A management system that promotes data circulation and full life cycle

Abstract

The present invention relates to the technical field of data circulation and full life cycle management. The present invention relates to a system for promoting data circulation and full life cycle management. It includes a data circulation platform establishment unit, a life cycle stage analysis unit, a to-be-matched data analysis unit, a platform data circulation unit, and a data self-extraction unit; the data circulation platform establishment unit is used to establish a data circulation platform, and to connect the data circulation platform with the data source of the factory department to obtain the factory data of the factory department; by deeply binding the factory data with the full life cycle of the product, not only is a stage feature library constructed based on historical data, and real-time data is accurately allocated to the corresponding stage through feature matching, but the stage allocation module also combines the business process to construct the corresponding relationship between data type and stage, and dynamically optimizes the matching rules through the feedback mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of data circulation and full life cycle management, and in particular to a system for promoting data circulation and full life cycle management. Background Art

[0002] In the field of factory production and operations, data management is the core link supporting production efficiency improvement, cost control and decision-making optimization. Existing technologies have achieved preliminary integration of factory production data, management data and other information by building a basic data collection and storage system.

[0003] However, each factory department operates independently or carries out data management work in a single business scenario. For example, the production department only manages production equipment data, and the sales department separately maintains customer order information. The closed storage of data in each department forms data silos. Data sharing between departments requires manual intervention and transmission, which can easily lead to information lag or deviation, seriously affecting the efficiency of cross-departmental collaboration. In order to reduce this situation, a management system that promotes data circulation and full life cycle management is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a system for promoting data circulation and full life cycle management to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, a system for promoting data circulation and full life cycle management is provided, which includes a data circulation platform establishment unit, a life cycle stage analysis unit, a to-be-matched data analysis unit, a platform data circulation unit, and a data self-extraction unit;

[0006] The data circulation platform establishment unit is used to establish a data circulation platform and connect the data circulation platform with the data source of the factory department to obtain the factory data of the factory department;

[0007] The life cycle stage analysis unit is used to perform life cycle stage analysis on factory products, classify historical factory data in combination with the life cycle stage, and assign life cycle stage to real-time factory data based on the classification results;

[0008] The to-be-matched data analysis unit is used to establish a to-be-matched data list for the factory department based on the data classification results, and at the same time analyze the life cycle stage of the factory products based on real-time factory data, and dynamically adjust the to-be-matched data list based on the analysis results;

[0009] The platform data circulation unit is used to compare the real-time factory data corresponding to the factory department with the list of data to be matched, obtain the difference data list through the comparison result, and then the data circulation platform circulates data to the factory department according to the difference data list;

[0010] The data self-extraction unit is used to perform semantic analysis on the acquired real-time factory data and display the semantic analysis results to the factory departments for the factory departments to perform data self-extraction.

[0011] As a further improvement of this technical solution, the data circulation platform establishment unit establishes a data circulation platform, collects data from factory departments through the data circulation platform, and at the same time, the data circulation platform circulates and distributes the collected data to factory departments, so that data can be shared among factory departments.

[0012] As a further improvement of the present technical solution, the data circulation platform establishment unit includes a transmission establishment module;

[0013] The transmission establishment module establishes a data connection between the management system of the factory department and the data circulation platform by using a data transmission protocol, thereby introducing the factory data generated by the factory department into the data circulation platform.

[0014] As a further improvement of the present technical solution, the life cycle stage analysis unit includes a data classification module and a stage allocation module;

[0015] The data classification module is used to perform life cycle analysis on factory products, obtain the life cycle stages of factory products in different states, and classify historical factory data in combination with the life cycle stages of factory products, and obtain the data type corresponding to the life cycle stage based on the data classification results;

[0016] The stage allocation module is used to perform data type analysis on the real-time factory data, obtain the data type of the real-time factory data, and then allocate the real-time factory data to a life cycle stage according to the data type.

[0017] As a further improvement of this technical solution, the life cycle stages in the data classification module include raw material procurement stage, production stage, quality inspection stage, sales stage, and after-sales stage;

[0018] One real-time factory data can be associated with multiple life cycle stages simultaneously.

