A global power station power data standardization encoding and compatibility method and system

Abstract

The application discloses a global power station power data standardization coding and compatibility method and system, relates to the technical field of power data processing and communication compatibility integration, and comprises the following steps: collecting multiple types of power operation data through an edge gateway, performing abnormal cleaning and structure classification on the data, performing multi-level standardization coding according to power station structure information and equipment attributes, and constructing a unified data index table. Format adaptation and protocol conversion processes are called to complete structure docking and communication compatibility of power data between heterogeneous systems. The method solves a series of traditional pain point problems such as disordered data collection, incomplete cleaning, non-uniform coding and non-compatible communication, and introduces automation, standardization and intelligent identification mechanisms at each stage, so that the whole scheme realizes essential optimization in data quality, platform compatibility and operation efficiency, and fully embodies the systematic innovation and practical advantage of the technical scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power data processing and communication compatibility, in particular to a global power station power data standardization coding and compatibility method and system. BACKGROUND

[0002] With the diversification of energy structure and the continuous evolution of smart grid technology, power production enterprises have generally deployed power stations and monitoring systems covering various energy types such as hydropower, wind power and photovoltaic power. Various types of power operation equipment continue to develop towards digitization and intelligentization, and have collected a large amount of operation data related to voltage, current, temperature, state and the like. At the same time, business platforms such as dispatching systems, operation and maintenance platforms and equipment management systems have also become diversified and heterogeneous. In order to improve management efficiency and realize equipment life cycle supervision, state analysis and accurate scheduling, it is urgent to build a unified power data expression and sharing mechanism to realize mutual recognition and interconnection between different data sources. Standardized coding and protocol compatibility are important foundations for ensuring the effective circulation of power data.

[0003] At present, for the processing of multi-source heterogeneous data in the power production process, most traditional technologies have serious problems such as non-uniform standards, non-standard structure and difficulty in cross-platform interaction. First, different power stations or equipment manufacturers lack unified standards for equipment identification, data structure and naming rules, resulting in that power data between various systems cannot be directly shared or matched. For example, the same voltage signal may use different field names or units in different systems, which brings significant obstacles to data fusion. Secondly, before entering the background platform, the data is often not structured, cleaned and classified, and a large number of redundant items, abnormal values and missing fields in the original data cannot be effectively identified and corrected, further reducing the accuracy and reliability of subsequent modeling, analysis and early warning. Thirdly, most existing solutions focus on local coding of a certain type of equipment or system, and lack a data coding hierarchy system covering the entire domain of power stations-equipment-signals. More importantly, in the process of cross-system data transmission, different platforms use different communication protocols, and traditional methods usually rely on manual configuration of conversion scripts or static field mapping, which lacks flexibility and is difficult to realize protocol automatic recognition, dynamic adaptation and bidirectional packaging processing. Therefore, the existing technology cannot meet the overall requirements of standardization, structurization and compatibility of the current power big data system in terms of data coding uniformity, abnormal data processing accuracy and communication protocol adaptation capability, and there is an urgent need for a more systematic, automated and standardized data coding and communication compatibility solution. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that the existing power data coding and transmission method lacks a unified data coding structure, cannot accurately clean and classify abnormal data, has poor cross-system communication protocol adaptation capability, and how to realize standardized power data structure and bidirectional communication packaging processing in a multi-protocol environment.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a global power station power data standardized coding and compatible method, comprising collecting multiple types of power operation data through an edge gateway, and performing abnormal cleaning and structure classification on the data.

[0007] According to the power station structure information and equipment attribute, a multi-level standardized coding is performed to construct a unified data index table.

[0008] A format adaptation and protocol conversion process is called to complete the structure docking and communication compatibility of power data between heterogeneous systems.

[0009] The protocol conversion process includes deploying a communication protocol adaptation component to adapt the structure of mainstream power communication protocols. A protocol identifier is built-in to automatically identify the target protocol type and select the corresponding template to call the field packaging logic. In the packaging process, a message integrity verification mechanism, a timestamp precision verification mechanism, and a signal uniqueness verification mechanism are set. For data interaction between cross-protocol systems, bidirectional adaptation and dynamic reconfiguration are performed. During the protocol conversion process, abnormal behaviors are recorded to the log system, and alarm notifications are pushed to the management terminal for review and reissue.

