Global power station power data standardization coding and compatibility method and system

Abstract

The invention discloses a global power station power data standardization coding and compatibility method and system, and relates to the technical field of power data processing and communication compatibility integration, and the method comprises the steps: collecting multiple types of power operation data through an edge gateway, and carrying out the exception cleaning and structure classification of the data. Executing multi-level standardized coding according to power station structure information and equipment attributes, and constructing a unified data index table; and calling a format adaptation and protocol conversion process to complete structural docking and communication compatibility of the power data between heterogeneous systems. According to the method, a series of traditional pain point problems of disordered data acquisition, incomplete cleaning, non-uniform coding, incompatible communication and the like are solved, and an automatic, standardized and intelligent identification mechanism is introduced in each stage, so that the whole set of scheme realizes essential optimization in the aspects of data quality, platform compatibility and operation efficiency; and the systematic innovation and practical advantages of the technical scheme are fully embodied.

Description

Technical Field

[0001] The present invention relates to the technical field of power data processing and communication compatible integration, and in particular to a global power station power data standardized encoding and compatible method and system. Background Art

[0002] With the diversification of energy structures and the continuous evolution of smart grid technology, power generation companies have generally deployed power stations and monitoring systems covering a variety of energy sources, including hydropower, wind power, and photovoltaics. Various types of power operating equipment continue to develop towards digitalization and intelligence, collecting a large amount of operational data including voltage, current, temperature, and status. At the same time, business platforms such as dispatching systems, operation and maintenance platforms, and equipment management systems are also becoming more diverse and heterogeneous. To improve management efficiency and achieve full-lifecycle equipment monitoring, status analysis, and precise scheduling, there is an urgent need to establish a unified power data representation and sharing mechanism to achieve mutual recognition and interoperability between different data sources. Standardized encoding and protocol compatibility have become crucial foundations for ensuring the effective flow of power data.

[0003] Currently, most traditional technologies for processing multi-source, heterogeneous data in the power generation process suffer from serious problems such as inconsistent standards, non-standardized structures, and difficulty in cross-platform interoperability. First, different power plants or equipment manufacturers lack unified standards for device identification, data structure, and naming conventions, resulting in the inability to directly share or match power data across various systems. For example, the same voltage signal may use different field names or units in different systems, significantly hindering data integration. Second, data often undergoes structured cleaning and classification before entering the backend platform. This makes it difficult to effectively identify and correct a large number of redundant items, outliers, and missing fields in the raw data, further reducing the accuracy and reliability of subsequent modeling, analysis, and early warning. Third, most existing solutions focus on local encoding for specific devices or systems, lacking a comprehensive data encoding hierarchy covering the entire power plant, equipment, and signal domain. More importantly, cross-system data transmission utilizes different communication protocols across different platforms. Traditional approaches often rely on manually configured conversion scripts or static field mapping, lacking flexibility and making it difficult to achieve automatic protocol recognition, dynamic adaptation, and bidirectional encapsulation. Therefore, the existing technology cannot meet the overall requirements of the current power big data system for standardization, structuring, and compatibility in terms of data encoding uniformity, abnormal data processing accuracy, and communication protocol adaptability. There is an urgent need for a more systematic, automated, and standardized data encoding and communication compatibility solution. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing power data encoding and transmission methods lack a unified data coding structure, abnormal data cannot be cleaned and classified with high precision, and the adaptability of cross-system communication protocols is poor, as well as how to realize standardized power data structure and two-way communication encapsulation processing in a multi-protocol environment.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a global power plant power data standardization encoding and compatibility method, including collecting multiple types of power operation data through an edge gateway, and performing abnormal cleaning and structural classification on the data.

[0007] Multi-level standardized coding is performed based on power plant structure information and equipment attributes to build a unified data index table.

[0008] Call the format adaptation and protocol conversion process to complete the structural docking and communication compatibility of power data between heterogeneous systems.

[0009] The protocol conversion process involves deploying a communication protocol adapter component to adapt the structure of mainstream power communication protocols. A built-in protocol identifier automatically identifies the target protocol type and selects the corresponding template to invoke field encapsulation logic. During the encapsulation process, mechanisms are implemented to verify message integrity, timestamp accuracy, and signal uniqueness. Bidirectional adaptation and dynamic reconfiguration are implemented for data exchange between cross-protocol systems. During the protocol conversion process, abnormal behavior is logged in a log system, and alert notifications are sent to the management terminal for review and reissue.

[0010] As a preferred solution of the global power plant power data standardization encoding and compatibility method described in the present invention, the collection of multiple types of power operation data through the edge gateway includes deploying gateway devices to obtain power plant operation data in real time, configuring data type identifiers and sampling time tags for each type of data, and temporarily storing the collected raw data in a local cache area, pushing it to the central data transfer channel at set intervals. During the push process, multi-level buffering and asynchronous transmission strategies are used to prevent data loss and transmission congestion, and identify the source, location and collection time metadata fields.

