Industrial product credible operation and maintenance method based on crowd sensing

Abstract

In order to solve the problem that a product operation and maintenance knowledge base is lack of flexibility and comprehensiveness due to the fact that an existing industrial product operation and maintenance information source is single, the invention provides an industrial product operation and maintenance knowledge graph construction method based on crowd sensing. Reliable user experience obtained after screening comprises specific problems encountered by the user in the actual production process and corresponding solving strategies, the reliable user experience is integrated into the basic knowledge graph of the industrial product, the knowledge graph is further perfected, and the efficiency of crowd sensing in operation and maintenance of the industrial product is improved. Furthermore, in order to solve the problem that in an existing industrial product operation and maintenance method, all links in the operation and maintenance process are difficult to effectively coordinate and share information, the invention provides an industrial product credible operation and maintenance method based on crowd sensing, and the operation and maintenance method relates to the whole process of real-time monitoring, fault early warning and maintenance decision support. All links can effectively cooperate and share information, and closed-loop management is formed.

Description

Technical Field

[0001] The present invention relates to the field of industrial product operation and maintenance. The industrial product described in the present invention refers to production and processing equipment. Background Art

[0002] In recent years, with the vigorous development of technologies such as microelectronics, industrial Internet of Things, and artificial intelligence, the manufacturing industry has developed rapidly towards automation, digitization, and intelligence. In the process of processing parts for industrial products, many key component parameters of industrial products change dynamically, which has a great impact on the quality of processed parts. Therefore, in order to ensure the quality of parts processing, it is necessary to monitor these performance parameters during the processing of industrial products, conduct fault warnings, and provide accurate and efficient maintenance decisions in a timely manner when faults occur.

[0003] As a structured, semantic and interconnected information representation method, knowledge graph can efficiently organize and manage the experience and knowledge accumulated in the operation and maintenance of industrial products, and provide accurate query functions, which is of great significance for providing maintenance decision support in the operation and maintenance of industrial products.

[0004] Existing industrial product operation and maintenance methods, such as the Chinese invention patent application with application number 202311870779.8, construct a knowledge graph of power equipment defects based on various official data collected, and use the relationships and rules in the knowledge graph of power equipment defects, combined with relevant algorithms to achieve the prediction of potential defects of power equipment. Another example is the Chinese invention patent application with application number 202311581518.4, which inputs the operating parameters of the equipment into the constructed equipment knowledge graph, uses machine learning algorithms to predict the health status of the equipment, determines whether the equipment is abnormal, and generates warning information.

[0005] The above research has promoted the industrial product operation and maintenance method, but only the industrial product information from the manufacturer is considered in the product operation and maintenance knowledge base information, resulting in the lack of flexibility and comprehensiveness of the product operation and maintenance knowledge base. At the same time, the method disclosed in the above patent application lacks the design of an integrated, full-process industrial product operation and maintenance process, resulting in difficulty in effective collaboration and information sharing among various links in the operation and maintenance process. Summary of the invention

[0006] In order to solve the problem that the existing industrial product operation and maintenance information source is single, resulting in the lack of flexibility and comprehensiveness of the product operation and maintenance knowledge base, the present invention provides a method for constructing an operation and maintenance knowledge graph of industrial products based on crowd intelligence perception.

[0007] Furthermore, in order to solve the technical problem that it is difficult to effectively coordinate and share information among various links in the operation and maintenance process in the existing industrial product operation and maintenance methods, the present invention provides a trusted operation and maintenance method for industrial products based on crowd intelligence perception.

[0008] The technical solution of the present invention is:

[0009] The method for constructing the operation and maintenance knowledge graph of industrial products based on crowd intelligence perception is special in that it includes the following steps:

[0010] Step 1: Based on the official data of industrial products, build a basic knowledge graph of industrial products based on deep learning;

[0011] Step 2: Based on crowd intelligence perception, improve the basic knowledge graph of industrial products constructed in step 1 to obtain the operation and maintenance knowledge graph of industrial products;

[0012] Step 2.1: Obtain the industrial product triple P: P = {M, C, F}; M is the module constituting the industrial product, C is the component of the module, and F is the attribute of the component;

[0013] Step 2.2: According to the mutual influence relationship between components belonging to different modules in the industrial product, construct the influence matrix E. The element value in the influence matrix E is 0 or 1, which is determined according to the following rules:

[0014]

[0015] Step 2.3: Collect user experience, and represent each user experience with keywords as U_e={Q, A}, where Q is the keyword set of the question and A is the keyword set of the answer; through the keyword set Q of the question and the industrial product triple P, the user experience can be assigned to the corresponding module of the industrial product;

[0016] Step 2.4: Filter out reliable user experiences from the collected user experiences;

[0017] Step 2.4.1: For each user experience, score the presence of the key words in its answer;

[0018] Step 2.4.2: For each user experience, rate the match between the keywords in the answer and the product module mentioned in the question;

[0019] Step 2.4.3: For each user experience, score the matching between the answer keywords and the product modules other than the question based on the influence matrix E;

[0020] Step 2.4.4: Based on the scores of steps 2.4.1-2.4.3, a fuzzy evaluation method is used to calculate a column vector B consisting of the comprehensive evaluation results of all user experiences:

[0021] Step 2.4.5: Set a threshold to filter out reliable user experience. If the value of an element in column vector B is greater than or equal to the threshold, the user experience corresponding to the element is considered reliable. Otherwise, the user experience corresponding to the element is unreliable.

[0022] Step 2.5: Add the screened reliable user experience to the basic knowledge graph of industrial products constructed in step 1 to obtain the operation and maintenance knowledge graph of industrial products.

[0023] Furthermore, step 1 is specifically as follows:

[0024] Step 1.1: Collect official data on industrial products and remove duplicates, missing values ​​and standardize data formats;

[0025] Step 1.2: Ontology modeling, including defining entities and relationships between them;

[0026] Step 1.3: Entity annotation;

[0027] For each type of official data processed in step 1.1, some data are randomly selected and annotated with BIOE based on the entities defined in step 1.2, so that they correspond one-to-one with the entities defined in step 1.2;

[0028] Step 1.4: Knowledge extraction;

[0029] Step 1.4.1: Use the official data after entity annotation in step 1.3 to train the BERT+Bi-LSTM+CRF neural network model, and use the trained BERT+Bi-LSTM+CRF model to extract entities from the remaining unlabeled official data to obtain industrial product entities;

[0030] Step 1.4.2: For the types of industrial product entities extracted in step 1.4.1, triple information of product knowledge is formed according to the relationship between the defined entities;

[0031] Step 1.4.3: Modify or delete the triplet information obtained in step 1.4.2, and retain the valid triplet information;

[0032] Step 1.5: Knowledge storage;

[0033] The Neo4j graph database is used to store the valid triple information obtained in step 1.4 to obtain the basic knowledge graph of industrial products.