[0010] As a preferred scheme of the global power station power data standardized coding and compatible method, wherein: the collecting multiple types of power operation data through an edge gateway includes deploying a gateway device to obtain the operation data of a power station in real time, configuring a data type identifier and a sampling time label for each type of data, and temporarily storing the collected raw data in a local cache area, and pushing to a central data transfer channel at a set interval, preventing data loss and transmission congestion through multi-level buffering and asynchronous transmission strategy during the pushing process, and identifying the source, location and collection time metadata fields.

[0011] As a preferred scheme of the global power station power data standardized coding and compatible method, wherein: the performing abnormal cleaning and structure classification on the data includes integrating a cleaning engine locally in the edge gateway, performing format consistency verification after receiving the data, and removing data fields with incomplete structure. Abnormal values are identified by setting thresholds, fluctuation windows and logical rules. The data marked as abnormal is interpolated according to the time proximity principle. After cleaning, the data is divided into power station information, equipment attribute and operation signal according to the field characteristics, and the mapping classification rules are defined by metadata dictionary and configuration template, and the classification labels are automatically annotated and written in the data index header.

[0012] As a preferred scheme of the global power station power data standardization coding and compatibility method, wherein: the multi-level standardization coding according to the power station structure information and equipment attribute includes generating a unique identification code for each power station, which is obtained by sequentially splicing a company code, a country code, a power station code and a unit type code to obtain a 15-bit power station identification. The equipment is coded according to the region, system type and equipment category number in a three-level combination manner, which is combined into a whole plant 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 a main index table and a multi-table joint query structure. A coding conflict detection mechanism is set to automatically retrieve the existing identification pool for all newly connected equipment. If the number is repeated, a unique code is regenerated according to the equipment deployment order and the standby number strategy to avoid duplication conflicts.

[0013] As a preferred scheme of the global power station power data standardization coding and compatibility method, wherein: the construction of the unified data index table includes establishing three main tables of a power station table, an equipment table and a signal table in a relational database for each coding object, respectively recording the meta information fields. In the time series database, a time series table group is established according to the equipment identification as the index, corresponding to each measuring point real-time data stream. The data index table is used to establish a relational query structure and a data tracking structure, and a unique constraint is set for all key fields. The data writing process is configured with a redundancy check bit and a data record version number.

[0014] As a preferred scheme of the global power station power data standardization coding and compatibility method, wherein: the format adaptation includes setting a format conversion controller, calling a structure mapping configuration table to convert the internal data format into a parseable format, and performing name mapping, default value filling and data type conversion according to the field definition rules. A three-stage verification mechanism is set in the adaptation process. The first stage is field coverage verification to judge whether all necessary fields are matched. The second stage is data type verification to check whether the field value type and the target format definition are consistent. The third stage is syntax encapsulation verification to detect the integrity of the overall data packet structure. After the verification, the encapsulation result is submitted to the protocol adaptation process for subsequent communication encapsulation.

[0015] As a preferred scheme of the global power station power data standardization coding and compatible method, the protocol conversion process comprises deploying a communication protocol adaptation component for realizing dynamic identification, structure packaging and bidirectional conversion of power data among different communication protocols, and the communication protocol adaptation component comprises a protocol identifier, a template calling controller, a packaging verifier and a log alarm management unit. The protocol identifier automatically judges the protocol type adopted by the target business system based on preset communication channel parameters, data message structure characteristics and communication header field mode matching, and supports mainstream power system communication protocols. After successful identification, the protocol identifier triggers the template calling controller to rearrange the fields, convert the value domain format and package the standardized data according to the field mapping template of the corresponding protocol. During the packaging process, the packaging verifier performs three-level integrity and accuracy verification, including message integrity verification, data length, segment header and tail identification and protocol definition consistency. Time stamp accuracy verification, whether the difference between the data generation time and the current packaging time meets the time limit. Signal uniqueness verification, whether the measurement point number contained in the message is bound to the current connection target device is verified. After successful execution of the packaging and verification process, the packaged data is submitted to the communication channel of the target system for transmission. If there is a protocol mismatch, field missing and syntax error problem in any packaging step, the packaging verifier will generate exception information, record the event, write it into the fault log table, and simultaneously push the alarm to the platform management terminal.