[0011] As a preferred solution of the global power plant power data standardization encoding and compatibility method described in the present invention, the above-mentioned abnormal cleaning and structural classification of the data includes: the edge gateway locally integrates a cleaning engine, performs format consistency verification after receiving the data, and eliminates data fields with incomplete structures. Identify abnormal values ​​by setting thresholds, fluctuation windows and logical rules. Interpolate and complete the data marked as abnormal based on the principle of time proximity. After the cleaning is completed, the data is divided into power plant information category, equipment attribute category and operation signal category according to the field characteristics. The mapping classification rules are defined by the metadata dictionary and configuration template, and the classification labels are automatically marked and written into the data index header.

[0012] As a preferred solution of the global power plant power data standardization coding and compatibility method described in the present invention, wherein: the multi-level standardized coding performed based on the power plant structure information and equipment attributes includes generating a unique identification code for each power plant, and sequentially splicing the company code, country code, power plant code and unit type code to obtain a 15-digit power plant identification. The equipment is coded using a three-level combination method according to the region, system type and equipment category number, which is combined into a plant-wide code, a system code and an equipment code. All identification results are written into data records through field mapping, and used as data primary keys to construct the main index table and multi-table joint query structure. A coding conflict detection mechanism is set up to automatically search the existing identification pool for all newly connected devices. If the number is repeated, the unique code is regenerated based on the equipment deployment sequence and the backup numbering strategy to avoid duplicate conflicts.

[0013] As a preferred embodiment of the global power plant power data standardization encoding and compatibility method described in the present invention, the construction of a unified data index table includes establishing three main tables for each coded object in a relational database: a power plant table, a device table, and a signal table, each recording metadata fields. In a time series database, a time series table group is established based on the device identifier as an index, corresponding to the real-time data stream of each measurement point. The data index table is used to establish a relational query structure and data tracking structure, setting unique constraints on all key fields, and configuring redundant check bits and data record version numbers during the data writing process.

[0014] As a preferred solution of the global power plant power data standardization encoding and compatibility method described in the present invention, the format adaptation includes setting up a format conversion controller, calling the structure mapping configuration table to convert the internal data format into a parsable format, and performing name mapping, default value filling and data type conversion according to the field definition rules. The adaptation process sets a three-stage verification mechanism. The first stage is the field coverage verification to determine whether all required fields are matched. The second stage is the data type verification to check whether the field value type is consistent with the target format definition. The third stage is the syntax encapsulation verification to detect the integrity of the overall data packet structure. After the verification is passed, the encapsulation result is submitted to the protocol adaptation process for subsequent communication encapsulation.

[0015] As a preferred embodiment of the global power plant power data standardization encoding and compatibility method described in the present invention, the protocol conversion process includes deploying a communication protocol adapter component to dynamically identify, encapsulate, and bidirectionally convert power data between different communication protocols. The communication protocol adapter component includes a protocol identifier, a template call controller, an encapsulation checker, and a log alarm management unit. The protocol identifier automatically determines the protocol type used by the target business system based on preset communication channel parameters, data message structure characteristics, and communication header field pattern matching, supporting mainstream power system communication protocols. Upon successful identification, the protocol identifier triggers the template call controller to rearrange fields, convert value range formats, and encapsulate the standardized data according to the corresponding protocol field mapping template. During the encapsulation process, the encapsulation checker performs three levels of integrity and accuracy verification, including message integrity verification, data length, and segment header and tail identifier consistency with protocol definitions; timestamp accuracy verification, comparing the difference between the data generation time and the current encapsulation time to ensure that it meets time constraints; and signal uniqueness verification, verifying whether the measurement point number contained in the message is bound to the currently connected target device. After the encapsulation and verification process is successfully executed, the encapsulated data will be submitted to the communication channel of the target system to complete the transmission. If protocol mismatch, field missing and syntax error problems occur during the execution of any encapsulation step, the encapsulation verifier will generate exception information, record the event, write it into the fault log table, and simultaneously push an alarm to the platform management terminal.

[0016] Another object of the present invention is to provide a global power plant power data standardized coding and compatibility system, which can be used to call the format adaptation and protocol conversion process through the protocol conversion module to complete the structural docking and communication compatibility of power data between heterogeneous systems, and solve the problems of existing power data coding and transmission methods such as the lack of a unified data coding structure, the inability to clean and classify abnormal data with high precision, the poor adaptability of cross-system communication protocols, and how to realize standardized power data structure and two-way communication encapsulation processing in a multi-protocol environment.

