An engine system based on data mining and analysis and its optimization method

Abstract

The present invention discloses an engine based on data mining and analysis and an optimization method thereof, which specifically relates to the field of data science, including a processor module, a memory module and a user information terminal, and also includes a full-process database construction module, a product full-process information mining module, a data standardization processing module, a product full-process quality comprehensive analysis module, a product full-process quality comprehensive judgment module and an interactive feedback module. The present invention establishes a detailed product full-process information library, deeply mines a full range of basic information from target market size to customer relationship maintenance, ensures comprehensive coverage and detailed monitoring of all aspects of product management, and through in-depth analysis of these massive data, the present invention also constructs a highly integrated mathematical model. The model can comprehensively evaluate the overall quality of product full-process management. This comprehensive evaluation method enables enterprises to comprehensively examine the effectiveness of product management from multiple dimensions.

Description

Technical Field

[0001] The present invention relates to the field of data science technology, and more specifically, to an engine based on data mining and analysis and an optimization method thereof. Background Art

[0002] Data mining, also known as information exploration or data mining, is an important field in computer science. It extracts hidden, previously unknown, but potentially valuable information from large amounts of data through a variety of methods, including statistics, online analytical processing, intelligence retrieval, machine learning, expert engines (relying on past empirical rules), and pattern recognition. The goal of data mining is usually to establish a decision model to predict future behavior based on past action data to support decision making in various application areas.

[0003] Data mining has a wide range of applications in business, finance, healthcare, social media, the Internet, and manufacturing. For example, in the business field, data mining can help companies discover market trends, predict sales, and analyze consumer behavior; in the financial field, data mining can be used for fraud detection, credit scoring, risk management, and portfolio optimization; in the healthcare field, data mining can discover potential disease patterns, predict disease risks, and provide personalized treatment recommendations.

[0004] Product full-process management based on data mining is an efficient and accurate management method. It uses data mining technology to extract valuable information from massive data to support decision-making and management of the entire life cycle of products, from design, production to sales and service.

[0005] However, there are still some shortcomings in its actual use. For example, the data mined by the existing technology is too single, which makes it difficult to accurately judge the overall situation of the whole process management of the product. In addition, there is currently a lack of comprehensive indicators for monitoring the whole process of the product. If monitoring is carried out only from a single dimension, the mutual influence between product potential, production efficiency and cost-effectiveness, supply chain quality, product marketing effect and product after-sales service quality in the whole process of product management will be ignored, and the quality and efficiency of the whole process management of the product cannot be better evaluated. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an engine based on data mining and analysis, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an engine based on data mining and analysis, comprising a processor module, a memory module and a user information terminal, characterized in that it also includes a full-process database construction module, a product full-process information mining module, a data standardization processing module, a product full-process quality comprehensive analysis module, a product full-process quality comprehensive judgment module and an interactive feedback module.

[0008] Preferably, the memory module includes all data texts of the engine based on data mining and analysis, and collects the information text output by each module in real time. The processor module is used to control the information text instructions output by each module. The user information terminal is an information output device for receiving the engine based on data mining and analysis.

[0009] Preferably, the whole process database construction module is used to record the information of each link from product conception, design, development, production, sales to after-sales service as feature text data and store it in the database;

[0010] The product whole process information mining module includes a product planning information collection unit, a product production information collection unit, a product sales information collection unit, a product after-sales information collection unit and a supply chain management information collection unit, which is used to select a first key text from the feature text data to obtain a first key text;

[0011] The data standardization processing module is used to perform standardization processing and analysis on the first key text data to obtain the second key text;

[0012] The product whole process quality comprehensive analysis module is used to import the second key text data obtained in the data standardization processing module into the mathematical model of product potential coefficient, the mathematical model of production efficiency and cost-effectiveness coefficient, the mathematical model of supply chain quality coefficient, the mathematical model of product marketing effect coefficient, the mathematical model of product after-sales service quality coefficient and the mathematical model of product whole process comprehensive evaluation coefficient, and process and analyze them to obtain a text output value based on the second key text;

[0013] The product whole process quality comprehensive judgment module is used to compare the text output value with the preset value, and input the comparison result into the interactive feedback module;

[0014] The interactive feedback module is used to import the comparison result into the administrator terminal, and when the text output value comparison result is lower than the preset value, an alarm will be issued to the administrator's equipment.

