Fire alarm linkage device based on new energy smart power station

Abstract

This invention relates to the field of safety monitoring technology for new energy power stations, and particularly to a fire alarm linkage device based on a smart new energy power station. The device includes a fire linkage center, an environmental sensing module, an intelligent analysis module, a linkage selection and control module, a linkage division module, and a linkage execution module. This invention constructs a real-time virtual 3D model and, with the aid of a fire risk model and scene type matching algorithm, can quickly identify potential fault characteristics and accurately locate risk positions. It also employs a quantitative scoring method to select the optimal solution, overcoming the limitations of traditional experience-based selection. Furthermore, through visualized analysis of the execution status, it ensures the effective implementation of linkage commands, resolving the problem of handling failures caused by single equipment malfunctions. Finally, it identifies deviation stages through deviation rate calculation and constructs a dynamic deviation list, forming a closed-loop mechanism of "solution execution - stage evaluation - dynamic optimization" to improve the timeliness and targeting of dynamic control.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology for new energy power stations, and in particular to a fire alarm linkage device based on a smart new energy power station. Background Technology

[0002] With the rapid development of the new energy industry, the scale and complexity of smart stations such as photovoltaic power stations, energy storage stations, and charging pile clusters continue to increase, posing a severe challenge to fire safety. These stations have dense equipment and highly concentrated energy, and the causes of fires are diverse—including not only obvious risks such as traditional smoke and open flames, but also potential faults that are highly concealed and develop rapidly, such as abnormal electrical parameters, leakage of characteristic gases, and thermal runaway of equipment. Moreover, the faults spread quickly and are difficult to handle, which can easily lead to major safety accidents and economic losses.

[0003] Currently, traditional fire alarm linkage devices have significant limitations. On the one hand, the data collection dimensions are limited, focusing mainly on basic physical signals such as smoke and temperature, lacking targeted collection of key hidden danger data such as electrical status (voltage, current, insulation parameters) and characteristic gas concentrations, resulting in delayed early fault identification. On the other hand, linkage control relies on experience-based scheme selection, lacking precise adaptation to the spatial layout and equipment distribution of the site. The implementation of the scheme lacks quantitative evaluation and dynamic adjustment mechanisms, which can easily lead to problems such as linkage delays, handling deviations, or excessive intervention, making it difficult to meet the fire protection needs of new energy smart sites for "early warning, precise control, and efficient handling".

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a fire alarm linkage device based on a new energy smart power station. By constructing a real-time virtual 3D model and using a fire risk model and scene type matching algorithm, it can quickly identify potential fault characteristics and accurately locate risk locations, solving the problems of delayed early warning and ambiguous risk location in traditional systems. Furthermore, it extracts alternative linkage schemes from a scheme library based on the target scenario and uses a quantitative scoring method to select the optimal scheme, overcoming the limitations of traditional experience-based selection. At the same time, through execution status visualization analysis, it ensures the effective implementation of linkage commands, solving the problem of handling failures caused by single equipment failures. Moreover, it identifies deviation stages through deviation rate calculation and constructs a dynamic deviation list, forming a closed-loop mechanism of "scheme execution - stage evaluation - dynamic optimization" to improve the timeliness and pertinence of dynamic control.

[0006] The objective of this invention can be achieved through the following technical solution: a fire alarm linkage device based on a new energy smart power station, comprising a fire linkage center, an environmental sensing module, an intelligent analysis module, a linkage selection and control module, a linkage division module, and a linkage execution module;

[0007] The environmental perception module comprehensively collects dynamic multidimensional data within the target site and sends it to the fire alarm control center for storage. At the same time, it collects static basic data of the target site and constructs a real-time virtual 3D model based on the static basic data.

[0008] The intelligent analysis module is used to match the dynamic multidimensional data of the target site under the current scene with the dynamic multidimensional data of different scenes retrieved, and set the scene type with the highest matching degree as the target scene.

