Pressing plate monitoring application model intelligent generation system and method

Abstract

The invention discloses an intelligent generation system and method for a pressing plate monitoring application model, and relates to the technical field of transformer substation relay protection, and the system comprises a data collection and analysis module which automatically reads the site investigation data of a transformer substation pressing plate, and completes the data standardization analysis and format conversion according to a modeling specification; the intelligent modeling engine module is used for generating a standardized ICD model file based on the data subjected to format conversion; the model verification optimization module automatically verifies the conformity of the ICD model and the specification, outputs a verification report and automatically corrects the deviation; the dynamic adaptation updating module monitors the layout of the pressing plate in real time and automatically triggers incremental updating of the model; the visual management module is used for providing model visual display, model version management and abnormity alarm; the data acquisition and analysis module, the intelligent modeling engine module, the model verification optimization module, the dynamic adaptation updating module and the visual management module are in communication connection; according to the invention, the modeling efficiency can be improved, and the accuracy of online monitoring of the pressing plate is improved.

Description

Technical Field

[0001] This invention belongs to the field of substation relay protection technology, specifically relating to an intelligent generation system and method for pressure plate monitoring application models. Background Technology

[0002] In substation relay protection, the pressure plate is a key control component. By monitoring the status of the pressure plate, potential safety hazards in the power system can be detected in a timely manner, and corresponding preventive measures can be taken in advance to ensure the safe operation of the power system. In order to facilitate pressure plate management and solve the inefficiency and safety hazards of manual inspection, pressure plate monitoring application models are usually used for monitoring.

[0003] Currently, the construction of the relay protection relay monitoring application model relies on the "Relay Protection Relay Protection Relay Protection Online Monitoring Modeling Specification". It involves manual modeling based on the on-site survey data of the substation relay protection relays. The on-site survey data includes cabinet distribution, relay protection relay parameters and location information, which results in low modeling efficiency and susceptibility to model deviation due to human error. Furthermore, the large number and wide distribution of relay protection relays in substations, along with significant differences in the types, locations, and devices to which different relay protection relays belong, make it difficult for manual modeling to meet the requirements of model consistency and standardization. It is also not convenient to quickly adapt to the dynamic speed adjustment of the substation relay protection relay layout, thus affecting the accuracy of online monitoring of relay protection relays. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent generation system and method for pressure plate monitoring application models, which can improve modeling efficiency and enhance the accuracy of online pressure plate monitoring, thereby solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The intelligent generation system for pressure plate monitoring application models includes: a data acquisition and parsing module that automatically reads on-site survey data of substation pressure plates and completes data standardization parsing and format conversion according to modeling specifications;

[0007] The intelligent modeling engine module generates standardized ICD model files based on the format-converted data;

[0008] The model validation and optimization module automatically verifies the conformity of the ICD model with the standard, outputs a validation report, and automatically corrects deviations.

[0009] The dynamic adaptation and update module monitors the pressure plate layout in real time and automatically triggers incremental model updates.

[0010] The visualization management module provides model visualization, model version management, and anomaly alerts;

[0011] The data acquisition and parsing module, intelligent modeling engine module, model verification and optimization module, dynamic adaptation and update module, and visualization management module are interconnected.

[0012] Preferably, the data acquisition and parsing module includes an Excel file parser, a core field identifier, a data format converter, a data cleaning filter, and a standardized data output device connected in sequence. The Excel file parser reads the survey data, the core field identifier identifies the core fields of the cabinet physical ID, pressure plate name, category, row, column, and IEDNAME to which it belongs, and the data format converter completes the format mapping according to the modeling specifications. The data cleaning filter uses a data redundancy identification algorithm to automatically remove or correct the converted data and generate standardized data.

[0013] Preferably, the intelligent modeling engine module includes a specification template library, a model generator, a dataset mapper, and an ICD file generator connected in sequence, wherein the specification template library is used to load preset IEC61850 modeling templates;

[0014] The model generator is used to automatically build the basic framework; the dataset mapper uses an attribute mapping matching algorithm based on the basic framework to calculate the optimal mapping relationship, generate a mapping relationship table, and associates the corresponding rules in the specification template library to ensure that the mapping is traceable. Then, it completes the data assignment in batches according to the mapping relationship table.

