Power distribution terminal parameter dynamic self-optimization method and system based on metadata driving and closed-loop feedback

Abstract

The invention provides a power distribution terminal parameter dynamic self-optimization method and system based on metadata driving and closed-loop feedback, and belongs to the technical field of power system automation. The method comprises the following steps: constructing and maintaining a four-dimensional dynamic association model of parameters, equipment, protocols and algorithms, and providing a standardized data base for parameter configuration; dynamically generating a parameter configuration view based on the parameter metadata, and executing multi-level security verification in editing, submitting and approving links; intelligently converting the approved parameter configuration data into a target terminal communication protocol instruction, and reliably issuing the target terminal communication protocol instruction to the power distribution terminal; and generating a parameter optimization strategy based on the multi-source real-time data driving algorithm model, executing the strategy, and dynamically adjusting the optimization algorithm according to execution feedback to form closed-loop optimization. According to the method, the problems of low efficiency, poor adaptability, insufficient safety, high technical barrier, lack of closed-loop capability and the like in the prior art are solved, and the intelligent level of operation and maintenance of the power distribution terminal is remarkably improved.

Description

Technical Field

[0001] This invention relates to a method and system for dynamic self-optimization of distribution terminal parameters based on metadata-driven and closed-loop feedback, belonging to the field of power system automation technology. Background Technology

[0002] With the rapid evolution of smart distribution networks towards higher resilience and digitalization, the refined and dynamic optimization of the parameters of distribution terminals (DTU / FTU / TTU), as the core nodes of power grid sensing and control, has become a key factor in improving power grid operation and maintenance efficiency and system reliability. However, the current parameter management model for distribution terminals suffers from a series of technical bottlenecks, severely restricting the development of smart distribution networks. Specifically, the existing technologies and their shortcomings are as follows: Inefficient configuration: Currently, configuring power distribution terminal parameters mainly relies on manual modification of configuration files item by item or the use of specialized tools, with a single parameter adjustment often taking more than 2 hours. The core of this problem lies in the lack of an automated mechanism for the generation, verification, and distribution of parameter configurations, resulting in excessive manual intervention, which is not only inefficient but also prone to errors.

[0003] Insufficient scenario adaptability: Existing technologies typically use static and fixed configuration templates, which cannot be adapted to different regions (such as urban and rural areas), equipment models (such as DTU and FTU), and operating scenarios (such as peak and off-peak loads). The essence of this is the lack of a flexible metadata-driven architecture, making parameter configuration difficult to adapt to the complex and ever-changing power grid operating environment.

[0004] Lack of safety mechanisms: Manual operation during parameter configuration can easily lead to safety issues such as parameter values ​​exceeding limits and protection setting mismatches, and there is a lack of real-time verification and traceability mechanisms throughout the entire configuration process. Several cases in the industry have involved line tripping due to human error, fully exposing the shortcomings of existing technologies in safety management.

[0005] Significant technical barriers exist: adjusting complex parameter strategies often relies on vendors modifying the underlying code, with development cycles lasting several weeks. The core issue lies in the failure to decouple configuration logic from the underlying code, limiting users' self-management capabilities and increasing maintenance costs and complexity.

[0006] Lack of closed-loop capability: Existing technologies exhibit fragmented processes in parameter configuration, security verification, execution, and effect feedback, failing to form a closed-loop autonomous mechanism of "generation-verification-execution-feedback-iteration." This makes it difficult for optimization strategies to dynamically evolve with the power grid's operating status and adapt to changes in the power grid in a timely manner.

[0007] To address the aforementioned issues, there is an urgent need for a method to resolve the problems of low efficiency, poor adaptability, insufficient security, high technical barriers, and lack of closed-loop capability in existing technologies. Summary of the Invention

[0008] The purpose of this invention is to provide a dynamic self-optimization method and system for distribution terminal parameters based on metadata-driven and closed-loop feedback. It aims to solve the problems of low efficiency, poor adaptability, insufficient security, high technical barriers and lack of closed-loop capability in the existing technology by constructing a metadata-driven dynamic adaptation architecture, designing a dynamic decision-making algorithm engine, establishing a full-process self-optimization closed-loop mechanism, realizing protocol-independent distribution, and reducing technical barriers, thereby significantly improving the intelligent level of distribution terminal operation and maintenance.