[0009] The linkage selection and control module is used to extract all alternative linkage schemes corresponding to the target scene, and to perform linkage control matching analysis on the positive and negative evaluation indicators of each collected alternative linkage scheme, and output the optimal linkage control scheme and collaborative feedback instructions.

[0010] When generating collaborative feedback instructions, the linkage division module is used to perform execution division and expected evaluation analysis of the optimal linkage control scheme and output a dynamic deviation list.

[0011] Preferably, the real-time virtual 3D model construction process is as follows:

[0012] S1: Collect dynamic multidimensional data and static basic data within the target site;

[0013] S2: Preprocess the collected static basic data to obtain initial basic data;

[0014] S3: Call the 3D modeling engine to build a virtual 3D framework of the station at a 1:1 scale based on the initial basic data to obtain the initial virtual 3D model;

[0015] S4: Import the collected dynamic multidimensional data into the initial virtual 3D model to obtain the real-time virtual 3D model. Preferably, the analysis process of the intelligent analysis module is as follows:

[0016] T1: Retrieve dynamic multidimensional data of the target site from the real-time virtual 3D model, and simultaneously call the pre-set fire risk model. After preprocessing the dynamic multidimensional data, input it into the pre-set fire risk model to determine whether there are potential fault characteristics in the target site.

[0017] T2: When the pre-set fire risk model identifies potential fault characteristics, it extracts the physical location information corresponding to the fault characteristics and transmits the location information to the real-time virtual 3D model to obtain the early warning virtual 3D model.

[0018] Preferably, it also includes T3: simultaneously retrieving dynamic multidimensional data under different scenarios of the target site, matching the dynamic multidimensional data under the current scenario of the target site with the retrieved dynamic multidimensional data under different scenarios, and setting the scenario type with the highest matching degree as the target scenario.

[0019] Preferably, the analysis process of the linkage selection and control module is as follows:

[0020] Based on the target scenario, all alternative linkage schemes corresponding to the target scenario are extracted from the preset linkage control scheme library. The execution process of each alternative linkage scheme is simulated to obtain the positive and negative evaluation indicators of each alternative linkage scheme.

[0021] The extreme value standardization method was used to convert positive and negative evaluation indicators into standard scores of 0-100.

[0022] Based on the formula: Scheme Optimization Score = ∑ (Indicator Standard Score Zi × Corresponding Weight Coefficient ai), the scheme optimization score of each candidate linkage scheme is obtained;

[0023] All alternative linkage schemes are sorted from highest to lowest according to their optimal scheme score, and the alternative linkage scheme with the highest score is the optimal linkage control scheme for the target fault scenario.

[0024] Preferably, the controlled equipment is controlled based on the optimal linkage control scheme, and the execution information (success / failure) of each controlled equipment within a preset time threshold is obtained. The controlled equipment that is executed successfully in the execution information is marked in green in the early warning virtual 3D model, and the controlled equipment that is executed unsuccessfully in the execution information is marked in yellow in the early warning virtual 3D model, thus obtaining the execution 3D model diagram;

[0025] Execution analysis is performed based on the 3D model diagram. If all controlled devices execute successfully, a collaborative feedback instruction is generated. If a single device fails to execute, the "backup device" instruction is automatically invoked, and an alarm message is sent to the operation and maintenance terminal. If multiple devices fail to execute, the "emergency manual intervention" process is triggered.

[0026] Preferably, the analysis process of the linkage division module is as follows:

[0027] The execution process of the optimal linkage control scheme is divided into the fault suppression start stage, the fault recovery and stabilization stage, and the post-recovery observation stage (representing the preset duration observation stage after the fault recurrence rate = 0).