[0017] As a preferred solution of the global power station power data standardization encoding and compatibility system described in the present invention, it includes: a data collection preprocessing module, a data index table construction module, and a protocol conversion module.

[0018] The collected data preprocessing module is used to collect multiple types of power operation data through the edge gateway and perform abnormal cleaning and structural classification on the data.

[0019] The data index table construction module is used to perform multi-level standardized coding based on power plant structure information and equipment attributes to construct a unified data index table.

[0020] The protocol conversion module is used to call the format adaptation and protocol conversion process to complete the structural docking and communication compatibility of power data between heterogeneous systems.

[0021] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for standardized coding and compatibility of global power plant power data.

[0022] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for standardizing and encoding global power plant power data and ensuring compatibility.

[0023] Beneficial effects of the present invention: The global power plant power data standardized coding and compatibility method provided by the present invention can collect and clean multiple types of power data on the edge side, ensuring that the data entering the main system is complete, standardized and traceable without relying on manual screening or post-cleaning, laying a high-quality data foundation for subsequent coding, indexing and platform integration.

[0024] The construction of standardized data coding and multi-level data indexing system has achieved a unified and standardized power data resource directory management capability, enabling the system to quickly locate any data source and form a multi-dimensional cross-index, greatly improving data scheduling efficiency, maintenance convenience, and the platform's versatility and scalability in multi-project and multi-device scenarios.

[0025] The two-way communication mechanism of format adaptation and protocol conversion achieves the goal of interconnection and interoperability, where data is generated in one place and available to multiple systems. It not only reduces the cost of secondary development and manual configuration between systems, but also significantly improves operation and maintenance efficiency and system flexibility, and has high engineering implementation value. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 This is an overall flow chart of a global power plant power data standardization encoding and compatibility method provided by the first embodiment of the present invention.

[0028] Figure 2 A system diagram of a global power plant power data standardization encoding and compatibility method provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0029] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0030] Example 1, with reference to Figure 1 , as one embodiment of the present invention, provides a global power plant power data standardization encoding and compatibility method, including: S1: Collect multiple types of power operation data through the edge gateway and perform anomaly cleaning and structural classification on the data.

[0031] Gateway devices are deployed to obtain the operating data of the power station in real time. Data type identifiers and sampling time tags are configured for each type of data. The collected raw data is temporarily stored in the local cache area and pushed to the central data transfer channel at set intervals. During the push process, multi-level buffering and asynchronous transmission strategies are used to prevent data loss and transmission congestion, and the source, location and collection time metadata fields are identified.

[0032] Furthermore, power station operating data includes voltage, current, frequency, power, temperature, wind speed, irradiance, and switch status. Once connected to the gateway, all collected data is immediately assigned three types of metadata tags: data type identifier (analog, status, or environmental variable), data collection timestamp, and collection source location (substation number and PV module number), forming a unified data structure.

[0033] The edge gateway integrates a local cleaning engine to perform format consistency checks on received data, removing incomplete data fields. It identifies outliers by setting thresholds, fluctuation windows, and logical rules. Data marked as outliers is interpolated and completed based on temporal proximity. After cleaning, the data is classified into power plant information, equipment attributes, and operating signals based on field characteristics. Classification rules are defined using a metadata dictionary and configuration templates, and classification labels are automatically added to the data index header.

[0034] After completing data cleaning and classification, the system embeds the classification label of the data (such as device attribute class, signal operation class) into the data meta field, and writes it into the encoding task pool in combination with the collection source information for subsequent structured encoders to generate standard numbers.

[0035] Furthermore, format consistency verification includes checking field integrity and the correct data format (e.g., integer / floating point / Boolean value). In this invention, the voltage signal is set to a normal range of 6.0kV to 6.6kV. The current signal is set to a normal range of 0A to 1000A. The wind speed is set to a normal range of 0 to 35°C. The ambient temperature is set to a normal range of -30°C to 60°C. State variables are only allowed to take values ​​of 0 or 1; values ​​of 2 or null are considered abnormal. The fluctuation window in this invention uses a default sliding window size of 5 consecutive sampling points. The sampling interval between each point is 5 seconds, corresponding to a window time width of 25 seconds. The mean μ and standard deviation σ of the sampled data within the window are calculated. If the absolute value of the difference between the current new sample value and the mean is greater than three times the standard deviation, it is marked as an abnormal fluctuation value. Logical rules process multi-field linked data with state dependencies and causal constraints, such as switch status and bus voltage, fan start commands and wind speed monitoring, etc. Each logical rule is defined as a condition set + judgment expression. The platform features a logic rule configuration table, allowing users to graphically configure conditional fields and judgment logic (such as IF-ELSE structures). Identified logic anomalies are tagged and can trigger event alerts.