[0009] To achieve the above objectives, the present invention employs the following technical solution: A dynamic self-optimization method for distribution terminal parameters based on metadata-driven and closed-loop feedback includes the following steps: S1: Construct and maintain a four-dimensional dynamic correlation model of parameters, devices, protocols, and algorithms to provide a standardized data foundation for parameter configuration; S2: Dynamically generate a parameter configuration view based on the parameter metadata, and perform multi-level security checks during the editing, submission and approval processes; S3: Intelligently converts the approved parameter configuration data into target terminal communication protocol instructions and reliably sends them to the power distribution terminal; S4: Generate parameter optimization strategies based on multi-source real-time data-driven algorithm models, execute the strategies, and dynamically adjust the optimization algorithm based on execution feedback to form a closed-loop optimization.

[0010] Preferably, the construction and maintenance of the four-dimensional dynamic correlation model of parameters, devices, protocols, and algorithms includes: Define the device model and establish the association mapping between terminal type, hardware characteristics and communication protocol type; Define parameter items, including parameter name, encoding, data type, value range, logical constraint rules, and their address mapping in the communication protocol; Create versioned parameter templates and associate them with specific device models and parameter items; A pre-defined algorithm model defines the algorithm's input and output parameters and computational logic; Define the operation and maintenance task model, including triggering conditions, execution action sequence and approval rules.

[0011] Preferably, step S2 includes: Based on the selected device type or scenario, load the associated parameter template or dynamically filter parameter items; Dynamically render the visual editing component based on parameter-based data types and constraint rules; Perform real-time validation of data types, value ranges, and logical relationship constraints during editing; Upon submission, parameter change comparison information is generated, and a multi-level approval process associated with the parameter importance level is triggered.

[0012] Preferably, step S3 includes: Dynamically load the corresponding protocol adapter plugin based on the protocol type of the target device; Based on the protocol point table address mapping relationship defined in the parameter items, the parameter values ​​are converted into data frames or instruction sequences that conform to the target protocol specification; Instructions are sent to the terminal through a write, confirmation, and verification mechanism, and failure retry and state synchronization are performed.

[0013] Preferably, step S4 includes: The operation and maintenance tasks can be orchestrated in a visual way, defining trigger conditions including timed, event-based, or manual methods, as well as execution actions including parameter modification, algorithm calculation, device control, or API calls. When the task is triggered, acquire real-time data from multiple sources and call the preset algorithm model to calculate the optimized parameter values; The algorithm automatically performs security checks on the parameter values ​​output and sends them to the approval process. After execution and distribution, the optimization results are evaluated based on the actual operating effect, and the coefficients in the algorithm model are dynamically adjusted to achieve closed-loop feedback optimization.

[0014] Preferably, the coefficients in the dynamic adjustment algorithm model include: The operational effectiveness is quantitatively evaluated using key performance indicators, including alarm reduction rate or action accuracy rate. The algorithm coefficients are dynamically updated using a formula with a learning rate based on the deviation between the actual and target values ​​of key performance indicators.

[0015] Preferably, the algorithm used to calculate the optimized parameter value is an overcurrent protection setting optimization algorithm, and its calculation formula is: , in, This is the temperature compensation coefficient. This is the load compensation factor. To standardize temperature difference, To standardize the load deviation, the load compensation coefficient is dynamically adjusted based on the closed-loop feedback results, as shown in the following formula: , in, The adjusted load compensation coefficient is... The preload compensation coefficient is the total load factor. Learning rate The system monitors and statistically analyzes the actual measured values ​​of key performance indicators. To achieve the target values ​​for key performance indicators.

[0016] Preferably, the alarm reduction rate is equal to the difference between the number of alarms before optimization and the number of alarms after optimization divided by the number of alarms before optimization; The accuracy rate of an action is equal to the number of correct actions divided by the sum of the number of correct actions, the number of incorrect actions, and the number of refusal actions.

[0017] A dynamic self-optimization system for distribution terminal parameters based on metadata-driven and closed-loop feedback includes the following modules: The metadata-driven modeling module is used to build a four-dimensional dynamic correlation model of parameters, devices, protocols, and algorithms, enabling standardized definition and dynamic expansion of power distribution terminal parameters. The dynamic view generation and security verification module is used to dynamically generate a visual configuration interface based on parameter metadata, and to perform real-time verification and multi-level security control throughout the entire editing, submission and approval process. The protocol intelligent adaptation and delivery module is used to generate, convert, and reliably deliver parameter commands to multi-protocol terminals through plug-in protocol adapters; The dynamic decision-making and closed-loop operation and maintenance module is used to drive algorithmic decisions based on multi-source real-time data, and achieve closed-loop self-optimization through task orchestration, execution feedback and parameter iteration.