[0028] For each stage, obtain the expected benchmark value of the optimal linkage solution; collect multi-dimensional target data for each stage in real time, including load recovery rate and equipment protection success rate;

[0029] Based on the multidimensional target data at each stage, calculate the deviation rate P between the actual value and the expected benchmark value of each quantitative indicator in the multidimensional target data at each stage:

[0030] The deviation rate P of each stage is processed to determine the deviation stage or the target stage. When a deviation stage exists, the quantitative indicator corresponding to the deviation rate P being less than zero is marked as the target indicator. A dynamic deviation list is constructed based on the target indicator and the deviation stage.

[0031] The beneficial effects of this invention are as follows:

[0032] (1) This invention uses an environmental perception module to synchronously collect dynamic multidimensional data and static basic data, and combines 1:1 scale virtual three-dimensional modeling and data association to construct a real-time virtual three-dimensional model. With the help of fire risk model and scene type matching algorithm, it can quickly identify potential fault characteristics, accurately locate risk locations, and present them intuitively through visualization, thus solving the problems of delayed early warning and ambiguous risk location in traditional systems.

[0033] (2) The present invention also extracts alternative linkage schemes from the scheme library based on the target scenario and uses a quantitative scoring method to screen the optimal scheme, which solves the limitation of traditional experience-based selection. At the same time, through mechanisms such as execution status visualization marking, automatic switching of backup equipment, and emergency manual intervention triggering, it ensures the effective implementation of linkage instructions, solves the problem of handling failure caused by single equipment failure, and identifies deviation stages by deviation rate calculation and constructs a dynamic deviation list to form a closed-loop mechanism of "scheme execution-stage evaluation-dynamic optimization", which effectively reduces the risk of execution anomalies and improves the timeliness and pertinence of dynamic control. Attached Figure Description

[0034] The invention will now be further described with reference to the accompanying drawings;

[0035] Figure 1 This is a flowchart of the system of the present invention;

[0036] Figure 2 This is a reference diagram for analyzing the environmental perception module of the present invention;

[0037] Figure 3 This is a reference diagram for analyzing the optimal linkage control scheme. Detailed Implementation

[0038] 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.

[0039] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments;

[0040] Example 1: Please refer to Figures 1 to 3 As shown, the present invention is a fire alarm linkage device based on a new energy smart power station, including a fire linkage center, an environmental sensing module, an intelligent analysis module, a linkage selection and control module, a linkage division module, and a linkage execution module. The fire linkage center and the environmental sensing module have a bidirectional communication connection, the environmental sensing module and the intelligent analysis module have a unidirectional communication connection, the intelligent analysis module and the linkage selection and control module and the fire linkage center have a unidirectional communication connection, the linkage selection and control module and the linkage division module have a unidirectional communication connection, and the fire linkage center and the linkage execution module have a unidirectional communication connection.

[0041] The environmental perception module comprehensively collects dynamic multidimensional data within the target site and sends it to the fire alarm control center for storage. The dynamic multidimensional data not only covers traditional smoke signals and open flame signals, but also focuses on collecting electrical status data and characteristic gas data. Among them, electrical status data includes equipment operating voltage, current, temperature and insulation status parameters, etc., and characteristic gas data includes various characteristic gas concentration parameters that may be generated by equipment failure or fire precursors, ensuring the comprehensiveness and relevance of data collection.

[0042] S1: The environmental perception module simultaneously collects static basic data of the target site. The static basic data includes the site's overall layout plan, equipment three-dimensional dimensions (such as the length / width / height of transformers, charging piles, and energy storage battery cabinets), equipment installation location coordinates (based on the site's internal positioning coordinate system, such as a three-dimensional coordinate system established with the site entrance as the origin), building structure data (such as wall locations, fire compartment divisions, and ventilation duct routing), and infrastructure distribution (such as the locations of fire hydrants, cable trenches, and emergency exits).