[0016] Another object of the present application is to provide a global power station power data standardization coding and compatible system which can call the format adaptation and protocol conversion process through the protocol conversion module to complete the structure docking and communication compatibility of power data among heterogeneous systems, and solve the problems of lack of unified data coding structure, high-precision cleaning and classification of abnormal data, poor cross-system communication protocol adaptation capability, and how to realize bidirectional communication packaging processing of standardized power data structure and multi-protocol environment in the existing power data coding and transmission method.

[0017] As a preferred scheme of the global power station power data standardization coding and compatible system, it comprises a collected data preprocessing module, a data index table construction module and a protocol conversion module.

[0018] The collected data preprocessing module is used for collecting multiple types of power operation data through an edge gateway, and performing abnormal cleaning and structure classification on the data.

[0019] The data index table construction module is used for performing multi-level standardization coding according to power station structure information and equipment attributes, and constructing a unified data index table.

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

[0021] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the global power station power data standardization coding and compatibility method.

[0022] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the global power station power data standardization coding and compatibility method.

[0023] The global power station power data standardization coding and compatibility method provided by the application has the advantages that the edge side multi-type power data acquisition and cleaning classification processing can ensure the data entering the main system to have completeness, standardization and traceability without relying on manual screening or later cleaning, and lays a high-quality data foundation for subsequent coding, indexing and platform integration.

[0024] The standardized data coding and multi-level data indexing system construction can achieve unified and standardized power data resource directory management capability, so that the system can quickly locate any data source and form multi-dimensional cross indexing, greatly improving data scheduling efficiency, maintenance convenience and platform versatility and scalability in multi-project and multi-device scenarios.

[0025] The format adaptation and protocol conversion bidirectional communication mechanism can achieve the interconnection and intercommunication target of data generated in one place and available in multiple systems, which not only reduces the secondary development and manual configuration cost between systems, but also significantly improves the operation and maintenance efficiency and system flexibility, and has high engineering landing value. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0027] Figure 1 A global power station power data standardization coding and compatibility method provided by the first embodiment of the application is provided.

[0028] Figure 2 A global power station power data standardization coding and compatibility method provided by the second embodiment of the application is provided. DETAILED DESCRIPTION

[0029] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0030] Embodiment 1, refer to Figure 1 For an embodiment of the present application, a global power station power data standardization coding and compatibility method is provided, comprising:

[0031] S1: Collecting multiple types of power operation data through the edge gateway, performing abnormal cleaning and structure classification on the data.

[0032] Deploying gateway devices to obtain the operation data of the power station in real time, configuring a data type identifier and a sampling time label for each type of data, and temporarily storing the collected raw data to a local cache area, and pushing to a central data transfer channel at a set interval, preventing data loss and transmission congestion through multi-level buffering and asynchronous transmission strategy during the pushing process, and identifying the source, location and collection time metadata fields.

[0033] Further, the operation data of the power station includes voltage, current, frequency, power, temperature, wind speed, irradiance, and switch state. All collected data is immediately given three types of metadata labels after accessing the gateway: data type identifier (analog quantity, state quantity, environmental variable), data collection timestamp, and collection source location (transformer unit number and photovoltaic component number) for forming a unified data structure.

[0034] The edge gateway locally integrates a cleaning engine, which performs format consistency verification after receiving the data and eliminates data fields with incomplete structure. Abnormal values are identified by setting threshold, fluctuation window and logical rules. The data marked as abnormal is interpolated and completed according to the time proximity principle. After cleaning, the data is divided into power station information class, device attribute class and operation signal class according to the field characteristics, and the mapping classification rules are defined by metadata dictionary and configuration template, and the classification labels are automatically annotated and written into the data index header.

[0035] After completing data cleaning and classification, the system embeds the classification labels of the data (such as device attribute class, signal operation class) into the data metadata field, and writes the collection source information into the coding task pool for subsequent structured encoder to generate standard number.