[0019] As a further improvement of the present technical solution, the to-be-matched data analysis unit includes a data list establishment module and a data list adjustment module;

[0020] The data list creation module divides the factory departments according to the data classification results of the data classification module, obtains the data types corresponding to the factory departments at the life cycle stages, and creates a list of to-be-matched data for the factory departments according to the data types corresponding to the life cycle stages;

[0021] The data list adjustment module is used to analyze the life cycle stage of the factory products according to the life cycle stage assigned by real-time factory data, determine the life cycle stage of the factory products through the analysis results, and then dynamically adjust the data list to be matched corresponding to the factory department according to the life cycle stage.

[0022] As a further improvement of this technical solution, the platform data circulation unit includes a difference comparison module and a data circulation module;

[0023] The difference comparison module is used to compare the real-time factory data owned by the factory department with the list of data to be matched, obtain the difference data between the real-time factory data and the list of data to be matched, and summarize the difference data to establish a list of difference data;

[0024] The data circulation module is used to identify the real-time factory data and the difference data list obtained through the data circulation platform. When the data circulation platform identifies the real-time factory data contained in the difference data list, the identified real-time factory data is circulated to the factory department corresponding to the difference data list. Conversely, when the data circulation platform does not identify the real-time factory data contained in the difference data list, monitoring is continued.

[0025] As a further improvement of the present technical solution, during the process of the data circulation module identifying factory data, the data circulation platform can remind the corresponding factory department based on the real-time factory data that is not identified in the difference data list, so that the factory department responsible for collecting the real-time factory data can upload the data.

[0026] As a further improvement of the present technical solution, the data self-extraction unit includes a semantic analysis module and a data display module;

[0027] The semantic analysis module is used to perform semantic analysis on the real-time factory data obtained by the data circulation platform to obtain semantic data corresponding to each real-time factory data;

[0028] The data display module is used to display each real-time factory data to the factory department based on semantic data. When the factory department requires real-time factory data outside the list of data to be matched, the real-time factory data is filtered according to the semantic data, and the filtered real-time factory data is self-extracted in the data circulation platform.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. This system promotes data circulation and full life cycle management by deeply binding factory data with the entire product life cycle. It not only builds a stage feature library based on historical data and accurately allocates real-time data to the corresponding stage through feature matching, but also the stage allocation module combines business processes to build a correspondence between data types and stages, and dynamically optimizes matching rules through a feedback mechanism. Secondly, it can also dynamically adjust the list of data to be matched in each department according to the real-time stage of the product to ensure that data management is synchronized with the rhythm of the product life cycle. This precise classification and dynamic adaptation of the entire life cycle allows data to accurately serve the business needs of each stage, greatly improving the business utilization value of data.

[0031] 2. This system promotes data circulation and management of the entire life cycle. By establishing a unit through the data circulation platform, the data sources of various factory departments are connected to the data circulation platform, the data source classification is clarified, and data collection and distribution are realized through standardized transmission protocols. At the same time, data missing between departments are identified through difference comparison, and the missing data is automatically circulated to the target department. At the same time, the circulation trajectory is recorded to ensure traceability. This process completely breaks the closed state of data in various departments in traditional factories, realizes accurate and efficient sharing of data between departments, avoids repeated data collection or information asymmetry problems, and provides data support for cross-departmental collaboration.

[0032] 3. This system promotes data circulation and full life cycle management system, automatically identifies differential data and completes cross-departmental circulation through preset rules, without the need for manual transmission one by one. At the same time, it performs semantic analysis on real-time data based on the factory domain knowledge graph and semantic tag library, and supports departments to independently screen and extract required data through fuzzy search to meet personalized needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is the overall structural principle diagram of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0035] See also Figure 1 As shown, the purpose of this embodiment is to provide a system to promote data circulation and full life cycle management, including a data circulation platform establishment unit, a life cycle stage analysis unit, a to-be-matched data analysis unit, a platform data circulation unit, and a data self-extraction unit;

[0036] The data circulation platform establishment unit is used to establish a data circulation platform and connect the data circulation platform with the data source of the factory department to obtain the factory data of the factory department;

[0037] The data circulation platform establishment unit establishes a data circulation platform, collects data from factory departments through the data circulation platform, and at the same time, the data circulation platform circulates and distributes the collected data to factory departments, so that data can be shared among factory departments.