[0015] Preferably, the first key text in the product whole process information mining module is as follows:

[0016] The first key text refers to target market size parameters, unit product cost budget parameters, product development cycle parameters, product R&D cost parameters, production line average daily production quantity parameters, production line production unit product time parameters, unit product raw material utilization rate parameters, production line equipment failure rate parameters, production line equipment maintenance cycle parameters, inventory turnover rate parameters, on-time delivery rate parameters, production demand rate parameters, sales target achievement rate parameters, sales cost parameters, sales profit parameters, advertising investment cost parameters, advertising investment effect parameters, after-sales service response time parameters, fault resolution rate parameters, customer relationship maintenance frequency parameters;

[0017] The target market size parameter refers to the target market size ;

[0018] The unit product cost budget parameter refers to the unit product cost budget ;

[0019] The product development cycle parameter refers to the product development cycle ;

[0020] The product development cost parameter refers to the product development cost ;

[0021] The average daily production quantity parameter of the production line refers to the average daily production quantity of the production line ;

[0022] The time required for the production line to produce a unit of product parameter refers to the time required for the production line to produce a unit of product ;

[0023] The unit product raw material utilization rate parameter refers to the unit product raw material utilization rate ;

[0024] The production line equipment failure rate parameter refers to the production line equipment failure rate ;

[0025] The production line equipment maintenance cycle parameter refers to the production line equipment maintenance cycle ;

[0026] The inventory turnover rate parameter refers to the inventory turnover rate ;

[0027] The on-time delivery rate parameter refers to the percentage of on-time delivery times at each node in the supply chain to the total number of deliveries. ;

[0028] The production-demand ratio parameter refers to the production-demand ratio ;

[0029] The sales target achievement rate parameter refers to the sales target achievement rate ;

[0030] The sales cost parameter refers to the sales cost ;

[0031] The sales profit parameter refers to the sales profit ;

[0032] The advertising investment cost parameter refers to the advertising investment cost ;

[0033] The advertising investment effect parameter refers to the advertising investment effect ;

[0034] The after-sales service response time parameter refers to the after-sales service response time ;

[0035] The fault resolution rate parameter refers to the fault resolution rate ;

[0036] The customer relationship maintenance frequency parameter refers to the customer relationship maintenance frequency .

[0037] Preferably, the sales target achievement rate parameter specifically refers to:

[0038] Sales target achievement rate = sales amount for the current quarter / target sales amount for the current quarter.

[0039] Preferably, the raw material utilization rate parameter per unit product specifically refers to:

[0040] Raw material utilization rate per unit product = actual raw material consumption per unit product / actual raw material consumption per unit product + additional raw material loss.

[0041] Preferably, the production-demand ratio parameter specifically refers to:

[0042] The ratio of the number of products produced (or services provided) at each node in the supply chain to the demand for that product at its downstream nodes.

[0043] Preferably, the advertising investment effect parameter specifically refers to:

[0044] Advertising investment effect parameter = advertising investment turnover in the current quarter / advertising investment turnover in the previous quarter.

[0045] Preferably, the Z-score standardization process is a commonly used data preprocessing method, which converts the original data into a standardized Z-Score by calculating the mean and standard deviation of the original data. This method can eliminate the magnitude differences between data, so that data of different magnitudes can be compared and comprehensively analyzed.

[0046] Preferably, the mathematical model of the product potential coefficient is as follows:

[0047] = + ,in Refers to the product potential coefficient, The target market size. Refers to the unit product cost budget, Refers to the product development cycle, Refers to product development costs, Average unit product cost budget for similar products in the industry, , Refers to other influencing factors.

[0048] Preferably, the mathematical model of the production efficiency and cost-effectiveness coefficient is as follows:

[0049] = + ,in Refers to production efficiency and cost-effectiveness coefficient, Refers to the average daily production quantity of the production line. Refers to the time required for a production line to produce a unit of product. Refers to the utilization rate of raw materials per unit product. Refers to the failure rate of production line equipment and the maintenance cycle of production line equipment , Refers to the average production line output of the industry, , Refers to other influencing factors.

[0050] Preferably, the mathematical model of the supply chain quality coefficient is as follows:

[0051] = + ,in Refers to the supply chain quality coefficient, Refers to inventory turnover rate, Refers to the percentage of on-time delivery times at each node in the supply chain to the total number of deliveries. Refers to the production-demand ratio, , Refers to other influencing factors.