[0036] Furthermore, format consistency checks include verifying the completeness of fields and the correctness of data formats (e.g., integer / floating-point / Boolean values). In this invention, the normal range for voltage signals is set to 6.0kV~6.6kV. The normal range for current signals is set to 0A~1000A. The normal range for wind speed is set to 0~35. The normal range for ambient temperature is set to -30~60. State variables are only allowed to take values ​​of 0 or 1; if a value of 2 or null appears, it is considered abnormal. The fluctuation window in this invention is set to a default sliding window size of 5 consecutive sampling points. The sampling period 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 sampled value and the mean is greater than three times the standard deviation, it is marked as an abnormal fluctuation value. Logical rules handle multi-field linked data with state dependencies and causal constraints, such as switch status and bus voltage, wind turbine start command and wind speed monitoring, etc. Each logical rule is defined in the form of a condition set + judgment expression. The platform includes a logic rule configuration table, allowing users to graphically configure condition fields and judgment logic (such as IF-ELSE structures). Identified logical anomalies are tagged and can trigger event alarms.

[0037] It should be noted that the structured collection and local cleaning of multi-source power operation data are achieved through edge gateways, and real-time anomaly identification and classification are performed by combining thresholds, sliding windows, and logical rules. Pre-filtering improves data quality, reduces the burden on the central system, and ensures the accuracy and stability of subsequent encoding and transmission.

[0038] To further enhance the intelligence and accuracy of signal anomaly detection, this invention introduces a signal segment information entropy index based on sliding time density normalization, used to quantify the stability of a certain type of operational data within the current time window:

[0039] ,

[0040] in, For the first Signal type in time window Normalized information entropy within, For the first signal in this type of window The statistical density of each segment This represents the total number of current signal segments. Let be the local density function of the signal in the time dimension. ∈[0,τ] represents the signal observation interval, and the integral term is the normalization factor.

[0041] When performing the cleaning process at the edge gateway, the platform uses each type of signal... As a unit, its position within the current sliding time window Information entropy analysis is performed on all sampled segments within the time frame. The sampled data is first grouped by a sliding window (default 5 points, 25 seconds), and the signal change trend, mean, and standard deviation of each group are statistically analyzed. Then, the local time density function of the current time segment is estimated based on the Gaussian kernel function. After normalizing all segments, calculate the normalized information entropy of the group. .

[0042] The system defaults to setting the threshold to the upper limit of the historical normal range entropy of this type of signal (this invention sets it to...). ),like If the signal exceeds this threshold, it indicates that the signal segment exhibits drastic fluctuations or atypical behavior, and the system will label this data segment as having an abnormal distribution. If the fluctuation window detection anomaly is also met, neighbor interpolation completion will be automatically initiated, and the anomaly record will be written to the event log. If only the information entropy is abnormal, it will be added to the observation list for review by the scheduler. All anomaly detection and entropy calculation processes are executed periodically by the edge nodes, with a cycle of 1 minute.

[0043] It should be noted that this entropy function replaces the traditional moving average method with a distribution information-driven quantitative index, enabling the anomaly detection mechanism to simultaneously consider the frequency of the signal and the distribution entropy. This avoids the misjudgment of boundary changes by simple threshold strategies and is suitable for real-time stability judgment of multi-type, non-stationary sampled signals.

[0044] S2: Perform multi-level standardized coding based on power plant structure information and equipment attributes to build a unified data index table.

[0045] A unique identifier is generated for each power station, consisting of a 15-digit identifier formed by sequentially concatenating the company code, country code, power station code, and unit type code. Equipment is coded using a three-level combination method based on its region, system type, and equipment category number, resulting in a plant-wide code, system code, and equipment code. All identification results are written to data records through field mapping and used as primary keys to construct the main index table and multi-table join query structures. A code conflict detection mechanism is implemented, automatically searching the existing identifier pool for all newly connected devices. If a duplicate number is found, a new unique code is generated based on the device deployment order and a backup numbering strategy to avoid duplicate conflicts.

[0046] Furthermore, this invention, referring to the "Electric Power Production Data Coding Standard," generates a 15-bit unique identifier for each power station, using the following composition rules:

[0047] Company code (4 digits): Set uniformly by the operating unit, such as the company's internal code "1085".

[0048] Country code (3 digits): Based on the GB / T2659 country / region code setting, such as "586" for China.

[0049] Power station code (4 digits): Determined according to the company's internal power station numbering system. For example, "0450" represents a certain hydropower station.