[0038] Identify various data sources within the factory, such as data generated by production systems and management systems. Categorize data sources based on factory department responsibilities and data ownership, and differentiate data generated by different departments to prepare for subsequent accurate data collection.

[0039] The data circulation platform establishment unit includes a transmission establishment module;

[0040] The transmission establishment module establishes a data connection between the management system of the factory department and the data circulation platform by utilizing the data transmission protocol, thereby introducing the factory data generated by the factory department into the data circulation platform.

[0041] Determine the data interface type (such as API, database, etc.) of the factory department management system, sort out the access rights and parameter requirements of the data interface, prepare for establishing the connection, and then prepare the receiving configuration of the data circulation platform, including data storage format, processing rules, etc., to ensure that it can adapt to the data characteristics of the management system;

[0042] Based on the determined data interface type, configure the corresponding connection parameters on the data circulation platform, establish an initial connection with the management system, perform a connection test, send a test data request, verify whether the management system can respond normally and return data, check whether the data format and content meet expectations, and adjust the connection parameters if not.

[0043] After the connection is established, factory data is obtained from the management system according to the preset transmission frequency (real-time, scheduled, etc.).

[0044] The life cycle stage analysis unit is used to analyze the life cycle stages of factory products, classify historical factory data according to the life cycle stages, and assign life cycle stages to real-time factory data based on the classification results;

[0045] The life cycle stage analysis unit includes a data classification module and a stage allocation module;

[0046] The data classification module is used to perform life cycle analysis on factory products, obtain the life cycle stages of factory products in different states, and classify historical factory data in combination with the life cycle stages of factory products. Based on the data classification results, the data type corresponding to the life cycle stage is obtained. The specific steps are as follows:

[0047] Historical data classification and stage mapping: Clean and preprocess historical factory data to remove noise data. Based on the defined stage characteristics, historical data is mapped to the corresponding lifecycle stage. For example, data records containing purchase order numbers are mapped to the raw material procurement stage, and data records containing production batch numbers are mapped to the production stage.

[0048] For data records across stages, multi-stage mapping is performed based on the association relationship between data fields;

[0049] Real-time data stage allocation and synchronous display: Acquire factory data in real time, extract feature fields from the data, and then match the real-time data features with the stage feature library to determine the lifecycle stage to which the data belongs.

[0050] The life cycle stages in the data classification module include raw material procurement stage, production stage, quality inspection stage, sales stage, and after-sales stage;

[0051] Raw material procurement stage: including supplier information, purchase order number, arrival time, etc.;

[0052] Production stage: involving production batches, equipment operating parameters, operators, etc.;

[0053] Quality inspection stage: covers inspection standards, test results, unqualified items, etc.;

[0054] Sales stage: including customer information, order amount, delivery time, etc.;

[0055] After-sales stage: involving customer feedback, maintenance records, return and exchange information, etc.

[0056] Data fields related to the characteristics of each stage are extracted from historical factory data to form a stage feature library.

[0057] One real-time factory data can be associated with multiple life cycle stages simultaneously.

[0058] The stage allocation module is used to analyze the data type of real-time factory data, obtain the data type of real-time factory data, and then allocate the real-time factory data to the corresponding life cycle stage according to the data type. The specific steps are as follows:

[0059] Data type identification preparation: sort out the factory business processes, identify the typical data types in each life cycle stage (raw material procurement, production, etc.), and build a basic database corresponding to data types and stages;

[0060] Then, the real-time factory data is preliminarily cleaned to remove duplicate and invalid data, ensure data quality, and prepare for accurate type identification;

[0061] Real-time data type analysis: Extract key attributes of real-time factory data (such as data format, business identifier, and associated objects) and determine the data type using preset rules (such as regular expression matching and business tag recognition). If the data type is complex or the rules are difficult to match, a machine learning classification model (such as Naive Bayes trained on historical data) is introduced to assist in identification and improve the accuracy of type judgment.

[0062] Lifecycle stage allocation: Based on the data type-stage mapping base library, real-time data of identified types is matched to the corresponding lifecycle stage. If the data is associated with multiple types (such as production quality inspection-related data), it is allocated to multiple stages.