[0015] The ICD file generator is used to assemble model components according to specifications and generate standard-compliant ICD files.

[0016] Preferably, the model generator includes an IED model generator, an LD model generator, and a GGIO model generator. The model generator loads the basic templates output by the specification template library, and then automatically constructs the device IED object, logical device framework, and logical nodes, generating the basic model framework and transmitting it to the dataset mapper.

[0017] The preferred process for assembling model components is as follows:

[0018] A1. Load the assigned data and IEC61850 standard component splicing template, initialize the ICD file frame, fill the file header information according to the standard, associate the defined device access points, and obtain standardized data.

[0019] A2. Construct the IED object, logical device, logical node, and dataset in sequence, ensuring that the dataset does not span logical devices;

[0020] A3. Establish hierarchical associations through component hierarchical association algorithms, fill in attributes according to specifications, and ensure that the specification name is uniquely determined by the attribute.

[0021] A4. Generate the original model component set by unifying the label and attribute codes according to the standard specifications.

[0022] The preferred, standard-compliant ICD file generation process is as follows:

[0023] B1. Receive the complete model data after splicing, verify the compliance of file structure, component attributes, and syntax encoding according to IEC61850 standard and modeling specifications, and automatically correct and repairable anomalies.

[0024] The compliance judgment criteria are as follows: when the actual attribute value is within the range of the attribute standard value, or within the set of attribute standard enumerated values, it is compliant; otherwise, it is non-compliant.

[0025] B2. If the verification is successful, an ICD model file conforming to the standard will be generated. If the verification fails, an error report will be output, and the verification will be repeated after correction.

[0026] Preferably, the model verification and optimization module includes a multi-rule verification engine, a specification compliance verifier, a data integrity verifier, a logical consistency verifier, an automatic corrector, and a verification report generator. The specification compliance verifier, data integrity verifier, and logical consistency verifier are connected in parallel and their ends are respectively connected to the multi-rule verification engine and the automatic corrector. The verification report generator is connected to the automatic corrector.

[0027] Preferably, the dynamic adaptation and update module includes a data change monitor, an incremental update calculator, a model adjuster, and a version manager connected in sequence. The data change monitor compares and examines the differences between data versions in real time, identifies operation items, and outputs a structured list of operation items, including addition items, deletion items, and modification items.

[0028] The incremental update calculator uses the change impact range algorithm to calculate the impact range of the model and outputs a list of impact ranges.

[0029] The model adjuster updates the model within the model's influence range;

[0030] Version managers are used to record update logs.

[0031] Preferably, the visualization management module includes a topology graph generator, a model details queryer, an anomaly alarm, a version management controller, and a manual fine-tuning interface. The version management controller is connected to the topology graph generator, the model details queryer, and the anomaly alarm, respectively, and the manual fine-tuning interface is connected to the version management controller.

[0032] Based on the above-described intelligent generation system for pressure plate monitoring application models, this invention also provides an intelligent generation method for pressure plate monitoring application models, including the following steps:

[0033] S1. Import the on-site survey data of the substation pressure plate, automatically identify the core fields and complete the data format conversion and standardization processing according to the online monitoring modeling specification of the relay protection pressure plate, remove redundant and erroneous data, and output a standardized data list.

[0034] S2. Based on the preset online monitoring modeling specification template library for relay protection pressure plates, automatically generate the basic framework for device modeling, logic device modeling, and logic node modeling, and preset a dataset that conforms to the specification.

[0035] S3. Automatically establish the mapping relationship between standardized survey data and model attributes, and batch complete the assignment of device version information, screen counter ledger, pressure plate position and status information in the corresponding model attributes;

[0036] S4. Start the multi-rule verification engine to verify the model's compliance with standards, data integrity, and logical consistency, output verification reports, automatically correct repairable deviations, and provide manual intervention prompts for anomalies that cannot be automatically corrected.

[0037] S5. After verification, an ICD model file conforming to the standard is automatically generated, and a visual model map is generated simultaneously.

[0038] S6. Monitor changes in exploration data versions in real time, automatically identify changes, trigger incremental model updates, and periodically and automatically verify the model's compliance with the latest specifications and provide adaptation suggestions.