[0050] Unit type code (4 digits): Set according to the category of power generation equipment, such as "1163" representing a hydro-generator unit.

[0051] The generated complete identifier example is: "108558604501163", which serves as the unique logical number of the power station in the system.

[0052] This invention sets up a three-level combined coding system for equipment objects in a power plant:

[0053] Plant code (2-4 digits): indicates the functional area number of the equipment in the power plant. For example, "00" is equipment shared by the whole plant, and "01" indicates the area of ​​Unit 1.

[0054] System code (5 digits): Three letters represent the system classification (e.g., "MKV" represents the measurement and control system), and two digits represent the specific sub-unit number under the system.

[0055] Device code (5 digits): The first two digits are the device type code (e.g., "PT" for voltage transformer), and the last three digits are the serial number of the same type of device, starting with "001".

[0056] Example: The equipment code for a voltage transformer under Unit 1 can be "01MKV01PT001". This code, combined with the power plant identifier, constitutes a complete and unique equipment identifier.

[0057] When all power plant identifiers and equipment codes enter the database, they will be automatically written into the corresponding fields through a mapping table mechanism to form the logical primary key field of the data table. This ensures that each record in the system has a unique identifier for data writing, indexing, and cross-table access.

[0058] In the relational database, three main tables—power station table, equipment table, and signal table—are created for each coded object, recording metadata fields respectively. In the time-series database, a time-series table group is created based on the equipment 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, setting unique constraints on all key fields, and configuring redundant check bits and data record version numbers during the data writing process.

[0059] Furthermore, the power station table fields include Station_ID, country code, geographical coordinates, and voltage level. The equipment table fields include Device_ID, the associated Station_ID, equipment model, and installation location. The signal table fields include Signal_ID, the associated Device_ID, signal type, and data unit. All tables are interconnected through unique identifier fields (Station_ID, Device_ID) to achieve primary and foreign key constraints and cross-table joins.

[0060] It should be noted that by establishing unified coding rules for power plants and equipment, and constructing logical primary keys and a standardized indexing system, data structure consistency and end-to-end unique tracking of equipment data are achieved. This effectively improves the standardization of data storage and query efficiency, supporting multi-source data fusion analysis and cross-table logical join calls.

[0061] The completed primary key structure will serve as a key identifier in the field mapping table, used for field reorganization, unique signal binding, and structural hierarchy positioning during protocol template invocation.

[0062] To address the risk of code conflicts in power plant equipment codes under large-scale access environments, this invention proposes a uniqueness verification mechanism based on multi-factor coupling of deployment location, topology level, and equipment attributes to calculate the structural reinforcement code for each device:

[0063] ,

[0064] in, For the first The device in the Unique reinforcement value within the layer structure. This is a multi-factor coupling function, which includes deployment location mapping values. System topology level value Device attribute hash encoding . This refers to the equipment aging index. and They represent the first The offset interference and noise disturbance values ​​of the equipment. This is the complexity factor of the current structural layer.

[0065] Whenever there is new equipment The first connection to a certain power station When using a layered architecture, the platform generates standard codes and initiates a uniqueness conflict detection process. At this time, the system reads the device deployment location in real time. Hierarchical attributes Equipment type and parameters An aging coefficient is generated by combining the manufacturing time of the equipment. Based on the equipment lifecycle Using 12 months as the default time axis, the numerator is constructed by calculating the exponentially weighted multi-factor coupled integral value, while simultaneously extracting related devices at the same level within the system. =1... The perturbation information (deployment overlap, frequency interference, etc.) is used to calculate the normalized denominator, ultimately yielding the uniqueness enhancement score. .

[0066] The system sets a scoring threshold of 0.65. If the score is lower than this value, it indicates a high degree of similarity to existing codes or significant overlap in location / function / model. In this case, a backup numbering strategy will be triggered, prioritizing the addition of an extended suffix to the device or adjusting the system code position to ensure logical uniqueness. If the score... If the score is between 0.65 and 0.8, the code will be added to a potential conflict list for scheduler confirmation. If the score is below 0.65, the spare number generator will be automatically activated, the spare field segment code will be called and an auxiliary index tag will be attached, and the code will be written to the unique code pool. If the score is above 0.8, the current code will be retained. The final determined code and uniqueness score will be written together to the device table and the code mapping log for subsequent tracking and retrospective lookup.