[0063] At the same time, a feedback mechanism is established. If it is found after allocation that the stage does not match the actual business scenario of the data (such as mismatch of abnormal data types), the corresponding relationship will be corrected manually or automatically, and the basic database will be updated.

[0064] The to-be-matched data analysis unit is used to create a to-be-matched data list for the factory department based on the data classification results. At the same time, it analyzes the life cycle stage of the factory products based on real-time factory data and dynamically adjusts the to-be-matched data list based on the analysis results.

[0065] The to-be-matched data analysis unit includes a data list establishment module and a data list adjustment module;

[0066] The data list creation module divides the factory departments according to the data classification results of the data classification module, obtains the data types corresponding to the factory departments at the life cycle stage, and creates a list of data to be matched for the factory departments based on the data types corresponding to the life cycle stages. The specific steps are as follows:

[0067] Department-stage correlation: First, clarify the responsibilities of each factory department (such as procurement, production, and quality inspection). Analyze each department's involvement in each stage of the product lifecycle (raw material procurement, production, etc.), determine the corresponding relationship between departments and stages, and then, based on the data classification results, extract the data types corresponding to each lifecycle stage, such as supplier data and purchase order data in the procurement stage; process parameters and work order data in the production stage, etc., to clarify the stage-data type mapping.

[0068] Factory department division and data matching: Based on departmental responsibilities and stage participation, each department is mapped to a specific lifecycle stage. The data scope that each department needs to focus on at different stages is divided. At the same time, for each department and the corresponding lifecycle stage, the corresponding data type is screened out to form the basic structure of the department's matching data.

[0069] Constructing a list of data to be matched: For each factory department, we organize and form a list of data to be matched based on the data type of the corresponding life cycle stage;

[0070] Establish a list update mechanism. When data classification results and department responsibilities are adjusted, the list of data to be matched is updated synchronously to ensure that it is consistent with actual business needs. The formula is as follows:

[0071] ;

[0072] Among them, M is department D k With stage S j The matching degree of data types measures the degree of fit between department responsibilities and stage data. k，i For Department D k The weight of the i-th responsibility feature reflects the impact of the responsibility on data requirements, S j，i Life cycle stage S j The adaptation coefficient of the i-th data type indicates the degree of association between the data type and the stage, and n is the total number of responsibilities and data type features involved in the matching.

[0073] The data list adjustment module is used to analyze the life cycle stage of factory products based on the life cycle stage assigned by real-time factory data. The life cycle stage of factory products is determined based on the analysis results, and then the list of data to be matched corresponding to the factory department is dynamically adjusted according to the life cycle stage. The specific steps are as follows:

[0074] Basic preparation for product lifecycle stage analysis: Establish a product lifecycle stage determination rule base, clarify the key determination indicators and state transition conditions for each stage, and define a data type weight matrix for each lifecycle stage to reflect the importance of different data types to stage determination;

[0075] Real-time data-driven stage analysis: Collect real-time factory data related to the product, extract data related to each stage based on the assigned lifecycle stage, and conduct a comprehensive analysis of the extracted data based on the judgment rule base. If both purchase order completion data and production work order start data exist, the product may be in the transition state from the raw material procurement stage to the production stage. If the quality inspection data shows that the failure rate exceeds the threshold and the after-sales feedback data increases, the product may be returned from the sales stage to the after-sales stage.

[0076] By analyzing the time series relationship and logical association of data, determine the most likely life cycle stage of the product;

[0077] Dynamic adjustment of the list of data to be matched: Based on the product's life cycle stage, the list of data to be matched corresponding to each factory department is updated. The specific formula is as follows:

[0078] ;

[0079] Among them, C jFor products in stage S j The confidence level of the data is higher, the higher the value, the more likely it is in this stage, m is the number of types of real-time data, w u is the weight of the u-th type of real-time data, reflecting the importance of this data type to the stage judgment, Q u，j For the u-th category real-time data and stage S j The matching degree, T u is the time validity coefficient of the u-th category real-time data.

