Automatic checking system for factory site table

Abstract

The invention relates to an automatic checking system for a factory station table, and belongs to the technical field of data checking. The system comprises a hardware layer and a data layer, wherein the data layer comprises a data access layer and a data storage layer; the data access layer is used for providing a data interface; the data storage layer serves as a database to store data; the service layer is configured with a preset algorithm, and the execution steps of the preset algorithm comprise: obtaining a to-be-checked factory site table, obtaining description information of the to-be-checked factory site table, and carrying out word segmentation processing on the description information to obtain a plurality of sub-character strings; carrying out normative judgment on each sub-character string; performing fuzzy matching on the sub-string and a standard description information base in response to the sub-string conforming to the specification; generating a check report based on the fuzzy matching result; and the display layer is used for displaying the check report. Through the establishment of the system, the automatic checking of the checking report is completed, the checking efficiency can be improved, the workload of personnel can be reduced, and the automatic and intelligent requirements of a modern power grid can be met.

Description

Technical Field

[0001] The invention relates to the technical field of data verification, in particular to an automatic verification system for a plant site table. Background Art

[0002] The plant and site table is usually a data table used to manage and record relevant information of specific industrial or engineering facilities (such as electricity, petroleum, chemical industry, etc.). Research has found that current automation professionals still rely on manual review of the provided plant and site tables, and compare them with standard monitoring information one by one. Due to the large number of station information points, the manual method is relatively backward, which requires a lot of time and energy, is inefficient, and inevitably leads to errors and omissions. At the same time, there is no fixed audit standard, and the audit standard is arbitrary. Summary of the invention

[0003] In order to solve the above-mentioned problems in the prior art, the present invention provides an automatic calibration system for plant site meters.

[0004] In the first aspect, an embodiment of the present application provides an automatic verification system for a plant site table, comprising: a hardware layer, including: an application server and a database server; the application server is used to execute a preset algorithm, and the database server is used to access and store data; a data layer, including: a data access layer and a data storage layer; the data access layer is used to provide a data interface; the data storage layer serves as a database to store data; a service layer, configured with the preset algorithm, and the preset algorithm execution steps include: obtaining the plant site table to be verified, obtaining the description information of the plant site table to be verified, performing word segmentation on the description information to obtain multiple substrings; performing a normative judgment on each substring; in response to the substring complying with the specification, fuzzy matching the substring with a standard description information library; generating a verification report based on the fuzzy matching result; a display layer, used to display the verification report.

[0005] Optionally, the preset algorithm execution step further specifically includes: inputting the substring into a pre-built enhanced support vector machine, and determining whether the substring complies with the standardization by calculating a decision function; wherein, a first label or a second label is output through the decision function; the first label represents compliance with the standardization, and the second label represents non-compliance with the standardization.

[0006] Optionally, the enhanced support vector machine is constructed by the following steps, including: obtaining a training sample set, wherein the training sample set is a set of known normative point table character strings, each point table character string includes a known label; using the training sample set to train the enhanced support vector machine until the optimal hyperplane is obtained.

[0007] Optionally, the preset algorithm execution step further specifically includes: preprocessing the description information of the plant site table to be calibrated to remove useless characters; using regular expressions to identify keywords in the preprocessed description information; and performing word segmentation processing on the description information based on the identified keywords to obtain multiple substrings.

[0008] Optionally, the data access layer configures a read-write separation mode, and the read-write separation mode is specifically configured as follows: when responding to a read data request, routing the read data request to a database instance pre-configured for read operations; and when responding to a write data request, sending the write data request to a primary database.

[0009] Optionally, when storing the original data of the plant site table to be calibrated, the data storage layer divides each data level according to the data type, and organizes the data into a tree structure according to the data level.

[0010] Optionally, the data access layer also configures a Redis service, and the Redis service is specifically configured as follows: when a data request is received, searching for data corresponding to the data request from the cache; when the data corresponding to the data request does not exist in the cache, sending a request to the database and storing the acquired data in the cache.

