Classification system and classification method for industrial control data

Abstract

The invention discloses a classification system and a classification method for industrial control data, and belongs to the technical field of industrial control, the classification system comprises a data screening module, a data classification module and a data checking module, the data screening module is used for carrying out basic analysis on collected data, constructing a fine data class according to the analyzed data, and checking the data class according to the data class; and the data classification module is used for carrying out statistics on communication points or similar points of the data classes, merging the data classes with a set amount according to requirements to form new data classification items, and marking and storing the data classification items. According to the method, links or merges of communicated projects can be formed, the redundancy of data classification is reduced, the data volume of each small project is refined under the condition of completing rapid classification of industrial control data, and the query convenience after data classification is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial control, and in particular relates to a classification system and a classification method for industrial control data. Background Art

[0002] With the rapid development of advanced IT technologies such as the Internet of Things, data, and artificial intelligence, the manufacturing industry is accelerating the integration of IT and OT in order to reshape the core competitiveness of enterprises. Under the general trend of networking and intelligence, industrial networks are moving from closed to open, and security issues are gradually being paid attention to by all sectors of society. Industrial control systems require large amounts of data such as images and voice signals and high-speed transmission.

[0003] Due to the large amount of network traffic data in industrial control systems, a commonly used classification method is to classify data based on industrial control projects. This classification method can quickly complete the classification, but the amount of data in each project is still large, which is not conducive to data query after data classification. Summary of the invention

[0004] In view of the problems existing in the prior art, the purpose of the present invention is to provide a classification system and classification method for industrial control data, which can form links or mergers of similar items, reduce the redundancy of data classification, and refine the data volume of each small item while completing the rapid classification of industrial control data, thereby ensuring the convenience of query after data classification.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: The present invention provides a classification system for industrial control data, including a data screening module, a data classification module and a data verification module; The data screening module is used to perform basic analysis on the collected data, build detailed data classes based on the analyzed data, and screen the data classes; The data classification module is used to count the common points or similar points of the data categories, merge the set amount of data categories as needed to form new data classification items, and mark and store each data classification item; The data verification module is used to perform sampling verification after data classification and keep records of sampling verification to ensure the accuracy of data classification.

[0006] A further improvement of the present invention is that, in the data screening module, a screening is performed once according to the type of data, and then a secondary screening is performed according to the length of the data string in the same type of data.

[0007] A further improvement of the present invention is that, during a screening process, data classification and comparison are performed, data of the same type are further merged, uncertain data are retained, and an alarm signal is triggered to remind manual operation, and the operation method is recorded after the manual operation is completed, and a link between this type of data and the operation method is established.

[0008] A further improvement of the present invention is that, during secondary screening, the length of the data string screened to the same category item is not greater than 1.5 times the length of the minimum data string.

[0009] A further improvement of the present invention is that, in the data classification module, the relevant categories of data classification include the category of information proposed by the data, the category of information presented in the data and the relevant categories of information derived from the data.

[0010] A further improvement of the present invention is that, in the data classification module, each individual data class is provided with a corresponding index, and a directory related to the index is added.

[0011] A further improvement of the present invention is that, in the data classification module, links between different data classes with correlation are established based on the correlation between the data classes; the terms included in the links include keywords, related words and distinguishing words.

[0012] A further improvement of the present invention is that, in the data verification module, the frequency of sampling verification is 0.1, and the amount of data for sampling verification is not less than 30.

[0013] The present invention also provides a method for classifying industrial control data. The method is based on the industrial control data classification system and comprises: Acquisition of industrial control data: Acquisition of industrial control data through multiple information channels, merging of identical industrial control data, and reduction of redundancy of industrial control data; Perform basic analysis and screening of industrial control data: perform the first data type screening and the second data string length screening in sequence; Finely classify data: Perform fine classification according to data information categories or related categories, then store and record each classification result separately, and establish links between related indexes and data categories; Verification after completing data classification: Use sampling method to ensure the rationality of data classification.

[0014] A further improvement of the present invention is that before each classification result is stored and recorded separately, data classes with high correlation are merged according to the data classification results.