[0036] It should be noted that the edge gateway enables structured collection and local cleaning of multi-source power operation data, combining thresholds, sliding windows, and logical rules for real-time anomaly identification and classification. Pre-filtering improves data quality, reduces the burden on central systems, and ensures the accuracy and stability of subsequent encoding and transmission.

[0037] To further improve the intelligence and accuracy of signal anomaly detection, this paper introduces a signal segment information entropy index based on sliding time density normalization to quantify the stability of a certain type of operating data within the current time window: , in, For the Class signal in time window The normalized information entropy within, The first The statistical density of the fragments, is the total number of current signal segments, is the local density function of the signal in the time dimension, ∈[0,τ] is the signal observation interval, and the integral term is the normalization factor.

[0038] When the edge gateway performs the cleaning process, the platform uses each type of signal As a unit, its current sliding time window The sampled data are first grouped by sliding windows (default 5 points, 25 seconds), and the signal change trend, average value and standard deviation in each group are counted. Then, the local time density function of the current time period is estimated based on the Gaussian kernel function. After normalizing all the fragments, the normalized information entropy of the group is calculated .

[0039] The system default threshold is the upper limit of the entropy of the normal range of the signal history (the present invention is set to ),like Exceeding this threshold indicates significant signal fluctuations or atypical behavior, and the system labels this data segment as a distribution anomaly. If the fluctuation window detection anomaly also meets the criteria, neighboring interpolation is automatically initiated, and the anomaly is recorded in the event log. If only the information entropy is abnormal, it is placed on the observation list for review by dispatchers. All anomaly detection and entropy calculation processes are performed regularly by edge nodes, with a period of 1 minute.

[0040] It should be noted that this entropy function replaces the traditional sliding average method with a quantitative indicator driven by distribution information, so that the anomaly detection mechanism can simultaneously consider the frequency and distribution entropy of the signal, avoiding the misjudgment of boundary changes by simple threshold strategies, and is suitable for real-time stability judgment of multi-type, non-stationary sampling signals.

[0041] S2: Perform multi-level standardized coding based on power plant structure information and equipment attributes to build a unified data index table.

[0042] A unique identification code is generated for each power plant, consisting of the company code, country code, power plant code, and unit type code, in sequence, to create a 15-digit plant identifier. Equipment is coded using a three-level combination of plant-wide, system, and equipment category codes, based on their region, system type, and equipment category. All identification results are written to data records through field mapping and used as primary keys to construct primary index tables and multi-table joint query structures. A coding conflict detection mechanism is implemented to automatically search the existing identification pool for all newly connected devices. If a number is duplicated, a unique code is regenerated based on the device deployment sequence and the backup numbering strategy to avoid duplicate conflicts.

[0043] Furthermore, the present invention refers to the "Electric Power Production Data Coding Standard" to generate a 15-digit unique identification code for each power station, using the following composition rules: Company code (4 digits): Uniformly set by the operating unit, such as the company's internal code is "1085".

[0044] Country code (3 digits): Set according to GB / T2659 country / region codes, such as "586" for China.

[0045] Power station code (4 digits): Determined according to the company's internal power station numbering system, for example, "0450" represents a hydropower station.

[0046] Unit type code (4 digits): set according to the type of power generation equipment, such as "1163" represents a hydro-turbine generator set.

[0047] An example of a generated complete identification is: "108558604501163", which is the unique logical number of the power station in the system.

[0048] The present invention sets three levels of combined coding for equipment objects in the power station: Plant-wide code (2 to 4 digits): Indicates the functional area number of the equipment in the power plant, such as "00" for equipment shared by the entire station, and "01" for the Unit 1 area.

[0049] System code (5 digits): The three letters represent the system classification (such as "MKV" represents the measurement and control system), and the two digits represent the specific sub-unit number under the system.

[0050] Equipment code (5 digits): The first two digits are the equipment type code (such as "PT" for voltage transformer), and the last three digits are the serial number of the same type of equipment, starting with "001".

[0051] For example, the device code of the voltage transformer under unit 1 can be "01MKV01PT001". This code, when combined with the power plant identification, constitutes a complete and unique device identification.

[0052] When data enters the database, all power station identification and equipment codes will be automatically written into the corresponding fields through the mapping table mechanism, forming the logical primary key field (Primary Key) of the data table, ensuring that each record in the system has a unique identification for data writing, indexing and cross-table calls.

[0053] In the relational database, three main tables—a power station table, a device table, and a signal table—are created for each coded object, each recording metadata fields. In the time series database, a time series table group is created based on the device identifier as an index, corresponding to the real-time data stream of each measurement point. The data index table is used to establish the relational query structure and data tracking structure, with unique constraints set for all key fields. Redundancy check bits and data record version numbers are configured during the data writing process.