[0080] The platform data circulation unit is used to compare the real-time factory data corresponding to the factory department with the list of data to be matched, and obtain the difference data list through the comparison results. Then, the data circulation platform circulates data to the factory department based on the difference data list;

[0081] The platform data circulation unit includes a difference comparison module and a data circulation module;

[0082] The difference comparison module is used to compare the real-time factory data owned by the factory department with the list of data to be matched, obtain the difference data between the real-time factory data and the list of data to be matched, summarize the difference data, and establish a difference data list;

[0083] Compare the real-time factory data with the list of data to be matched item by item. Using pre-set comparison rules (such as whether the data item exists, whether the value is within a reasonable range, whether the format is consistent, etc.), identify data that exists in the real-time data but is missing from the list to be matched (positive differences), and data that exists in the list to be matched but is missing from the real-time data (negative differences).

[0084] Classify and summarize the identified difference data, create a difference data list according to dimensions such as data type, department, and related business, and record detailed information about the difference data.

[0085] The data circulation module is used to identify the real-time factory data and the difference data list obtained through the data circulation platform. When the data circulation platform identifies the real-time factory data included in the difference data list, the identified real-time factory data will be circulated to the factory department corresponding to the difference data list. Conversely, when the data circulation platform does not identify the real-time factory data included in the difference data list, the monitoring will continue.

[0086] The data circulation platform traverses the difference data list and searches for the corresponding target factory department for each difference data. When it identifies that the real-time factory data contains a data item in the difference data list, it extracts the data item from the source department and circulates it to the target department. At the same time, it records the data circulation trajectory (such as circulation time, source, destination, etc.) to ensure data traceability.

[0087] If no data item in the difference data list is identified, the data circulation platform continues to monitor the real-time data and waits for the appearance of data that meets the conditions.

[0088] During the process of the data circulation module identifying factory data, the data circulation platform can remind the corresponding factory department based on the real-time factory data that is not identified in the difference data list, so that the factory department responsible for collecting the real-time factory data can upload the data.

[0089] The data self-extraction unit is used to perform semantic analysis on the acquired real-time factory data and display the semantic analysis results to the factory departments for the factory departments to perform data self-extraction.

[0090] The data self-extraction unit includes a semantic analysis module and a data display module;

[0091] The semantic analysis module is used to perform semantic analysis on the real-time factory data obtained by the data circulation platform to obtain the semantic data corresponding to each real-time factory data;

[0092] Build a factory domain knowledge graph to sort out the semantic relationships between data items (such as equipment failures, associations, downtime, maintenance records, etc.). At the same time, define a semantic tag library to preset semantic tags for different types of real-time factory data (such as production progress, quality indicators, energy consumption data, etc.);

[0093] Perform natural language processing (NLP) on real-time factory data to extract key entities and attributes in the data, and then map the extracted entities and attributes to the knowledge graph to generate semantic data.

[0094] The data display module is used to display each real-time factory data to the factory department based on semantic data. When the factory department requires real-time factory data outside the list of data to be matched, the real-time factory data is filtered according to the semantic data, and the filtered real-time factory data is self-extracted in the data circulation platform.

[0095] Semantic data is categorized and displayed based on the responsibilities of factory departments. When a department requires data outside the to-be-matched list, the system automatically filters the associated real-time data through a fuzzy search using semantic tags. The formula is as follows:

[0096] ;

[0097] Among them, S is the matching degree between the data item and the query semantics, the higher the value, the more relevant it is, b is the number of semantic labels, α b is the weight of the bth semantic tag, Sim is the similarity, R b is the bth semantic label, and F is the query semantics.

[0098] Extract screening results and generate visual reports to support departments in independently obtaining the required data.

