Industry knowledge structured modeling method of knowledge graph based on semantic association

Abstract

The invention belongs to the technical field of knowledge maps, and discloses an industry knowledge structured modeling method of a knowledge map based on semantic association. Collecting industry production data; constructing a production knowledge graph according to the industry production data; obtaining personalized demand content; performing feature extraction on the personalized demand content to obtain actual demand feature data; inputting the actual demand feature data into the demand evaluation model to obtain a demand evaluation score, and analyzing and processing the demand evaluation score to obtain a demand rationality level; inputting the actual demand feature data and the production knowledge graph into a parameter construction model to obtain a corresponding production parameter set; inputting the demand rationality level, the production parameter set and the production knowledge graph into a demand construction model to obtain predicted demand feature data; the matching degree of the customer demand and the system prediction is dynamically evaluated, a production parameter set meeting the personalized demand of the customer is generated, and the production accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of knowledge graph technology, and more specifically, to a method for structured modeling of industry knowledge based on a knowledge graph with semantic associations. Background Art

[0002] A variety of intelligent devices are widely used in enterprise production activities. These devices can generate massive multi-source data in real time. These data cover different types, dimensions and structures, involving a large spatial and temporal span of production, and are mainly used to improve production reliability, repeatability and product quality. Business process data modeling aims to accurately identify bottlenecks and waste links in the production process through deep integration and analysis of multi-source data, and propose corresponding improvement strategies based on data-driven, so as to achieve efficiency and flexibility of business processes. By constructing multi-source data into a business process model, it can effectively support the quantitative analysis of lean production processes, optimize process structures, and improve production efficiency and overall benefits. Therefore, how to build a unified business process data model in a distributed information system environment and realize dynamic analysis and optimization of complex business processes through the model is a key technical problem that needs to be solved in this field.

[0003] In order to improve the flexibility and application adaptability of knowledge graphs in complex environments, the current Chinese patent application with publication number CN117992619A proposes an industry knowledge graph modeling method and system based on an attribute graph, the method comprising: creating basic attributes and basic relationships of the industry knowledge graph, wherein all data is stored using an attribute graph database as a storage engine; creating business attributes and business relationships required for industry knowledge applications by combining basic attributes and basic relationships; filling in business relationships between different industry knowledge ontologies to form an ontology relationship graph, combining business attributes and business relationships, and adding business knowledge constraints to form an industry knowledge ontology; creating extensible industry knowledge instances based on the industry knowledge ontology; filling in business relationships between different industry knowledge instances to form an instance relationship graph; forming an industry knowledge graph based on the industry knowledge ontology, the ontology relationship graph, the industry knowledge instance, and the instance relationship graph to realize industry knowledge business.

[0004] Although the above method can meet most scenarios, research and practical application of the above method and existing technology have found that the above method and existing technology have at least the following defects:

[0005] Existing technologies have shown obvious limitations in responding to diverse and personalized needs. The production process is difficult to dynamically adjust according to customer needs, and it is impossible to respond quickly to complex demand scenarios. The setting of production parameters usually relies on manual analysis or adjustment methods based on fixed rules, lacking intelligent optimization capabilities, resulting in the inability to achieve dynamic adjustment and real-time feedback, causing large fluctuations in product quality and waste of production materials, which in turn leads to product parameter settings that do not fully match customer needs, affecting customer experience and market competitiveness.

[0006] In view of this, the present invention proposes an industry knowledge structured modeling method based on a semantically associated knowledge graph to solve the above problems. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for structuring industry knowledge modeling based on a knowledge graph of semantic association, comprising:

[0008] Collect industry production data;

[0009] Build a production knowledge graph based on industry production data;

[0010] Get personalized demand content;

[0011] Extract features from personalized demand content to obtain actual demand feature data;

[0012] Input the actual demand characteristic data into the demand assessment model to obtain the demand assessment score, and analyze and process the demand assessment score to obtain the demand rationality level;

[0013] Input the actual demand feature data and production knowledge graph into the parameter construction model to obtain the corresponding production parameter set;

[0014] Input the demand rationality level, production parameter set and production knowledge graph into the demand construction model to obtain the predicted demand feature data;

[0015] Extract features from actual demand feature data and predicted demand feature data to obtain demand matching degree;

[0016] Comprehensively analyze the demand matching degree, production parameter set and production knowledge graph, generate an updated production parameter set, send instructions to the smart factory, adjust the production parameters to the updated production parameter set and carry out production.