[0054] Furthermore, the power station table fields include Station_ID, country code, geographic coordinates, and voltage level. The equipment table fields include Device_ID, Station_ID, device model, and installation location. The signal table fields include Signal_ID, Device_ID, signal type, and data unit. All tables use unique identification fields (Station_ID and Device_ID) to implement primary and foreign key constraints and cross-table joins.

[0055] It should be noted that by establishing unified power plant and equipment coding rules, building a logical primary key and standardized index system, we achieve data structure consistency and unique tracking of equipment data throughout the entire process. This effectively improves data storage standardization and query efficiency, supporting multi-source data fusion analysis and cross-table logical association calls.

[0056] The constructed primary key structure will serve as the key identifier in the field mapping table and will be used for field reorganization, unique signal binding, and structural hierarchical positioning during the protocol template call process.

[0057] To address the risk of conflict in power station equipment coding in a large-scale access environment, this paper proposes a uniqueness verification mechanism based on the multi-factor coupling of deployment location, topology level, and equipment attributes to calculate the structural enhancement code of each device: , in, For the The device in Uniqueness enforcement value within the layer structure. is a multi-factor coupling function, which includes the deployment location mapping value , system topology level value , device attribute hash code . is the equipment aging index, and Respectively represent The offset interference and noise disturbance values ​​of the equipment, is the complexity factor of the current structure layer.

[0058] Whenever a new device The first When the layer structure system is established, the platform will generate a standard code for it and start the unique conflict detection process. At this time, the system will read the device deployment location in real time. , hierarchical attributes , equipment type and parameters , and generate the aging coefficient based on the manufacturing time of the equipment . Based on the equipment life cycle (Default 12 months) is the time axis, and the exponential weighted multi-factor coupling integral value is calculated to form the numerator, and the associated devices at the same level in the system are extracted at the same time =1... The disturbance information (deployment overlap, frequency interference, etc.) is calculated, the normalized denominator is calculated, and the uniqueness enhancement score is finally obtained. .

[0059] The system sets the scoring threshold to 0.65. If the score is lower than this value, it means that the existing code is highly similar or the location / function / model overlaps seriously, and the backup numbering strategy will be triggered, giving priority to adding an extended tail number to the device, or adjusting the system code position to ensure logical uniqueness. If the score is between 0.65 and 0.8, the code is added to the potential conflict list for dispatcher confirmation. If it is below 0.65, the backup number generator is automatically activated, calling the backup field segment code, appending the index tag, and writing it into the unique code pool. If it is greater than 0.8, the current code is retained. The finalized code and uniqueness score are written to the device table and code mapping log for subsequent tracking and review.

[0060] It should be noted that by introducing multi-dimensional topology and device attribute factors and combining them with a time-varying exponential filtering mechanism, adaptive avoidance of coding conflicts in complex heterogeneous device scenarios is achieved, enabling the platform to have a coding uniqueness guarantee mechanism under large-scale expansion.

[0061] S3: Call the format adaptation and protocol conversion process to complete the structural docking and communication compatibility of power data between heterogeneous systems.

[0062] A format conversion controller is established, calling the structure mapping configuration table to convert the internal data format into a parsable format. Name mapping, default value filling, and data type conversion are performed according to the field definition rules. The adaptation process uses a three-stage verification mechanism. The first stage is field coverage verification, which determines whether all required fields match. The second stage is data type verification, which checks whether the field value type is consistent with the target format definition. The third stage is syntax encapsulation verification, which checks the integrity of the overall data packet structure. After passing the verification, the encapsulation result is submitted to the protocol adaptation process for subsequent communication encapsulation.

[0063] Furthermore, a preferred solution for performing name mapping, default value filling, and data type conversion according to field definition rules specifically includes: in the present invention, field definition rules are used to guide the format conversion controller to convert internal standardized data into a data structure that can be parsed by the target system, and its specific content includes the following three aspects: Field Name Mapping Table: The system maintains a field mapping configuration table that is used to map the data fields in the platform of the present invention to the field names required by the target system. This table can automatically select templates based on different target systems.

[0064] Field type constraint table: Each field must clearly define its corresponding data type in the target system. For example, the Val field must be a Float, TimeTag must be in ISO8601 timestamp format, and DevCode must be a string of no more than 20 characters. The conversion controller checks and performs type conversion or format reconstruction based on these rules.

[0065] Field integrity and default value table: For mandatory fields required by the target system, if they are missing in the source data, the system will search for the default value of the corresponding field in the "Default Value Configuration Table" to fill in the data structure and avoid verification failure.