[0018] Preferably, the dynamic view generation and security verification module includes: The intelligent view generation unit is used to dynamically render UI components based on parameter metadata. The real-time validation engine unit is used to perform data type, value range, enumeration, and logical relationship validations during parameter editing. A multi-level approval control unit is used to automatically match approval nodes and record approval results based on the importance level of parameters.

[0019] The advantages of this invention are: it constructs a metadata-driven dynamic adaptation architecture, and through a four-dimensional association model of parameters, devices, protocols, and algorithms, it realizes flexible expansion and scenario-based adaptive adaptation of parameter templates, breaking the limitations of static templates.

[0020] The design of a dynamic decision-making algorithm engine is based on real-time data from multiple sources, such as weather, load, and equipment status. A parameter optimization calculation model is established to achieve intelligent generation and dynamic adjustment of optimal parameters, thus eliminating reliance on human experience.

[0021] Establish a closed-loop mechanism for full-process self-optimization, and achieve continuous autonomous optimization of parameter strategies through a closed-loop chain of "algorithm decision-making → security verification → reliable deployment → effect feedback → algorithm iteration".

[0022] It enables protocol-independent distribution and supports plug-and-play functionality for multi-protocol terminals (IEC104 / Modbus, etc.) through plug-in adapters, solving the problem of heterogeneous protocols from multiple vendors.

[0023] Lowering the technical barrier, the platform enables dynamic generation of parameter configuration views and automated operation and maintenance task orchestration through a low-code platform, allowing ordinary operation and maintenance personnel to independently complete complex optimization operations. Attached Figure Description

[0024] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0025] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0026] Figure 2 An entity relationship diagram (ER diagram) for the core business data model (metadata).

[0027] Figure 3 Flowchart for loading dynamic parameter templates and generating views.

[0028] Figure 4 Flowchart for parameter editing and real-time verification.

[0029] Figure 5 Flowchart for multi-level approval and security control of parameters.

[0030] Figure 6 This is a flowchart for dynamic decision-making and closed-loop operation and maintenance task execution.

[0031] Figure 7 This is a schematic diagram of the overall system architecture of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1 like Figure 1 As shown, a dynamic self-optimization method for distribution terminal parameters based on metadata-driven and closed-loop feedback includes the following steps: S1: Construct and maintain a four-dimensional dynamic correlation model of parameters, devices, protocols, and algorithms to provide a standardized data foundation for parameter configuration; S2: Dynamically generate a parameter configuration view based on the parameter metadata, and perform multi-level security checks during the editing, submission and approval processes; S3: Intelligently converts the approved parameter configuration data into target terminal communication protocol instructions and reliably sends them to the power distribution terminal; S4: Generate parameter optimization strategies based on multi-source real-time data-driven algorithm models, execute the strategies, and dynamically adjust the optimization algorithm based on execution feedback to form a closed-loop optimization.

[0034] As a refinement of the above embodiments, the construction and maintenance of the four-dimensional dynamic correlation model of parameters, devices, protocols, and algorithms includes: S101: Core Entity Design: Utilize the graphical modeling tools of a low-code platform to define the core business data model and relationships (see ER diagram). Figure 2 ),include: (1) Device model: Define terminal type (DTU, FTU, TTU, etc.), hardware characteristics (such as CPU model, storage capacity), and supported communication protocol types (IEC104 / Modbus, etc.), and establish the association mapping between devices and protocols. (2) Device instance: Record the unique ID, line, geographical location and associated device model of the physical terminal to achieve precise binding between physical device and abstract model. (3) Parameter definition: accurately describe the parameter metadata, including name, encoding, data type (int / float / enumeration, etc.), value range (min / max), constraint rules (value range verification, logical relationship constraints such as "overcurrent I segment set value > II segment set value"), and reduction point table address mapping (associated with the register address of the communication protocol), laying the foundation for protocol conversion. (4) Parameter templates: Create versioned configuration sets, associate them with specific device models, and include parameter item definitions and default values ​​or configuration rules (such as formula references). They support quick reuse of templates, version comparison and rollback management.

[0035] (5) Dynamic parameter configuration: Real-time parameter values, configuration status (pending / issuing / successful / failed) and historical version records (including operator, time and reason for change) of storage device instances, so as to achieve full life cycle traceability of parameters.

[0036] (6) Algorithm model library: Pre-set optimization algorithms (such as overcurrent protection adaptive algorithm and load prediction correction algorithm), define algorithm input parameters (such as temperature and load rate), output parameters (such as protection setpoint) and calculation logic, and support algorithm version management and dynamic calling.