[0043] S2: Preprocess the collected static basic data to obtain initial basic data. Through CAD drawing vectorization, parameter standardization (such as unifying equipment size units to millimeters and retaining coordinate accuracy to centimeters), data deduplication and completion (for missing equipment parameters, supplement them through equipment manufacturer's instructions or on-site measurements), ensure the integrity and consistency of static basic data, and lay the data foundation for model building;

[0044] S3: Using a 3D modeling engine (such as Unity), a virtual 3D framework for the site is built at a 1:1 scale based on the initial base data, resulting in an initial virtual 3D model. This involves first constructing the main structure of the site building (such as the walls, roof, and floor of the factory, control room, and equipment room), then embedding the 3D models of the equipment one by one according to the coordinate parameters (standard models can be called from the equipment model library, or customized models can be created for special equipment), and finally adding infrastructure models (such as visual models of fire protection system pipelines, ventilation openings, and power grid lines) to form an initial virtual 3D model that perfectly matches the physical site's spatial structure and equipment layout.

[0045] S4: Import the collected dynamic multidimensional data into the initial virtual 3D model to obtain the real-time virtual 3D model, and send it to the fire alarm linkage center for storage;

[0046] For example, temperature sensor data of a transformer can be associated with the corresponding monitoring point of the transformer in a virtual model, and smoke sensor data can be associated with the virtual area of ​​its installation location.

[0047] The intelligent analysis module is used to perform real-world scene fitting analysis on the dynamic multidimensional data of the collected target stations. The specific real-world scene fitting analysis process is as follows:

[0048] T1: Retrieve dynamic multidimensional data of the target site from the real-time virtual 3D model, and simultaneously call the pre-set fire risk model. After preprocessing the dynamic multidimensional data (such as noise reduction, enhancement, etc.), input it into the pre-set fire risk model to determine whether there are potential fault characteristics of the target site (such as electrical parameters exceeding the normal fluctuation range, abnormal increase in characteristic gas concentration).

[0049] T2: Once the pre-set fire risk model identifies potential fault characteristics, it extracts the physical location information (such as equipment number and installation coordinates) corresponding to the fault characteristics and transmits the location information to the real-time virtual 3D model to obtain the early warning virtual 3D model. That is, the real-time virtual 3D model accurately marks the specific location of the potential fault through visualization methods such as red highlighting and icon marking based on the received location information. At the same time, it displays relevant data of the fault characteristics (such as abnormal parameter values, characteristic gas types and concentrations), which makes it easier for staff to intuitively grasp the risk situation and provides clear location guidance for subsequent linkage and disposal.

[0050] T3: Simultaneously retrieve dynamic multidimensional data of different scenarios (such as thermal runaway of energy storage batteries, overheating of photovoltaic modules, leakage of characteristic gases, short circuit of grid interface, etc.) of the target site, match the dynamic multidimensional data of the target site under the current scenario with the dynamic multidimensional data of different scenarios retrieved, set the scenario type with the highest matching degree as the target scenario, and send the target scenario to the fire linkage center for storage.

[0051] For example, the combination of excessive concentration of characteristic gas and abnormal temperature can be used to determine an "early stage of thermal runaway in energy storage batteries".

[0052] Example 2: The linkage selection and control module is used to extract all alternative linkage schemes corresponding to the target scene, and to perform linkage control matching analysis on the positive and negative evaluation indicators of each collected alternative linkage scheme. The specific linkage control matching analysis process is as follows:

[0053] Based on the target scenario, extract all alternative linkage schemes corresponding to the target scenario from the preset linkage control scheme library;

[0054] The execution process of each alternative linkage scheme was simulated to obtain positive and negative evaluation indicators for each alternative linkage scheme.

[0055] Positive evaluation indicators include risk diffusion suppression rate and equipment protection success rate;

[0056] Negative evaluation indicators include disposal cost data (such as the total cost of inert gas consumables, downtime energy loss, etc.) and total linkage delay time;

[0057] The extreme value standardization method was used to convert positive and negative evaluation indicators into standard scores of 0-100.