[0066] A communication protocol adapter component is deployed to dynamically identify, encapsulate, and convert power data between different communication protocols. The component includes a protocol identifier, a template call controller, an encapsulation checker, and a log and alarm management unit. The protocol identifier automatically determines the protocol type used by the target business system based on preset communication channel parameters, data message structure characteristics, and communication header field pattern matching. It supports mainstream power system communication protocols. Upon successful identification, the protocol identifier triggers the template call controller, which rearranges the fields, converts the value range format, and encapsulates the standardized data according to the corresponding protocol field mapping template. During the encapsulation process, the encapsulation checker performs three levels of integrity and accuracy verification, including message integrity verification, data length, and segment header and footer identifiers, ensuring consistency with protocol definitions. It also performs timestamp accuracy verification, comparing the difference between the data generation time and the current encapsulation time to ensure that it meets time constraints. It also performs signal uniqueness verification, verifying that the measurement point number contained in the message is bound to the currently connected target device. After the encapsulation and verification process is successfully executed, the encapsulated data will be submitted to the communication channel of the target system to complete the transmission. If protocol mismatch, field missing and syntax error problems occur during the execution of any encapsulation step, the encapsulation verifier will generate exception information, record the event, write it into the fault log table, and simultaneously push an alarm to the platform management terminal.

[0067] It should be noted that through format adaptation and protocol conversion mechanisms, power data can be intelligently connected from standardized structures to multi-protocol systems, supporting dynamic identification, encapsulation, and two-way communication. This improves data interoperability between heterogeneous platforms, ensures accurate data transmission between different systems, and significantly enhances platform compatibility and engineering deployment flexibility.

[0068] To address the issues of stability, overhead, and matching rate differences in the encapsulation paths of various communication protocols, this paper constructs a protocol scoring function based on field dynamic behavior to select the optimal protocol template in the encapsulation process: , in, For the The comprehensive adaptation score of the protocol template. For the The structural importance of class fields in encapsulation templates. The number of encapsulation failures that occurred in the most recent interaction for this field. The average time it takes to encapsulate the field. This is a compatibility error in the field structure. is the protocol complexity evaluation value. It is the function of field packet loss rate change during the encapsulation process. Indicates the current The total number of field categories to be encapsulated in a protocol template. Indicates the current traversal Class fields.

[0069] Field packet loss rate change function Expressed as: , in, For the The field encapsulation packet loss rate change function of a protocol template during the encapsulation process. The template basic packet loss rate level coefficient represents the steady-state upper limit of the protocol encapsulation failure rate. The exponential growth coefficient controls the rate of packet loss accumulation during the encapsulation process. The periodic packet loss disturbance caused by factors such as network load in the periodic disturbance amplitude control package is used. is the disturbance frequency. The initial phase perturbation determines the initial offset of the oscillation on the time axis. This function comprehensively reflects the transmission jitter trend and cycle loss characteristics of the current protocol during encapsulation, and its value is used to dynamically adjust the normalization penalty of the scoring function.

[0070] When the power platform is ready to push data to other heterogeneous business systems, the protocol identifier will first automatically determine the communication protocol type used by the target system (such as IEC104, MODBUS, etc.). After the identification is completed, the platform will extract all available protocol templates { }, and perform scoring calculation on each template in turn. , the system counts the field types it contains , and calculate the historical frequency of encapsulation failures field by field , conversion time , structural error , and field priority . At the same time, extract the template in the recent Packet loss rate function over time , and its integral value is used as the stability index.

[0071] Scoring function The above four dimensions are integrated into a unified evaluation value. The platform sets the scoring threshold of all templates to 0.60 by default. If the highest scoring template If this value is exceeded, the template is selected as the packaging solution for this round. Otherwise, the system prompts you to perform field mapping or structural supplementation. Scoring results are recorded in the packaging log for auditing and model iteration by operations personnel. The scoring function automatically updates the weight model every 24 hours to adapt to changes in field importance and evolving business needs.

[0072] Example 2, reference Figure 2 , which is an embodiment of the present invention, provides a global power station power data standardization encoding and compatibility system, including a data collection preprocessing module 100, a data index table construction module 200, and a protocol conversion module 300.

[0073] S4: The data collection preprocessing module 100 is used to collect multiple types of power operation data through the edge gateway and perform abnormal cleaning and structural classification on the data.

[0074] The collected data preprocessing module 100 includes an edge collection submodule 101 and a data cleaning and classification submodule 102 .

[0075] Furthermore, the edge acquisition submodule 101 is used to deploy edge gateway acquisition devices to sample multiple types of power operation data, including parameters such as voltage, current, frequency, temperature, wind speed, etc., and assign a data type identifier, sampling timestamp and acquisition location label to each type of data to achieve data structure standardization and traceability identification at the edge.