[0052] Preferably, the mathematical model of the product marketing effect coefficient is as follows:

[0053] = + ,in Refers to the product marketing effect coefficient, Refers to the sales target achievement rate. Refers to sales cost, Refers to sales profit, Refers to advertising investment costs, Refers to the effect of advertising investment. , Refers to other influencing factors,

[0054] Preferably, the mathematical model of the product after-sales service quality coefficient is as follows:

[0055] = + , Refers to the product after-sales service quality coefficient, Refers to the after-sales service response time, Refers to the fault resolution rate and the frequency of customer relationship maintenance , , Refers to other influencing factors.

[0056] Preferably, the mathematical model of the comprehensive evaluation coefficient of the whole process of the product is as follows:

[0057] = ,in Refers to the comprehensive evaluation coefficient of the whole process of the product. Refers to the product potential coefficient, Refers to production efficiency and cost-effectiveness coefficient, Refers to the supply chain quality coefficient, Refers to the product marketing effect coefficient, Refers to the product after-sales service quality coefficient, , , , , Refers to the weight coefficient.

[0058] Preferably, the weight coefficient , , , , It is calculated by the entropy method, and the specific calculation steps of the entropy method are as follows:

[0059] Data standardization: Since the measurement units of various indicators may be different, in order to eliminate the impact of the dimension, the collected data needs to be standardized. Commonly used standardization methods include Z-score standardization and range method standardization;

[0060] Calculate the proportion of indicators: Calculate the proportion of the i-th sample value under the j-th indicator to the sum of all sample values ​​of the indicator. This step is the basis for the subsequent calculation of information entropy;

[0061] Calculate information entropy: According to the definition and calculation formula of information entropy, calculate the information entropy ej of the jth indicator. The smaller the information entropy, the greater the degree of variation of the indicator and the greater the amount of information provided;

[0062] Calculate information entropy redundancy: Information entropy redundancy dj is the complement of information entropy, that is, dj=1-ej. The greater the information entropy redundancy, the more information the indicator contains, and the greater the impact on the comprehensive evaluation;

[0063] Calculate weight: Calculate the weight of each indicator in the comprehensive evaluation according to its information entropy redundancy. The weight coefficient is the ratio of the information entropy redundancy of each indicator to the sum of the information entropy redundancy of all indicators.

[0064] The product whole process quality comprehensive judgment module is used to compare the product whole process comprehensive evaluation coefficient with the preset value, and input the comparison result into the interactive feedback module.

[0065] Preferably, the preset value is a warning value for evaluating product potential, production efficiency and cost-effectiveness, supply chain quality, product marketing effect and product after-sales service quality in the whole process management of the product obtained based on industry practical experience. When the comprehensive evaluation coefficient of the whole process of the product in the target area is lower than the preset value, it means that the product potential of the product is small, the production efficiency and cost-effectiveness are low, the supply chain quality is poor, the product marketing effect is poor and the product after-sales service quality is low, and corresponding measures need to be taken to optimize the product potential, production efficiency and cost-effectiveness, supply chain quality, product marketing effect and product after-sales service quality in the whole process management of the product.

[0066] Preferably, when the comparison result of the interactive feedback module is lower than a preset value, the engine will trigger an alarm function on the terminal, send a warning signal to the management personnel in the form of sound, vibration and pop-up window, and give relevant parameters, and use artificial intelligence to help management personnel analyze the reasons affecting the comprehensive evaluation coefficient of the whole product process, and further track down the specific aspects affecting the whole product process management, such as small product potential, low production efficiency and cost-effectiveness, poor supply chain quality, poor product marketing effect and low product after-sales service quality, and provide targeted optimization methods.

[0067] Preferably, a method based on data mining and analysis includes:

[0068] S1: Construction of the whole process database: used to record the information of each link from product conception, design, development, production, sales to after-sales service as feature text data and store it in the database;

[0069] S2: Product whole process information mining: including product planning information collection unit, product production information collection unit, product sales information collection unit, product after-sales information collection unit and supply chain management information collection unit, used to select the first key text from the feature text data to obtain the first key text;

[0070] S3: Data standardization processing: used to perform standardization processing and analysis on the first key text data to obtain the second key text;