[0039] The intelligent generation system and method for pressure plate monitoring application model proposed in this invention have the following advantages compared with the prior art:

[0040] 1. This invention, through the coordinated operation of a data acquisition and parsing module, an intelligent modeling engine module, a model verification and optimization module, a dynamic adaptation and update module, and a visualization management module, can automatically complete standardized mapping, avoiding problems such as category confusion and attribute misfilling when manually comparing with specifications for modeling. It ensures that the model is completely consistent with the online monitoring modeling specifications for relay protection pressure plates, reducing monitoring deviations from the source. Furthermore, it identifies issues from multiple dimensions, including specification compliance, data integrity, and logical consistency, and automatically corrects problems that can be automatically corrected, thereby improving modeling efficiency.

[0041] 2. This invention periodically and automatically verifies the conformity of the model with the latest modeling specifications, ensuring that the model meets the standard requirements in the long term, avoiding the failure of monitoring logic due to specification iteration, and ensuring the long-term accuracy of online monitoring of the pressure plate.

[0042] 3. This invention automatically identifies core fields in survey data through a standardized import and parsing module, eliminating the need for manual field-by-field entry. It also automatically completes format conversion and removes redundant and erroneous data, improving data processing efficiency. Based on a preset template library of online monitoring modeling specifications for relay protection pressure plates, and the basic framework of intelligent modeling engine, logic devices, and logic nodes, it completes attribute mapping and assignment in batches, shortening the modeling time for a single cabinet and significantly reducing the cost of repetitive operations. Attached Figure Description

[0043] Figure 1 A system block diagram according to an embodiment of the present invention is shown;

[0044] Figure 2 A block diagram of a data acquisition and parsing module according to an embodiment of the present invention is shown;

[0045] Figure 3 A block diagram of an intelligent modeling engine module according to an embodiment of the present invention is shown;

[0046] Figure 4 A block diagram of a dynamic adaptation and update module according to an embodiment of the present invention is shown;

[0047] Figure 5 A block diagram of the model verification and optimization module according to an embodiment of the present invention is shown;

[0048] Figure 6 A flowchart of a method according to an embodiment of the present invention is shown. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] This invention provides, for example Figure 1-5 The intelligent generation system for the pressure plate monitoring application model shown includes a data acquisition and parsing module, an intelligent modeling engine module, a model verification and optimization module, a dynamic adaptation and update module, and a visualization management module connected in sequence. The data acquisition and parsing module automatically reads the on-site survey data of the substation pressure plate and completes the data standardization parsing and format conversion according to the modeling specifications.

[0051] The data acquisition and parsing module includes an Excel file parser, a core field recognizer, a data format converter, a data cleaning filter, and a standardized data output device connected in sequence. Specifically, it reads survey data through the Excel file parser:

[0052] The system receives on-site survey data of substation pressure plates in Excel format, automatically verifies the integrity of the file, outputs an error message and terminates the process if the verification fails, and automatically identifies the core sheet pages of substation, cabinet and pressure plate in the file, reads the data of each sheet page in the preset order, generates the raw data matrix, stores the read raw data in the temporary buffer area, and records the data reading log.

[0053] The core field recognizer identifies the core fields of the cabinet's physical ID, pressure plate name, category, row, column, and IEDNAME. Specifically, it first loads a preset core field library containing key fields required by the modeling specifications. Using a string similarity matching algorithm, it traverses the column names of the original data matrix and compares them with the core field library. It automatically identifies fields with a matching degree greater than 80%, marks the matching fields, and prompts for manual confirmation. It establishes the correspondence between the identified fields and the modeling specification attributes, and generates a field mapping table to provide a basis for format conversion.

[0054] The data format converter completes the format mapping according to the modeling specifications. Specifically, it loads the format conversion rule library corresponding to the "Relay Protection Plate Online Monitoring Modeling Specification", and then converts the format of the identified core fields according to the rule library, such as converting "6 / 7 / 23" to "2023 / 06 / 07". For fields with null values ​​or disordered formats, it fills in the default value or corrects the format according to the specifications to ensure that the data format meets the modeling requirements.

[0055] The data cleaning filter uses a data redundancy identification algorithm to automatically remove or correct the transformed data, generating standardized data. The formula for the data redundancy identification algorithm is:

[0056] ,

[0057] in, This represents the data deviation value. For a single data field value, Here, n represents the standard value of the field and the number of data rows.