[0037] (7) Operation and maintenance task model: Define automated process templates, including triggering conditions (timed / event / manual), execution action sequence (parameter modification / device control / API call, etc.), approval rules (graded according to parameter importance), etc., to provide a basic model for automated operation and maintenance. S102: Initialization Mechanism: The system pre-sets common equipment models (such as DTU-A type, FTU-B type), basic parameter item definition library (such as protection type / measurement type parameters), default parameter templates (such as residential area / industrial area scene templates) and other basic data, and realizes rapid initialization and deployment through the metadata batch import interface (supporting Excel / JSON format).

[0038] As a refinement of the above embodiments, step S2 includes: S201: Intelligent View Generation Mechanism (Refer to...) Figure 3 ): (1) After the operation and maintenance personnel select the target equipment type (or instance) and parameter scenario (such as "summer peak protection configuration"), the system automatically loads the associated parameter template (or dynamically selects applicable items from the parameter item library).

[0039] (2) Intelligent rendering of visual editing components based on parameter item metadata (data type, constraint rules): ① Numeric type → Slider / input box with range limit + unit display; ② Enumeration type → Drop-down selection box (options come from the set of enumeration values ​​defined in the metadata); ③ Boolean type → Switch control (displays "Enable / Disable" text description); ④ Text type → Text boxes with format validation (such as IP address format validation); The generated interface is a dynamically customized parameter configuration view, requiring no manual UI code writing and adapting to parameter differences between different device models. S202: Real-time Verification Engine Design (Reference) Figure 4 ): (1) Real-time verification during editing: When maintenance personnel edit parameter values, the system triggers the real-time verification engine to synchronously perform data type verification (such as blocking non-integer input), value range verification (highlighting prompts when exceeding min / max), enumeration option verification (restricting input of non-preset values), and logical relationship constraint verification (such as "overflow I segment set value must be greater than II segment"). Error information is displayed immediately (red border + prompt text) to intercept errors from the source. (2) Forced review upon submission: After submission, the system automatically generates a parameter change comparison table, highlights the modified items (original value → target value) and the input box for the reason for change, and ensures that the operation and maintenance personnel confirm that there are no errors before entering the approval process.

[0040] S203: Parameter Approval and Safety Control (Refer to) Figure 5 ): After submission, the configuration data enters a multi-level approval process. The system automatically matches the approval node (e.g., team technician → dispatch center) based on the parameter's importance level (e.g., primary protection parameter / secondary measurement parameter) and modification range threshold (e.g., setting change ≥ 20%). Approving personnel can view modification details, difference comparisons, and verification results, and approve or reject the modification (a reason must be provided for rejection). Only parameters that have passed all approvals can proceed to the distribution stage. The above steps enable intelligent dynamic rendering of UI components based on parameter metadata, and build a multi-level verification chain that runs through the entire process of editing, submitting, and approving, ensuring the security and accuracy of the configuration process.

[0041] As a refinement of the above embodiments, step S3 includes: (1) Protocol intelligent adaptation mechanism: The system queries the communication protocol type (such as IEC60870-5-104, ModbusTCP) based on the target device instance ID, and dynamically loads the corresponding protocol adapter plugin (supports hot-swapping) through the plugin management platform to achieve automatic matching between the protocol and the device without manual intervention. (2) Command generation and conversion logic: The protocol adapter converts the approved parameter configuration data (parameter item encoding → target value) into data frames or command sequences that conform to the target protocol specification. The conversion process strictly follows the address mapping relationship of the protocol point table in the parameter item definition (such as the ocr1 parameter being mapped to the 0x3F01 address of IEC104) to ensure the accuracy of the command. (3) Reliable data transmission and state synchronization technology: The instructions are sent asynchronously to the communication service layer via a message queue, and the communication service layer establishes a connection with the terminal through the corresponding physical interface (Ethernet / 4G). The system employs a three-step mechanism: ① Write the parameter value to the device register; ② Read the confirmation information returned by the device; ③ Verify that the actual parameter value has been written correctly to ensure reliable delivery. Implement a failure retry mechanism (default ≤ 3 times, configurable) and failure alarm notification (SMS / system pop-up), update the status of dynamic parameter configuration (success / failure / timeout) and operation log (including time, operator, device ID, parameter change items, original value, target value, protocol instructions, execution results, etc.), and support full process traceability. As a refinement of the above embodiments, step S4 includes: (1) Task visualization and orchestration mechanism: The operation and maintenance task model is built using a graphical process designer (drag-and-drop operation) on a low-code platform. Core components include: Trigger conditions: Supports timed triggering (e.g., daily at 14:00), event triggering (e.g., "line load rate ≥ 90%" "temperature ≥ 35℃"), and manual triggering. Multiple conditions can be combined using logical operators (AND / OR) (e.g., "timed at 14:00 AND temperature ≥ 35℃").