[0071] S4: Comprehensive analysis of product quality throughout the entire process: used to import the second key text data obtained in the data standardization processing module into the mathematical model of product potential coefficient, the mathematical model of production efficiency and cost-effectiveness coefficient, the mathematical model of supply chain quality coefficient, the mathematical model of product marketing effect coefficient, the mathematical model of product after-sales service quality coefficient and the mathematical model of comprehensive evaluation coefficient of the entire product process, and process and analyze them to obtain a text output value based on the second key text;

[0072] S5: Comprehensive judgment of product quality throughout the entire process: used to compare the text output value with the preset value, and input the comparison result into the interactive feedback module;

[0073] S6: Interactive feedback: used to import the comparison results into the administrator terminal. When the text output value comparison result is lower than the preset value, an alarm will be issued to the administrator's device.

[0074] Technical effects and advantages of the present invention:

[0075] 1. The present invention establishes a product full process information database and mines basic information in the database. The basic information includes target market size information, unit product cost budget information, product development cycle information, product R&D cost information, average daily production quantity of production lines, time required for production of unit products by production lines, unit product raw material utilization rate information, production line equipment failure rate information, production line equipment maintenance cycle information, inventory turnover rate information, on-time delivery rate information, production demand rate information, sales target achievement rate information, sales cost information, sales profit information, advertising investment cost information, advertising investment effect information, after-sales service response time information, fault resolution rate information, customer relationship maintenance frequency information, covering the five aspects of product full process management, namely, product planning information, product production information, product sales information, product after-sales information, and supply chain management information, thereby providing a more comprehensive and detailed monitoring means for product full process management;

[0076] 2. By conducting an in-depth analysis of the collected data, the present invention establishes a mathematical model that can comprehensively measure the overall quality of product management throughout the entire process. By establishing such a comprehensive model, the present invention can better measure the overall quality of product management throughout the entire process from different dimensions. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0078] Figure 2 It is a schematic diagram of the structure of the whole process information mining module of the present invention. DETAILED DESCRIPTION

[0079] 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.

[0080] As attached Figure 1 An engine based on data mining and analysis is shown, comprising: a processor module, a memory module and a user information terminal, and also comprising a full-process database construction module, a product full-process information mining module, a data standardization processing module, a product full-process quality comprehensive analysis module, a product full-process quality comprehensive judgment module and an interactive feedback module.

[0081] The output end of the full-process database construction module is telecommunication connected to the input end of the product full-process information mining module, the output end of the product full-process information mining module is telecommunication connected to the input end of the data standardization processing module, the output end of the data standardization processing module is telecommunication connected to the input end of the product full-process quality comprehensive analysis module, the output end of the product full-process quality comprehensive analysis module is telecommunication connected to the input end of the product full-process quality comprehensive judgment module, the output end of the product full-process quality comprehensive judgment module is telecommunication connected to the input end of the interactive feedback module, and the processor module and the memory module input ends are telecommunication connected to the output ends of the full-process database construction module, the product full-process information mining module, the data standardization processing module, the product full-process quality comprehensive analysis module, the product full-process quality comprehensive judgment module and the interactive feedback module.

[0082] The memory module includes all data texts of the engine based on data mining and analysis, and collects the information texts output by each module in real time. The processor module is used to centrally control the information text instructions output by each module. The user information terminal is an information output device that receives the engine based on data mining and analysis;

[0083] The whole process database construction module is used to record the information of each link from product conception, design, development, production, sales to after-sales service as feature text data and store it in the database;

[0084] The product whole process information mining module includes a product planning information collection unit, a product production information collection unit, a product sales information collection unit, a product after-sales information collection unit and a supply chain management information collection unit, which is used to select a first key text from the feature text data to obtain a first key text;

[0085] The first key text in the product whole process information mining module in the preferred technical solution of this application is as follows:

[0086] In this embodiment, it should be specifically explained that the first key text refers to the target market size parameter, unit product cost budget parameter, product development cycle parameter, product R&D cost parameter, production line average daily production quantity parameter, production line production unit product time parameter, unit product raw material utilization rate parameter, production line equipment failure rate parameter, production line equipment maintenance cycle parameter, inventory turnover rate parameter, on-time delivery rate parameter, production demand rate parameter, sales target achievement rate parameter, sales cost parameter, sales profit parameter, advertising investment cost parameter, advertising investment effect parameter, after-sales service response time parameter, fault resolution rate parameter, customer relationship maintenance frequency parameter;

[0087] The target market size parameter refers to the target market size ;