[0015] Compared with the prior art, the present invention has at least the following beneficial technical effects: The present invention provides a classification system and classification method for industrial control data. When classifying industrial control data, the industrial control data is first acquired, and then basic analysis and screening of the industrial control data, fine classification of the data and verification after data classification are performed in sequence. In the above operations, the classification system can be uniformly controlled by the classification system. During the classification process, the refinement of individual data items can be completed. At the same time, links or mergers of similar items can be formed to reduce the redundancy of data classification. Under the condition of completing the rapid classification of industrial control data, the data volume of each small item is refined, ensuring the convenience of query after data classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A structural block diagram of a classification system for industrial control data of the present invention; Figure 2 The figure is a flow chart of a method for classifying industrial control data according to the present invention. DETAILED DESCRIPTION

[0017] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0018] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0019] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0020] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a communication; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0021] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0022] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0023] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0024] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0025] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0026] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0027] Example 1 See also Figure 1 , this embodiment provides an industrial control data classification system, including a data screening module, a data classification module and a data verification module; The data screening module is used to perform basic analysis on the collected data, build detailed data classes based on the analyzed data, and screen the data classes; The data classification module is used to count the common points or similar points of the data categories, merge the set amount of data categories as needed to form new data classification items, and mark and store each data classification item; The data verification module is used to perform sampling verification after data classification and keep records of sampling verification to ensure the accuracy of data classification.

[0028] See also Figure 2 In this embodiment, in the data screening module, a primary screening is performed according to the type of data, and then a secondary screening is performed according to the length of the data string in the same type of data. The primary screening can screen out the major categories of data, and the secondary screening can perform a refined screening on the primary screened data. Indeed, the primary screening is usually used to preliminarily classify the data according to larger categories or types. This is the first step in data processing and helps to simplify complex data sets into more manageable parts. The secondary screening is based on the primary screening, and the classified data is screened more carefully and specifically to meet specific analysis or processing needs.

[0029] In the data screening module, a screening is performed once according to the data type, and then a secondary screening is performed according to the length of the data string in the same type of data; In a screening process, data classification and comparison are carried out, data of the same type are further merged, uncertain data are retained, and an alarm signal is triggered to remind manual operation. After the manual operation is completed, the operation method is recorded and a link between the data and the operation method is established; During the secondary screening, the length of the data string screened to the same category item shall not exceed 1.5 times the length of the minimum data string.

[0030] See also Figure 2In this embodiment, in the data classification module, the relevant categories of data classification include the category of information proposed by the data, the category of information mainly presented in the data, and the relevant categories of information derived from the data. Extracting multiple features from the data string and classifying them separately according to the features can ensure the accuracy of data classification. In the process of data classification, extracting multiple features from the data string and classifying them according to these features can significantly improve the accuracy of data classification. Feature extraction is a key step in machine learning, data mining and data analysis. It involves extracting useful information or attributes from raw data. These attributes (i.e., features) can best describe the data and help subsequent model training or classification tasks.

[0031] See also Figure 2 In this embodiment, in the data classification module, each individual data class is provided with a corresponding index, and a directory about the index is added. According to the index directory, the efficiency of retrieval after data classification can be improved, and at the same time, the accuracy of the retrieved data is guaranteed. In the process of data management and classification, not only attention should be paid to the screening and classification of data, but also the efficiency and accuracy of subsequent data retrieval should be considered. Extracting multiple features of the data and classifying them according to these features can significantly improve the accuracy of data classification. At the same time, establishing a reasonable index directory can greatly improve the retrieval efficiency after data classification and ensure the accuracy of retrieved data.

[0032] See also Figure 2 Specifically, in the data classification module, based on the correlation between data classes, links between different data classes with correlation are established; in the links, the terms included are keywords, related words, and distinguishing words. In the data classification module, in order to understand and manage data more comprehensively, especially when there are potential correlations between different data classes, it is an effective method to establish links between these classes. Such links can not only help users explore and discover related data more conveniently, but also provide strong support for data analysis, mining, and decision support. When constructing these links, it is crucial to include terms such as keywords, related words, and distinguishing words.

[0033] By using correlation, in the retrieval after data classification, it can be ensured that related data is retrieved at the same time, avoiding the omission of retrieval results. In the data classification module, establishing links based on the correlation between data classes is a very important step, which is directly related to the completeness and accuracy of data retrieval. By identifying the correlation between different data classes and establishing links between these classes, a broader and deeper search can be achieved during data retrieval, thereby ensuring that related data is retrieved at the same time, avoiding the omission of retrieval results.