[0058] Generate a structured data list that meets the modeling requirements using a standardized data output device;

[0059] When generating the structured data list, the standardized data after cleaning is reorganized according to the model hierarchy required by the modeling specifications to generate a structured data list. The structured data list includes device modeling data, logical device data, and logical node data. The format conformity of the reorganized data is verified again to ensure that there are no format deviations. The structured data list is output to the intelligent modeling engine module in JSON format, and a data parsing report is generated simultaneously, which can be downloaded and viewed manually, completing the data acquisition and parsing process.

[0060] The intelligent modeling engine module generates standardized ICD model files based on the format-converted data.

[0061] The intelligent modeling engine module includes a specification template library, a model generator, a dataset mapper, and an ICD file generator connected in sequence. The specification template library is used to load preset IEC61850 modeling templates. The IEC61850 modeling template includes standard configurations of devices, logic devices, logic nodes, and datasets.

[0062] The model generator is used to automatically build the basic framework. The model generator includes the IED model generator, LD model generator and GGIO model generator. The model generator loads the basic template output by the specification template library, and then automatically builds the device IED object, logical device framework and logical nodes, and generates the basic model framework and transmits it to the dataset mapper.

[0063] The dataset mapper, based on the basic framework, uses an attribute mapping matching algorithm to calculate the optimal mapping relationship and generate a mapping relationship table. This table records information such as mapping fields, target attributes, similarity, model level, and associated datasets. It also associates the corresponding rules from the specification template library to ensure traceability. Data is then assigned in batches according to the mapping relationship table. If a one-to-many or many-to-one conflict occurs between fields and attributes, the specification priority is prioritized over similarity priority, and the optimal one is retained. The attribute mapping matching algorithm formula is:

[0064] ,

[0065] In the formula, Sim() is a cosine similarity function used to calculate the matching degree between data fields and model attributes, representing the optimal mapping relationship between data fields and model attributes. For a single field in standardized data, The target attribute in the model;

[0066] The ICD file generator is used to assemble model components according to specifications and generate standard-compliant ICD files.

[0067] The model components include devices, logic equipment, logic nodes, and datasets. The assembly of the model components follows the IEC 61850 standard. The assembly process for the model components is as follows:

[0068] A1. Load the assigned data and IEC61850 standard component splicing template, initialize the ICD file frame, fill the file header information according to the standard, associate the defined device access points, and obtain standardized data.

[0069] A2. Construct the IED object, logical device, logical node, and dataset in sequence, ensuring that the dataset does not span logical devices;

[0070] A3. Establish hierarchical associations through component hierarchical association algorithms, fill in attributes according to specifications, and ensure that the specification name is uniquely determined by the attribute.

[0071] The formula for the component hierarchy association algorithm is:

[0072] ,

[0073] In the formula, For a complete set of component hierarchical relationships, For the i-th level parent component, The j-th child component corresponding to the i-th level parent component, where N is the total number of component levels. This represents the parent-child component relationship.

[0074] A4. Generate the original model component set by unifying the label and attribute codes according to the standard specifications:

[0075] The standard ICD file generation process is as follows:

[0076] B1. Receive the complete model data after splicing, verify the compliance of file structure, component attributes, and syntax encoding according to IEC61850 standard and modeling specifications, and automatically correct and repairable anomalies.

[0077] The compliance judgment criteria are as follows: The attribute is compliant when the actual attribute value is within the range of the attribute standard value or within the attribute standard enumeration value set; otherwise, it is non-compliant. The attribute standard value range is determined based on the relay protection pressure plate online monitoring modeling specification and the equipment parameter constraint table and logical node attribute value range in the IEC61850 series standards. The standard enumeration value set is determined based on the relay protection pressure plate online monitoring modeling specification and its appendices. For example, the hard pressure plate modeling clearly defines the pressure plate category enumeration value and the logical node attribute enumeration definition.

[0078] B2. If the verification is successful, an ICD model file conforming to the standard will be generated. If the verification fails, an error report will be output, and the verification will be repeated after correction.

[0079] The intelligent modeling engine module can improve modeling efficiency, shorten the modeling time for a single-screen cabinet, and reduce standard deviations caused by manual modeling.