[0011] Optionally, the plant site table to be calibrated is a four-remote information point table.

[0012] Optionally, the preset algorithm execution step further specifically includes: performing fuzzy matching by using edit distance.

[0013] Optionally, the verification report includes at least a data field verification result and a value verification result.

[0014] The beneficial effects of the present invention include: the embodiment of the present application provides an automatic calibration system for plant site tables, which provides hardware basic support through the hardware layer, effectively accesses data through the data layer, executes automatic calibration of algorithms through the service layer, and finally displays the calibration report through the presentation layer. By building this system, automatic calibration of the calibration report is completed, which can improve calibration efficiency, reduce personnel workload, and meet the automation and intelligence requirements of modern power grids.

[0015] At the same time, at the algorithm level, this application designs a three-level layout. First, the description information of the complex plant site table is disassembled into multiple substrings to make it easier for the system to handle, thereby providing a solid foundation for subsequent verification and evaluation analysis. Secondly, each substring is subjected to normative judgment (such as whether it is an allowed character type, format, etc.) to ensure the accuracy and compliance of the data, so as to reduce the decision-making risks caused by data accuracy and compliance. Furthermore, the most critical point is to fuzzy match the substrings that meet the specifications with the standard description information library. It should be noted that the description information may come from different data sources (different sites, etc.), and there are problems such as spelling errors, different wording or inconsistent standards. Then, these differences can be effectively identified and processed through fuzzy matching (that is, according to fuzzy matching, the standard description information with the highest similarity is determined). In other words, with the help of fuzzy matching, this system can effectively improve the accuracy and completeness of the verification, avoiding common errors and omissions in the manual verification process. It is equivalent to enhancing the consistency of the verification results by formulating unified audit standards and eliminating subjective differences caused by non-fixed audit standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 An automatic calibration system for a plant site table provided by an embodiment of the present invention;

[0017] Figure 2 Another automatic calibration system for plant site tables provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0019] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0020] The plant and site table is usually a data table used to manage and record relevant information of specific industrial or engineering facilities (such as electricity, petroleum, chemical industry, etc.). Research has found that current automation professionals still rely on manual review of the provided plant and site tables, and compare them with standard monitoring information one by one. Due to the large number of station information points, the manual method is relatively backward, which requires a lot of time and energy, is inefficient, and inevitably leads to errors and omissions. At the same time, there is no fixed audit standard, and the audit standard is arbitrary.

[0021] In view of the above problems, the present application proposes the following embodiments to solve the above technical problems.

[0022] See also Figure 1 , the embodiment of the present application provides an automatic calibration system for a plant site table, comprising:

[0023] The hardware layer includes: application servers and database servers; application servers are used to execute preset algorithms, and database servers are used to access and store data.

[0024] It should be noted that the hardware layer serves as the hardware basic support for the automatic verification system of the plant site table provided in the embodiment of the present application to ensure the efficiency and stability of the system when processing large amounts of data.

[0025] The data layer includes: data access layer and data storage layer; the data access layer is used to provide data interface; the data storage layer serves as a database to store data.

[0026] Among them, the data layer corresponds to the database server of the hardware layer.

[0027] The service layer is configured with a preset algorithm, and the preset algorithm execution steps include: obtaining a plant site table to be calibrated, obtaining description information of the plant site table to be calibrated, performing word segmentation on the description information to obtain multiple substrings; making a normative judgment on each substring; in response to a substring meeting the specification, fuzzy matching the substring with a standard description information library; and generating a calibration report based on the fuzzy matching result.

[0028] The service layer corresponds to the application server of the hardware layer. The service layer is configured with a software algorithm (ie, the above-mentioned preset algorithm) to perform automatic verification of the plant site table.

[0029] It should be noted that the automatic verification algorithm for the plant site table in the embodiment of the present application involves three key algorithm contents. The first part is the word segmentation processing of the description information of the plant site table to be verified to obtain multiple substrings. The second part is to make a normative judgment on each substring (such as whether it is an allowed character type, format, etc.). The third part is to perform fuzzy matching of the substrings that meet the specifications with the standard description information library to determine the standard description with the highest similarity. Through the three parts of the algorithm content, a verification report is then produced.