[0099] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for promoting data circulation and full life cycle management, characterized by: It includes a data circulation platform establishment unit, a life cycle stage analysis unit, a data analysis unit for matching, a platform data circulation unit, and a data self-extraction unit; The data circulation platform establishment unit is used to establish a data circulation platform and connect the data circulation platform with the data source of the factory department to obtain the factory data of the factory department; The life cycle stage analysis unit is used to perform life cycle stage analysis on factory products, classify historical factory data in combination with the life cycle stage, and assign life cycle stage to real-time factory data based on the classification results; The to-be-matched data analysis unit is used to establish a to-be-matched data list for the factory department based on the data classification results, and at the same time analyze the life cycle stage of the factory products based on real-time factory data, and dynamically adjust the to-be-matched data list based on the analysis results; The platform data circulation unit is used to compare the real-time factory data corresponding to the factory department with the list of data to be matched, obtain the difference data list through the comparison result, and then the data circulation platform circulates data to the factory department according to the difference data list; The data self-extraction unit is used to perform semantic analysis on the acquired real-time factory data and display the semantic analysis results to the factory departments for the factory departments to perform data self-extraction.

2. The system for promoting data circulation and full life cycle management according to claim 1, characterized in that: The data circulation platform establishment unit establishes a data circulation platform, collects data from factory departments through the data circulation platform, and at the same time, the data circulation platform circulates and distributes the collected data to factory departments, so that data can be shared among factory departments.

3. The system for promoting data circulation and full life cycle management according to claim 1, characterized in that: The data circulation platform establishment unit includes a transmission establishment module; The transmission establishment module establishes a data connection between the management system of the factory department and the data circulation platform by using a data transmission protocol, thereby introducing the factory data generated by the factory department into the data circulation platform.

4. The system for promoting data circulation and full life cycle management according to claim 1, characterized in that: The life cycle stage analysis unit includes a data classification module and a stage allocation module; The data classification module is used to perform life cycle analysis on factory products, obtain the life cycle stages of factory products in different states, and classify historical factory data in combination with the life cycle stages of factory products, and obtain the data type corresponding to the life cycle stage based on the data classification results; The stage allocation module is used to perform data type analysis on the real-time factory data, obtain the data type of the real-time factory data, and then allocate the real-time factory data to a life cycle stage according to the data type.

5. The system for promoting data circulation and full life cycle management according to claim 4, characterized in that: The life cycle stages in the data classification module include raw material procurement stage, production stage, quality inspection stage, sales stage, and after-sales stage; One real-time factory data can be associated with multiple life cycle stages simultaneously.

6. The system for promoting data circulation and full life cycle management according to claim 1, characterized in that: The to-be-matched data analysis unit includes a data list establishment module and a data list adjustment module; The data list creation module divides the factory departments according to the data classification results of the data classification module, obtains the data types corresponding to the factory departments at the life cycle stages, and creates a list of to-be-matched data for the factory departments according to the data types corresponding to the life cycle stages; The data list adjustment module is used to analyze the life cycle stage of the factory products according to the life cycle stage assigned by real-time factory data, determine the life cycle stage of the factory products through the analysis results, and then dynamically adjust the data list to be matched corresponding to the factory department according to the life cycle stage.

7. The system for promoting data circulation and full life cycle management according to claim 1, characterized in that: The platform data circulation unit includes a difference comparison module and a data circulation module; The difference comparison module is used to compare the real-time factory data owned by the factory department with the list of data to be matched, obtain the difference data between the real-time factory data and the list of data to be matched, and summarize the difference data to establish a list of difference data; The data circulation module is used to identify the real-time factory data and the difference data list obtained through the data circulation platform. When the data circulation platform identifies the real-time factory data contained in the difference data list, the identified real-time factory data is circulated to the factory department corresponding to the difference data list. Conversely, when the data circulation platform does not identify the real-time factory data contained in the difference data list, monitoring is continued.

8. The system for promoting data circulation and full life cycle management according to claim 7, characterized in that: During the process of the data circulation module identifying the factory data, the data circulation platform can remind the corresponding factory department based on the real-time factory data that is not identified in the difference data list, so that the factory department responsible for collecting the real-time factory data can upload the data.

9. The system for promoting data circulation and full life cycle management according to claim 1, characterized in that: The data self-extraction unit includes a semantic analysis module and a data display module; The semantic analysis module is used to perform semantic analysis on the real-time factory data obtained by the data circulation platform to obtain semantic data corresponding to each real-time factory data; The data display module is used to display each real-time factory data to the factory department based on semantic data. When the factory department requires real-time factory data outside the list of data to be matched, the real-time factory data is filtered according to the semantic data, and the filtered real-time factory data is self-extracted in the data circulation platform.

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