[0017] Furthermore, the method for comprehensively analyzing the demand matching degree, the production parameter set and the production knowledge graph includes:

[0018] Preset demand matching threshold;

[0019] If the demand matching degree is greater than or equal to the demand matching degree threshold, the actual demand feature data matches the predicted demand feature data, and the production parameter set is used as the updated production parameter set;

[0020] If the demand matching degree is less than the demand matching degree threshold, the actual demand feature data does not match the predicted demand feature data, and a demand mismatch report is generated. After the customer reconfirms the demand, the updated actual demand feature data is obtained; the updated actual demand feature data and the production knowledge graph are input into the parameter construction model to obtain the corresponding production parameter set, which is marked as the production parameter set to be changed, and the production parameter set to be changed is used as the updated production parameter set;

[0021] Send instructions to the smart factory to adjust the production parameters to the updated production parameter set and start production.

[0022] Furthermore, the method for obtaining the demand rationality level includes:

[0023] Preset evaluation score thresholds and , Less than ; The demand rationality levels include third-level rationality, second-level rationality and first-level rationality;

[0024] If the needs assessment score is greater than or equal to , then the demand rationality level is level 3 rationality, and the system does not issue unreasonable demand instructions;

[0025] If the needs assessment score is greater than or equal to , and less than , then the demand rationality level is level 2 rationality, and the system does not issue unreasonable demand instructions;

[0026] If the needs assessment score is less than , then the demand rationality level is level one rationality, and the system issues an unreasonable demand instruction.

[0027] Furthermore, the method for obtaining the demand matching degree includes:

[0028] Subtract the predicted product type in the predicted demand feature data from the expected product type in the actual demand feature data to obtain the product type difference; subtract the predicted cost in the predicted demand feature data from the expected cost in the actual demand feature data to obtain the cost difference; subtract the predicted pass rate in the predicted demand feature data from the expected pass rate in the actual demand feature data to obtain the pass rate difference; subtract the predicted accuracy in the predicted demand feature data from the expected accuracy in the actual demand feature data to obtain the accuracy difference; subtract the predicted delivery cycle in the predicted demand feature data from the expected delivery cycle in the actual demand feature data to obtain the delivery cycle difference;

[0029] Perform weighted operations on the product type difference, cost difference, pass rate difference, precision difference and delivery cycle difference to obtain the first result of demand matching; sum the squares of the weight coefficients corresponding to the product type difference, cost difference, pass rate difference, precision difference and delivery cycle difference to obtain the second result of demand matching;

[0030] The first result of the demand matching degree is divided by the second result of the demand matching degree to obtain a third result of the demand matching degree, and the third result of the demand matching degree is squared to obtain the demand matching degree.

[0031] Furthermore, the method for acquiring the actual demand characteristic data includes:

[0032] Extract the expected product type from the personalized demand content, create a product type key-value pair, the value of the product type key-value pair is the expected product type in the personalized demand content; extract the expected cost from the personalized demand content, create a cost key-value pair, the value of the cost key-value pair is the expected cost in the personalized demand content;

[0033] Extract the expected pass rate from the personalized demand content, create a pass rate key-value pair, the value of which is the expected pass rate in the personalized demand content; extract the expected accuracy from the personalized demand content, create a accuracy key-value pair, the value of which is the expected accuracy in the personalized demand content; extract the expected delivery cycle from the personalized demand content, create a delivery cycle key-value pair, the value of which is the expected delivery cycle in the personalized demand content;

[0034] The product type key-value pair, cost key-value pair, pass rate key-value pair, precision key-value pair and delivery cycle key-value pair are constructed into actual demand feature data.

[0035] Furthermore, the method for constructing a production knowledge graph based on the industry production data includes:

[0036] The temperature, pressure, processing speed, cutting depth and coating thickness in the production process variables are converted into corresponding knowledge nodes and added to the first knowledge node set; the equipment load, current, voltage and vibration frequency in the equipment operation data are converted into corresponding knowledge nodes and added to the second knowledge node set; the qualified rate, precision, strength and sealing in the production quality indicators are converted into corresponding knowledge nodes and added to the third knowledge node set; the composition, specification, ductility and density in the raw material characteristics are converted into corresponding knowledge nodes and added to the fourth knowledge node set;

[0037] Using natural language processing technology to process industry production documents to obtain relationships between knowledge nodes; the relationships between knowledge nodes include influence, determination, restriction and association;

[0038] The first knowledge node set, the second knowledge node set, the third knowledge node set, the fourth knowledge node set and the relationships between the knowledge nodes are constructed into a knowledge graph to obtain a production knowledge graph.