[0034] Furthermore, step 2.4 is specifically as follows:

[0035] Step 2.4.1: For each user experience, score the presence of the key words in its answer;

[0036] For each user experience U_e, determine whether the answer keyword set A is empty. If it is empty, the score is 0, and step 2.4.2 and step 2.4.3 are skipped. At the same time, the score of the two evaluation indicators of step 2.4.2 and step 2.4.3 for this user experience is 0; if it is not empty, the score is 1, and step 2.4.2 is entered;

[0037] Step 2.4.2: For each user experience, score the match between the keywords of the answer and the product module in the question;

[0038] For each user experience U_e, first assign the user experience to the corresponding product module according to its question keyword Q, and then match the answer keyword A in the user experience with the components and component attributes in the product module assigned according to the question keyword. If the match is successful, the score is 0.5 and go to step 2.4.3; if the match is unsuccessful, the score is 0 and go to step 2.4.3;

[0039] Step 2.4.3: For each user experience, score the matching of its answer keywords with other product modules other than the question based on the influence matrix E;

[0040] For each user experience U_e, if the answer keyword A in a user experience mentions the components of other modules other than the product module assigned to the question keyword in step 2.4.2, and according to the influence matrix E, it is determined that the components of other modules influence the components mentioned in the question keyword of this user experience, then the score is 0.5 and the process goes to step 2.4.4; otherwise, the score is 0 and the process goes to step 2.4.4;

[0041] Step 2.4.4: Use the following formula to calculate the column vector B consisting of the comprehensive evaluation results of all user experiences:

[0042] B=R·W T

[0043] Among them, W is the weight vector of the evaluation index, W = [ω1ω2ω3], ω1ω2ω3 are the weights of the existence score of the answer keyword, the matching score of the answer keyword and the product module in the question, and the matching score of the answer keyword and the product module outside the question, respectively. R is the fuzzy evaluation matrix, The elements in the matrix R are the scores of the three evaluation indicators obtained in steps 2.4.1-2.4.3. The rows in the matrix R represent different user experiences, and the columns represent the three evaluation indicators of user experience.

[0044] Step 2.4.5: Set a threshold to filter out reliable user experience. If the value of an element in column vector B is greater than or equal to the threshold, the user experience corresponding to the element is considered reliable. Otherwise, the user experience corresponding to the element is unreliable.

[0045] Furthermore, in step 2.4.4, the values ​​of ω1ω2ω3 are 0.4, 0.4 and 0.2 respectively, and the threshold in step 2.4.5 is set to 0.5.

[0046] Furthermore, step 2.5 is specifically as follows:

[0047] Step 2.5.1: Remove duplicates from reliable user experiences;

[0048] Step 2.5.2: Based on the ontology model constructed in step 1.2, the reliable user experience after deduplication is divided into entities and relationships are added to form triple information of user experience;

[0049] Step 2.5.3: Use the Neo4j graph database to add and save the triple information of user experience obtained in step 2.5.2 to the basic knowledge graph of industrial products obtained in step 1 to obtain the improved operation and maintenance knowledge graph of industrial products.

[0050] The present invention also provides a method for trusted operation and maintenance of industrial products based on crowd intelligence perception, which is special in that it includes the following steps:

[0051] Step 1: Use physical sensors to build industrial products with perception capabilities;

[0052] Step 2: Industrial product status monitoring and fault warning;

[0053] Step 2.1: Set corresponding fault warning threshold ranges for each key component of the industrial product;

[0054] Step 2.2: Industrial products with sensing capabilities monitor the operating status data of their key components in real time;

[0055] Step 2.3: Display the operating status data of each key component monitored in step 2.2 in real time, and compare them with the corresponding fault warning threshold range regularly. If they are all within the fault warning threshold range, it means that the industrial product is operating normally, and return to step 2.2; if the operating status data of a key component is not within the corresponding fault warning threshold range, it means that the industrial product is operating abnormally, generate a fault description and send it to the front end to notify the user, and go to step 3;

[0056] Step 3: Generate industrial product maintenance decisions;

[0057] Step 3.1: Input the fault description obtained in step 2.2 into the pre-trained TextCNN model to obtain the fault type to which the fault description data belongs; the TextCNN model is trained using a fault description data set constructed based on common fault types of industrial products, and each fault type in the fault description data set is correspondingly provided with a Neo4j query statement;

[0058] Step 3.2: According to the fault type obtained in step 3.1, determine the corresponding Neo4j query language;

[0059] Step 3.3: Input the Neo4j query language obtained in step 3.2 into the operation and maintenance knowledge graph constructed by the above-mentioned method for constructing the operation and maintenance knowledge graph of industrial products based on crowd intelligence perception to obtain the maintenance decision.

[0060] Furthermore, in step 2.1, based on the historical operating data and statistical laws of each key component, the 3σ principle is adopted to set the fault warning threshold range of each key component to (μ-3σ,μ+3σ), where μ and σ are the mean and standard deviation of the historical operating data of each key component, respectively.

[0061] Furthermore, in step 2.3, basic information of industrial products is also displayed in real time, including product model, product size, product processing principle, product processing accuracy, product processing size, product components and component parameters.

[0062] The advantages of the present invention are:

[0063] 1. In the process of constructing the knowledge graph of industrial product operation and maintenance, in order to improve the comprehensiveness and flexibility of the knowledge graph, the present invention designs a reliability evaluation process to evaluate and screen the reliability of user experience. The reliable user experience obtained after screening includes the specific problems encountered by users in the actual production process and the corresponding solutions. These reliable user experiences are of great value in product operation and maintenance. The present invention integrates these reliable user experiences into the basic knowledge graph of industrial products, thereby further improving the knowledge graph and improving the efficiency of crowd intelligence perception in industrial product operation and maintenance.

[0064] 2. The operation and maintenance method of the present invention realizes real-time monitoring and fault warning of industrial products by combining static data and dynamic data of industrial products. By applying microelectronic technology, industrial products can sense the operating status of their internal key components and use them efficiently, and the collected dynamic perception data is combined with the static basic information of industrial products to realize real-time monitoring of industrial products. The collected operating parameters of key components are compared with the fault warning threshold range set based on statistical laws. If an abnormality is found, a fault description is generated based on the template, thereby realizing the precise positioning of industrial product faults and providing effective support for operation and maintenance decisions.

[0065] 3. The present invention constructs an operation and maintenance knowledge graph of industrial products based on deep learning. The operation and maintenance knowledge graph contains text information such as expert technical manuals and reliable user experience, and is stored in the Neo4j graph database, so that the operation and maintenance knowledge base has efficient management and data retrieval functions. The TextCNN model is used to classify industrial product fault descriptions, and a corresponding Neo4j query language is set for each type of fault description. When an industrial product fault description is obtained, the trained TextCNN model and the improved industrial product operation and maintenance knowledge graph can quickly help users find fault repair solutions, which helps to improve maintenance efficiency and maintenance results.

[0066] 4. The operation and maintenance method of the present invention involves a full-process industrial product operation and maintenance process of real-time monitoring, fault warning, and maintenance decision support. Each link can effectively collaborate and share information to form a closed-loop management, ultimately providing a reliable operation and maintenance method for industrial products.