[0030] Presentation layer, used to display verification reports.

[0031] Finally, a presentation layer is provided to show the automatically generated verification report to the user.

[0032] In summary, the embodiment of the present application provides an automatic calibration system for plant site tables. The system provides hardware basic support through the hardware layer, effectively accesses data through the data layer, executes automatic calibration through the service layer, and finally displays the calibration report through the presentation layer. By building this system, automatic calibration of the calibration report is completed, which can improve calibration efficiency, reduce personnel workload, and meet the automation and intelligence needs of modern power grids.

[0033] At the same time, at the algorithm level, this application designs a three-level layout. First, the description information of the complex plant site table is disassembled into multiple substrings to make it easier for the system to handle, thereby providing a solid foundation for subsequent verification and evaluation analysis. Secondly, each substring is subjected to normative judgment (such as whether it is an allowed character type, format, etc.) to ensure the accuracy and compliance of the data, so as to reduce the decision-making risks caused by data accuracy and compliance. Furthermore, the most critical point is to fuzzy match the substrings that meet the specifications with the standard description information library. It should be noted that the description information may come from different data sources (different sites, etc.), and there are problems such as spelling errors, different wording or inconsistent standards. Then, these differences can be effectively identified and processed through fuzzy matching (that is, according to fuzzy matching, the standard description information with the highest similarity is determined). In other words, with the help of fuzzy matching, this system can effectively improve the accuracy and completeness of the verification, avoiding common errors and omissions in the manual verification process. It is equivalent to enhancing the consistency of the verification results by formulating unified audit standards and eliminating subjective differences caused by non-fixed audit standards.

[0034] Optionally, the plant site table to be calibrated is a table of four remote information points.

[0035] It should be noted that the four-remote information point table usually refers to a table used to record and manage information related to data collection and monitoring in the fields of remote sensing, telemetry, remote control and telemetry. The four-remote information point table usually contains the following contents: information point identification, information point type, information point location, data collection frequency, sensor information, etc.

[0036] In other words, the system provided by the embodiment of the present application can automatically complete the import and management of the four-remote information point table, and realize the segmentation and standardized verification of the point table description information through algorithms such as word segmentation and fuzzy matching.

[0037] The automatic calibration system of the plant site table is described in detail below.

[0038] See also Figure 2 Among them, the hardware layer serves as the basic support, and in addition to application servers and database servers, it can also include hardware devices such as firewalls and switches.

[0039] Specifically, the application server is responsible for the execution of core business logic, including but not limited to key tasks such as importing plant site tables, word segmentation processing, and calculation of fuzzy matching algorithms. It is the core of the entire system operation.

[0040] The database server is responsible for storing various types of data, including but not limited to plant site tables, standard description information base, equipment ledgers and other important information, to ensure fast access and security of data.

[0041] Firewalls and switches build a network security barrier for the system, protecting the internal network from external attacks, while also ensuring the security and reliability of data interaction between multiple systems.

[0042] The data layer is mainly composed of the data access layer and the data storage layer.

[0043] Optionally, the data access layer is responsible for providing an efficient data access interface, including configuration with Redis service, reservoir access DAO (Data Access Object) mechanism and read-write separation mode, ensuring fast access to data between system components.

[0044] Specifically, the read-write separation mode is specifically configured as follows: in response to a read data request, the read data request is routed to a database instance preconfigured for read operations; and in response to a write data request, the write data request is sent to a primary database.

[0045] It should be noted that read-write separation is a key link in the design of the data layer. In order to improve system performance, an innovative read-write separation strategy is adopted. This strategy divides database operations into two categories: read operations and write operations, which are processed by different database instances, so as to reduce the pressure on a single database and improve data processing capabilities. When the system needs to read data, such as retrieving information in the plant site table, the read request will be routed to the database instance dedicated to read operations, thereby avoiding the impact of read operations on write operations. For write operations, they will be sent directly to the main database to ensure data consistency.