[0042] Execution actions include: batch parameter modification (calling predefined templates or dynamically calculated values); algorithm calculation (binding to preset algorithms in the algorithm model library, inputting external data such as meteorological API temperature values ​​and SCADA load rates, and outputting optimized parameter values); equipment control (restart / opening / closing, requiring secondary confirmation); work order generation and notification (via SMS / WeChat); and external API calls (such as real-time data interfaces from meteorological bureaus). Approval nodes: Multi-level approval processes are embedded based on the task risk level (high / medium / low) (e.g., high-risk tasks require approval from the scheduling director).

[0043] (2) Execution logic of dynamic decision-making algorithm: Input layer: When a task is triggered, it automatically acquires real-time data from multiple sources, including meteorological data (temperature, humidity), power grid data (line load rate, voltage), and equipment status data (terminal temperature, running time).

[0044] Computation layer: Invokes preset algorithm models to perform calculations. Example algorithm formula (overcurrent protection setting optimization): , in: This is the temperature compensation coefficient (default 0.8, generated based on historical data training). This is the load compensation factor (default 1.2, dynamically adjusted via machine learning). Standardized temperature difference (35℃ as the reference temperature); Standardized load deviation (85% is the baseline load rate).

[0045] Output layer: Generates parameter modification instructions (e.g., ocr1=68.0A) and decision basis (e.g., "6℃, load rate 92%, it is recommended to increase the overcurrent stage I setting by 3A").

[0046] (3) Closed-loop execution and feedback technology: Automatically trigger the verification process: The parameter values ​​output by the algorithm automatically call the real-time verification engine (value range / logical constraint verification) in technical module two.

[0047] Mandatory approval control: After real-time verification, the process enters the multi-level approval process of technical module three, and the agreement is issued after approval.

[0048] Execution feedback mechanism: Success: Record the actual running results (e.g., no overload alarms within 1 hour after optimization, normal action response time), and execute. The coefficient dynamic adjustment algorithm recalculates (such as) (Increased from 1.2 to 1.25).

[0049] Failure: Trigger an alarm notification (including anomaly details), generate a diagnostic report (e.g., "Low setpoint caused malfunction"), and execute... The coefficient dynamic adjustment algorithm recalculates (e.g., reduce) (Up to 1.1).

[0050] Formula for dynamic adjustment algorithm of coefficients: , in, The adjusted load compensation coefficient is... The preload compensation coefficient is the total load factor. The learning rate is initially 0.05. Quantitative calculation of key performance indicators: Alarm reduction rate = (Number of alarms before optimization - Number of alarms after optimization) / Number of alarms before optimization.

[0051] Action accuracy rate = Number of correct actions / (Number of correct actions + Number of incorrect actions + Number of refusal actions).

[0052] End-to-end status tracking: Updates the status of operation and maintenance tasks (success / failure) and execution logs (input data, algorithm output values, and feedback results), providing data support for algorithm iteration and forming a complete closed loop of "decision-execution-feedback-optimization".

[0053] It should be noted that the present invention brings the following technical effects: 1. Breakthrough in efficiency: Through metadata-driven view generation, automated verification and distribution, the time for a single parameter configuration is reduced from ≥2 hours to ≤5 minutes, with an efficiency improvement of >99%. The core reason is the full automation of the configuration process. 2. Significantly enhanced scene adaptability: The parameter metadata and template mechanism support differentiated configurations for different devices, regions, and scenes, achieving adaptation without modifying the underlying code, which is more than 10 times more efficient than the static template solution. 3. Comprehensive upgrade of security mechanisms: The real-time verification engine intercepts more than 99% of parameter errors, the multi-level approval process ensures compliance, and the operation log enables full-process traceability, reducing security risks to near zero. 4. Leap in decision-making intelligence: The dynamic decision-making algorithm achieves adaptive parameter optimization based on multi-source real-time data, eliminating reliance on human experience and improving the accuracy of parameter optimization by more than 30% in complex scenarios.

[0054] 5. Industry-leading closed-loop capability: The automated operation and maintenance task engine realizes a complete closed loop of "perception-decision-execution-feedback-optimization", which improves the operation and maintenance response speed by 80% and the system reliability by more than 15% compared with the traditional method.