[0039] Furthermore, the training method of the demand assessment model includes:

[0040] A demand assessment data set is collected in advance, wherein the demand assessment data set includes G groups of demand assessment data and demand assessment scores corresponding to the G groups of demand assessment data, where G is a positive integer greater than 0, and the demand assessment data includes actual demand feature data; the demand assessment data set is divided into a training set and a test set, the demand assessment data in the training set is used as the input of the demand assessment model, and the demand assessment scores in the training set are used as the output of the demand assessment model, with minimizing the sum of the prediction accuracies of all predicted demand assessment scores as the training goal; training is stopped when the sum of the prediction accuracies converges; the demand assessment model is a naive Bayes model or a support vector machine model.

[0041] Furthermore, the training method of the demand construction model includes:

[0042] A demand construction data set is collected in advance, wherein the demand construction data set includes K groups of demand construction data and predicted demand feature data corresponding to the K groups of demand construction data, where K is a positive integer greater than 0, and the demand construction data includes a demand rationality level, a production parameter set, and a production knowledge graph; the demand construction data set is divided into a training set and a test set, the demand construction data in the training set is used as the input of the demand construction model, and the predicted demand feature data in the training set is used as the output of the demand construction model, with minimizing the sum of the prediction accuracies of all predicted predicted demand feature data as the training goal; training is stopped when the sum of the prediction accuracies converges; the demand construction model is a random forest model or a recurrent neural network model.

[0043] Furthermore, the training method of the parameter construction model includes:

[0044] A parameter construction data set is collected in advance, the parameter construction data set includes H groups of parameter construction data, and a production parameter set corresponding to the H groups of parameter construction data, H is a positive integer greater than 0, and the parameter construction data includes actual demand feature data and a production knowledge graph; the parameter construction data set is divided into a training set and a test set, the parameter construction data in the training set is used as the input of the parameter construction model, and the production parameter set in the training set is used as the output of the parameter construction model, with minimizing the sum of the prediction accuracies of all predicted production parameter sets as the training goal; the training is stopped when the sum of the prediction accuracies converges; the parameter construction model is a CaffeNet model.

[0045] Furthermore, the industry production data includes production process variables, equipment operating status, production quality indicators and raw material characteristics; the production process variables include temperature, pressure, processing speed, cutting depth and coating thickness; the equipment operation data includes equipment load, current, voltage and vibration frequency; the production quality indicators include pass rate, precision, strength and sealing; the raw material characteristics include composition, specification, ductility and density; the personalized demand content includes expected product type, expected cost, expected pass rate, expected precision and expected delivery cycle.

[0046] The technical effects and advantages of the industry knowledge structured modeling method based on the semantically associated knowledge graph of the present invention are as follows:

[0047] By introducing knowledge graph technology, the complex production knowledge, process parameters and actual data in the smart factory are associated and modeled, so as to achieve the goal of flexible and efficient production through personalized customization of needs. Through dynamic evaluation of demand matching, the degree of matching between customer needs and system predictions can be quickly judged, and the demand rationality level, production parameter set and production knowledge graph are combined for comprehensive analysis to generate the optimal production parameter set that meets the personalized needs of customers, avoid production errors caused by deviations, and improve production accuracy and customer satisfaction. By adaptively optimizing production parameters, reducing manual intervention and shortening adjustment time, ensure that each step of rule setting and algorithm implementation has sufficient flexibility and accuracy. Meet diverse and personalized production needs and realize flexible manufacturing. Reduce resource waste and unnecessary cost expenditure by adjusting and optimizing production processes in real time.

[0048] This solution achieves closed-loop control from customer demand to production parameter optimization by combining knowledge graph technology, demand assessment model and parameter optimization, comprehensively improving the production efficiency, flexibility and customer satisfaction of smart factories, and has broad application value and technological leadership in the field of intelligent manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of an industry knowledge structured modeling system based on a semantically associated knowledge graph according to Embodiment 1 of the present invention;

[0050] Figure 2 This is a flow chart of a method for structurally modeling industry knowledge based on a semantically associated knowledge graph according to Embodiment 2 of the present invention;

[0051] Figure 3 A flow chart of the method for comprehensive analysis of demand matching, production parameter set and production knowledge graph;

[0052] Figure 4 The flowchart of the method for obtaining the rationality level of requirements;

[0053] Figure 5 Schematic diagram of the production knowledge graph. DETAILED DESCRIPTION

[0054] 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.

[0055] Example 1

[0056] See also Figure 1 As shown, the industry knowledge structured modeling system based on the semantically associated knowledge graph described in this embodiment includes a first acquisition module, a first processing module, a second acquisition module, a second processing module, a demand assessment module, a parameter construction module, a demand construction module, a demand matching module and a parameter determination module, and each module realizes data transmission through wired and / or wireless connections.