[0046] Optionally, the data access layer also configures a Redis service, which is specifically configured as follows: when a data request is received, the data corresponding to the data request is searched from the cache; when the data corresponding to the data request does not exist in the cache, a request is sent to the database, and the acquired data is stored in the cache.

[0047] Specifically, the Redis service in the data access layer is used as a cache mechanism to store frequently accessed data, such as basic device information and common query results. When the system receives a data request, the Redis service first attempts to obtain the data from the cache. If the corresponding data does not exist in the cache, a request is made to the database, and the obtained data is stored in the cache for subsequent quick access. This strategy can effectively reduce the number of direct accesses to the database and reduce the database load.

[0048] The data storage layer uses a database to save various information required by the system, such as plant site tables, standard specification signal libraries, equipment ledgers, etc., and improves data access performance through a read-write separation mechanism to ensure stable operation of the system.

[0049] Optionally, the data storage layer, when storing the original data of the plant site table to be calibrated, divides each data level according to the data type, and organizes the data into a tree structure according to the data level.

[0050] Specifically, in the data storage layer, data organization follows certain structural principles to facilitate efficient data storage and retrieval. For example, when storing the plant site table, the data storage layer will organize the data into a tree structure based on the type of equipment, voltage level and other attributes, which is convenient for hierarchical query. For the four remote standard description information library (corresponding to the four remote information point table), each record will contain fields such as signal name, equipment type, signal category, etc. These fields help to quickly locate and compare the standardization of the signal in the subsequent data verification process.

[0051] In addition, between the data layer and the service layer, by defining a clear data access interface, the consistency and security of data transmission are ensured. When the service layer needs to obtain or update data, it calls the interface provided by the data access layer through the API (Application Programming Interface) to complete the data access operation. In this way, the data layer not only ensures efficient storage and retrieval of data, but also provides reliable data support for the service layer, ensuring the efficiency and stability of the entire system operation.

[0052] The preset algorithm for service layer configuration is described below.

[0053] Optionally, the preset algorithm execution steps further specifically include: preprocessing the description information of the site table of the plant to be checked to remove useless characters; using regular expressions to identify keywords in the preprocessed description information; and segmenting the description information according to the identified keywords to obtain multiple substrings.

[0054] Specifically, during the import of the four remote information point tables, the system will first perform preliminary processing on the received data through the application server, including classifying the point table information according to attributes such as voltage level and equipment type. This process involves the segmentation of the point table description information.

[0055] In order to ensure the accuracy of segmentation, the embodiment of the present application adopts an innovative word segmentation algorithm. Assuming that there is a point table description information S, the algorithm divides S into a series of substrings s 1 ,s 2 ,…,s n , where n represents the number of substrings after segmentation. The goal of segmentation is to make each substring s i It can correspond to a specific function or device type as much as possible, so as to facilitate the subsequent comparison and analysis with the standard specification signal library. The specific steps of the segmentation algorithm are as follows: pre-process the input point table description information S to remove irrelevant characters, such as punctuation marks, spaces, etc.; use regular expressions to identify keywords in the description information; divide S into several substrings s according to the keywords 1 ,s 2 ,…,s n , ensuring that each s i All have clear meanings.

[0056] Optionally, the preset algorithm execution step further specifically includes: performing fuzzy matching by using edit distance.

[0057] Semantic fuzzy matching plays a vital role in the system. It can be used to process the description information in the four-remote information point table, by segmenting the point table description into several substrings with practical significance, and then fuzzily matching with the standard description in the four-remote standard description information library.

[0058] Specifically, if there is a point table describing information D, the algorithm divides D into d 1 ,d 2 ,…,d n , where d i represents the substring after segmentation, and n represents the number of substrings after segmentation.

[0059] After that, the algorithm calculates each d i Similarity sim with the standard description S ( d i ,S ) , which can be derived from the edit distance. The edit distance is a method to measure the similarity between two strings. It represents the minimum number of edit operations (insertion, deletion, substitution) required to transform one string into another.