[0080] The model verification and optimization module automatically verifies the conformity of the ICD model with the standard, outputs a verification report, and automatically corrects deviations.

[0081] The model validation and optimization module includes a multi-rule validation engine, a specification compliance validator, a data integrity validator, a logical consistency validator, an automatic corrector, and a validation report generator. The specification compliance validator, data integrity validator, and logical consistency validator are connected in parallel, with their ends connected to the multi-rule validation engine and the automatic corrector, respectively. The validation report generator is connected to the automatic corrector. Through multi-dimensional validation of specification compliance, data integrity, and logical consistency, and automatic correction of the model, the module improves the comprehensiveness and accuracy of model validation and reduces misjudgments in pressure plate monitoring caused by model deviation.

[0082] The multi-rule verification engine loads verification rules, performs structured parsing of the verification rules, calculates task allocation weights using a load balancing scheduling algorithm, and distributes tasks in parallel to the data integrity verifier, logical consistency verifier, and specification compliance verifier based on the calculated task allocation weights. It also receives feedback from the data integrity verifier, logical consistency verifier, and specification compliance verifier in real time, classifies them according to severity, and builds an anomaly list.

[0083] The load balancing scheduling algorithm formula is:

[0084] ,

[0085] in, Assign weights to the tasks of the i-th type of validator. The number of rules assigned to the i-th type of validator. The processing capability of the i-th type of checker The correlation coefficient, Let be the matching degree between rule j and the i-th type of validator, and m be the total number of validation rules. Let be the measurement weight coefficient for the j-th verification rule. The adapter type coefficient for the j-th validation rule;

[0086] The specification compliance checker uses a uniqueness check algorithm for compliance verification. The formula for the uniqueness check algorithm is as follows:

[0087] ,

[0088] in, To ensure the compliance rate of GGIO's name uniqueness, This represents the total number of GGIO nodes. For the set of d attribute values ​​of GGIO nodes, The number of duplicate elements in the set of attribute values ​​for d, when When =100%, it is considered compliant. When the percentage is less than 100%, extract the GGIO nodes corresponding to duplicate d attribute values ​​and mark them as an anomaly of duplicate GGIO names.

[0089] The data integrity verifier uses a data integrity compliance rate algorithm to verify data integrity. The formula for the data integrity compliance rate algorithm is as follows:

[0090] ,

[0091] in, To ensure data integrity compliance rate, This represents the total number of required attributes. The number of exceptions in the required attributes of class p. The number of required attribute categories, when At 5%, it is judged as basically compliant. At 5%, according to Arrange abnormal attributes in descending order, and prioritize marking core attributes as abnormal.

[0092] The logical consistency checker uses a consistency conflict determination algorithm to perform logical consistency checks. The formula for the consistency conflict determination algorithm is as follows:

[0093] ,

[0094] in, The result represents the logical consistency judgment, where Q is the total number of logical association rules. Let q be the standard value of the logical rule. Let q be the actual value of the attribute corresponding to the q-th rule. For the matching function, that is, when When f=1, When f=0, When =1, all logical rules are compliant. When f=0, reverse the search to find the rule q corresponding to f=0 and mark it as a logical conflict item;

[0095] The automatic corrector categorizes exceptions into those that can be automatically corrected and those that cannot, based on the exception list forwarded by the multi-rule verification engine. Exceptions that can be automatically corrected include null value filling, value inversion, and format errors, while exceptions that cannot be automatically corrected include cross-ID issues in the dataset, missing core attributes, and complex logical conflicts. Automatic correction is performed on exceptions that can be automatically corrected.

[0096] An automatic correction formula is used to address null value imperfections. The formula is as follows:

[0097] ,

[0098] In the formula, The corrected value. This is the default value. This is the average of historical data. Let x be the t-th data value, and x be the number of historical data entries;

[0099] The anomaly of inverted values ​​is automatically corrected using a replacement formula, as follows:

[0100] ,

[0101] In the formula, The corrected value. For logical NOT operation, This is an abnormal value;

[0102] The conversion formula is used to automatically correct formatting errors. The conversion formula is as follows:

[0103] ,

[0104] In the formula, The corrected value. () is a string to number conversion function. [] is the rounding function. This is an abnormal value.