[0057] The first acquisition module is used to collect industry production data; the industry production data includes production process variables, equipment operation status, production quality indicators and raw material characteristics; the production process variables include temperature, pressure, processing speed, cutting depth and coating thickness; the equipment operation data includes equipment load, current, voltage and vibration frequency; the production quality indicators include qualified rate, precision, strength and sealing; the raw material characteristics include composition, specification, ductility and density.

[0058] The industry production data is obtained through the background data management system of the smart factory or enterprise.

[0059] The first processing module is used to construct a production knowledge graph based on industry production data.

[0060] The method for constructing a production knowledge graph based on the industry production data includes:

[0061] The temperature, pressure, processing speed, cutting depth and coating thickness in the production process variables are converted into corresponding knowledge nodes and added to the first knowledge node set; the equipment load, current, voltage and vibration frequency in the equipment operation data are converted into corresponding knowledge nodes and added to the second knowledge node set; the qualified rate, precision, strength and sealing in the production quality indicators are converted into corresponding knowledge nodes and added to the third knowledge node set; the composition, specification, ductility and density in the raw material characteristics are converted into corresponding knowledge nodes and added to the fourth knowledge node set;

[0062] For example, temperature is converted into a temperature knowledge node, pressure is converted into a pressure knowledge node, processing speed is converted into a processing speed knowledge node, cutting depth is converted into a cutting depth knowledge node, and coating thickness is converted into a coating thickness knowledge node.

[0063] Using natural language processing technology to process industry production documents to obtain relationships between knowledge nodes; the relationships between knowledge nodes include influence, determination, restriction and association;

[0064] The first knowledge node set, the second knowledge node set, the third knowledge node set, the fourth knowledge node set and the relationships between the knowledge nodes are constructed into a knowledge graph, thereby obtaining a production knowledge graph.

[0065] For example, the heating temperature affects the pass rate, and the relationship between the heating temperature and the pass rate can be obtained; process variables affect quality indicators, material properties affect quality indicators, equipment status affects process variables, and equipment status is associated with process variables.

[0066] It should be noted that Figure 5 As a schematic diagram of the production knowledge graph, production documents refer to records, text descriptions or technical documents related to the production process. These contents exist in the form of natural language, such as production logs, equipment manuals, operating manuals, process specifications, etc. These documents contain a lot of information about production processes, equipment operation, quality control and raw material characteristics.

[0067] The second acquisition module is used to obtain personalized demand content; the personalized demand content includes expected product type, expected cost, expected qualification rate, expected accuracy and expected delivery cycle.

[0068] The second processing module is used to extract features from personalized demand content to obtain actual demand feature data.

[0069] The method for acquiring the actual demand feature data includes:

[0070] Extract the expected product type from the personalized demand content, create a product type key-value pair, the key of the product type key-value pair is "expected product type", and the value of the product type key-value pair is the expected product type in the personalized demand content; extract the expected cost from the personalized demand content, create a cost key-value pair, the key of the cost key-value pair is "expected cost", and the value of the cost key-value pair is the expected cost in the personalized demand content;

[0071] Extract the expected pass rate from the personalized demand content, create a pass rate key-value pair, the key of the pass rate key-value pair is "expected pass rate", and the value of the pass rate key-value pair is the expected pass rate in the personalized demand content; extract the expected accuracy from the personalized demand content, create a accuracy key-value pair, the key of the accuracy key-value pair is "expected accuracy", and the value of the accuracy key-value pair is the expected accuracy in the personalized demand content; extract the expected delivery cycle from the personalized demand content, create a delivery cycle key-value pair, the key of the delivery cycle key-value pair is "expected delivery cycle", and the value of the delivery cycle key-value pair is the expected delivery cycle in the personalized demand content;

[0072] The product type key-value pair, cost key-value pair, pass rate key-value pair, precision key-value pair and delivery cycle key-value pair are constructed into actual demand feature data.

[0073] It should be noted that each key-value pair corresponds to a specific demand feature of the customer, so that the demand feature can be quantified into a specific value or category, which is convenient for multi-dimensional comparison and analysis, and can well express the personalized needs of customers, thereby achieving the effect of personalized modeling and meeting the needs of different customers. The demand features in the form of key-value pairs can be directly input into the machine learning model as feature vectors for demand prediction, classification or optimization.

[0074] Examples of actual demand feature data are as follows:

[0075] "Actual demand feature data":{

[0076] / / Product type key-value pair

[0077] {“Expected Product Type”: “Threaded Steel Pipe”},

[0078] / / Cost key-value pair

[0079] {“Expected cost”: “5 million”},

[0080] / / Qualified rate key-value pair

[0081] {“Expected pass rate”: “95%”},

[0082] / / Precision key-value pair

[0083] {"Expected Accuracy":"0.9"},

[0084] / / Delivery cycle key-value pair

[0085] {“Expected delivery period”: “2024-1-10 to 2024-2-14”}

[0086] };

[0087] The demand assessment module is used to input the actual demand characteristic data into the demand assessment model to obtain the demand assessment score, and analyze and process the demand assessment score to obtain the demand rationality level.