[0034] See also Figure 2In this embodiment, the frequency of sampling verification in the data verification module is 0.1, and the amount of data for sampling verification is not less than 30. Verification by sampling can ensure high accuracy of verification, and at the same time, reduce the manpower and time consumption of all verification. The data verification module is a vital part of data quality management, which is used to verify the accuracy, completeness and consistency of data. The frequency of sampling verification: 0.1, which usually means that 1 out of every 10 data items needs to be selected for verification. This frequency can be adjusted according to the reliability, importance and verification cost of the data. The lower limit of the amount of data for sampling verification: 30, that is, regardless of the total size of the data set, at least 30 data items must be verified. This is to ensure the adequacy and representativeness of the verification, especially when the data set is small.

[0035] Example 2 See also Figure 2 This embodiment provides a classification method for industrial control data. The specific implementation steps of the classification method are as follows: Step 1: Obtain industrial control data: Obtain industrial control data through multiple information channels, merge the same industrial control data, and reduce the redundancy of industrial control data; Step 2: Perform basic analysis and screening of industrial control data: perform the first data type screening and the second data string length screening in sequence; Step 3: Finely classify the data: Finely classify the data according to the data information category or related categories, then store and record each classification result separately, and establish links between related indexes and data categories; Step 4: Verification after completing data classification: Use sampling method to ensure the rationality of data classification.

[0036] The classification system is controlled by the classification system. During the classification process, the refinement of individual data items can be completed. At the same time, the links or mergers of the interrelated items can be formed to reduce the redundancy of data classification. Under the condition of completing the rapid classification of industrial control data, the data volume of each small item is refined to ensure the convenience of query after data classification. See also Figure 2 In this embodiment, in step 3, after data classification is completed and before storage, data classes with high correlation can be merged according to the data classification results. After data classification is completed and before storage, merging data classes with high correlation according to the data classification results is an effective strategy to optimize data storage structure and improve data utilization efficiency.

[0037] Example 3 This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for evaluating the dynamic aerodynamic load of a wind turbine generator set are implemented.

[0038] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0039] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0040] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0041] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0042] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0043] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A classification system for industrial control data, characterized in that: It includes data screening module, data classification module and data verification module; The data screening module is used to perform basic analysis on the collected data, build detailed data classes based on the analyzed data, and screen the data classes; The data classification module is used to count the common points or similar points of the data categories, merge the set amount of data categories as needed to form new data classification items, and mark and store each data classification item; The data verification module is used to perform sampling verification after data classification and keep records of sampling verification to ensure the accuracy of data classification.

2. The classification system for industrial control data according to claim 1, characterized in that: In the data screening module, a screening is performed once according to the data type, and then a secondary screening is performed according to the length of the data string in the same type of data.

3. The classification system for industrial control data according to claim 2, characterized in that: During a screening process, data is classified and compared, data of the same type is merged, uncertain data is retained, and an alarm signal is triggered to remind manual operation. After the manual operation is completed, the operation method is recorded and a link between this type of data and the operation method is established.

4. The classification system for industrial control data according to claim 3, characterized in that: During the secondary screening, the length of the data string screened to the same category item shall not exceed 1.5 times the length of the minimum data string.

5. The classification system for industrial control data according to claim 1, characterized in that: In the data classification module, the relevant categories of data classification include the category of the information proposed by the data, the category of the information presented in the data and the relevant categories of the information derived from the data.

6. The classification system for industrial control data according to claim 1, characterized in that: In the data classification module, each individual data class is set with a corresponding index, and a directory about the index is added.

7. The classification system for industrial control data according to claim 1, characterized in that: In the data classification module, based on the correlation between data classes, links between different data classes with correlation are established; In the link, the terms included are keywords, related words and distinguishing words.

8. The classification system for industrial control data according to claim 1, characterized in that: In the data verification module, the frequency of sampling verification is 0.1, and the amount of data for sampling verification is not less than 30.

9. A method for classifying industrial control data, characterized in that: The method is based on a classification system for industrial control data according to any one of claims 1 to 6, comprising: Acquisition of industrial control data: Acquisition of industrial control data through multiple information channels, merging of identical industrial control data, and reduction of redundancy of industrial control data; Perform basic analysis and screening of industrial control data: perform the first data type screening and the second data string length screening in sequence; Finely classify data: Perform fine classification according to data information categories or related categories, then store and record each classification result separately, and establish links between related indexes and data categories; Verification after completing data classification: Use sampling method to ensure the rationality of data classification.

10. The method for classifying industrial control data according to claim 9, characterized in that: In step three, before storing and recording each classification result separately, the data classes with high correlation are merged according to the data classification results.

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