[0067] It should be noted that by introducing multidimensional 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.

[0068] S3: Invoke the format adaptation and protocol conversion process to complete the structural docking and communication compatibility of power data between heterogeneous systems.

[0069] A format conversion controller is established to call 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. A three-stage verification mechanism is implemented during the adaptation process: the first stage is field coverage verification, determining whether all required fields match; the second stage is data type verification, checking whether the field value types are consistent with the target format definition; and the third stage is syntax encapsulation verification, checking the overall data packet structure integrity. After successful verification, the encapsulation result is submitted to the protocol adaptation process for subsequent communication encapsulation.

[0070] Furthermore, a preferred scheme for performing name mapping, default value filling, and data type conversion according to field definition rules specifically includes the following three aspects: In this invention, field definition rules are used to guide the format conversion controller to convert internal standardized data into a data structure that the target system can parse.

[0071] Field Name Mapping Table: The system maintains a field mapping configuration table to map data fields within the platform to the field names required by the target system. This table can automatically select a template based on different target systems.

[0072] Field type constraint table: The data type of each field in the target system must be clearly defined. For example, the Val field must be Float, TimeTag must be in ISO8601 timestamp format, and DevCode must be a string type with no more than 20 characters. The conversion controller checks and performs type conversion or format reconstruction according to this rule.

[0073] Field Integrity and Default Value Table: For required fields in the target system, if they are missing in the source data, the system will look up the default value for the corresponding field in the "Default Value Configuration Table" to populate the data structure and avoid validation failure.

[0074] A communication protocol adaptation component is deployed to achieve dynamic identification, structural encapsulation, and bidirectional conversion of power data across different communication protocols. This component includes a protocol identifier, a template invocation controller, an encapsulation verifier, 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 packet structure characteristics, and communication header field pattern matching, supporting mainstream power system communication protocols. Upon successful identification, the protocol identifier triggers the template invocation controller, which, according to the corresponding protocol's field mapping template, rearranges fields, converts value domain formats, and groups and encapsulates standardized data. During encapsulation, the encapsulation verifier performs three levels of integrity and accuracy verification, including: message integrity verification (data length, segment header and footer identifiers must match protocol definitions); timestamp accuracy verification (compare the difference between the data generation time and the current encapsulation time to ensure it meets timeliness requirements); and signal uniqueness verification (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 any protocol mismatch, missing field, or syntax error occurs during any encapsulation step, the encapsulation verifier will generate exception information, record the event, write it to the fault log table, and simultaneously push an alarm to the platform management terminal.

[0075] It should be noted that, through format adaptation and protocol conversion mechanisms, intelligent interconnection of power data from standardized structures to multi-protocol systems is achieved, supporting dynamic identification, encapsulation, and bidirectional communication. This enhances the data interoperability between heterogeneous platforms, ensures accurate data transmission between different systems, and significantly improves platform compatibility and engineering deployment flexibility.

[0076] To address the issues of stability, overhead, and matching rate differences in encapsulation paths for various communication protocols, this invention constructs a protocol scoring function based on dynamic field behavior for selecting the optimal protocol template during the encapsulation process.

[0077] ,

[0078] in, For the first The overall compatibility score of each protocol template. For the first The structural importance of class fields in the encapsulation template. This represents the number of encapsulation failures that have occurred in the most recent interactions with this field. Encapsulate the average time taken for the field. This is due to a structural compatibility error in the field. This is the protocol complexity evaluation value. This is a function that describes the change in packet loss rate of fields during the encapsulation process. Indicates the current number The total number of field categories to be encapsulated in each protocol template. Indicates the th traversal of the current iteration. Class field.

[0079] Field packet loss rate change function Represented as:

[0080] ,

[0081] in, For the first The packet loss rate change function is used in the field encapsulation process of a protocol template. The template-based 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. This refers to the periodic packet loss disturbance caused by factors such as network load in the periodic disturbance amplitude control package. The frequency is the disturbance frequency. The initial phase of the 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 normalize the scoring function.