[0088] The training method of the demand assessment model includes:

[0089] A demand assessment data set is collected in advance, and the demand assessment data set includes G groups of demand assessment data and demand assessment scores corresponding to the G groups of demand assessment data, where G is a positive integer greater than 0, and the demand assessment data includes actual demand feature data; the demand assessment score is evaluated by multiple technical personnel based on the actual demand feature data, and corresponding scores are performed, and after removing the maximum and minimum values, the average value is taken to obtain the demand assessment score; the demand assessment data set is divided into a training set and a test set, and the demand assessment data in the training set is used as the input of the demand assessment model, and the demand assessment score in the training set is used as the output of the demand assessment model, with minimizing the sum of the prediction accuracies of all predicted demand assessment scores as the training goal; and training is stopped when the sum of the prediction accuracies converges; the demand assessment model is a naive Bayes model or a support vector machine model.

[0090] like Figure 4 As shown, the method for obtaining the demand rationality level includes:

[0091] Preset evaluation score thresholds and , Less than ; The demand rationality levels include third-level rationality, second-level rationality and first-level rationality. The higher the rationality level, the higher the demand rationality;

[0092] If the needs assessment score is greater than or equal to , then the demand content is reasonable, the demand rationality level is level 3 rationality, and the system does not issue unreasonable demand instructions;

[0093] If the needs assessment score is greater than or equal to , and less than , then the demand content is slightly unreasonable, the demand rationality level is level 2 rationality, and the system does not issue unreasonable demand instructions;

[0094] If the needs assessment score is less than , then the demand content is seriously unreasonable, the demand rationality level is level one rationality, and the system issues an unreasonable demand instruction.

[0095] It should be noted that by setting multi-level thresholds, the rationality level of demand can be accurately divided, and a simple one-size-fits-all judgment method can be avoided, thereby improving the flexibility and applicability of the evaluation. When the demand is seriously unreasonable, the system can issue a warning in time and terminate the production process to avoid waste of resources and potential losses, and improve the safety and reliability of the system; for slightly unreasonable demands, the system allows continued operation to avoid excessive interference with production efficiency, while providing space for optimizing minor problems. The dynamic judgment mechanism based on demand assessment scores provides production managers with a scientific decision-making basis and reduces the impact of subjective judgment on the production process. By clarifying the judgment criteria for the rationality of demand and the hierarchical response mechanism, customers can optimize their needs based on system feedback, so as to obtain solutions that meet expectations more quickly. This hierarchical judgment mechanism realizes the intelligence and controllability of demand rationality judgment, and provides strong technical support for improving the intelligence level and flexibility of production systems.

[0096] The parameter construction module is used to input the actual demand feature data and the production knowledge graph into the parameter construction model to obtain the corresponding production parameter set.

[0097] The training method of the parameter construction model includes:

[0098] A parameter construction data set is collected in advance, the parameter construction data set includes H groups of parameter construction data, and a production parameter set corresponding to the H groups of parameter construction data, H is a positive integer greater than 0, and the parameter construction data includes actual demand feature data and a production knowledge graph; the parameter construction data set is divided into a training set and a test set, the parameter construction data in the training set is used as the input of the parameter construction model, and the production parameter set in the training set is used as the output of the parameter construction model, with minimizing the sum of the prediction accuracies of all predicted production parameter sets as the training goal; the training is stopped when the sum of the prediction accuracies converges; the parameter construction model is a CaffeNet model.

[0099] It should be noted that the actual demand feature data includes multi-dimensional demand indicators such as product type, cost, qualified rate, precision and delivery cycle, which represent the personalized requirements of customers for products. The production knowledge graph constructs multi-level production factors such as production process, equipment operation parameters, quality control indicators and raw material characteristics and their relationships through nodes and edges. Through the parameter construction model, the actual demand feature data can be mapped to the corresponding nodes of the production knowledge graph to complete the semantic matching and association between demand features and production factors. The production knowledge graph contains constraint rules between production factors (for example, higher precision requirements may reduce processing speed; shorter delivery cycle may increase equipment operation load). The parameter construction model uses these rules to reason and evaluate the input requirements to determine a reasonable set of production parameters.

[0100] The demand construction module is used to input the demand rationality level, production parameter set and production knowledge graph into the demand construction model to obtain predicted demand feature data.

