboring company's behavior records;

[0048] C4.3: Based on the extracted material feature information, use clustering algorithms to divide neighboring companies into different pattern groups;

[0049] C4.4: Use the similarity calculation formula to match the target company's customer characteristics with the pattern groups of neighboring companies, and find the neighboring company group that is most similar to the target company's customers;

[0050] C4.5: Based on the matching results, select companies with transaction model characteristics that match the target company's business needs from a group of neighboring companies with similar customers. Recommend products from these companies to generate a recommendation list.

[0051] C4.6: Optimize the recommendation list based on feedback from target companies.

[0052] Specifically, the formula for generating the recommendation list in C4.5 is:

[0053] ;

[0054] in, represents the recommendation degree of target company a for product r, represents the average rating of all products by target company a, represents the set of neighboring companies similar to the target company a, represents the similarity between target company a and neighbor company j, represents the actual rating of product r by neighbor company j, represents the average rating of all products by neighbor company j, express The absolute value of .

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The present invention proposes an intelligent industrial cluster management system based on big data analysis, and optimizes and improves the architecture, operation steps and processes. The system has the advantages of simple process, low investment and operation costs, and low production work costs.

[0057] 2. The present invention proposes an intelligent industrial cluster management system based on big data analysis. By collecting and analyzing the research directions and project data of similar companies in the supply chain, this data processing and forecasting system can accurately predict the material requirements and number of parts types of the target company, effectively support the company's research planning and procurement strategy, and improve the efficiency and accuracy of supply chain management.

[0058] 3. The present invention proposes an intelligent industrial cluster management system based on big data analysis. By combining customer portraits, market segmentation and advanced recommendation algorithms, the system can accurately match and recommend potential partners or customers. At the same time, through closed-loop optimization of feedback data, it continuously optimizes the prediction model and matching process, further improving the accuracy and efficiency of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is the architecture diagram of the intelligent industrial cluster management system based on big data analysis of the present invention;

[0060] Figure 2 This is a flowchart of the principle implementation of the intelligent industrial cluster management system based on big data analysis of the present invention;

[0061] Figure 3 This is a flowchart for implementing the association rule mining strategy of the intelligent industrial cluster management system based on big data analysis of the present invention;

[0062] Figure 4 A flow chart for constructing a potential customer model for the intelligent industrial cluster management system based on big data analysis in the present invention. DETAILED DESCRIPTION

[0063] Example 1

[0064] See also Figure 1-Figure 2 The present invention provides an embodiment of an intelligent industrial cluster management system based on big data analysis, comprising the following steps:

[0065] Data processing module, model prediction module, demand analysis module, matching and recommendation module, optimization module;

[0066] The data processing module is used to collect research directions, project data, and material usage of similar companies from the supply chain, and perform preprocessing to generate the target company data set;

[0067] The model prediction module is used to load the pre-built regression analysis prediction model and train it using the target company's data set to obtain a trained regression analysis prediction model for subsequent real-time prediction;

[0068] The demand analysis module is used to analyze the materials needed for the company's next research based on the forecast results, determine the types and quantities of parts required, and optimize the parts procurement plan through association rule mining strategies to reduce inventory costs and improve supply chain efficiency;

[0069] The matching and recommendation module is used to build a potential customer model using customer profiling and market segmentation methods, match the required parts with the potential customer model, identify potential companies or customers, and recommend potential partners or customers through an improved collaborative filtering algorithm;

[0070] The optimization module is used to collect feedback data and optimize the regression analysis prediction model, part determination and potential customer matching process based on the feedback data to form a closed-loop optimization system.

[0071] The model prediction module includes: model loading unit, model training unit, and real-time prediction unit;

[0072] Model loading unit, used to load pre-built regression analysis prediction model;

[0073] A model training unit is used to train the regression analysis prediction model using the target company data set and adjust the regression analysis prediction model parameters to improve the prediction accuracy;

[0074] The real-time prediction unit is used to input the real-time acquired data into the trained regression analysis prediction model to perform real-time prediction.