[0067] 5. The group intelligence perception of the present invention is embodied in:

[0068] 1) Multi-source information collection: Collect user experience from the exchanges and discussions of multiple users in industrial product forums. These user experiences are diverse and rich.

[0069] 2) Design processing and screening and evaluation process: By screening, evaluating and filtering the information posted by users, reliable user experiences can be extracted from it. These reliable user experiences contain useful knowledge information.

[0070] 3) Improve the knowledge graph of industrial product operation and maintenance: By enriching the knowledge base with reliable user experience obtained after screening, the basic knowledge graph of industrial products can be enriched. The obtained operation and maintenance knowledge graph can provide specific scenarios and solutions during the actual use of industrial products. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is an overall flow chart of the operation and maintenance method of the present invention.

[0072] Figure 2 It is a flow chart for constructing the operation and maintenance knowledge graph of industrial products in the present invention.

[0073] Figure 3 It is a reliability evaluation flow chart of user experience in the present invention. DETAILED DESCRIPTION

[0074] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0075] like Figure 1-3As shown, the industrial product trusted operation and maintenance method based on crowd intelligence perception provided by the present invention includes the following steps:

[0076] Step 1: Build a basic knowledge graph of industrial products based on deep learning;

[0077] Step 1.1: Data collection and data processing;

[0078] Collect official data about industrial products, including basic information about industrial products (such as product specifications, components of industrial products, and parameter information of each component), instructions for use of industrial products, test documents for industrial products, and official maintenance plans for industrial products; the types of official data collected include structured data (product specification data and parameter data of each component of the product) and unstructured data (instructions, usage logs, test documents, and maintenance reports). The collected official data needs to be processed (de-duplication, processing of missing values, and standardization of data formats);

[0079] Step 1.2: ontology modeling;

[0080] The essence of ontology modeling is to describe entities, attributes, relationships and their hierarchical structures in a specific field. In layman's terms, it is to refine industrial product information data to form a conceptual representation of information data. Therefore, it is necessary to define various class concepts, and the specific knowledge content is the instantiation of the class, for example, a 3D printer is an instance of a product. Entity and relationship types are important components of the ontology model. The ontology modeling in this step mainly includes the definition of entities and relationships.

[0081] The specific process is as follows:

[0082] Step 1.2.1: Define entities;

[0083] Entities include product entities, attribute entities, and operation and maintenance entities; product entities include products (i.e., conceptual representations of instances (e.g., 3D printers)), product modules, and product components, where product components generally represent specific objects that require operation and maintenance; attribute entities include the working principles of industrial products, the specifications of industrial products and the dimensions of their components, the processing accuracy of industrial products, the normal operating parameters of product components, and the material properties of processed parts; operation and maintenance entities include fault phenomena, fault causes, and fault solutions;

[0084] Step 1.2.2: Define relationships;

[0085] Relationships include relationships between entity elements within a class and relationships between entities of different classes. Predefined relationship types include "contains", "has", "occurs", "results in", and "resolves".

[0086] Step 1.3: Entity annotation;

[0087] For each type of official data obtained after processing in step 1.1, some data are randomly selected and BIOE annotated based on the various entities defined in step 1.2.1. B, I and E represent the beginning, middle and end of the entity respectively, and O represents the non-entity part of the sentence. BIOE annotation needs to be operated according to the entities defined in step 1.2.1, so as to correspond the official data to the various entities defined in step 1.2.1 one by one.

[0088] Step 1.4: Knowledge extraction;

[0089] Step 1.4.1: Use the official data with entity annotation in step 1.3 above to train the BERT+Bi-LSTM+CRF neural network model, and use the trained BERT+Bi-LSTM+CRF model to extract entities from the remaining unlabeled official data to obtain industrial product entities;

[0090] Step 1.4.2: For the types of industrial product entities extracted in step 1.4.1, triple information of product knowledge is formed according to the relationship types between predefined entities. The triple information format is <entity 1, category 1>-relationship-<entity 2, category 2>, such as <3D printer, product>-include-<printing platform module, product module>;

[0091] Step 1.4.3: The triple information obtained in step 1.4.2 is screened, and whether there is erroneous triple information is determined based on the entity type and the relationship between entities. The erroneous triple information is corrected (including deduplication, supplementation of missing parts) or deleted, and finally the valid triple information is retained for subsequent operations.

[0092] Step 1.5: Knowledge storage;

[0093] After knowledge extraction, the Neo4j graph database is used to store the valid triple information obtained in step 1.4. The Neo4j graph database stores data in the form of nodes and relationships, defines the extracted entities and relationships as node and relationship types, and stores and defines the attributes of related nodes. At this point, the construction of the basic knowledge graph of industrial products is completed.

[0094] Step 2: Based on crowd intelligence perception, improve the basic knowledge graph of industrial products constructed in step 1 to obtain the operation and maintenance knowledge graph of industrial products;

[0095] The basic knowledge graph of industrial products obtained in step 1 is improved with user experience. User experience can usually be obtained from industrial product community forums and generally consists of questions and answers. Whether the collected user experience can be added to the knowledge graph requires reliability judgment, and reliable user experience is added to the basic knowledge graph of industrial products.

[0096] The specific method is as follows:

[0097] Step 2.1: Obtain the industrial product triples represented by the structure and attributes of the industrial products;

[0098] According to the functional units of industrial products, user experience needs to be allocated to different modules of industrial products in the future. Therefore, the module set of industrial products is first defined as follows:

[0099] P M ={M1, M2...M X}

[0100] Among them, M1, M2...M X They are the 1st, 2nd, …, Xth modules in the industrial product respectively;

[0101] The components and component attributes of each module are defined as follows:

[0102] M i ={C1, C2...C Y}

[0103] C j ={F1, F2...F Z}

[0104] Among them, C1, C2...C Y Module M i The first, second, ..., Yth components in F1, F2, ..., F Z Component C j The first, second, ..., Zth attributes of M; i is 1, 2, ..., X, j is 1, 2, ..., Y, respectively; M i , C j 、F k It can be obtained from the basic knowledge graph of industrial products constructed in step 3, where k is 1, 2, …, Z respectively.

[0105] Combining M, C, and F, we can get the industrial product triple P that represents the structure and attributes of the industrial product:

[0106] P = {M, C, F}.