[0082] When a power platform prepares to push data to other heterogeneous business systems, the protocol identifier first automatically determines the communication protocol type used by the target system (such as IEC104, MODBUS, etc.). After identification, the platform extracts a set of all available protocol templates. }, and then perform scoring calculations for each template in sequence. For each template The system statistics include the types of fields it contains. And calculate the historical frequency of encapsulation failures field by field. Conversion time Structural errors and field priority Simultaneously extract the template from the most recent Packet loss rate function within time Its integral value is used as a stability index.

[0083] Scoring function The above four dimensions are integrated into a unified evaluation score. The platform defaults to a scoring threshold of 0.60 for all templates. If the highest-scoring template... If the value exceeds this limit, the template is selected as the encapsulation scheme for this round. Otherwise, the system prompts for field mapping or structure supplementation. The scoring results are recorded in the encapsulation log for auditing by operations and maintenance personnel and for model iteration. The scoring function automatically updates the weight model every 24 hours to adapt to changes in field importance and evolving business needs.

[0084] Example 2, refer to Figure 2 As an embodiment of the present invention, a global power plant power data standardization coding and compatibility system is provided, including a data acquisition preprocessing module 100, a data index table construction module 200, and a protocol conversion module 300.

[0085] S4: The data preprocessing module 100 is used to collect various types of power operation data through the edge gateway and perform anomaly cleaning and structure classification on the data.

[0086] The data preprocessing module 100 includes an edge acquisition submodule 101 and a data cleaning and classification submodule 102.

[0087] Furthermore, the edge acquisition submodule 101 is used to deploy edge gateway acquisition devices to sample various types of power operation data, including parameters such as voltage, current, frequency, temperature, and wind speed. It also assigns a data type identifier, sampling timestamp, and acquisition location label to each type of data, thereby achieving data structure standardization and traceability identification at the edge.

[0088] The data cleaning and classification submodule 102 is used to perform anomaly detection and structural classification on the collected raw data. Based on the set threshold judgment rules, sliding window fluctuation analysis algorithm, and state logic condition identification mechanism, it removes or interpolates out-of-limit values, abrupt changes, and logically conflicting values. It also classifies the data into power plant information, equipment attribute, and operation signal categories according to field metadata attributes, and labels the corresponding categories in the data header.

[0089] It should be noted that the edge acquisition submodule 101 is the starting point of the data preprocessing module 100, and 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, and is a necessary prerequisite for subsequent encoding and index table construction.

[0090] S5: The data index table construction 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.

[0091] The data index table construction module 200 includes an encoding generation submodule 201 and a main table index construction submodule 202.

[0092] Furthermore, the coding 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 composed of the whole plant code, system code, and equipment code to form a unique identifier. A coding conflict detection mechanism is established to automatically judge and handle duplicate number conflicts.

[0093] The main table index construction submodule 202 is used to create three metadata main tables—the power plant table, the equipment table, and the signal table—in the relational database, using the power plant identifier and equipment code as primary keys. Simultaneously, a real-time data table group is created in the time-series database, indexed by the equipment code, to record continuous sequences of various signal values ​​changing over time. All table structures are configured with unique primary key constraints and data version number fields to ensure query efficiency and data consistency.

[0094] It should be noted that the unique identifiers for power plants and equipment output by the encoding generation submodule 201 provide the basis for establishing logical ownership relationships for the data. The main table index construction submodule 202 is the core mechanism for realizing cross-system data tracking and field association calls, ensuring that the platform has the ability to join multiple tables and perform efficient scheduling.

[0095] S6: 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.

[0096] The protocol conversion module 300 includes a format adaptation submodule 301 and a protocol encapsulation submodule 302.

[0097] Furthermore, the format adaptation submodule 301 is used to establish format conversion control logic. Based on the structure mapping configuration table and field definition rules, it performs name mapping, type conversion and default value filling on the standardized data structure, and sets 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.

[0098] The protocol encapsulation submodule 302 is used to deploy communication protocol adaptation 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 verification processor. The system automatically identifies the target communication protocol type, reassembles the field order and encapsulated content format according to the template, and performs data length, timestamp accuracy, and signal ID uniqueness verification. If the verification fails, an exception log is generated and a system alarm is pushed.

[0099] It should be noted that the format adaptation submodule 301 is the entry point of the protocol conversion module 300, determining the semantic and structural compatibility of the data. The protocol encapsulation submodule 302 provides bidirectional interface capabilities for cross-system data communication and is a key logical unit for the platform to achieve interoperability among multiple service systems.