[0105] The verification report generator receives the original anomaly lists from the specification compliance verifier, data integrity verifier, and logical consistency verifier, as well as the correction records and uncorrected anomaly lists from the automatic corrector. It categorizes and organizes these anomalies by verification dimension and anomaly severity, generating a structured and visual report. This report includes a verification overview, anomaly details, and data attachments. The verification overview includes the total number of verification rules, the total number of anomalies, the number of automatically corrected anomalies, the number of uncorrected anomalies, and various compliance rates. The anomaly details include the anomaly location, the cause of the anomaly, the processing result, and processing suggestions. Data attachments include screenshots of anomaly attributes and a comparison table before and after correction.

[0106] The dynamic adaptation and update module monitors the pressure plate layout in real time and automatically triggers incremental model updates.

[0107] The dynamic adaptation and update module includes a data change monitor, an incremental update calculator, a model adjuster, and a version manager connected in sequence. The data change monitor compares and examines the differences between data versions in real time, identifies operation items, and outputs a structured list of operation items, including addition items, deletion items, and modification items.

[0108] The data change monitor collects different versions of substation pressure plate field survey data in real time, establishes a version ledger, compares the latest collected version with the historical baseline version field by field and item by item, identifies data differences, and categorizes operation items according to the type of difference. Pressure plate data not present in the baseline version and newly added pressure plate data in the target version are summarized as new items, pressure plate data present in the baseline version and removed pressure plate data in the target version are summarized as deleted items, and pressure plate data that exists in both the baseline and target versions but whose attribute values ​​have changed are summarized as modified items.

[0109] The incremental update calculator uses the change impact range algorithm to calculate the model's impact range and outputs a list of impact ranges; the formula for the change impact range algorithm is:

[0110] ,

[0111] In the formula, The model needs to be adjusted due to changes in data. For a single changed data record, F() is the association function between the data item and the model component, and z is the number of changed data items;

[0112] The model adjuster updates the model within its influence range; the model adjuster receives a list of influence ranges and only adjusts the models within those ranges. Incremental updates are performed on the model components within the scope, and the updated model fragments are output. For example, if a new cabinet is added, LPMONT

[10] is created, and if a pressure plate is deleted, the corresponding GGIO node is removed.

[0113] The version manager is used to record update logs; specifically, it receives updated model fragments, synchronizes the model, and records version update logs.

[0114] By updating the model, the model response time can be reduced, the full model reconstruction can be avoided, maintenance costs can be reduced, and the dynamic adjustment requirements of substation pressure plate layout can be adapted.

[0115] The visualization management module provides model visualization, model version management, and anomaly alerts.

[0116] The visualization management module includes a topology graph generator, a model details queryer, an anomaly alarm, a version management controller, and a manual fine-tuning interface. The version management controller is connected to the topology graph generator, the model details queryer, and the anomaly alarm, respectively. The manual fine-tuning interface is connected to the version management controller. The topology graph generator uses a hierarchical structure generation algorithm to extract the hierarchical relationship of the model and construct a set of topology graph nodes and connecting edges, and outputs a visualization topology graph to the display interface.

[0117] The formula for the hierarchical structure generation algorithm is:

[0118] ,

[0119] In the formula, Visualize the topology of the pressure plate model. For the set of nodes in the topological graph, This represents the connection relationships between nodes.

[0120] The model details query tool is used to output detailed information to the display interface; the output detailed information includes the pressure plate category, its IEDID, and its row or column position.

[0121] The anomaly alarm device monitors the model status in real time. When an anomaly is detected, it triggers an audible and visual alarm, marks the anomaly point, and outputs alarm information to the version management controller.

[0122] The version management controller is used to receive the visual topology map, detailed information and alarm information pushed to the display interface, and associate it with the model version to achieve full-process data synchronization. Specifically, it receives the topology data from the topology map generator, the query results from the model details query generator, and the alarm information from the anomaly alarm generator, integrates them into a unified data model through a data synchronization algorithm, and pushes it to the visual display interface in real time to ensure that the topology map, details and alarm data are consistent. At the same time, it assigns a unique version number to each model update and links historical version data in parallel. When the manual fine-tuning interface initiates parameter modification, it synchronously triggers the model adjuster to update, ensuring that the operation, model and visualization full-process data are synchronized.