[0107] Step 2.2: Construct the impact matrix;

[0108] Considering that there may be an influence relationship between components of different modules, a two-dimensional influence matrix E is constructed to represent the influence relationship. Each element in the influence matrix E takes a value of 0 or 1, which is determined according to the following rules:

[0109]

[0110] For example, if an industrial product has two modules, namely module 1 and module 2; module 1 includes components A and component B, and module 2 includes components C and component D; if only component A in module 1 and component C in module 2 affect each other, component A in module 1 and component D in module 2 do not affect each other, and component B in module 1 and components C and D in module 2 do not affect each other, then only the influence relationship between components in different modules is considered, and the constructed influence matrix and the module components corresponding to its rows and columns are as follows:

[0111]

[0112] Step 2.3: Collect user experience and express it with keywords;

[0113] The collected user experience includes questions and answers. Keywords are extracted from the collected user experience. The keywords extracted from the questions include the module components and component attributes of the industrial product problems. The keywords extracted from the answers include the module components and component attributes in the questions and the components of different modules that affect each other. Finally, based on the extracted keywords, each piece of collected user experience can be represented as U_e:

[0114] U_e={Q、A}

[0115] Among them, Q represents the keyword set of the question, A represents the keyword set of the answer, and the collected user experience can be assigned to the corresponding product module through the keywords of the question and the triplet P obtained in step 2.1.

[0116] Step 2.4: Evaluate the reliability of user experience based on fuzzy evaluation and select reliable user experience;

[0117] Step 2.4.1: For each user experience, score the presence of the key words in its answer;

[0118] For each user experience U_e, determine whether the answer keyword set A is empty. If the answer keyword set A is empty, the score is 0, and step 2.4.2 and step 2.4.3 are skipped. At the same time, the score of the two evaluation indicators of step 2.4.2 and step 2.4.3 for this user experience is 0; if the answer keyword set A is not empty, the score of the evaluation indicator of this step for this user experience is 1, and go to step 2.4.2;

[0119] Step 2.4.2: For each user experience, score the match between the keywords of the answer and the product module in the question;

[0120] For each user experience U_e, first assign the user experience to the corresponding product module according to its question keyword Q, and then match the answer keyword A in the user experience with the components and component attributes in the product module assigned according to the question keyword. If the match is successful, the evaluation index of this step is scored as 0.5, and proceed to step 2.4.3; if the match is unsuccessful, the evaluation index of this step is scored as 0, and proceed to step 2.4.3;

[0121] Step 2.4.3: For each user experience, score the matching of its answer keywords with other product modules other than the question based on the influence matrix E;

[0122] For each user experience U_e, if the answer keyword A in a user experience mentions the components of other modules other than the product module assigned according to the question keyword in step 2.4.2, and the components of other modules influence each other with the components mentioned in the question keyword of this user experience (which can be judged according to the influence matrix E, at this time the value of the element at the corresponding position in the influence matrix E is 1), the evaluation index of this step is scored 0.5, and the process goes to step 2.4.4; otherwise, the evaluation index of this step is scored 0, and the process goes to step 2.4.4;

[0123] Step 2.4.4: Based on the scores of steps 2.4.1-2.4.3, a fuzzy evaluation method is used to calculate a column vector B consisting of the comprehensive evaluation results of all user experiences;

[0124] B=R·W T

[0125] Among them, W is the weight vector of the evaluation index, W = [ω1ω2ω3], ω1ω2ω3 are the weights of the existence score of the answer keyword, the matching score of the answer keyword and the product module in the question, and the matching score of the answer keyword and the product module outside the question, respectively. R is the fuzzy evaluation matrix, The elements in the matrix R are the scores of the three evaluation indicators obtained in steps 2.4.1-2.4.3. The rows in the matrix R represent different user experiences, and the columns represent the three evaluation indicators of user experience.

[0126] Step 2.4.5: Set a threshold and judge the reliability of each user experience based on the column vector B. If the value of an element in the column vector B is greater than or equal to the threshold, the user experience corresponding to the element is considered reliable. Otherwise, the user experience corresponding to the element is unreliable.

[0127] Step 2.5: Add the reliable user experience screened out in step 2.4.5 to the basic knowledge graph of industrial products obtained in step 1;

[0128] Step 2.5.1: Remove duplicates from reliable user experiences;

[0129] Step 2.5.2: Based on the ontology model constructed in step 1.2, the reliable user experience after deduplication is divided into entities and relationships are added to form triple information of user experience;

[0130] Step 2.5.3: Use the Neo4j graph database to add and save the triple information of user experience obtained in step 2.5.2 to the basic knowledge graph of industrial products obtained in step 1 to obtain a complete operation and maintenance knowledge graph of industrial products, so as to improve the practicality, flexibility and comprehensiveness of maintenance decision support.

[0131] Step 3: Use physical sensors to build industrial products with perception capabilities;

[0132] Step 3.1: Analyze the structure and working principle of industrial products to determine the key components and their operating parameters that need to be monitored during the operation of industrial products. These components and their operating parameters are closely related to the performance and stable operation of industrial products.

[0133] Step 3.2: Select corresponding physical sensors based on the key components and operating parameters that need to be monitored during the operation of industrial products, and deploy the selected physical sensors on the industrial products, so that the industrial products can easily obtain the operating parameter data of the key components of the industrial products (such as the temperature of the processing platform, the speed of the motor, and the position of the processed parts), thereby having the ability to perceive their operating status.

[0134] Step 4: Real-time operation status monitoring and fault warning of industrial products;

[0135] Step 4.1: Set corresponding fault warning threshold ranges for each key component of the industrial product;

[0136] To achieve industrial product failure warning, it is necessary to set the failure warning threshold range of each key component of the industrial product. According to the historical operation data of the key components of the industrial product, the failure warning threshold range of each key component is set based on statistical laws. Furthermore, the failure warning threshold range should also be adjusted according to the performance changes of the industrial product and the operation data in the recent period to ensure sensitivity and accuracy.

[0137] Step 4.2: Industrial products with sensing capabilities monitor the operating status data of their key components in real time;

[0138] During the manufacturing process of industrial products, physical sensors mounted on industrial products are used to collect real-time processing data of key components to reflect the real-time operation status of industrial products, and real-time display is performed to achieve the purpose of monitoring. First, a database is used on the back end of the server to store the information of each sensor, including sensor number, sensor type, installation location, fault warning threshold range, current state value, and abnormal parameters. Then, the raw data collected by the physical sensor is denoised, filtered, calibrated and packaged, and the packaged sensor data is sent to the back end server using a suitable data transmission protocol (such as HTTP, MQTT, etc.). After receiving the sensor data, the back end server verifies, merges and stores it, and the back end server updates the current state value of each sensor in real time according to the sensor number. Then, communication between the front end and the back end of the server (such as WebSocket, etc.) is established to obtain real-time sensor data updates, and the front end interface is designed to display the real-time operation data of key components of industrial products in a visual way. At the same time, basic information of industrial products is displayed, including product model, product size, product processing principle, product processing accuracy, product processing size, product components and their component parameters. Real-time monitoring of industrial products is achieved by combining static data and dynamic data of industrial products.

[0139] Step 4.3: Implement real-time monitoring and early warning logic on the server backend;

[0140] The sensor information is used to regularly check whether the current state value of each sensor is within the set fault warning threshold range. If not, an abnormality is detected. The sensor information with abnormal data is read according to the sensor number, and a fault description is generated according to the template and the user is notified through the notification mechanism. At the same time, step 5 is entered; if it is, it indicates that the industrial product is operating normally, and the sensor data is still regularly checked to see if it is within the set fault warning threshold. The fault description template is as follows:

[0141] The [parameter] of the current [position] is abnormal, the current value is [status value], and the threshold range is [threshold range].