[0076] The data cleaning and classification submodule 102 is used to detect anomalies and classify the structure of the collected raw data. Based on the set threshold judgment rules, sliding window fluctuation analysis algorithm, and state logic condition recognition mechanism, it removes or interpolates values ​​that exceed the limit, sudden change, and logical conflict. It also divides the data into power plant information, equipment attribute, and operation signal categories based on field meta-attributes, and annotates the corresponding classification labels in the data header.

[0077] It should be noted that the edge acquisition submodule 101 is the starting point for the data preprocessing module 100. Its output determines the quality of the sampled data and the degree of field standardization in subsequent modules. The data cleaning and classification submodule 102 provides data quality assurance and logical classification basis, which is a necessary prerequisite for subsequent coding and index table construction.

[0078] S5: Constructing a data index table The module 200 is used to perform multi-level standardized coding based on the power plant structure information and equipment attributes to construct a unified data index table.

[0079] The data index table construction module 200 includes a code generation submodule 201 and a main table index construction submodule 202 .

[0080] Furthermore, the code generation submodule 201 is used to generate standardized codes based on the power plant structure information and equipment attributes. The power plant code is composed of the company code, country code, power plant code, and unit type code. The equipment code is a combination of the whole plant code, system code and equipment code to form a unique identifier, and a code conflict detection mechanism is established to automatically determine and handle duplicate numbering conflicts.

[0081] The master table index construction submodule 202 is used to create three master metadata tables in the relational database: a power station table, a device table, and a signal table, using the power station identifier and device code as primary keys. Simultaneously, a real-time data table group is created in the time series database, using the device code as an index. This group records the continuous sequence of various signal values ​​over time. All table structures are configured with primary key unique constraints and data version number fields to ensure query efficiency and data consistency.

[0082] It should be noted that the unique identifiers of power plants and equipment output by the code generation submodule 201 provide the basis for establishing logical ownership relationships for data. The main table index construction submodule 202 is the core mechanism for achieving cross-system data tracking and field association calls, ensuring the platform has the ability to combine multiple tables and achieve efficient scheduling.

[0083] S6: The protocol conversion module 300 is used to call the format adaptation and protocol conversion process to complete the structural docking and communication compatibility of power data between heterogeneous systems.

[0084] The protocol conversion module 300 includes a format adaptation submodule 301 and a protocol encapsulation submodule 302 .

[0085] Furthermore, the format adaptation submodule 301 is used to establish format conversion control logic, perform name mapping, type conversion and default value filling on the standardized data structure according to the structure mapping configuration table and field definition rules, and set up a three-stage verification mechanism: including field coverage verification, data type consistency verification and syntax structure integrity verification, to ensure that the converted data meets the input requirements of the target system.

[0086] The protocol encapsulation submodule 302 deploys communication protocol adapter components, supporting mainstream power communication protocol formats such as DL / T634.5101 and IEC60870-5-104. It includes a protocol identifier, a field encapsulator, and a validation processor. The system automatically identifies the target communication protocol type, reorganizes the field order and encapsulated content format according to the template, and performs verification of data length, timestamp accuracy, and signal ID uniqueness. If verification fails, an exception log is generated and a system alert is sent.

[0087] It should be noted that the format adaptation submodule 301 is the entry point of the protocol conversion module 300 and determines the semantic and structural compatibility of the data. The protocol encapsulation submodule 302 provides a bidirectional interface capability for cross-system data communication and is the key logical unit for the platform to achieve interoperability among multiple business systems.

Claims

1. A global power plant power data standardization encoding and compatibility method, characterized by: include: Collect multiple types of power operation data through edge gateways and perform abnormal cleaning and structural classification on the data; Implement multi-level standardized coding based on power plant structure information and equipment attributes to build a unified data index table; Invoke the format adaptation and protocol conversion process to complete the structural docking and communication compatibility of power data between heterogeneous systems; The protocol conversion process includes deploying a communication protocol adapter component to perform structural adaptation of the mainstream power communication protocol; The built-in protocol identifier automatically identifies the target protocol type and selects the corresponding template to call the field encapsulation logic; during the encapsulation process, the message integrity verification mechanism, timestamp accuracy verification mechanism and signal uniqueness verification mechanism are set; for data interaction between cross-protocol systems, two-way adaptation and dynamic reconfiguration are performed, and abnormal behavior during the protocol conversion process is recorded in the log system, and alarm notifications are pushed to the management terminal for review and reissue.

2. The method for global power plant power data standardization, encoding and compatibility according to claim 1, characterized in that: The collection of multiple types of power operation data through the edge gateway includes: Gateway devices are deployed to obtain the operating data of the power station in real time. Data type identifiers and sampling time tags are configured for each type of data. The collected raw data is temporarily stored in the local cache area and pushed to the central data transfer channel at set intervals. During the push process, multi-level buffering and asynchronous transmission strategies are used to prevent data loss and transmission congestion, and the source, location and collection time metadata fields are identified.