[0055] 6. Significantly reduced technical barriers: Configuration and operation orchestration are achieved through zero-coding / low-coding methods. Ordinary operation and maintenance personnel can operate independently after simple training, breaking the technical monopoly of vendors and reducing response time from weeks to hours.

[0056] Example 2 The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. This embodiment takes the dynamic optimization of overcurrent protection parameters of a 10kV line DTU terminal as an example to demonstrate the closed-loop self-optimization process driven by dynamic decision-making and metadata.

[0057] 1. Metadata modeling: (1) Define the device model as “DTU-A type”, specify that its supported communication protocol is “IEC104”, and its hardware characteristics are “support overcurrent I / II / III stage protection”.

[0058] (2) Define core parameter items: Create “Overcurrent Protection Setting_I Segment” (ocr1), data type float, unit A, value range [10.0, 100.0], default value 50.0, logical constraint “ocr1>Overcurrent Protection Setting_II Segment”, protocol address mapping 0x3F01; similarly define related parameters such as “Overcurrent Protection Setting_II Segment” (ocr2).

[0059] (3) Establish the parameter template "DTU-A Basic Protection Template", associate it with the "DTU-A Type" device model, and include parameter items such as ocr1 and ocr2 and default values, and support version management.

[0060] (4) Enter the device instance: DTU device, ID=DTU-001, line “Chengdong Line #1”, geographical location “Chengdong Industrial Zone”, associated device model “DTU-A type”, and complete the binding of physical device and metadata model.

[0061] (5) Define the algorithm model: Add "overcurrent protection adaptive optimization algorithm" to the algorithm model library, associate the input parameters (real-time temperature, real-time load rate) and output parameters (ocr1 optimization value), and the algorithm formula is as described in technical module four.

[0062] (6) Create an operation and maintenance task model: Define the task template for "Summer Peak Overcurrent Protection Optimization", with the trigger condition being "14:00 every day AND the highest temperature of the day ≥ 35℃", the execution action being "call the overcurrent protection adaptive optimization algorithm → automatic parameter adjustment → generate execution report", and the approval node being "team technician → dispatch center protection specialist".

[0063] 2. Dynamic view generation and security verification (refer to...) Figure 3 , Figure 4 ): (1) The maintenance personnel select the device instance "DTU-001" and the scenario "overcurrent protection configuration" through the system interface, and the system automatically loads the associated "DTU-A basic protection template V1.0".

[0064] (2) Based on the parameter item metadata, the system dynamically generates a visual configuration view: the ocr1 parameter is rendered as a "slider + input box" component (slider range 10.0-100.0A, input box displays the current value 50.0A in real time), and automatically associates logical constraints to prompt "must be greater than the ocr2 value (current 40.0A)".

[0065] (3) According to the summer load characteristics of the industrial area, the maintenance personnel input ocr1=65.0A. The real-time verification engine immediately verifies that it is within the range of [10.0,100.0] and satisfies "65.0>40.0". The verification is successful (green prompt "parameter is valid").

[0066] (4) After submission, the system generates a parameter change comparison table (highlighting OCR1 from 50.0A to 65.0A). The maintenance personnel fill in the reason for the change, "Summer load increases, protection setting value is increased", and then submit it for approval.

[0067] 3. Intelligent Protocol Adaptation and Distribution (Refer to...) Figure 5 ) (1) The system identifies OCR1 as a “Level 1 protection setting” and automatically triggers a two-level approval process: the team technician reviews the modification details and verification results, and approves it after confirming that it meets the load characteristics of the industrial area; the protection specialist in the dispatch center reviews the logical constraints and on-site necessity before approving it.

[0068] (2) After approval, the system automatically calls the “IEC104 protocol adapter” to convert ocr1=65.0A into a protocol instruction frame with address 0x3F01 (a telemetry setting instruction conforming to the IEC104 standard).

[0069] (3) Issue commands through the “command writing - device confirmation - value verification” mechanism: ① Write commands to DTU-001 ② Receive “write successful” confirmation returned by the device ③ Read the value of device register 0x3F01 after 3 seconds, which is 65.0A, and the verification is successful. The system updates the dynamic parameter configuration status to “successfully issued” and records the operation log (time, operator, parameter values ​​before and after change, etc.).

[0070] 4. Dynamic decision-making and closed-loop operation and maintenance execution (refer to...) Figure 6 ) Taking the "Summer Peak Overcurrent Protection Optimization" task as an example, the closed-loop self-optimization process is demonstrated: (1) Task triggering: At 14:00 on a certain day, the system detects "the highest temperature of the day is 36℃≥35℃" through the meteorological API, which meets the triggering conditions, and the task is instantiated and executed.