[0058] Based on the formula: Scheme Optimization Score = ∑ (Indicator Standard Score Zi × Corresponding Weight Coefficient ai), the scheme optimization score of each candidate linkage scheme is obtained, where Zi represents the standard score Zi corresponding to the i-th indicator, ai represents the weight coefficient ai corresponding to the i-th indicator, and i is a natural number greater than zero.

[0059] All alternative linkage schemes are sorted from highest to lowest according to their optimal scheme score, and the alternative linkage scheme with the highest score is the optimal linkage control scheme for the target fault scenario.

[0060] By using the above-mentioned quantitative selection method, we can get rid of the limitations of traditional "experience-based selection", and scientifically select the optimal linkage control scheme based on data and quantitative indicators. This ensures the accuracy, efficiency and economy of fire alarm linkage in new energy smart stations, while providing a traceable and reproducible decision-making basis for the continuous optimization of linkage schemes.

[0061] For example: "Energy storage battery compartment A3 area - precursor to thermal runaway", "Package transformer B1 - risk of insulation breakdown";

[0062] Warning signs of battery thermal runaway: 1. Start the ventilation fan for exhaust; 2. Pre-activate the fire extinguishing device (to be triggered after risk confirmation); 3. Disconnect the battery cluster in this compartment from the main circuit;

[0063] Insulation breakdown in the transformer substation: 1. Disconnect the transformer substation circuit breaker; 2. Close the high-voltage isolation cabinet; 3. Start the fire sprinkler pump in the transformer substation area;

[0064] Based on the optimal linkage control scheme, the controlled equipment is controlled, and the execution information (success / failure) of each controlled equipment within the preset time threshold is obtained. The controlled equipment that is executed successfully in the execution information is marked in green in the early warning virtual 3D model, and the controlled equipment that is executed unsuccessfully in the execution information is marked in yellow in the early warning virtual 3D model, thus obtaining the execution 3D model diagram;

[0065] Execution analysis is performed based on the 3D model diagram. If all controlled devices execute successfully, a collaborative feedback instruction is generated.

[0066] If a single device fails to execute, the "backup device" command will be automatically invoked (such as activating the backup fire extinguishing device in the area), and an alarm message will be sent to the operation and maintenance terminal (such as "Fire extinguishing device 1 of energy storage compartment A3 has failed, backup device has been activated").

[0067] If multiple devices fail to execute (e.g., all power outage commands in a certain area fail), the "emergency manual intervention" process is triggered, automatically dialing the site maintenance personnel's phone number and highlighting and flashing the location of the faulty equipment in the execution 3D model to guide manual handling;

[0068] The linkage control steps can achieve seamless connection "from early warning to response", which not only ensures the accuracy and real-time performance of control commands, but also improves the reliability of the system through anomaly handling and iterative optimization, meeting the needs of diverse fire emergency scenarios in new energy smart power stations.

[0069] When generating collaborative feedback instructions, the linkage partitioning module is used to perform execution partitioning and expected evaluation analysis on the optimal linkage control scheme. The specific execution partitioning and expected evaluation analysis process is as follows:

[0070] The execution process of the optimal linkage control scheme is divided into the fault suppression start stage, the fault recovery and stabilization stage, and the post-recovery observation stage (representing the preset duration observation stage after the fault recurrence rate = 0).

[0071] For each stage, the expected benchmark values ​​for obtaining the optimal linkage scheme are as follows: fault current decay rate ≥ 50A / ms, equipment protection success rate ≥ 95%, and peak temperature of the fault area ≤ 80℃.

[0072] Real-time collection of multi-dimensional target data at each stage, including load recovery rate, equipment protection success rate, etc.

[0073] Based on the multidimensional target data at each stage, calculate the deviation rate between the actual value and the expected benchmark value of each quantitative indicator (positive quantitative indicators: equipment protection success rate, load recovery rate, etc.; negative quantitative indicators: fault area temperature peak, stability delay, etc.) in the multidimensional target data at each stage:

[0074] The deviation rate P of the positive quantitative indicator = (actual value - expected benchmark value) / expected benchmark value × 100%;

[0075] The deviation rate P of the negative quantitative indicator = (expected benchmark value - actual value) / expected benchmark value × 100%;

[0076] The deviation rate P of each stage is processed for discrimination. If the deviation rate P is ≥ 0, the corresponding stage is determined to be the standard-achieving stage. If there is a deviation rate P < 0, the corresponding stage is determined to be the deviation stage.