[0101] The training method of the demand construction model includes:

[0102] A demand construction data set is collected in advance, wherein the demand construction data set includes K groups of demand construction data and predicted demand feature data corresponding to the K groups of demand construction data, where K is a positive integer greater than 0, and the demand construction data includes a demand rationality level, a production parameter set, and a production knowledge graph; the demand construction data set is divided into a training set and a test set, the demand construction data in the training set is used as the input of the demand construction model, and the predicted demand feature data in the training set is used as the output of the demand construction model, with minimizing the sum of the prediction accuracies of all predicted predicted demand feature data as the training goal; training is stopped when the sum of the prediction accuracies converges; the demand construction model is a random forest model or a recurrent neural network model.

[0103] It should be noted that when the demand rationality level is evaluated as secondary rationality, customer demand is in a slightly unreasonable range, which may cause a certain deviation between the demand feature data predicted by the system and the actual customer demand, thereby affecting the accuracy of production decisions and the ability to meet customer personalized needs. In order to avoid this problem and improve the matching degree between demand feature data and actual customer needs, the present invention proposes an optimization strategy based on comprehensive analysis, which combines the demand rationality level, production parameter set and production knowledge graph to perform multi-dimensional association reasoning and dynamic optimization.

[0104] By comprehensively considering the rationality level of demand, the model can identify the reasonable range of customer demand; combined with the production parameter set, it can evaluate the response level of current production capacity to demand; using the production knowledge map, the logical relationship and adjustable space between various production factors are analyzed. Based on this, the system uses the constraint optimization algorithm to correct the demand feature data and introduce the optimal demand feature data that can be achieved under the current production conditions, ensuring that it strikes a balance between technical feasibility and demand matching.

[0105] The demand matching module is used to extract features from actual demand feature data and predicted demand feature data to obtain demand matching degree.

[0106] The method for obtaining the demand matching degree includes:

[0107] ;

[0108] in, For demand matching, To predict product types from demand feature data, is the expected product type, To predict the cost in the demand feature data, is the expected cost, To predict the qualified rate in the demand feature data, is the expected pass rate, To predict the prediction accuracy in demand feature data, For the expected accuracy, To predict the delivery cycle from the demand feature data, is the expected delivery cycle, , , and is the weight coefficient.

[0109] The parameter determination module is used to conduct a comprehensive analysis of the demand matching degree, production parameter set and production knowledge graph, generate an updated production parameter set, send instructions to the smart factory, adjust the production parameters to the updated production parameter set and carry out production.

[0110] like Figure 3 As shown, the method for comprehensively analyzing the demand matching degree, production parameter set and production knowledge graph includes:

[0111] Preset demand matching threshold;

[0112] If the demand matching degree is greater than or equal to the demand matching degree threshold, the actual demand feature data matches the predicted demand feature data, and the production parameter set is used as the updated production parameter set;

[0113] If the demand matching degree is less than the demand matching degree threshold, the actual demand feature data does not match the predicted demand feature data, and a demand mismatch report is generated. After the customer reconfirms the demand, the updated actual demand feature data is obtained; the updated actual demand feature data and the production knowledge graph are input into the parameter construction model to obtain the corresponding production parameter set, which is marked as the production parameter set to be changed, and the production parameter set to be changed is used as the updated production parameter set;

[0114] Send instructions to the smart factory to adjust the production parameters to the updated production parameter set and start production.

[0115] It should be noted that by setting the matching threshold, it is possible to quickly determine the matching status of actual demand feature data and predicted demand feature data, ensuring that production is entered only when the demand is accurate, thereby reducing production errors caused by demand deviations and improving production accuracy. In the case of insufficient demand matching, the system generates a demand mismatch report and requires customers to reconfirm, ensuring that the revised demand is closer to the actual expectations of customers, improving demand adaptation capabilities and customer satisfaction. The updated actual demand feature data is combined with the production knowledge graph to dynamically generate a new set of production parameters, and the production plan is optimized through intelligent adjustment of the model to ensure that the production process can flexibly adapt to changes in demand.

[0116] In the case of sufficient demand matching, the existing production parameter set is directly adopted to simplify the process; in the case of insufficient demand matching, the intelligent adjustment mechanism is used to quickly generate updated production parameters, reduce human intervention, and improve overall production efficiency and response speed. Closed-loop control is achieved throughout the entire process from demand assessment to production parameter adjustment to final production, ensuring that production operations meet customer needs. At the same time, it provides a technical foundation for dynamic optimization and lays a solid foundation for intelligent production.