[0142] Step 5: Generate industrial product maintenance decisions;

[0143] Step 5.1: Train the TextCNN model (the model can be trained in advance and used directly during fault monitoring);

[0144] Step 5.1.1: According to the common fault types of industrial products, generate fault description data of each common fault type according to the same fault description template in step 4.3, and construct a fault description data set from these fault description data. The fault description data set includes different fault descriptions of industrial products and corresponding fault types; at the same time, set a Neo4j query statement for each fault type in the fault data set;

[0145] Step 5.1.2: Use the constructed fault description dataset and the existing conventional method to train the TextCNN model so that the TextCNN model can classify the fault description data in the fault description dataset into fault types.

[0146] Step 5.2: Obtain maintenance decision plan;

[0147] Step 5.2.1: Use the trained TextCNN model to classify the industrial product fault description data generated in step 4.3 to obtain the fault type to which the fault description data belongs;

[0148] Step 5.2.2: According to the fault type obtained in step 5.2.1, determine the corresponding Neo4j query language;

[0149] Step 5.2.3: Input the Neo4j query language obtained in step 5.2.2 into the industrial product operation and maintenance knowledge graph constructed in step 2.5. The industrial product operation and maintenance knowledge graph outputs maintenance decisions, including maintenance steps, replacement parts recommendations, and actions that maintenance personnel can take.

[0150] It can be seen from the above process that the maintenance decision obtained by the present invention is obtained by combining the inference of the machine learning model and the information in the industrial product operation and maintenance knowledge graph, which can provide users with practical solutions and ultimately realize the trusted operation and maintenance process of industrial products.

[0151] Example:

[0152] This embodiment uses an FDM 3D printer as an example to illustrate the present invention.

[0153] See also Figure 1 , the process of this embodiment is as follows:

[0154] Step 1: Construct a basic knowledge graph of FDM 3D printers based on deep learning;

[0155] Step 1.1: Data collection and data processing;

[0156] Collect official data about FDM 3D printers, including the user manual of the corresponding model, expert technical manuals, usage logs, and official maintenance plans. Most of this official data is text data, which is unstructured data and needs to be deduplicated and missing value processed before it can be used.

[0157] Step 1.2: ontology modeling;

[0158] For the operation and maintenance of FDM 3D printers, ontology modeling includes the definition of entities and relationships.

[0159] Step 1.2.1: Define entities;

[0160] Entities include product entities, attribute entities, and operation and maintenance entities. Product entities include products, product modules, and product components, which are represented by Product, Product_Module, and Product_Component respectively. Attribute entities include the attributes of products and product components, and the attributes are uniformly represented by Feature. Operation and maintenance entities include fault phenomena, fault causes, and fault solutions, which are represented by Fault, Cause, and Solution respectively.

[0161] Step 1.2.2: Define relationships;

[0162] Relationships include those between entity elements of the same type and those between entities of different types. The predefined relationship types include Include, Have, Happen, Lead to, and Solution, which are represented by Include, Has_feature, Happen, Lead to, and Overcome respectively. The relationships between predefined entities are as follows: Product contains Product_Module, Product_Module contains Product_Component, Product and Product_Component have Feature, Product_Component has Fault, Cause causes Fault, and Solution solves Cause.

[0163] Step 1.3: Entity annotation;

[0164] Based on the various entities defined in step 1.2.1, the BIOE annotation method is used to annotate the official data of some 3D printers obtained after processing in step 1.1. The entity part in the official data is annotated according to the entity defined in step 1.2.1. B, I and E represent the beginning, middle and end of the entity respectively. The non-entity part in the official data is annotated as O. BIOE annotation needs to be operated according to the entities defined in step 1.2.1, and the official data is matched one by one with the various entities defined in step 1.2.1. For example, the printing platform is used as a product module, and the printing platform is annotated by BIOE as follows: "printing" is annotated as "B-Product_Module", "printing" is annotated as "I-Product_Module", "flat" is annotated as "I-Product_Module", and "platform" is annotated as "E-Product_Module".

[0165] Step 1.4: Knowledge extraction;

[0166] The BERT+Bi-LSTM+CRF neural network model is used to extract entities (the model can be built based on the PyTorch framework using the existing method).

[0167] Using the official data of the 3D printer annotated in step 1.3, the existing conventional method is used to train the BERT+Bi-LSTM+CRF neural network model. The first layer of the model uses the pre-trained BERT to receive the official data and convert each word in the received official data into a corresponding embedding vector; the second layer uses the Bi-LSTM layer to process the input sequence (i.e., the output of BERT) in the forward and reverse directions respectively, capturing the contextual features of each position in the input sequence; the third CRF layer adds constraints to the output of the Bi-LSTM layer and decodes it, outputting the entity type corresponding to each word in the corresponding official data.

[0168] For the remaining unlabeled official data of 3D printers, the trained BERT+Bi-LSTM+CRF neural network model is used to extract entities and obtain the entities of FDM 3D printers.

[0169] For the extracted entities, according to the predefined relationships in step 1.2.2, relationships are added to the extracted entities to form triple information of 3D printer knowledge in the format of <entity 1, type>-relationship-<entity 2, type>.

[0170] Finally, the triple information of 3D printer knowledge needs to be deduplicated, supplemented, and deleted.

[0171] Step 1.5: Knowledge storage;

[0172] After knowledge extraction, the Neo4j graph database is used to store the triple information of the 3D printer knowledge obtained in step 1.4.

[0173] Neo4j graph database stores data in the form of nodes and relationships. Each node in the Neo4j graph database represents an entity. The node label is the type of the entity. The node can add some attributes of the corresponding entity (such as attribute 1: attribute 1 content...). The relationship between nodes is connected by edges. The edges are the relationship between entities, and the edges can also add attributes. We define the extracted entities and relationships as node and relationship types, store and define the attributes related to the entities, and store the triple information of 3D printer knowledge in the Neo4j graph database. For example, first define the label "Product_Component"; then add a node named nozzle, and add node attributes to the node (including detailed information of the nozzle); finally, define the relationship and establish the relationship between the entity nodes. In this way, by storing the triple information of 3D printer knowledge in the Neo4j graph database (such as <print nozzle module, Product_Module, Include, nozzle, Product_Component>), a knowledge base is formed, and the basic knowledge graph of 3D printers is formed.