[0060] Assume di The lengths of and S are m and p respectively, then the edit distance ED(d i , S) can be obtained through dynamic programming, defining a two-dimensional array dp[m+1][p+1], where dp[i][j] represents d i The edit distance between the first i characters of and the first j characters of S.

[0061] Initialize dp[i][0]=i, dp[0][j]=j, the recursive formula is:

[0062] dp[i][j]=min(dp[i-1][j]+1, dp[i][j-1]+1, dp[i-1][j-1]+cost(i,j));

[0063] Where cost(i, j) is the character matching cost. i When the i-th character of is equal to the j-th character of S, cost(i, j)=0, otherwise cost(i, j)=1.

[0064] It should be noted that the edit distance can handle common problems such as spelling errors, missing characters or extra characters, making the matching process more fault-tolerant. Therefore, in the embodiment of the present application, it is applied to the fuzzy matching process configured in the preset algorithm in the service layer.

[0065] Optionally, the preset algorithm execution step further specifically includes: inputting the substring into a pre-built enhanced support vector machine, and determining whether the substring complies with the standardization by calculating a decision function; wherein, a first label or a second label is output through the decision function; the first label represents compliance with the standardization, and the second label represents non-compliance with the standardization.

[0066] Optionally, the enhanced support vector machine is constructed by the following steps, including: obtaining a training sample set, wherein the training sample set is a set of known normative point table character strings, each point table character string includes a known label; and using the training sample set to train the enhanced support vector machine until the optimal hyperplane is obtained.

[0067] Specifically, in the automatic verification process of the plant site table, the embodiment of the present application proposes an enhanced support vector machine (ESVM) algorithm for model construction and service optimization to solve the problem of normative judgment of the site table description information. The ESVM algorithm is a supervised learning method used for classification and regression analysis. On this basis, ESVM introduces a feature selection mechanism to improve the robustness and generalization ability of the classifier.

[0068] Specifically, obtain the training sample set D = {(x1 ,y 1 ), (x 2 ,y 2 ),...,(x l ,y l )}, where x i It can represent the feature vector (i.e., point table string) of the i-th description information, y i Indicates the corresponding label, y i ∈{-1, +1}, indicating whether the description information complies with the specification. The training sample set is a set of known normative point table strings, and each point table string includes a known label.

[0069] The goal of the ESVM algorithm is to find an optimal hyperplane w in the feature space. T x+b=0 (w is the weight vector, x is the input feature vector, b represents the bias, and T represents the transpose operation), so that all positive samples are on one side of the hyperplane, all negative samples are on the other side, and the distance between the two types of samples and the hyperplane is as far as possible.

[0070] ESVM finds the optimal hyperplane by maximizing the interval M between the support vectors on both sides of the decision boundary and the hyperplane, which is defined as In order to find the optimal hyperplane, it is necessary to minimize the norm of w ||w|| while satisfying the classification conditions of all sample points. Therefore, the optimization goal of ESVM can be expressed as:

[0071]

[0072] subjectto:y i (w T x i +b)≥1-ξ i , i=1,...,l;

[0073] ξ i ≥0, i=1, ..., l;

[0074] Among them, subject to represents the constraint, C is the regularization parameter, which controls the degree of penalty for misclassification, and ξ i is a slack variable that allows some sample points to be within the decision boundary or misclassified. The above optimization problem can be solved by introducing Lagrange multipliers and dual forms, and the decision function is:

[0075]

[0076] Among them, α i is a Lagrange multiplier, and only the α corresponding to the support vector i >0.

[0077] In addition, the feature selection mechanism of ESVM sorts and filters the features and only retains the features that contribute most to the classification. This not only simplifies the model, improves the speed of training and prediction, but also enhances the generalization ability and robustness of the model.

[0078] Through the trained ESVM model, the new substring x can be new Make a normative judgment, that is, calculate f(x new ) and determine x based on its sign new Whether it meets the specifications (i.e. outputting the first label or the second label). This method not only improves the accuracy of the verification, but also provides a scientific basis for the subsequent generation of verification reports.