[0075] The demand analysis module includes: demand forecasting unit, parts determination unit, and procurement optimization unit;

[0076] Demand forecasting unit, used to analyze the materials needed for the company's next research based on the forecast results;

[0077] Furthermore, the specific process of analyzing the materials required for the company's research based on the forecast results includes:

[0078] (1) Obtain and analyze the forecast results obtained by the model forecast module, including accurate understanding of the quantity, type, and trend of change of materials required for the company's research projects in different time periods. At the same time, the forecast results may be presented in the form of charts, reports, or data tables;

[0079] (2) Compare the forecast results with historical data to analyze the growth or decrease trend of material demand. At the same time, it is necessary to combine seasonal factors, industry development trends, etc. to more comprehensively understand the reasons for changes in material demand;

[0080] (3) Further analyze the supply of materials, including evaluating factors such as supplier reliability, delivery cycle, and material quality to ensure that the company can obtain the required materials in a timely and stable manner. In addition, it is necessary to consider substitutes and alternatives for materials to reduce supply chain risks.

[0081] Parts determination unit, which uses bill of materials and supply chain management knowledge to determine the type and quantity of parts required;

[0082] The procurement optimization unit discovers the association relationships between parts by introducing association rule mining strategies, and optimizes parts procurement plans based on these relationships, such as batch procurement and order consolidation, to reduce procurement costs and improve supply chain efficiency.

[0083] The matching and recommendation module includes: model building unit, matching unit, and recommendation unit;

[0084] Model building unit, used to build potential customer models using customer profiling and market segmentation methods;

[0085] Matching unit, used to match the required parts with potential customer models and identify potential companies or customers;

[0086] The recommendation unit is used to introduce an improved collaborative filtering algorithm to recommend potential partners or customers.

[0087] Specifically, the overall implementation process of the intelligent industrial cluster management system based on big data analysis includes:

[0088] Step S1: Collect research directions, project data, and material usage of similar companies through supply chain data, clean the initial data set, remove redundant and abnormal data, and perform standardization to form the target company data set;

[0089] Step S2: Load the pre-built regression analysis prediction model and train it using the target company dataset to obtain a trained regression analysis prediction model;

[0090] Step S3: Input the target company data set acquired in real time into the trained regression analysis prediction model to make predictions based on the company's research direction, material quantity, and type;

[0091] Step S4: Based on the forecast results, the company analyzes the materials needed for the next research and uses the bill of materials and supply chain management knowledge to determine the required part types and quantities. At the same time, an association rule mining strategy is introduced to discover the associations between parts. Based on these associations, the parts procurement plan is optimized to reduce inventory costs and improve supply chain efficiency.

[0092] Step S5: Utilize customer profiling and market segmentation methods to build a potential customer model, match the required parts with the potential customer model, identify potential companies or customers, and introduce an improved collaborative filtering algorithm to recommend potential partners or customers, thereby improving matching accuracy and satisfaction.

[0093] Step S6: The matching results are returned to the company for its decision-making reference. At the same time, feedback data is collected, including prediction accuracy and procurement efficiency. The regression analysis prediction model, parts determination and potential customer matching process are optimized based on the feedback data to form a closed-loop optimization system.

[0094] Example 2

[0095] See also Figure 3 In this embodiment, the demand analysis module includes a procurement optimization unit, which is configured with an association rule mining strategy. The association rule mining strategy is used to discover the association relationship between parts and optimize the parts procurement plan based on the association relationship. The specific steps of the association rule mining strategy include:

[0096] A1: Obtain the target company dataset ,in, represents the nth target company data, where n represents the number of target company data;

[0097] A2: Set a minimum support threshold based on the company's business needs and data characteristics and the minimum confidence threshold ;

[0098] A3: Based on the target company dataset , using a counter to traverse , count the frequency of occurrence of each target company's data , organize each target company’s data and its corresponding occurrence frequency to generate a frequency table;

[0099] like , then abandon it;

[0100] like , then retain The corresponding target company data forms a frequent itemset;