[0117] Example 2

[0118] See also Figure 2 As shown, this embodiment provides an industry knowledge structured modeling method based on a knowledge graph of semantic association, and also includes:

[0119] Collect industry production data;

[0120] Build a production knowledge graph based on industry production data;

[0121] Get personalized demand content;

[0122] Extract features from personalized demand content to obtain actual demand feature data;

[0123] Input the actual demand characteristic data into the demand assessment model to obtain the demand assessment score, and analyze and process the demand assessment score to obtain the demand rationality level;

[0124] Input the actual demand feature data and production knowledge graph into the parameter construction model to obtain the corresponding production parameter set;

[0125] Input the demand rationality level, production parameter set and production knowledge graph into the demand construction model to obtain the predicted demand feature data;

[0126] Extract features from actual demand feature data and predicted demand feature data to obtain demand matching degree;

[0127] Comprehensively analyze the demand matching degree, production parameter set and production knowledge graph, generate an updated production parameter set, send instructions to the smart factory, adjust the production parameters to the updated production parameter set and carry out production.

[0128] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0129] 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. A method for structural modeling of industry knowledge based on a knowledge graph with semantic association, characterized in that: include: Collect industry production data; Build a production knowledge graph based on industry production data; Get personalized demand content; Extract features from personalized demand content to obtain actual demand feature data; Input the actual demand characteristic data into the demand assessment model to obtain the demand assessment score, and analyze and process the demand assessment score to obtain the demand rationality level; Input the actual demand feature data and production knowledge graph into the parameter construction model to obtain the corresponding production parameter set; Input the demand rationality level, production parameter set and production knowledge graph into the demand construction model to obtain the predicted demand feature data; Extract features from actual demand feature data and predicted demand feature data to obtain demand matching degree; Comprehensively analyze the demand matching degree, production parameter set and production knowledge graph, generate an updated production parameter set, send instructions to the smart factory, adjust the production parameters to the updated production parameter set and carry out production.

2. According to claim 1, a method for structurally modeling industry knowledge based on a knowledge graph of semantic association, characterized in that: Methods for comprehensive analysis of demand matching, production parameter sets, and production knowledge graphs include: Preset demand matching threshold; If the demand matching degree is greater than or equal to the demand matching degree threshold, the actual demand feature data matches the predicted demand feature data, and the production parameter set is used as the updated production parameter set; If the demand matching degree is less than the demand matching degree threshold, the actual demand feature data does not match the predicted demand feature data, and a demand mismatch report is generated. After the customer reconfirms the demand, the updated actual demand feature data is obtained; the updated actual demand feature data and the production knowledge graph are input into the parameter construction model to obtain the corresponding production parameter set, which is marked as the production parameter set to be changed, and the production parameter set to be changed is used as the updated production parameter set; Send instructions to the smart factory to adjust the production parameters to the updated production parameter set and start production.

3. The method for structurally modeling industry knowledge based on a semantically associated knowledge graph according to claim 2 is characterized in that: The method for obtaining the demand rationality level includes: Preset evaluation score thresholds and , Less than ; The demand rationality levels include third-level rationality, second-level rationality and first-level rationality; If the needs assessment score is greater than or equal to , then the demand rationality level is level 3 rationality, and the system does not issue unreasonable demand instructions; If the needs assessment score is greater than or equal to , and less than , then the demand rationality level is level 2 rationality, and the system does not issue unreasonable demand instructions; If the needs assessment score is less than , then the demand rationality level is level one rationality, and the system issues an unreasonable demand instruction.

4. The method for structurally modeling industry knowledge based on a semantically associated knowledge graph according to claim 3 is characterized in that: The method for obtaining the demand matching degree includes: Subtract the predicted product type in the predicted demand feature data from the expected product type in the actual demand feature data to obtain the product type difference; subtract the predicted cost in the predicted demand feature data from the expected cost in the actual demand feature data to obtain the cost difference; subtract the predicted pass rate in the predicted demand feature data from the expected pass rate in the actual demand feature data to obtain the pass rate difference; subtract the predicted accuracy in the predicted demand feature data from the expected accuracy in the actual demand feature data to obtain the accuracy difference; subtract the predicted delivery cycle in the predicted demand feature data from the expected delivery cycle in the actual demand feature data to obtain the delivery cycle difference; Perform weighted operations on the product type difference, cost difference, pass rate difference, precision difference and delivery cycle difference to obtain the first result of demand matching; sum the squares of the weight coefficients corresponding to the product type difference, cost difference, pass rate difference, precision difference and delivery cycle difference to obtain the second result of demand matching; The first result of the demand matching degree is divided by the second result of the demand matching degree to obtain a third result of the demand matching degree, and the third result of the demand matching degree is squared to obtain the demand matching degree.