Claims

1. A global power plant power data standardization coding and compatibility method, characterized in that, include: Multiple types of power operation data are collected through edge gateways, and anomaly cleaning and structural classification are performed on the data. Based on the power plant structure information and equipment attributes, multi-level standardized coding is performed to construct a unified data index table; The process of calling format adaptation and protocol conversion is used to complete the structural docking and communication compatibility of power data between heterogeneous systems; The protocol conversion process includes deploying communication protocol adaptation components and performing structural adaptation to mainstream power communication protocols. 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, it sets up message integrity verification mechanism, timestamp accuracy verification mechanism and signal uniqueness verification mechanism; for data interaction between cross-protocol systems, it performs bidirectional adaptation and dynamic reconfiguration; during the protocol conversion process, it records abnormal behavior to the log system and pushes alarm notifications to the management terminal for review and resending. Data anomaly cleaning and structure classification include, The edge gateway integrates a local cleaning engine to perform format consistency checks on received data and remove data fields with incomplete structures. Outliers are identified by setting thresholds, fluctuation windows, and logical rules; data marked as outliers are then interpolated and completed based on the principle of temporal proximity. After cleaning, the data is divided into power plant information, equipment attribute and operation signal categories 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. Multi-level standardized coding is performed based on power plant structural information and equipment attributes, including: A unique identifier is generated for each power station, which is a 15-digit power station identifier obtained by sequentially concatenating the company code, country code, power station code and unit type code. Equipment is coded using a three-level combination method based on its region, system type, and equipment category number, resulting in a plant-wide code, a system code, and an equipment code. All identification results are written to data records through field mapping and used as data primary keys to construct the main index table and multi-table join query structure; A coding conflict detection mechanism is set up to automatically search the existing identifier pool for all newly connected devices. If the number is duplicated, a unique code is regenerated based on the device deployment order and the backup numbering strategy to avoid duplicate conflicts. Building a unified data index table includes, In the relational database, three main tables are created for each coded object: a power station table, an equipment table, and a signal table, which record metadata fields 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.

2. The global power plant power data standardization coding and compatibility method as described in claim 1, characterized in that: The collection of various types of power operation data through the edge gateway includes, Deploy gateway devices to acquire power plant operation data in real time, configure data type identifiers and sampling time tags for each type of data, temporarily store the collected raw data in the local cache, and push it to the central data relay 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 global power plant power data standardization coding and compatibility method as described in claim 1 or 2, 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 is set up with a three-stage verification mechanism. The first stage is field coverage verification, which determines whether all required fields are matched. 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 successful verification, the encapsulation result is submitted to the protocol adaptation process for subsequent communication encapsulation.

4. The global power plant power data standardization coding and compatibility method as described in claim 3, characterized in that: The protocol conversion process includes, The communication protocol adaptation component is deployed to realize the dynamic identification, structural encapsulation and bidirectional conversion of power data between different communication protocols. The communication protocol adaptation 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 packet structure characteristics, and communication header field pattern matching, supporting mainstream power system communication protocols. After successful identification, the protocol identifier triggers the template call controller, which rearranges the fields, converts the value domain format, and groups and encapsulates the standardized data 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 footer identifiers and protocol definition consistency; Timestamp accuracy verification: compare the difference between the data generation time and the current encapsulation time to see if it meets the timeliness limit; Signal uniqueness verification: verify whether the measurement point number contained in the message has a binding relationship with 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 any protocol mismatch, missing field, or syntax error occurs during any encapsulation step, the encapsulation verifier will generate exception information, record the event, write it to the fault log table, and simultaneously push an alarm to the platform management terminal.

5. A global power plant power data standardization coding and compatibility system, employing a global power plant power data standardization coding and compatibility method as described in any one of claims 1 to 4, characterized in that: It includes a data acquisition preprocessing module (100), a data index table construction module (200), and a protocol conversion module (300). The data preprocessing module (100) is used to collect various types of power operation data through the edge gateway and perform anomaly cleaning and structure classification on the data; The data index table construction 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; 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.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the global power plant power data standardization coding and compatibility method as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the global power plant power data standardization coding and compatibility method as described in any one of claims 1 to 4.

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