[0101] A4: Sort the frequent itemsets in descending order and construct an FP tree in the sorted order. At the same time, insert each target company data into the FP tree in the sorted order of the frequent itemsets and update the node count. In the FP tree, the tree node represents the target company data, and the node count represents the target company data in Frequency of occurrence ,The paths of the tree represent the co-occurrence relationships of the target ,company data;

[0102] A5: Generate all non-empty true subsets of frequent itemsets from the FP tree, and for each non-empty true subset Z, calculate the credibility between it and the complement B ,in, Representing item sets exist The number of times it appears in Represents the item set Z in The number of times it appears in Indicates the implied logical relationship. represents a union;

[0103] Non-empty proper subsets refer to all subsets of a set, excluding the empty set and the set itself. For example, for the set {a, b, c}, its non-empty proper subsets include {a}, {b}, {c}, {a, b}, {a, c}, {b, c}; then each frequent item set is traversed to generate its non-empty proper subsets.

[0104] A6: Based on the minimum credibility threshold ,like , then retain the association rules corresponding to the frequent item set to form the association rule set R;

[0105] A7: Output the filtered association rule set R to form association rules between parts;

[0106] A8: Interpret the generated association rules between parts, identify the relationships between parts, analyze purchasing patterns based on parts purchase records, and present the analysis results as an association rule network diagram. Based on the analysis results, formulate targeted inventory management strategies.

[0107] The relationships between parts include complementarity and substitution; purchasing patterns include which parts are frequently purchased together and which parts have a correlation in purchase order or time. Furthermore, when presented as an association rule network diagram, the antecedents and consequents of the association rule are represented as nodes, and the relationships as edges.

[0108] like , then consider bulk purchases or combined orders, where represents the support of the association rule, and , Represents association rules support, represents the number of items containing association rule A and association rule D, Indicates the total number of items;

[0109] like , then optimize the inventory management strategy, where Indicates the credibility of the association rule;

[0110] In summary, inventory management strategies include considering bundling inventory or combination management for parts that are often purchased together; for parts with substitution relationships, their cost-effectiveness can be evaluated, and more suitable parts can be selected for inventory control; and inventory levels and replenishment strategies can be adjusted according to seasonal or cyclical changes in purchasing patterns.

[0111] A9: Implement the optimized procurement plan and monitor its execution.

[0112] Example 3

[0113] See also Figure 4 In this embodiment, the matching and recommendation module includes a model building unit and a recommendation unit. The model building unit is used to build a potential customer model using customer portraits and market segmentation methods. The specific steps of building the potential customer model include:

[0114] B1: Obtain target company dataset , and extract features from the target company dataset, and build customer profiles based on the extracted features;

[0115] B2: Based on the customer profile data, a clustering algorithm is used to segment the market into different customer groups, and potential customer characteristics are identified from the segmented market groups. At the same time, data corresponding to the potential customer characteristics are extracted from the customer profile data to generate customer feature data. The clustering algorithm is prior art in this field and does not constitute an inventive solution of this application, so it will not be described in detail here.

[0116] B3: Loading a pre-built decision tree model and training the pre-built decision tree model using customer feature data to enable it to learn the characteristics and patterns of potential customers, obtaining a trained decision tree model, and saving the trained decision tree model as a potential customer model. The decision tree model is prior art in this field and does not constitute an inventive solution of the present application, and is not described in detail here.

[0117] B4: Input real-time target company data into the potential customer model and calculate the target company data based on the preset threshold. , convert the identification results of the potential customer model into a potential customer list, classify the potential customers, prioritize the potential customers based on the company's potential needs, and output the classified and prioritized potential customer list to a report;

[0118] Furthermore, the specific steps of B4 include:

[0119] (1) Obtain real-time target company data and potential customer models;

[0120] (2) Set thresholds based on business needs , used to convert the recognition results of the potential customer model into a potential customer list;

[0121] (3) Input the real-time target company data into the potential customer model to obtain the recognition results of the potential customer model;

[0122] (4) Based on the preset threshold, the identification results are converted into a list of potential customers.