[0174] Step 2: Improve the basic knowledge graph of 3D printers constructed in step 1 based on crowd intelligence perception;

[0175] Step 2.1: Obtain the industrial product triplet represented by the structure and attributes of the 3D printer;

[0176] The FDM 3D printer module set is defined as follows:

[0177] P M ={M1: print head module, M2: print platform module, M3: motion system module,

[0178] M4: Extrusion system module}

[0179] The components and component attributes that define each product module are as follows:

[0180] A collection of components and component properties of the print head module:

[0181] M1={C1: nozzle, C2: heating block, C3: thermistor}

[0182] C1={F1: maximum nozzle temperature, F2: nozzle diameter, F3: nozzle flow rate}

[0183] C2={F1: heating power, F2: heating area, F3: heating block material}

[0184] C3={F1: working range, F2: measurement accuracy, F3: installation dimensions}

[0185] A collection of components and component properties of the print platform module:

[0186] M2={C1: printing bed, C2: printing model, C3: heating device}

[0187] C1={F1: surface material, F2: size and shape, F3: surface flatness}

[0188] C2={F1: model layer thickness, F2: model material, F3: support structure}

[0189] C3={F1: heating power, F2: overall size, F3: working range}

[0190] The components and component properties of the motion system module are as follows:

[0191] M3={C1: X-axis, Y-axis, Z-axis motors, C2: X-axis, Y-axis belts, C3: Z-axis screw}

[0192] C1={F1: torque, F2: step angle, F3: maximum speed}

[0193] C2={F1: belt type, F2: belt width, F3: belt material}

[0194] C3={F1: screw type, F2: screw diameter, F3: screw pitch}

[0195] A collection of components and component properties for the Extrusion System module:

[0196] M4={C1: extrusion motor, C2: feed gear, C3: cooling fan}

[0197] C1={F1: torque, F2: step angle, F3: maximum speed}

[0198] C2={F1: gear material, F2: tooth shape, F3: gear diameter}

[0199] C3={F1: fan speed, F2: fan air volume, F3: working area}

[0200] The above modules, components, and component attributes can be obtained from the basic knowledge graph of FDM 3D printers constructed in step 1. Combining modules, components, and attributes can obtain the triple P representing the structure and attributes of the FDM 3D printer:

[0201] P = {M, C, F};

[0202] Step 2.2: Construct the impact matrix;

[0203] Considering that there may be influence relationships between components of different modules in the FDM 3D printer, an influence matrix is ​​constructed according to the influence relationships between components of different modules in step 2.1.

[0204] The nozzle in the print nozzle module affects each other with the print bed and print model in the print platform module, the Z-axis motor and Z-axis lead screw in the motion system module, and the extrusion motor, feed gear and cooling fan in the extrusion system module; the heating block in the print nozzle module affects each other with the print model in the print platform module and the extrusion motor in the extrusion system module; the print bed in the print platform module also affects each other with the X-axis Y-axis motor and X-axis Y-axis belt in the motion system module; the print model in the print platform module also affects each other with the X-axis Y-axis Z-axis motor, X-axis Y-axis belt, Z-axis lead screw in the motion system module, and the extrusion motor, feed gear and cooling fan in the extrusion system module. A two-dimensional influence matrix E is constructed to represent the influence relationship. Each element in the influence matrix E represents the influence relationship between different modules, and the element value is 0 or 1. Only the influence between different module components is considered, and the influence relationship between different components in the same module is not considered. If the different module components represented by the rows and columns affect each other, the corresponding influence matrix element value is 1, otherwise it is 0. The influence matrix E constructed in this embodiment is:

[0205]

[0206] Among them, the rows and columns of the influence matrix E represent the nozzle (PT), heating block (JK), thermistor (RZ) in the print nozzle module, the print bed (DC), print model (DM), heating device (JZ) in the print platform module, X-axis Y-axis Z-axis motor (XJ), X-axis Y-axis belt (XD), Z-axis screw (ZG) in the motion system module, and extrusion motor (JJ), feed gear (JL), cooling fan (LS) in the extrusion system module. The elements in the matrix are determined according to whether the components represented by the rows and columns affect each other. If there is a mutual influence relationship, the corresponding element is 1, otherwise it is 0. At the same time, only the influence between components of different modules is considered. When establishing the influence matrix E, it is recommended that the column names and row names correspond to the components of each module in the 3D printer, and are arranged in sequence to facilitate subsequent index search;

[0207] Step 2.3: Collect user experience and express it with keywords;

[0208] The collected user experience includes questions and answers. Keywords are extracted from the collected user experience. The keywords extracted from the questions include the module components and component attributes of the 3D printer problem. The keywords extracted from the answers include the module components and component attributes in the questions and the components of different modules that affect each other. Finally, based on the extracted keywords, each piece of collected user experience can be represented as U_e:

[0209] U_e={Q、A}

[0210] Among them, Q represents the keyword set of the question, A represents the keyword set of the answer, and the user experience can be assigned to the corresponding module in the 3D printer through the keywords of the question and the triplet P obtained in step 2.1.

[0211] Step 2.4: Evaluate the reliability of user experience based on fuzzy evaluation and select reliable user experience;

[0212] The existence of answer keywords (first indicator), the matching of answer keywords and the 3D printer modules mentioned in the corresponding questions (second indicator), and the matching of answer keywords and 3D printer product modules other than the corresponding questions (third indicator) are used as evaluation indicators to score each user experience, and then the comprehensive evaluation result is calculated based on the fuzzy evaluation matrix and the weight of each indicator. Considering that user experience can be used as an alternative for operation and maintenance, the selected user experience must meet the first indicator and at least one of the second and third indicators. The evaluation weights and thresholds are set according to the above rules. When calculating the comprehensive evaluation results, the weights of the first, second, and third evaluation indicators can be defined as 0.4, 0.4, and 0.2 respectively, and the threshold is set to 0.5.

[0213] The calculated comprehensive evaluation results of each user experience are compared with the threshold. If the comprehensive evaluation result of a user experience is greater than or equal to the threshold, the user experience is considered to be reliable;

[0214] Step 2.5: Add the reliable user experience screened out in step 2.4 to the 3D printer basic knowledge graph established in step 1 to obtain the 3D printer operation and maintenance knowledge graph.

[0215] Step 3: Use physical sensors to build an FDM-based 3D printer with sensing capabilities;

[0216] Step 3.1: The FDM 3D printer realizes the layer-by-layer stacking process by controlling the movement of the extruder and the printing platform, as well as the heating and cooling of the molten material. Therefore, it is determined that the key components of the FDM 3D printer are the nozzle, the printing platform and the motor. The operating parameters of these key components are: the temperature, moving speed, pressure and position of the nozzle, the temperature, moving speed and position of the printing platform, the speed, vibration parameters and voltage of the motor.

[0217] Step 3.2: Corresponding physical sensors are configured on the key components determined in step 3.1, so that the FDM 3D printer can monitor its own operating status during the printing process and has the ability to perceive its operating status.