[0088] The unit product cost budget parameter refers to the unit product cost budget ;

[0089] The product development cycle parameter refers to the product development cycle ;

[0090] The product development cost parameter refers to the product development cost ;

[0091] The average daily production quantity parameter of the production line refers to the average daily production quantity of the production line ;

[0092] The time required for the production line to produce a unit of product parameter refers to the time required for the production line to produce a unit of product ;

[0093] The unit product raw material utilization rate parameter refers to the unit product raw material utilization rate ;

[0094] The production line equipment failure rate parameter refers to the production line equipment failure rate ;

[0095] The production line equipment maintenance cycle parameter refers to the production line equipment maintenance cycle ;

[0096] The inventory turnover rate parameter refers to the inventory turnover rate ;

[0097] The on-time delivery rate parameter refers to the percentage of on-time delivery times at each node in the supply chain to the total number of deliveries. ;

[0098] The production-demand ratio parameter refers to the production-demand ratio ;

[0099] The sales target achievement rate parameter refers to the sales target achievement rate ;

[0100] The sales cost parameter refers to the sales cost ;

[0101] The sales profit parameter refers to the sales profit ;

[0102] The advertising investment cost parameter refers to the advertising investment cost ;

[0103] The advertising investment effect parameter refers to the advertising investment effect ;

[0104] The after-sales service response time parameter refers to the after-sales service response time ;

[0105] The fault resolution rate parameter refers to the fault resolution rate ;

[0106] The customer relationship maintenance frequency parameter refers to the customer relationship maintenance frequency .

[0107] It should be specifically noted in this embodiment that the parameters are screened from the database based on the business importance of each link from product conception, design, development, production, sales to after-sales service, and these parameters are collected by conventional means, such as collecting the average daily production quantity parameters of the production line and the time parameters required for the production of a unit product of the production line through the production log. Therefore, this embodiment does not make specific restrictions;

[0108] It should be specifically explained in this embodiment that the sales target achievement rate parameter specifically refers to:

[0109] Sales target achievement rate = sales amount for the current quarter / target sales amount for the current quarter;

[0110] It should be specifically explained in this embodiment that the raw material utilization rate parameter of unit product specifically refers to:

[0111] Raw material utilization rate per unit product = actual raw material consumption per unit product / actual raw material consumption per unit product + additional loss of raw materials;

[0112] It should be specifically explained in this embodiment that the production-demand ratio parameter specifically refers to:

[0113] The ratio of the number of products produced (or services provided) at each node in the supply chain to the demand for that product at its downstream nodes;

[0114] It should be specifically explained in this embodiment that the advertising investment effect parameter specifically refers to:

[0115] Advertising investment effect parameter = advertising investment turnover in the current quarter / advertising investment turnover in the previous quarter;

[0116] The data standardization processing module is used to perform standardization processing and analysis on the first key text data to obtain the second key text;

[0117] It should be specifically noted that the standardization process specifically refers to Z-score standardization process, which converts the original data into a standardized Z-score by calculating the mean and standard deviation of the original data. This method can eliminate the magnitude differences between data, so that data of different magnitudes can be compared and comprehensively analyzed.

[0118] The comprehensive product quality analysis throughout the entire process is used to import the second key text data obtained in the data standardization processing module into the mathematical model of the product potential coefficient, the mathematical model of the production efficiency and cost-effectiveness coefficient, the mathematical model of the supply chain quality coefficient, the mathematical model of the product marketing effect coefficient, the mathematical model of the product after-sales service quality coefficient, and the mathematical model of the comprehensive evaluation coefficient of the product throughout the entire process, and process and analyze them to obtain a text output value based on the second key text;

[0119] It should be specifically explained in this embodiment that the mathematical model of the product potential coefficient is as follows:

[0120] = + ,in Refers to the product potential coefficient, The target market size. Refers to the unit product cost budget, Refers to the product development cycle, Refers to product development costs, Average unit product cost budget for similar products in the industry, , Refers to other influencing factors;

[0121] It should be specifically explained in this embodiment that the mathematical model of the production efficiency and cost-effectiveness coefficient is as follows:

[0122] = + ,in Refers to production efficiency and cost-effectiveness coefficient, Refers to the average daily production quantity of the production line. Refers to the time required for a production line to produce a unit of product. Refers to the utilization rate of raw materials per unit product. Refers to the failure rate of production line equipment and the maintenance cycle of production line equipment , Refers to the average production line output of the industry, , Refers to other influencing factors;