[0123] (5) Use decision tree classification algorithm to classify potential customers into hot customers, warm customers, and cold customers, where hot customers are likely to place orders within one month, warm customers are likely to place orders within three months, and cold customers are likely to place orders after three months;

[0124] (6) Prioritize the classified potential customers based on the company's potential needs, using factors such as the customer's purchasing power and the intensity of potential needs;

[0125] (7) Use the CRM system to output the classified and prioritized list of potential customers into a report. The report should include the customer's basic information, classification information, priority ranking, and possible purchase intentions. The CRM system is the existing technology in this field and is not an inventive solution of this application. It will not be described in detail here.

[0126] B5: Regularly monitor and evaluate the potential customer model, and adjust the potential customer model based on the evaluation results.

[0127] The recommendation unit is configured with an improved collaborative filtering algorithm, which is used to recommend potential partners or customers. The specific steps of the improved collaborative filtering algorithm include:

[0128] C1: Obtain target company dataset ,according to , build a company-customer matrix , where rows represent companies, columns represent customers, and matrix elements represent the company's behavior records towards customers;

[0129] C2: Using an improved similarity calculation method, using the company-customer matrix Calculate the similarity between companies and store the similarity between companies in a company similarity matrix Among them, The rows and columns in represent companies, and the matrix elements represent the similarity between companies;

[0130] C3: For the target company, sort the companies from high to low according to the company similarity matrix. Based on the company similarity sorting results, select the K companies that are most similar to it as neighboring companies;

[0131] C4: Analyze the transaction patterns of neighboring companies based on their behavioral records. Combined with the target company's business needs and customer characteristics, a recommendation list is generated for the target company.

[0132] C5: Use accuracy evaluation indicators to evaluate the recommendation results and optimize the recommendation process based on the evaluation results. The accuracy evaluation indicators are prior art in this field and are not the inventive solution of this application, so they will not be described in detail here.

[0133] The specific formula of the improved similarity calculation method in C2 is:

[0134] ;

[0135] in, express The similarity with V, and V represent two companies of the same type in the company-customer matrix The behavior records in , m represents the number of companies of the same type, represents the weight of the i-th dimension, express The i-th element of represents the i-th element of V, express The offset in the i-th dimension, represents the offset of V in the i-th dimension, represents the nonlinear term coefficient, and express and The nonlinear transformation function of and Represents a positive number.

[0136] It should be noted that the formula in the present invention introduces the weighting factor , so that when calculating similarity, it can be differentiated according to the importance of different features, which enhances the flexibility and adaptability of the formula; secondly, the formula and represents the adjustment or offset of the feature value, which helps to more accurately measure the similarity between two objects in a specific situation; in addition, by adding the regularization term , which can capture the nonlinear relationship between features and further improve the accuracy and comprehensiveness of similarity calculation; Finally, the denominator and As a small constant term, it effectively avoids the situation where the denominator is zero, and enhances the stability and robustness of the formula. In general, the formula in the present invention is improved in terms of flexibility, accuracy, comprehensiveness and stability.

[0137] The specific steps of C4 include:

[0138] C4.1: Collect behavioral records of neighboring companies, as well as the target company's business needs and customer characteristics, and pre-process the collected data. Behavioral records include transaction data and product usage records.

[0139] C4.2: Extract the material characteristic information required for research on neighboring companies based on their behavioral records, including transaction frequency, transaction amount, and product category distribution;

[0140] C4.3: Based on the extracted material feature information, use a clustering algorithm to classify neighboring companies into different pattern groups. The clustering algorithm is prior art in this field and does not constitute the inventive solution of this application, so it will not be described in detail here;

[0141] C4.4: Use the similarity calculation formula to match the target company's customer characteristics with the pattern groups of neighboring companies, and find the neighboring company group that is most similar to the target company's customers;

[0142] C4.5: Based on the matching results, select companies with transaction model characteristics that match the target company's business needs from a group of neighboring companies with similar customers. Recommend products from these companies to generate a recommendation list.

[0143] C4.6: Optimize the recommendation list based on feedback from target companies.