[0123] The data synchronization algorithm formula is:

[0124] ,

[0125] in, Let y be the data consistency coefficient, and y be the total number of synchronized data items. For source data, For target data, () is the consistency judgment function;

[0126] The manual fine-tuning interface is used to directly modify model parameters, and the modification records are automatically synchronized to the version management controller.

[0127] By setting up the visual management module, maintenance personnel can quickly locate the pressure plate position and associated model information, shorten the anomaly response time, and ensure monitoring continuity.

[0128] Based on the above-described intelligent generation system for pressure plate monitoring application models, this invention also provides an intelligent generation method for pressure plate monitoring application models, such as... Figure 6 As shown, it includes the following steps:

[0129] S1. Import the on-site survey data of the substation pressure plate, automatically identify the core fields and complete the data format conversion and standardization processing according to the online monitoring modeling specification of the relay protection pressure plate, remove redundant and erroneous data, and output a standardized data list.

[0130] S2. Based on the preset online monitoring modeling specification template library for relay protection pressure plates, automatically generate the basic framework for device modeling, logic device modeling, and logic node modeling, and preset a dataset that conforms to the specification.

[0131] S3. Automatically establish the mapping relationship between standardized survey data and model attributes, and batch complete the assignment of device version information, screen counter ledger, pressure plate position and status information in the corresponding model attributes;

[0132] S4. Start the multi-rule verification engine to verify the model's compliance with standards, data integrity, and logical consistency, output verification reports, automatically correct repairable deviations, and provide manual intervention prompts for anomalies that cannot be automatically corrected.

[0133] S5. After verification, an ICD model file conforming to the standard is automatically generated, and a visual model map is generated simultaneously.

[0134] S6. Monitor changes in exploration data versions in real time, automatically identify changes, trigger incremental model updates, and periodically and automatically verify the model's compliance with the latest specifications and provide adaptation suggestions.

[0135] The standardized import and parsing module for survey data automatically identifies core fields in the survey data, eliminating the need for manual field-by-field entry. It also automatically performs format conversion and removes redundant and erroneous data, improving data processing efficiency. Based on a preset template library of online monitoring modeling specifications for relay protection pressure plates, and the basic framework of intelligent modeling engine, logic devices, and logic nodes, it completes attribute mapping and assignment in batches, shortening the modeling time for a single cabinet and significantly reducing the cost of repetitive operations.

[0136] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A pressure plate monitoring application model intelligent generation system, characterized in that: include: The data acquisition and parsing module automatically reads on-site survey data of substation pressure plates and completes standardized data parsing and format conversion according to modeling specifications. The intelligent modeling engine module generates standardized ICD model files based on the format-converted data; The model validation and optimization module automatically verifies the conformity of the ICD model with the standard, outputs a validation report, and automatically corrects deviations. The dynamic adaptation and update module monitors the pressure plate layout in real time and automatically triggers incremental model updates. The visualization management module provides model visualization, model version management, and anomaly alerts; The data acquisition and parsing module, intelligent modeling engine module, model verification and optimization module, dynamic adaptation and update module, and visualization management module are interconnected.

2. The intelligent generation system for pressure plate monitoring application model according to claim 1, characterized in that: The data acquisition and parsing module includes an Excel file parser, a core field identifier, a data format converter, a data cleaning filter, and a standardized data output device connected in sequence. The Excel file parser reads the survey data, the core field identifier identifies the core fields of the cabinet physical ID, pressure plate name, category, row, column, and IEDNAME to which it belongs, and the data format converter completes the format mapping according to the modeling specifications. The data cleaning filter uses a data redundancy identification algorithm to automatically remove or correct the converted data and generate standardized data.

3. The intelligent generation system for pressure plate monitoring application model according to claim 2, characterized in that: The intelligent modeling engine module includes a specification template library, a model generator, a dataset mapper, and an ICD file generator connected in sequence. The specification template library is used to load preset IEC61850 modeling templates. The model generator is used to automatically build the basic framework; the dataset mapper uses an attribute mapping matching algorithm based on the basic framework to calculate the optimal mapping relationship, generate a mapping relationship table, and associates the corresponding rules in the specification template library to ensure that the mapping is traceable. Then, it completes the data assignment in batches according to the mapping relationship table. The ICD file generator is used to assemble model components according to specifications and generate standard-compliant ICD files.