[0123] It should be specifically explained in this embodiment that the mathematical model of the supply chain quality coefficient is as follows:

[0124] = + ,in Refers to the supply chain quality coefficient, Refers to inventory turnover rate, Refers to the percentage of on-time delivery times at each node in the supply chain to the total number of deliveries. Refers to the production-demand ratio, , Refers to other influencing factors;

[0125] It should be specifically explained in this embodiment that the mathematical model of the product marketing effect coefficient is as follows:

[0126] = + ,in Refers to the product marketing effect coefficient, Refers to the sales target achievement rate. Refers to sales cost, Refers to sales profit, Refers to advertising investment costs, Refers to the effect of advertising investment. , Refers to other influencing factors;

[0127] It should be specifically explained in this embodiment that the mathematical model of the product after-sales service quality coefficient is as follows:

[0128] = + , Refers to the product after-sales service quality coefficient, Refers to the after-sales service response time, Refers to the fault resolution rate and the frequency of customer relationship maintenance , , Refers to other influencing factors;

[0129] It should be specifically explained in this embodiment that the mathematical model of the comprehensive evaluation coefficient of the whole process of the product is as follows:

[0130] = ,in Refers to the comprehensive evaluation coefficient of the whole process of the product. Refers to the product potential coefficient, Refers to production efficiency and cost-effectiveness coefficient, Refers to the supply chain quality coefficient, Refers to the product marketing effect coefficient, Refers to the product after-sales service quality coefficient, , , , , Refers to the weight coefficient;

[0131] It should be specifically explained in this embodiment that the weight coefficient , , , , It is calculated by the entropy method, and the specific calculation steps of the entropy method are as follows:

[0132] Data standardization: Since the measurement units of various indicators may be different, in order to eliminate the impact of the dimension, the collected data needs to be standardized. Commonly used standardization methods include Z-score standardization and range method standardization;

[0133] Calculate the proportion of indicators: Calculate the proportion of the i-th sample value under the j-th indicator to the sum of all sample values ​​of the indicator. This step is the basis for the subsequent calculation of information entropy;

[0134] Calculate information entropy: According to the definition and calculation formula of information entropy, calculate the information entropy ej of the jth indicator. The smaller the information entropy, the greater the degree of variation of the indicator and the greater the amount of information provided;

[0135] Calculate information entropy redundancy: Information entropy redundancy dj is the complement of information entropy, that is, dj=1-ej. The greater the information entropy redundancy, the more information the indicator contains, and the greater the impact on the comprehensive evaluation;

[0136] Calculate weight: Calculate the weight of each indicator in the comprehensive evaluation according to its information entropy redundancy. The weight coefficient is the ratio of the information entropy redundancy of each indicator to the sum of the information entropy redundancy of all indicators.

[0137] The product whole process quality comprehensive judgment module is used to compare the text output value with the preset value, and input the comparison result into the interactive feedback module;

[0138] What needs to be specifically explained in this embodiment is that the preset value is a warning value for evaluating product potential, production efficiency and cost-effectiveness, supply chain quality, product marketing effect and product after-sales service quality in the whole process management of the product obtained based on industry practical experience. When the comprehensive evaluation coefficient of the whole process of the product in the target area is lower than the preset value, it means that the product potential of the product is small, the production efficiency and cost-effectiveness are low, the supply chain quality is poor, the product marketing effect is poor and the product after-sales service quality is low. Corresponding measures need to be taken to optimize the product potential, production efficiency and cost-effectiveness, supply chain quality, product marketing effect and product after-sales service quality in the whole process management of the product.

[0139] The interactive feedback module is used to import the comparison result into the administrator terminal, and when the text output value comparison result is lower than the preset value, an alarm will be issued to the administrator's device;

[0140] What needs to be specifically explained in this embodiment is that when the comparison result of the interactive feedback module is lower than the preset value, the engine will trigger the alarm function on the terminal, send a warning signal to the management personnel in the form of sound, vibration and pop-up window, and give relevant parameters, and use artificial intelligence to help management personnel analyze the reasons affecting the comprehensive evaluation coefficient of the entire product process, and further track down specific aspects affecting the management of the entire product process, such as small product potential, low production efficiency and cost-effectiveness, poor supply chain quality, poor product marketing effect and low quality of product after-sales service, and provide targeted optimization methods.