[0144] The formula for generating the recommendation list in C4.5 is:

[0145] ;

[0146] in, represents the recommendation degree of target company a for product r, represents the average rating of all products by target company a, represents the set of neighboring companies similar to the target company a, represents the similarity between target company a and neighbor company j, represents the actual rating of product r by neighbor company j, represents the average rating of all products by neighbor company j, express The absolute value of .

[0147] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention. These are all protected by the present invention.

[0148] If the technical solution disclosed herein involves personal information, the product using the technical solution disclosed herein has clearly informed the individual of the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution disclosed herein involves sensitive personal information, the product using the technical solution disclosed herein has obtained the individual's separate consent before processing the sensitive personal information and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, a clear and prominent sign is set to inform that the individual has entered the personal information collection scope and that personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that they agree to the collection of their personal information; or on the personal information processing device, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload their personal information. The personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. Intelligent industrial cluster management system based on big data analysis, characterized by: include: Data processing module, model prediction module, demand analysis module, matching and recommendation module, optimization module; The data processing module is used to collect research directions, project data, and material usage of similar companies from the supply chain, and perform preprocessing to generate a target company data set; The model prediction module is used to load a pre-built regression analysis prediction model and train it using the target company data set to obtain a trained regression analysis prediction model; The demand analysis module is used to analyze the materials required for the company's subsequent research based on the forecast results, determine the required part types and quantities, and optimize the parts procurement plan through association rule mining strategies; The matching and recommendation module is used to build a potential customer model using customer profiling and market segmentation methods, match the required parts with the potential customer model, identify potential companies or customers, and recommend potential partners or customers through an improved collaborative filtering algorithm; The optimization module is used to collect feedback data and optimize the regression analysis prediction model, part determination and potential customer matching process based on the feedback data to form a closed-loop optimization system; The matching and recommendation module includes a model building unit and a recommendation unit; The recommendation unit is configured with an improved collaborative filtering algorithm, which is used to recommend potential partners or customers. The specific steps of the improved collaborative filtering algorithm include: C1: Obtain target company dataset ,according to , build a company-customer matrix , where rows represent companies, columns represent customers, and matrix elements represent the company's behavior records towards customers; C2: Using an improved similarity calculation method, using the company-customer matrix Calculate the similarity between companies and store the similarity between companies in a company similarity matrix Among them, The rows and columns in represent companies, and the matrix elements represent the similarity between companies; C3: For the target company, sort the companies from high to low according to the company similarity matrix. Based on the company similarity sorting results, select the K companies that are most similar to it as neighboring companies; C4: Analyze the transaction patterns of neighboring companies based on their behavioral records. Combined with the target company's business needs and customer characteristics, a recommendation list is generated for the target company. C5: Use accuracy evaluation indicators to evaluate the recommendation results and optimize the recommendation process based on the evaluation results; The specific formula of the improved similarity calculation method in C2 is: ； in, express The similarity with V, and V represent two companies of the same type in the company-customer matrix The behavior records in , m represents the number of companies of the same type, represents the weight of the i-th dimension, express The i-th element of represents the i-th element of V, express The offset in the i-th dimension, represents the offset of V in the i-th dimension, represents the nonlinear term coefficient, and express and The nonlinear transformation function of and Represents a positive number.

2. The intelligent industrial cluster management system based on big data analysis according to claim 1, characterized in that: The demand analysis module includes a procurement optimization unit, which is configured with an association rule mining strategy. The association rule mining strategy is used to discover the association relationship between parts and optimize the parts procurement plan based on the association relationship. The specific steps of the association rule mining strategy include: A1: Obtain the target company dataset ,in, represents the nth target company data, where n represents the number of target company data; A2: Set a minimum support threshold based on the company's business needs and data characteristics and the minimum confidence threshold ; A3: Based on the target company dataset , using a counter to traverse , count the frequency of occurrence of each target company's data , organize each target company’s data and its corresponding occurrence frequency to generate a frequency table; like , then abandon it; like , then retain The corresponding target company data forms a frequent itemset; A4: Sort the frequent itemsets in descending order and construct an FP tree in the sorted order. At the same time, insert each target company data into the FP tree in the sorted order of the frequent itemsets and update the node count. In the FP tree, the tree node represents the target company data, and the node count represents the target company data in Frequency of occurrence ,The paths of the tree represent the co-occurrence relationships of the target ,company data.