[0071] (2) Dynamic decision calculation: The task automatically calls "Overcurrent Protection Adaptive Optimization Algorithm V1.0" to obtain real-time data (temperature 36℃, load rate of Chengdong Line 92%), and calculates: Optimized value = 65.0 × (1 + 0.8 × 0.1 + 1.2 × 0.014) = 68.0A.

[0072] (3) Automatic parameter adjustment: The system sets the target value of OCR1 to 68.0A and automatically triggers the verification process (verifies that 68.0 is within the range of [10.0, 100.0] and > OCR2 = 40.0A). After the verification is passed, the approval process is initiated (same as step 3).

[0073] (4) Issuance and feedback: After approval, ocr1=68.0A was successfully issued to DTU-001. The system continuously monitored the line operation status (no overload alarm occurred from 14:00 to 16:00), and the optimization was deemed effective.

[0074] (5) Algorithm iteration: Based on success feedback, the system iterates through... The coefficient adjustment formula is calculated as follows (assuming actual) ,Target ), load compensation factor The version has been adjusted from 1.2 to 1.25 to improve decision-making accuracy under high-load scenarios and complete the "decision-execution-feedback-optimization" closed loop.

[0075] As can be seen from this embodiment, the present invention achieves flexible parameter adaptation through metadata-driven approach, intelligent optimization through dynamic decision-making algorithm, and continuous iteration through full-process closed-loop mechanism. It effectively solves the problems of low efficiency, poor adaptation, decision lag, and high security risks in traditional parameter management, and significantly improves the level of intelligent operation and maintenance of power distribution terminals.

[0076] Example 3 like Figure 7 As shown, a dynamic self-optimization system for distribution terminal parameters based on metadata-driven and closed-loop feedback includes the following modules: The metadata-driven modeling module is used to build a four-dimensional dynamic correlation model of parameters, devices, protocols, and algorithms, enabling standardized definition and dynamic expansion of power distribution terminal parameters. The dynamic view generation and security verification module is used to dynamically generate a visual configuration interface based on parameter metadata, and to perform real-time verification and multi-level security control throughout the entire editing, submission and approval process. The protocol intelligent adaptation and delivery module is used to generate, convert, and reliably deliver parameter commands to multi-protocol terminals through plug-in protocol adapters; The dynamic decision-making and closed-loop operation and maintenance module is used to drive algorithmic decisions based on multi-source real-time data, and achieve closed-loop self-optimization through task orchestration, execution feedback and parameter iteration.

[0077] As a refinement of the above embodiments, the dynamic view generation and security verification module includes: The intelligent view generation unit is used to dynamically render UI components based on parameter metadata. The real-time validation engine unit is used to perform data type, value range, enumeration, and logical relationship validations during parameter editing. A multi-level approval control unit is used to automatically match approval nodes and record approval results based on the importance level of parameters.

[0078] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic self-optimization of distribution terminal parameters based on metadata-driven and closed-loop feedback, characterized in that, Includes the following steps: S1: Construct and maintain a four-dimensional dynamic correlation model of parameters, devices, protocols, and algorithms to provide a standardized data foundation for parameter configuration; S2: Dynamically generate a parameter configuration view based on the parameter metadata, and perform multi-level security checks during the editing, submission and approval processes; S3: Intelligently converts the approved parameter configuration data into target terminal communication protocol instructions and reliably sends them to the power distribution terminal; S4: Generate parameter optimization strategies based on multi-source real-time data-driven algorithm models, execute the strategies, and dynamically adjust the optimization algorithm based on execution feedback to form a closed-loop optimization.

2. The dynamic self-optimization method for distribution terminal parameters based on metadata-driven and closed-loop feedback as described in claim 1, characterized in that, The four-dimensional dynamic correlation model for constructing and maintaining parameters, devices, protocols, and algorithms includes: Define the device model and establish the association mapping between terminal type, hardware characteristics and communication protocol type; Define parameter items, including parameter name, encoding, data type, value range, logical constraint rules, and their address mapping in the communication protocol; Create versioned parameter templates and associate them with specific device models and parameter items; A pre-defined algorithm model defines the algorithm's input and output parameters and computational logic; Define the operation and maintenance task model, including triggering conditions, execution action sequence and approval rules.