[0141] A method based on data mining and analysis, comprising:

[0142] S1: Construction of the whole process database: used to record the information of each link from product conception, design, development, production, sales to after-sales service as feature text data and store it in the database;

[0143] S2: Product whole process information mining: including product planning information collection unit, product production information collection unit, product sales information collection unit, product after-sales information collection unit and supply chain management information collection unit, used to select the first key text from the feature text data to obtain the first key text;

[0144] S3: Data standardization processing: used to perform standardization processing and analysis on the first key text data to obtain the second key text;

[0145] S4: Comprehensive analysis of product quality throughout the entire process: used to import the second key text data obtained in the data standardization processing module into the mathematical model of product potential coefficient, the mathematical model of production efficiency and cost-effectiveness coefficient, the mathematical model of supply chain quality coefficient, the mathematical model of product marketing effect coefficient, the mathematical model of product after-sales service quality coefficient and the mathematical model of comprehensive evaluation coefficient of the entire product process, and process and analyze them to obtain a text output value based on the second key text;

[0146] S5: Comprehensive judgment of product quality throughout the entire process: used to compare the text output value with the preset value, and input the comparison result into the interactive feedback module;

[0147] S6: Interactive feedback: used to import the comparison results into the administrator terminal. When the text output value comparison result is lower than the preset value, an alarm will be issued to the administrator's device.

[0148] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0149] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An engine system based on data mining and analysis, comprising: The processor module, the memory module and the user information terminal are characterized in that they also include a full-process database construction module, a product full-process information mining module, a data standardization processing module, a product full-process quality comprehensive analysis module, a product full-process quality comprehensive judgment module and an interactive feedback module; The memory module stores all data texts of the engine system based on data mining and analysis, and collects information texts output by each module in real time. The processor module is used to centrally control the information text instructions output by each module. The user information terminal is a device for receiving information output by the engine system based on data mining and analysis. The whole process database construction module is used to record the information of each link from product conception, design, development, production, sales to after-sales service as feature text data and store it in the database; The product whole process information mining module includes a product planning information collection unit, a product production information collection unit, a product sales information collection unit, a product after-sales information collection unit and a supply chain management information collection unit, which is used to select a first key text from the feature text data to obtain a first key text; The first key text refers to target market size parameters, unit product cost budget parameters, product development cycle parameters, product R&D cost parameters, production line average daily production quantity parameters, production line production unit product time parameters, unit product raw material utilization rate parameters, production line equipment failure rate parameters, production line equipment maintenance cycle parameters, inventory turnover rate parameters, on-time delivery rate parameters, production demand rate parameters, sales target achievement rate parameters, sales cost parameters, sales profit parameters, advertising investment cost parameters, advertising investment effect parameters, after-sales service response time parameters, fault resolution rate parameters, customer relationship maintenance frequency parameters; The data standardization processing module is used to perform standardization processing and analysis on the first key text data to obtain the second key text; The product whole process quality comprehensive analysis module is used to import the second key text data obtained in the data standardization processing module into the mathematical model of product potential coefficient, the mathematical model of production efficiency and cost-effectiveness coefficient, the mathematical model of supply chain quality coefficient, the mathematical model of product marketing effect coefficient, the mathematical model of product after-sales service quality coefficient and the mathematical model of product whole process comprehensive evaluation coefficient, and process and analyze them to obtain a text output value based on the second key text; The mathematical model of the product potential coefficient is as follows: = + ,in Refers to the product potential coefficient, The target market size. Refers to the unit product cost budget, Refers to the product development cycle, Refers to product development costs, Refers to the average unit product cost budget of the same type of products in the industry. , Refers to other influencing factors; The mathematical model of production efficiency and cost-effectiveness coefficient is as follows: = + ,in Refers to production efficiency and cost-effectiveness coefficient, Refers to the average daily production quantity of the production line. Refers to the time required for a production line to produce a unit of product. Refers to the utilization rate of raw materials per unit product. Refers to the failure rate of production line equipment, Refers to the maintenance cycle of production line equipment. Refers to the average production line output of the industry, , Refers to other influencing factors; The mathematical model of the supply chain quality coefficient is as follows: = + ,in Refers to the supply chain quality coefficient, Refers to inventory turnover rate, Refers to the percentage of on-time delivery times at each node in the supply chain to the total number of deliveries. Refers to the production-demand ratio, , Refers to other influencing factors; The mathematical model of the product marketing effect coefficient is as follows: = + ,in Refers to the product marketing effect coefficient, Refers to the sales target achievement rate. Refers to sales cost, Refers to sales profit, Refers to advertising investment costs, Refers to the effect of advertising investment. , Refers to other influencing factors; The mathematical model of the product after-sales service quality coefficient is as follows: = + , Refers to the product after-sales service quality coefficient, Refers to the after-sales service response time, Refers to the failure resolution rate, Refers to the frequency of customer relationship maintenance, , Refers to other influencing factors; The mathematical model of the comprehensive evaluation coefficient of the whole process of the product is as follows: = ,in Refers to the comprehensive evaluation coefficient of the whole process of the product. Refers to the product potential coefficient, Refers to production efficiency and cost-effectiveness coefficient, Refers to the supply chain quality coefficient, Refers to the product marketing effect coefficient, Refers to the product after-sales service quality coefficient, , , , , Refers to the weight coefficient; The product whole process quality comprehensive judgment module is used to compare the text output value with the preset value, and input the comparison result into the interactive feedback module; The preset values ​​are warning values ​​obtained based on industry practical experience to evaluate product potential, production efficiency and cost-effectiveness, supply chain quality, product marketing effect and product after-sales service quality in the whole process management of products; The interactive feedback module is used to import the comparison result into the administrator terminal, and when the text output value comparison result is lower than the preset value, an alarm will be issued to the administrator's equipment.