[0218] Step 4: Real-time operation status monitoring and fault warning of FDM 3D printer;

[0219] Step 4.1: Set corresponding fault warning threshold ranges for each key component of the FDM 3D printer;

[0220] First, the historical operation data of the key components of the 3D printer (nozzle, printing platform, motor) are obtained. Under the premise that the amount of historical operation data is large enough, the 3σ principle is used based on statistical laws to set the corresponding fault warning threshold range of each key component. The 3σ principle is: the probability that the operation data is distributed in (μ-σ, μ+σ) is 0.6827; the probability that the operation data is distributed in (μ-2σ, μ+2σ) is 0.9545; the probability that the operation data is distributed in (μ-3σ, μ+3σ) is 0.9973. Based on the 3σ principle, after calculating the mean μ and standard deviation σ of the historical operation data of each key component of the 3D printer, the corresponding fault warning threshold range is set for each key component as (μ-3σ, μ+3σ).

[0221] Furthermore, historical data can be reviewed regularly, and the fault warning threshold range of each key component can be flexibly adjusted according to the performance changes and actual conditions of the 3D printer.

[0222] Step 4.2: The FDM 3D printer with perception capability monitors the operating status data of its key components in real time;

[0223] When the 3D printer is processing parts, the data collected by the physical sensors on it include: the temperature, movement speed, pressure and position of the nozzle, the temperature, movement speed and position of the printing platform, the speed, vibration parameters and voltage of the motor.

[0224] Each installed sensor is uniquely numbered with an ID, and the sensor type Type, installation location Location, fault warning threshold range Threshold, current state value Value, and abnormality parameter Bool are stored. Use the MySQL database to store the information of each deployed sensor in the storage format of {ID, Type, Location, Threshold, Value, Bool}.

[0225] After the physical sensor collects the data, first of all, the data collected by the physical sensor is denoised, filtered, and calibrated, and then packaged in JSON format. Write a backend server program and use the MQTT protocol to send the packaged data to the backend server. After the backend server receives the sensor data, it performs data analysis to extract the sensor monitoring parameters and verify the integrity of the sensor data. The verified data is stored in the above-mentioned database connected to the backend server. The backend server updates the current status value of each sensor in real time according to the sensor number. Then, establish WebSocket communication between the front end and the server back end, use the Web framework to build the front end application, design a visual interface, and the server uses WebSocket to push new sensor data to the front end. The front end receives real-time data updates through WebSocket and uses the chart library in the visual interface to display the real-time operating parameter data of key components of the 3D printer in real time. At the same time, display the basic information of the 3D printer, including the 3D printer model, processing principle, processing accuracy, processing materials, motion system parameters, printing platform parameters, and the properties of each component. Step 4.3: Implement real-time monitoring and early warning logic on the server back end;

[0226] The backend server updates the current status value of the corresponding sensor in real time according to the sensor number ID, and compares it with the fault warning threshold range Threshold set in step 4.1 in real time. If the current status value of a sensor is not within the fault warning threshold range of the key component it monitors, a parameter abnormality is detected. At this time, the Bool value in the sensor information corresponding to the sensor is updated to True, otherwise, the Bool value in the sensor information corresponding to the sensor always remains False.

[0227] When the Bool value in a certain sensor information is updated to True, the sensor information with abnormal data is read according to the sensor number ID, the fault description data is generated according to the fault description template, and WebSocket real-time communication is used to send abnormal alarm notifications, namely fault description data, to the user on the front end using the browser, pop-up window or other methods.

[0228] The above fault description template is:

[0229] The [Type] of the current [Location] is abnormal, the current value is [Value], and the threshold range is [Threshold].

[0230] Step 5: Generate FDM 3D printer maintenance decision;

[0231] Step 5.1: Train the TextCNN model.

[0232] Step 5.1.1: Based on the common fault types of FDM 3D printers, generate the fault description data corresponding to these common fault types according to the same fault description template in step 4.3, so as to construct a fault description dataset. The fault description dataset includes different fault descriptions of FDM 3D printers and corresponding fault types, such as "The current printer nozzle temperature is abnormal, the current value is 270℃, and the threshold range is 250-260℃.", and annotate the corresponding fault type, such as "Nozzle temperature is abnormal" mentioned above. At the same time, set a Neo4j query statement for each fault type in the fault description dataset.

[0233] Step 5.1.2: Use the constructed fault description dataset and use existing conventional methods to train the TextCNN model.

[0234] Step 5.2: Obtain maintenance decision;

[0235] Step 5.2.1: After the fault description data obtained in step 4.3 is input into the TextCNN model trained in step 5.1, the TextCNN model outputs the fault type to which the fault description data belongs;

[0236] Step 5.2.2: According to the fault type obtained in step 5.2.1, determine the corresponding Neo4j query language;

[0237] Step 5.2.3: Input the Neo4j query language obtained in step 5.2.2 into the 3D printer operation and maintenance knowledge graph constructed in step 2.5. The 3D printer operation and maintenance knowledge graph outputs maintenance decisions, including maintenance steps, replacement parts suggestions, and actions that maintenance personnel can take. For example, the fault description data obtained in step 4.3 is: "The current temperature of the printer nozzle is abnormal, the current value is 270℃, and the threshold is 250-260℃." The TextCNN model outputs that the fault description data belongs to the "abnormal nozzle temperature" fault type. The Neo4j query language corresponding to the fault type is used in combination with the 3D printer operation and maintenance knowledge graph to query relevant information and generate a maintenance decision: "Check the nozzle temperature sensor. The temperature sensor may need to be replaced or cleaned and maintained." etc.

[0238] It can be seen from the above process that the maintenance decision obtained in this embodiment is obtained by combining the inference of the machine learning model and the information in the operation and maintenance knowledge graph of the 3D printer, which can provide users with practical and feasible fault solutions and ultimately realize the trusted operation and maintenance process of the FDM 3D printer.

Claims

1. A method for constructing an operation and maintenance knowledge graph of industrial products based on crowd intelligence perception, characterized in that: The following steps are involved: Step 1: Based on the official data of industrial products, build a basic knowledge graph of industrial products based on deep learning; Step 2: Based on crowd intelligence perception, improve the basic knowledge graph of industrial products constructed in step 1 to obtain the operation and maintenance knowledge graph of industrial products; Step 2.1: Obtain the industrial product triple P: P = {M, C, F}; M is the module constituting the industrial product, C is the component of the module, and F is the attribute of the component; Step 2.2: According to the mutual influence relationship between components belonging to different modules in the industrial product, construct the influence matrix E. The element value in the influence matrix E is 0 or 1, which is determined according to the following rules: Step 2.3: Collect user experience, and represent each user experience with keywords as U_e={Q, A}, where Q is the keyword set of the question and A is the keyword set of the answer; through the keyword set Q of the question and the industrial product triple P, the user experience can be assigned to the corresponding module of the industrial product; Step 2.4: Filter out reliable user experiences from the collected user experiences; Step 2.4.1: For each user experience, score the presence of the key words in its answer; Step 2.4.2: For each user experience, rate the match between the keywords in the answer and the product module mentioned in the question; Step 2.4.3: For each user experience, score the matching between the answer keywords and the product modules other than the question based on the influence matrix E; Step 2.4.4: Based on the scores of steps 2.4.1-2.4.3, a fuzzy evaluation method is used to calculate a column vector B consisting of the comprehensive evaluation results of all user experiences: Step 2.4.5: Set a threshold to filter out reliable user experience. If the value of an element in column vector B is greater than or equal to the threshold, the user experience corresponding to the element is considered reliable. Otherwise, the user experience corresponding to the element is unreliable. Step 2.5: Add the screened reliable user experience to the basic knowledge graph of industrial products constructed in step 1 to obtain the operation and maintenance knowledge graph of industrial products.