4. The intelligent generation system for pressure plate monitoring application model according to claim 3, characterized in that: The model generator includes an IED model generator, an LD model generator, and a GGIO model generator. The model generator loads the basic templates output by the specification template library, and then automatically constructs the device IED object, logical device framework, and logical nodes, generating the basic model framework and transmitting it to the dataset mapper.

5. The intelligent generation system for pressure plate monitoring application model according to claim 4, characterized in that: The process of assembling model components is as follows: A1. Load the assigned data and IEC61850 standard component splicing template, initialize the ICD file frame, fill the file header information according to the standard, associate the defined device access points, and obtain standardized data. A2. Construct the IED object, logical device, logical node, and dataset in sequence, ensuring that the dataset does not span logical devices; A3. Establish hierarchical associations through component hierarchical association algorithms, fill in attributes according to specifications, and ensure that the specification name is uniquely determined by the attribute. A4. Generate the original model component set by unifying the labels and attribute codes according to the standard specifications.

6. The intelligent generation system for pressure plate monitoring application model according to claim 5, characterized in that: The standard ICD file generation process is as follows: B1. Receive the complete model data after splicing, verify the compliance of file structure, component attributes, and syntax encoding according to IEC61850 standard and modeling specifications, and automatically correct and repairable anomalies. The compliance judgment criteria are as follows: when the actual attribute value is within the range of the attribute standard value, or within the set of attribute standard enumerated values, it is compliant; otherwise, it is non-compliant. B2. If the verification is successful, an ICD model file conforming to the standard will be generated. If the verification fails, an error report will be output, and the verification will be repeated after correction.

7. The intelligent generation system for pressure plate monitoring application model according to claim 6, characterized in that: The model validation and optimization module includes a multi-rule validation engine, a specification compliance validator, a data integrity validator, a logical consistency validator, an automatic corrector, and a validation report generator. The specification compliance validator, data integrity validator, and logical consistency validator are connected in parallel and their ends are respectively connected to the multi-rule validation engine and the automatic corrector. The validation report generator is connected to the automatic corrector.

8. The intelligent generation system for pressure plate monitoring application model according to claim 7, characterized in that: The dynamic adaptation and update module includes a data change monitor, an incremental update calculator, a model adjuster, and a version manager connected in sequence. The data change monitor compares and examines the differences between data versions in real time, identifies operation items, and outputs a structured list of operation items, including addition items, deletion items, and modification items. The incremental update calculator uses the change impact range algorithm to calculate the impact range of the model and outputs a list of impact ranges. The model adjuster updates the model within the model's influence range; Version managers are used to record update logs.

9. The intelligent generation system for pressure plate monitoring application model according to claim 8, characterized in that: The visualization management module includes a topology graph generator, a model details queryer, an anomaly alarm, a version management controller, and a manual fine-tuning interface. The version management controller is connected to the topology graph generator, the model details queryer, and the anomaly alarm, respectively, and the manual fine-tuning interface is connected to the version management controller.

10. A method for intelligently generating a pressure plate monitoring application model, based on the intelligent generation system for a pressure plate monitoring application model according to any one of claims 1-9, characterized in that: Includes the following steps: S1. Import the on-site survey data of the substation pressure plate, automatically identify the core fields and complete the data format conversion and standardization processing according to the online monitoring modeling specification of the relay protection pressure plate, remove redundant and erroneous data, and output a standardized data list. S2. Based on the preset online monitoring modeling specification template library for relay protection pressure plates, automatically generate the basic framework for device modeling, logic device modeling, and logic node modeling, and preset a dataset that conforms to the specification. S3. Automatically establish the mapping relationship between standardized survey data and model attributes, and batch complete the assignment of device version information, screen counter ledger, pressure plate position and status information in the corresponding model attributes; S4. Start the multi-rule verification engine to verify the model's compliance with standards, data integrity, and logical consistency, output verification reports, automatically correct repairable deviations, and provide manual intervention prompts for anomalies that cannot be automatically corrected. S5. After verification, an ICD model file conforming to the standard is automatically generated, and a visual model map is generated simultaneously. S6. Monitor changes in exploration data versions in real time, automatically identify changes, trigger incremental model updates, and periodically and automatically verify the model's compliance with the latest specifications and provide adaptation suggestions.

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