2. The engine system based on data mining and analysis according to claim 1, characterized in that: The first key text in the product full process information mining module is as follows: The target market size parameter refers to the target market size ; The unit product cost budget parameter refers to the unit product cost budget ; The product development cycle parameter refers to the product development cycle ; The product development cost parameter refers to the product development cost ; The average daily production quantity parameter of the production line refers to the average daily production quantity of the production line ; The time required for the production line to produce a unit product parameter refers to the time required for the production line to produce a unit product ; The unit product raw material utilization rate parameter refers to the unit product raw material utilization rate ; The production line equipment failure rate parameter refers to the production line equipment failure rate ; The production line equipment maintenance cycle parameter refers to the production line equipment maintenance cycle ; The inventory turnover rate parameter refers to the inventory turnover rate ; The on-time delivery rate parameter refers to the percentage of on-time delivery times at each node in the supply chain to the total number of deliveries. ; The production-demand ratio parameter refers to the production-demand ratio ; The sales target achievement rate parameter refers to the sales target achievement rate ; The sales cost parameter refers to the sales cost ; The sales profit parameter refers to the sales profit ; The advertising investment cost parameter refers to the advertising investment cost ; The advertising investment effect parameter refers to the advertising investment effect ; The after-sales service response time parameter refers to the after-sales service response time ; The fault resolution rate parameter refers to the fault resolution rate ; The customer relationship maintenance frequency parameter refers to the customer relationship maintenance frequency .

3. A method based on data mining and analysis, implemented based on the engine system based on data mining and analysis described in any one of claims 1-2, characterized in that: include: S1: Construction of the whole process database: used to record the information of each link from product conception, design, development, production, sales to after-sales service as feature text data and store it in the database; S2: Product whole process information mining: including product planning information collection unit, product production information collection unit, product sales information collection unit, product after-sales information collection unit and supply chain management information collection unit, used to select the first key text from the feature text data to obtain the first key text; S3: Data standardization processing: used to perform standardization processing and analysis on the first key text data to obtain the second key text; S4: Comprehensive analysis of product quality throughout the entire process: used to import the second key text data obtained in the data standardization processing module into the mathematical model of product potential coefficient, the mathematical model of production efficiency and cost-effectiveness coefficient, the mathematical model of supply chain quality coefficient, the mathematical model of product marketing effect coefficient, the mathematical model of product after-sales service quality coefficient and the mathematical model of comprehensive evaluation coefficient of the entire product process, and process and analyze them to obtain a text output value based on the second key text; S5: Comprehensive judgment of product quality throughout the entire process: used to compare the text output value with the preset value, and input the comparison result into the interactive feedback module; S6: Interactive feedback: used to import the comparison results into the administrator terminal. When the text output value comparison result is lower than the preset value, an alarm will be issued to the administrator's device.

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