3. The method for global power plant power data standardization, encoding and compatibility according to claim 1 or 2, characterized in that: The abnormal cleaning and structural classification of data includes: The edge gateway integrates a local cleaning engine to perform format consistency checks after receiving data and remove incomplete data fields. Identify outliers by setting thresholds, fluctuation windows, and logical rules; interpolate and complete data marked as outliers based on the principle of temporal proximity; After cleaning is completed, the data is divided into power plant information category, equipment attribute category and operation signal category according to field characteristics. The mapping classification rules are defined through metadata dictionary and configuration template, and the classification labels are automatically marked and written into the data index header.

4. The method for global power plant power data standardization, encoding and compatibility according to claim 3, characterized in that: The multi-level standardized coding according to the power plant structure information and equipment attributes includes: Generate a unique identification code for each power plant, which is a 15-digit power plant identification code composed of the company code, country code, power plant code and unit type code in sequence; Equipment is coded using a three-level combination method based on the region, system type, and equipment category, which is composed of the plant code, system code, and equipment code. All identification results are written into data records through field mapping and used as data primary keys to construct primary index tables and multi-table joint query structures; Set up a coding conflict detection mechanism to automatically search the existing identification pool for all newly connected devices. If the number is repeated, a unique code is regenerated based on the device deployment order and the backup numbering strategy to avoid duplication conflicts.

5. The method for global power plant power data standardization, encoding and compatibility according to any one of claims 1, 2 or 4, characterized in that: The construction of the unified data index table includes: In the relational database, three main tables, namely power station table, equipment table and signal table, are established for each coding object, and the meta-information fields are recorded respectively; In the time series database, a time series table group is created based on the device identifier as the index, corresponding to the real-time data stream of each measurement point; The data index table is used to establish relational query structures and data tracking structures, set unique constraints on all key fields, and configure redundant check bits and data record version numbers during the data writing process.

6. The method for global power plant power data standardization, encoding and compatibility according to claim 5, characterized in that: The format adaptation includes: Set up a format conversion controller, call the structure mapping configuration table to convert the internal data format into a parsable format, and perform name mapping, default value filling and data type conversion according to the field definition rules; The adaptation process sets up a three-stage verification mechanism. The first stage is field coverage verification to determine whether all required fields are matched; the second stage is data type verification to check whether the field value type is consistent with the target format definition; The third stage is syntax encapsulation verification, which checks the integrity of the overall data packet structure; After verification, the encapsulation result is submitted to the protocol adaptation process for subsequent communication encapsulation.

7. The method for global power plant power data standardization, encoding and compatibility according to any one of claims 1, 2, 4 or 6, characterized in that: The protocol conversion process includes: Deploy a communication protocol adapter component to achieve dynamic identification, structural encapsulation, and bidirectional conversion of power data between different communication protocols. The communication protocol adapter component includes a protocol identifier, a template call controller, an encapsulation verifier, and a log alarm management unit. The protocol identifier automatically determines the protocol type used by the target business system based on preset communication channel parameters, data message structure characteristics, and communication header field pattern matching, supporting mainstream power system communication protocols. After successful identification, the protocol identifier triggers the template to call the controller, which rearranges the fields, converts the value range format, and encapsulates the standardized data in groups according to the field mapping template of the corresponding protocol. During the encapsulation process, the encapsulation verifier performs three levels of integrity and accuracy verification, including message integrity verification, data length, segment header and tail identifiers, and consistency with protocol definitions; Timestamp accuracy check: compare the difference between data generation time and current packaging time to see if it meets the time limit; signal uniqueness check: verify whether the measurement point number contained in the message is bound to the currently connected target device; After the encapsulation and verification process is successfully executed, the encapsulated data will be submitted to the communication channel of the target system to complete the transmission. If protocol mismatch, field missing and syntax error problems occur during the execution of any encapsulation step, the encapsulation verifier will generate exception information, record the event, write it into the fault log table, and simultaneously push an alarm to the platform management terminal.

8. A global power plant power data standardization encoding and compatibility system, characterized by: It includes a data collection pre-processing module (100), a data index table construction module (200), and a protocol conversion module (300); The collected data pre-processing module (100) is used to collect multiple types of power operation data through an edge gateway, and perform abnormal cleaning and structural classification on the data; The data index table construction module (200) is used to perform multi-level standardized coding based on power station structure information and equipment attributes to construct a unified data index table; The protocol conversion module (300) is used to call the format adaptation and protocol conversion process to complete the structural docking and communication compatibility of power data between heterogeneous systems.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the global power plant power data standardization encoding and compatibility method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the global power plant power data standardization encoding and compatibility method according to any one of claims 1 to 7 are implemented.

Images