5. According to claim 4, a method for structural modeling of industry knowledge based on a knowledge graph of semantic association, characterized in that: The method for acquiring the actual demand feature data includes: Extract the expected product type from the personalized demand content, create a product type key-value pair, the value of the product type key-value pair is the expected product type in the personalized demand content; extract the expected cost from the personalized demand content, create a cost key-value pair, the value of the cost key-value pair is the expected cost in the personalized demand content; Extract the expected pass rate from the personalized demand content, create a pass rate key-value pair, the value of which is the expected pass rate in the personalized demand content; extract the expected accuracy from the personalized demand content, create a accuracy key-value pair, the value of which is the expected accuracy in the personalized demand content; extract the expected delivery cycle from the personalized demand content, create a delivery cycle key-value pair, the value of which is the expected delivery cycle in the personalized demand content; The product type key-value pair, cost key-value pair, pass rate key-value pair, precision key-value pair and delivery cycle key-value pair are constructed into actual demand feature data.

6. The method for structurally modeling industry knowledge based on a semantically associated knowledge graph according to claim 5, characterized in that: The method for constructing a production knowledge graph based on the industry production data includes: The temperature, pressure, processing speed, cutting depth and coating thickness in the production process variables are converted into corresponding knowledge nodes and added to the first knowledge node set; the equipment load, current, voltage and vibration frequency in the equipment operation data are converted into corresponding knowledge nodes and added to the second knowledge node set; the qualified rate, precision, strength and sealing in the production quality indicators are converted into corresponding knowledge nodes and added to the third knowledge node set; the composition, specification, ductility and density in the raw material characteristics are converted into corresponding knowledge nodes and added to the fourth knowledge node set; Using natural language processing technology to process industry production documents to obtain relationships between knowledge nodes; the relationships between knowledge nodes include influence, determination, restriction and association; The first knowledge node set, the second knowledge node set, the third knowledge node set, the fourth knowledge node set and the relationships between the knowledge nodes are constructed into a knowledge graph to obtain a production knowledge graph.

7. The method for structurally modeling industry knowledge based on a semantically associated knowledge graph according to claim 6 is characterized in that: The training method of the demand assessment model includes: A demand assessment data set is collected in advance, wherein the demand assessment data set includes G groups of demand assessment data and demand assessment scores corresponding to the G groups of demand assessment data, where G is a positive integer greater than 0, and the demand assessment data includes actual demand feature data; the demand assessment data set is divided into a training set and a test set, the demand assessment data in the training set is used as the input of the demand assessment model, and the demand assessment scores in the training set are used as the output of the demand assessment model, with minimizing the sum of the prediction accuracies of all predicted demand assessment scores as the training goal; training is stopped when the sum of the prediction accuracies converges; the demand assessment model is a naive Bayes model or a support vector machine model.

8. The method for structurally modeling industry knowledge based on a semantically associated knowledge graph according to claim 7 is characterized in that: The training method of the demand construction model includes: A demand construction data set is collected in advance, wherein the demand construction data set includes K groups of demand construction data and predicted demand feature data corresponding to the K groups of demand construction data, where K is a positive integer greater than 0, and the demand construction data includes a demand rationality level, a production parameter set, and a production knowledge graph; the demand construction data set is divided into a training set and a test set, the demand construction data in the training set is used as the input of the demand construction model, and the predicted demand feature data in the training set is used as the output of the demand construction model, with minimizing the sum of the prediction accuracies of all predicted predicted demand feature data as the training goal; training is stopped when the sum of the prediction accuracies converges; the demand construction model is a random forest model or a recurrent neural network model.

9. The method for structurally modeling industry knowledge based on a knowledge graph of semantic association according to claim 8, characterized in that: The training method of the parameter construction model includes: A parameter construction data set is collected in advance, the parameter construction data set includes H groups of parameter construction data, and a production parameter set corresponding to the H groups of parameter construction data, H is a positive integer greater than 0, and the parameter construction data includes actual demand feature data and a production knowledge graph; the parameter construction data set is divided into a training set and a test set, the parameter construction data in the training set is used as the input of the parameter construction model, and the production parameter set in the training set is used as the output of the parameter construction model, with minimizing the sum of the prediction accuracies of all predicted production parameter sets as the training goal; the training is stopped when the sum of the prediction accuracies converges; the parameter construction model is a CaffeNet model.

10. The method for structurally modeling industry knowledge based on a knowledge graph of semantic association according to claim 9, characterized in that: The industry production data includes production process variables, equipment operating status, production quality indicators and raw material characteristics; the production process variables include temperature, pressure, processing speed, cutting depth and coating thickness; the equipment operation data includes equipment load, current, voltage and vibration frequency; the production quality indicators include pass rate, precision, strength and sealing; the raw material characteristics include composition, specification, ductility and density; the personalized demand content includes expected product type, expected cost, expected pass rate, expected precision and expected delivery cycle.

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