[0079] Please continue reading Figure 2 In addition, the service layer also integrates functional modules such as template management, rule configuration service, workflow engine, semantic fuzzy matching algorithm, enhanced support vector machine algorithm, model construction, file service, account service, report service, log service, and permission management. These modules work together to realize the automatic verification and evaluation analysis of the plant site table.

[0080] The template management and rule configuration service defines the business logic framework, which is used to maintain the equipment spacing rules under different substation types and voltage levels, ensuring that the point table information can be verified according to the correct rules. The rule configuration service is responsible for maintaining the rule model library of the hierarchical relationship of "substation type → voltage level → spacing type → equipment", ensuring that the point table can be accurately verified according to these rules when verifying the plant site table.

[0081] The workflow engine is another important component in the service layer. It is responsible for coordinating the collaboration between service components to ensure that each task can be executed according to the predetermined process. For example, when checking the four remote information point tables, the workflow engine will control the sequential execution of a series of tasks such as point table import, rule matching, and verification analysis, and record the results of each stage for subsequent analysis and report generation.

[0082] The service layer transmits the verification results to the presentation layer in a friendly way through the report service. The log service and permission management ensure the traceability and security of system operations.

[0083] Optionally, the verification report includes at least data field verification results and value verification results.

[0084] Specifically, the data field verification results may include checking necessary fields in the plant site table (such as plant site name, location, equipment information, etc.), verifying the logical relationship between the fields in the plant site table, and ensuring consistency between the data.

[0085] The numerical verification results may specifically include whether the data entered in the plant site table is within a reasonable range, such as whether the equipment capacity is within the allowable range, whether the voltage is within reasonable limits, etc.

[0086] Please continue reading Figure 2 , the following describes the presentation layer:

[0087] The display layer is the front-end part of the system, responsible for presenting the information processed by the service layer to the user in an intuitive and friendly way. The display layer mainly includes basic data maintenance management, interval rule model library, four-remote standard description information library, plant site table import management, statistical analysis, four-remote point table auxiliary verification, verification report query, system management and other functional modules. These modules cooperate with each other to provide users with a complete set of plant site table automatic verification and evaluation analysis experience.

[0088] The basic data maintenance and management function module provides functions such as substation basic data maintenance, primary and secondary equipment ledger maintenance, and equipment ledger maintenance. Users can use this module to maintain basic information of the substation, such as geographical location, voltage level, etc., and can manage the relevant information of primary equipment (such as transformers, circuit breakers, etc.) and secondary equipment (such as relay protection devices, communication equipment, etc.) in the substation. This function module supports users to easily add, edit and delete information of related equipment to ensure the real-time and accuracy of all data. In addition, this module also supports the visualization of data to help users understand the basic situation of the substation more intuitively.

[0089] The interval rule model library establishes the hierarchical relationship of "substation type → voltage level → interval type → equipment", maps it step by step, and forms an interval rule model library with specific relationships. This rule library not only clarifies the specifications of equipment intervals under different substation types and voltage levels, but also provides the necessary reference basis for subsequent plant site table verification. Users can browse these rules and view and edit them through a graphical interface to ensure that the entry of point table information is consistent with the actual equipment situation.

[0090] The four-remote standard description information base forms a four-remote information standard rule base based on the signal acquisition specifications. This rule base lists the standard format and requirements of the four-remote information (telemetry, telesignaling, remote control, and remote adjustment) in detail to ensure the consistency and readability of the information. Users can consult these standards and quickly locate the required information through the search and filtering functions provided by the display layer to ensure that the signal information in the point table is consistent with the specifications.

[0091] The plant site table import management module provides the function of importing old or new site tables, and supports importing files in Excel format. After importing, the system can perform tree-shaped cascade display management of the point table according to the voltage level and the area to which the plant station belongs, making it convenient for users to browse and manage the point table information. This function allows users to easily import existing point table information into the system and classify and display it. The display layer also provides a preview function, allowing users to preview the point table data before formally importing to ensure the correctness of the data.