3. The intelligent industrial cluster management system based on big data analysis according to claim 2, characterized in that: The specific steps of the association rule mining strategy also include: A5: Generate all non-empty true subsets of frequent itemsets from the FP tree, and for each non-empty true subset Z, calculate the credibility between it and the complement B ,in, Representing item sets exist The number of times it appears in Represents the item set Z in The number of times it appears in Indicates the implied logical relationship. represents a union; A6: Based on the minimum credibility threshold ,like , then retain the association rules corresponding to the frequent item set to form the association rule set R; A7: Output the filtered association rule set R to form association rules between parts.

4. The intelligent industrial cluster management system based on big data analysis according to claim 3, characterized in that: The specific steps of the association rule mining strategy also include: A8: Interpret the generated association rules between parts, identify the relationships between parts, analyze purchasing patterns based on parts purchase records, and present the analysis results as an association rule network diagram. Based on the analysis results, formulate targeted inventory management strategies. like , then consider bulk purchases or combined orders, where represents the support of the association rule, and , Represents association rules support, represents the number of items containing association rule A and association rule D, Indicates the total number of items; like , then optimize the inventory management strategy, where Indicates the credibility of the association rule; A9: Implement the optimized procurement plan and monitor the execution of the procurement plan.

5. The intelligent industrial cluster management system based on big data analysis according to claim 4, characterized in that: The matching and recommendation module includes a model building unit and a recommendation unit. The model building unit is used to build a potential customer model using customer portraits and market segmentation methods. The specific steps of building the potential customer model include: B1: Obtain target company dataset , and extract features from the target company dataset, and build customer profiles based on the extracted features; B2: Based on customer profile data, use clustering algorithms to segment the market into different customer groups and identify potential customer characteristics from the segmented market groups. At the same time, extract data corresponding to potential customer characteristics from the customer profile data to generate customer feature data. B3: Load the pre-built decision tree model and train it using customer feature data to learn the characteristics and patterns of potential customers. This results in a trained decision tree model, which is then saved as a potential customer model. B4: Input real-time target company data into the potential customer model and calculate the target company data based on the preset threshold. , convert the identification results of the potential customer model into a potential customer list, classify the potential customers, prioritize the potential customers based on the company's potential needs, and output the classified and prioritized potential customer list to a report; B5: Regularly monitor and evaluate the potential customer model, and adjust the potential customer model based on the evaluation results.

6. The intelligent industrial cluster management system based on big data analysis according to claim 5, characterized in that: The specific steps of C4 include: C4.1: Collect behavioral records of neighboring companies, as well as the target company's business needs and customer characteristics, and pre-process the collected data; C4.2: Extract the material characteristics information required for the neighboring company research based on the neighboring company's behavior records; C4.3: Based on the extracted material feature information, use clustering algorithms to divide neighboring companies into different pattern groups; C4.4: Use the similarity calculation formula to match the target company's customer characteristics with the pattern groups of neighboring companies, and find the neighboring company group that is most similar to the target company's customers; C4.5: Based on the matching results, select companies with transaction model characteristics that match the target company's business needs from a group of neighboring companies with similar customers. Recommend products from these companies to generate a recommendation list. C4.6: Optimize the recommendation list based on feedback from target companies.

7. The intelligent industrial cluster management system based on big data analysis according to claim 6, characterized in that: The formula for generating the recommendation list in C4.5 is: ； in, represents the recommendation degree of target company a for product r, represents the average rating of all products by target company a, represents the set of neighboring companies similar to the target company a, represents the similarity between target company a and neighbor company j, represents the actual rating of product r by neighbor company j, represents the average rating of all products by neighbor company j, express The absolute value of .

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