3. The dynamic self-optimization method for distribution terminal parameters based on metadata-driven and closed-loop feedback as described in claim 1, characterized in that, Step S2 includes: Based on the selected device type or scenario, load the associated parameter template or dynamically filter parameter items; Dynamically render the visual editing component based on parameter-based data types and constraint rules; Perform real-time validation of data types, value ranges, and logical relationship constraints during editing; Upon submission, parameter change comparison information is generated, and a multi-level approval process associated with the parameter importance level is triggered.

4. The dynamic self-optimization method for distribution terminal parameters based on metadata-driven and closed-loop feedback according to claim 1, characterized in that, Step S3 includes: Dynamically load the corresponding protocol adapter plugin based on the protocol type of the target device; Based on the protocol point table address mapping relationship defined in the parameter items, the parameter values ​​are converted into data frames or instruction sequences that conform to the target protocol specification; Instructions are sent to the terminal through a write, confirmation, and verification mechanism, and failure retry and state synchronization are performed.

5. The dynamic self-optimization method for distribution terminal parameters based on metadata-driven and closed-loop feedback according to claim 1, characterized in that, Step S4 includes: The operation and maintenance tasks can be orchestrated in a visual way, defining trigger conditions including timed, event-based, or manual methods, as well as execution actions including parameter modification, algorithm calculation, device control, or API calls. When the task is triggered, acquire real-time data from multiple sources and call the preset algorithm model to calculate the optimized parameter values; The algorithm automatically performs security checks on the parameter values ​​output and sends them to the approval process. After execution and distribution, the optimization results are evaluated based on the actual operating effect, and the coefficients in the algorithm model are dynamically adjusted to achieve closed-loop feedback optimization.

6. The method for dynamic self-optimization of distribution terminal parameters based on metadata-driven and closed-loop feedback according to claim 5, characterized in that, The coefficients in the dynamic adjustment algorithm model include: The operational effectiveness is quantitatively evaluated using key performance indicators, including alarm reduction rate or action accuracy rate. The algorithm coefficients are dynamically updated using a formula with a learning rate based on the deviation between the actual and target values ​​of key performance indicators.

7. The dynamic self-optimization method for distribution terminal parameters based on metadata-driven and closed-loop feedback according to claim 6, characterized in that, The algorithm used to calculate the optimized parameter value is an overcurrent protection setting optimization algorithm, and its calculation formula is as follows: ， in, This is the temperature compensation coefficient. This is the load compensation factor. To standardize temperature difference, To standardize the load deviation, the load compensation coefficient is dynamically adjusted based on the closed-loop feedback results, as shown in the following formula: ， in, The adjusted load compensation coefficient is... The preload compensation coefficient is the total load factor. Learning rate The system monitors and statistically analyzes the actual measured values ​​of key performance indicators. To achieve the target values ​​for key performance indicators.

8. The dynamic self-optimization method for distribution terminal parameters based on metadata-driven and closed-loop feedback according to claim 6, characterized in that, The alarm reduction rate is equal to the difference between the number of alarms before optimization and the number of alarms after optimization, divided by the number of alarms before optimization. The accuracy rate of an action is equal to the number of correct actions divided by the sum of the number of correct actions, the number of incorrect actions, and the number of refusal actions.

9. A dynamic self-optimization system for distribution terminal parameters based on metadata-driven and closed-loop feedback, characterized in that, The method for dynamic self-optimization of distribution terminal parameters based on metadata-driven and closed-loop feedback as described in any one of claims 1-8 includes the following modules: The metadata-driven modeling module is used to build a four-dimensional dynamic correlation model of parameters, devices, protocols, and algorithms, enabling standardized definition and dynamic expansion of power distribution terminal parameters. The dynamic view generation and security verification module is used to dynamically generate a visual configuration interface based on parameter metadata, and to perform real-time verification and multi-level security control throughout the entire editing, submission and approval process. The protocol intelligent adaptation and delivery module is used to generate, convert, and reliably deliver parameter commands to multi-protocol terminals through plug-in protocol adapters; The dynamic decision-making and closed-loop operation and maintenance module is used to drive algorithmic decision-making based on multi-source real-time data, and achieve closed-loop self-optimization through task orchestration, execution feedback and parameter iteration.

10. The dynamic self-optimization system for distribution terminal parameters based on metadata-driven and closed-loop feedback as described in claim 1, characterized in that, The dynamic view generation and security verification module includes: The intelligent view generation unit is used to dynamically render UI components based on parameter metadata. The real-time validation engine unit is used to perform data type, value range, enumeration, and logical relationship validations during parameter editing. A multi-level approval control unit is used to automatically match approval nodes and record approval results based on the importance level of parameters.

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