[0092] The statistical analysis module is responsible for displaying the verification results after processing by the service layer. Users can use this module to view the results of the normative verification of the existing four-remote information point table provided by the old station or substation equipment manufacturer. After the system imports the existing four-remote information point table, it uses algorithms such as word segmentation and fuzzy matching to segment the point table description information, and then verifies the normativeness and completeness of the four-remote information standard rule base, and outputs the verification results. The display layer intuitively displays the verification results in the form of charts, lists, etc., to help users quickly understand the verification status.

[0093] The verification report query module supports users to view the verification analysis results of the plant station table according to the entire site or the selected specific interval, and export the verification report. It allows users to easily view the verification results and export the results for further analysis or archiving.

[0094] The display layer presents the calibration results to users through an intuitive interface, ensuring that users can easily and quickly obtain and manage the calibration information of the plant site table, thereby improving the efficiency and accuracy of automatic calibration and evaluation analysis of the plant site table.

[0095] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0096] An embodiment of the present application further 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 in the above-mentioned various algorithm embodiments can be implemented.

[0097] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0099] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0100] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An automatic calibration system for a plant station table, characterized in that: include: The hardware layer includes: an application server and a database server; the application server is used to execute a preset algorithm, and the database server is used to access and store data; The data layer includes: a data access layer and a data storage layer; the data access layer is used to provide a data interface; the data storage layer is used as a database to store data; The service layer is configured with the preset algorithm, and the preset algorithm execution steps include: obtaining a plant site table to be calibrated, obtaining description information of the plant site table to be calibrated, performing word segmentation processing on the description information to obtain multiple substrings; performing normative determination on each substring; in response to the substring meeting the norm, performing fuzzy matching on the substring with a standard description information library; and generating a calibration report based on the fuzzy matching result; The presentation layer is used to present the verification report.

2. The automatic verification system of the plant station table according to claim 1 is characterized in that: The preset algorithm execution step further specifically includes: Inputting the substring into a pre-built enhanced support vector machine, and determining whether the substring complies with the norm by calculating a decision function; Among them, the first label or the second label is output through the decision function; the first label represents compliance with the norms, and the second label represents non-compliance with the norms.

3. The automatic verification system of the plant station table according to claim 2 is characterized in that: The enhanced support vector machine is constructed by the following steps, including: Acquire a training sample set, wherein the training sample set is a known and standardized point table character string set, and each point table character string includes a known label; The enhanced support vector machine is trained using the training sample set until an optimal hyperplane is obtained.

4. The automatic verification system of the plant station table according to claim 1 is characterized in that: The preset algorithm execution step also specifically includes: preprocessing the description information of the plant site table to be calibrated to remove useless characters; using regular expressions to identify keywords in the preprocessed description information; and segmenting the description information according to the identified keywords to obtain multiple substrings.

5. The automatic verification system of the plant station table according to claim 1 is characterized in that: The data access layer configures a read-write separation mode; The read-write separation mode is specifically configured to: in response to a read data request, route the read data request to a preconfigured database instance for read operations; and in response to a write data request, send the write data request to a primary database.

6. The automatic verification system of the plant station table according to claim 1 is characterized in that: The data storage layer, when storing the original data of the plant site table to be calibrated, divides each data level according to the data type, and organizes the data into a tree structure according to the data level.

7. The automatic verification system of the plant station table according to claim 1 is characterized in that: The data access layer also configures Redis service; The Redis service is specifically configured to: when a data request is received, search for data corresponding to the data request from the cache; when the data corresponding to the data request does not exist in the cache, send a request to the database and store the acquired data in the cache.

8. The automatic verification system of the plant station table according to claim 1 is characterized in that: The plant site table to be calibrated is a table of four remote information points.

9. The automatic verification system of the plant station table according to claim 1 is characterized in that: The preset algorithm execution step further specifically includes: performing fuzzy matching by using edit distance.

10. The automatic verification system of the plant station table according to claim 1, characterized in that: The verification report at least includes a data field verification result and a value verification result.

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