2. The method for constructing an operation and maintenance knowledge graph of industrial products based on crowd intelligence perception according to claim 1 is characterized in that: Step 1 is as follows: Step 1.1: Collect official data on industrial products and remove duplicates, missing values ​​and standardize data formats; Step 1.2: Ontology modeling, including defining entities and relationships between them; Step 1.3: Entity annotation; For each type of official data processed in step 1.1, some data are randomly selected and annotated with BIOE based on the entities defined in step 1.2, so that they correspond one-to-one with the entities defined in step 1.2; Step 1.4: Knowledge extraction; Step 1.4.1: Use the official data after entity annotation in step 1.3 to train the BERT+Bi-LSTM+CRF neural network model, and use the trained BERT+Bi-LSTM+CRF model to extract entities from the remaining unlabeled official data to obtain industrial product entities; Step 1.4.2: For the types of industrial product entities extracted in step 1.4.1, triple information of product knowledge is formed according to the relationship between the defined entities; Step 1.4.3: Modify or delete the triplet information obtained in step 1.4.2, and retain the valid triplet information; Step 1.5: Knowledge storage; The Neo4j graph database is used to store the valid triple information obtained in step 1.4 to obtain the basic knowledge graph of industrial products.

3. The method for constructing an operation and maintenance knowledge graph of industrial products based on crowd intelligence perception according to claim 2 is characterized in that: Step 2.4 is as follows: Step 2.4.1: For each user experience, score the presence of the key words in its answer; For each user experience U_e, determine whether the answer keyword set A is empty. If it is empty, the score is 0, and step 2.4.2 and step 2.4.3 are skipped. At the same time, the score of the two evaluation indicators of step 2.4.2 and step 2.4.3 for this user experience is 0; if it is not empty, the score is 1, and step 2.4.2 is entered; Step 2.4.2: For each user experience, score the match between the keywords of the answer and the product module in the question; For each user experience U_e, first assign the user experience to the corresponding product module according to its question keyword Q, and then match the answer keyword A in the user experience with the components and component attributes in the product module assigned according to the question keyword. If the match is successful, the score is 0.5 and go to step 2.4.3; if the match is unsuccessful, the score is 0 and go to step 2.4.3; Step 2.4.3: For each user experience, score the matching of its answer keywords with other product modules other than the question based on the influence matrix E; For each user experience U_e, if the answer keyword A in a user experience mentions the components of other modules other than the product module assigned to the question keyword in step 2.4.2, and according to the influence matrix E, it is determined that the components of other modules influence the components mentioned in the question keyword of this user experience, then the score is 0.5 and the process goes to step 2.4.4; otherwise, the score is 0 and the process goes to step 2.4.4; Step 2.4.4: Use the following formula to calculate the column vector B consisting of the comprehensive evaluation results of all user experiences: B=R·W T Where W is the weight vector of the evaluation index, W = [ω1 ω2 ω3], ω1 ω2 ω3 are the weights of the existence score of the answer keyword, the matching score of the answer keyword and the product module in the question, and the matching score of the answer keyword and the product module outside the question, respectively. R is the fuzzy evaluation matrix. The elements in the matrix R are the scores of the three evaluation indicators obtained in steps 2.4.1-2.4.

3. The rows in the matrix R represent different user experiences, and the columns represent the three evaluation indicators of user experience. Step 2.4.5: Set a threshold to filter out reliable user experience. If the value of an element in column vector B is greater than or equal to the threshold, the user experience corresponding to the element is considered reliable. Otherwise, the user experience corresponding to the element is unreliable.

4. The method for constructing an operation and maintenance knowledge graph of industrial products based on crowd intelligence perception according to claim 3 is characterized in that: In step 2.4.4, the values ​​of ω1ω2ω3 are 0.4, 0.4 and 0.2 respectively, and the threshold in step 2.4.5 is set to 0.

5.

5. The method for constructing an operation and maintenance knowledge graph of industrial products based on crowd intelligence perception according to claim 3 or 4, characterized in that: Step 2.5 is as follows: Step 2.5.1: Remove duplicates from reliable user experiences; Step 2.5.2: Based on the ontology model constructed in step 1.2, the reliable user experience after deduplication is divided into entities and relationships are added to form triple information of user experience; Step 2.5.3: Use the Neo4j graph database to add and save the triple information of user experience obtained in step 2.5.2 to the basic knowledge graph of industrial products obtained in step 1 to obtain the improved operation and maintenance knowledge graph of industrial products.

6. The trusted operation and maintenance method of industrial products based on crowd intelligence perception is characterized by: The following steps are involved: Step 1: Use physical sensors to build industrial products with perception capabilities; Step 2: Industrial product status monitoring and fault warning; Step 2.1: Set corresponding fault warning threshold ranges for each key component of the industrial product; Step 2.2: Industrial products with sensing capabilities monitor the operating status data of their key components in real time; Step 2.3: Display the operating status data of each key component monitored in step 2.2 in real time, and compare them with the corresponding fault warning threshold range regularly. If they are all within the fault warning threshold range, it means that the industrial product is operating normally, and return to step 2.2; if the operating status data of a key component is not within the corresponding fault warning threshold range, it means that the industrial product is operating abnormally, generate a fault description and send it to the front end to notify the user, and go to step 3; Step 3: Generate industrial product maintenance decisions; Step 3.1: Input the fault description obtained in step 2.2 into the pre-trained TextCNN model to obtain the fault type to which the fault description data belongs; the TextCNN model is trained using a fault description data set constructed based on common fault types of industrial products, and each fault type in the fault description data set is correspondingly provided with a Neo4j query statement; Step 3.2: According to the fault type obtained in step 3.1, determine the corresponding Neo4j query language; Step 3.3: Input the Neo4j query language obtained in step 3.2 into the operation and maintenance knowledge graph constructed by the operation and maintenance knowledge graph construction method for industrial products based on crowd intelligence perception as described in any one of claims 1-5 to obtain a maintenance decision.

7. The method for trusted operation and maintenance of industrial products based on crowd intelligence perception according to claim 6 is characterized in that: In step 2.1, based on the historical operating data and statistical laws of each key component, the 3σ principle is used to set the fault warning threshold range of each key component to (μ-3σ,μ+3σ), where μ and σ are the mean and standard deviation of the historical operating data of each key component, respectively.

8. The method for trusted operation and maintenance of industrial products based on crowd intelligence perception according to claim 7 is characterized in that: In step 2.3, basic information of industrial products is also displayed in real time, including product model, product size, product processing principle, product processing accuracy, product processing size, product components and component parameters.

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