[0077] When there is a deviation phase, the quantitative indicator corresponding to the deviation rate P being less than zero is marked as the target indicator, and a dynamic deviation list is constructed based on the target indicator and the deviation phase.

[0078] The linkage execution module is used to respond to the dynamic deviation list and display it immediately, so that the scheme can be adjusted for the deviation stage based on the dynamic deviation list, thereby reducing the risk of abnormal execution of the entire optimal linkage control scheme and achieving the expected execution effect.

[0079] In summary, by synchronously collecting dynamic multidimensional data and static basic data through the environmental perception module, and combining 1:1 scale virtual 3D modeling and data association, a real-time virtual 3D model is constructed. With the help of fire risk model and scene type matching algorithm, potential fault characteristics can be quickly identified, risk locations can be accurately located, and the results can be presented intuitively through visualization. This solves the problems of delayed early warning and ambiguous risk location in traditional systems, and provides a clear basis for subsequent coordinated response.

[0080] Furthermore, it extracts alternative linkage solutions from the solution library based on the target scenario and uses a quantitative scoring method to select the optimal solution, thus solving the limitations of traditional experience-based selection. At the same time, through mechanisms such as execution status visualization marking, automatic switching of backup equipment, and emergency manual intervention triggering, it ensures the effective implementation of linkage instructions, solves the problem of handling failure caused by single equipment failure, and identifies deviation stages by deviation rate calculation and builds a dynamic deviation list, forming a closed-loop mechanism of "solution execution - stage evaluation - dynamic optimization", which effectively reduces the risk of execution anomalies and improves the timeliness and pertinence of dynamic control.

[0081] The threshold is set for comparative analysis of results to determine whether they are good or bad. The value of the threshold is determined by a combination of large-scale model analysis of sample data and human experience. It can also be adjusted appropriately based on seasonal or common-sense influencing factors.

[0082] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A fire alarm linkage device based on a new energy smart power station, characterized in that, It includes a fire alarm control center, an environmental sensing module, an intelligent analysis module, a control and linkage module, a linkage division module, and a linkage execution module; The environmental perception module comprehensively collects dynamic multidimensional data within the target site and sends it to the fire alarm control center for storage. At the same time, it collects static basic data of the target site and constructs a real-time virtual 3D model based on the static basic data. The intelligent analysis module is used to match the dynamic multidimensional data of the target site under the current scene with the dynamic multidimensional data of different scenes retrieved, and set the scene type with the highest matching degree as the target scene. The linkage selection and control module is used to extract all alternative linkage schemes corresponding to the target scene, and to perform linkage control matching analysis on the positive and negative evaluation indicators of each collected alternative linkage scheme, and output the optimal linkage control scheme and collaborative feedback instructions. When generating collaborative feedback instructions, the linkage division module is used to perform execution division and expected evaluation analysis of the optimal linkage control scheme and output a dynamic deviation list.

2. The fire alarm linkage device based on new energy smart power stations according to claim 1, characterized in that, The real-time virtual 3D model construction process is as follows: S1: Collect dynamic multidimensional data and static basic data within the target site; S2: Preprocess the collected static basic data to obtain initial basic data; S3: Call the 3D modeling engine to build a virtual 3D framework of the station at a 1:1 scale based on the initial basic data to obtain the initial virtual 3D model; S4: Import the collected dynamic multidimensional data into the initial virtual 3D model to obtain the real-time virtual 3D model.

3. The fire alarm linkage device based on new energy smart power stations according to claim 1, characterized in that, The analysis process of the intelligent analysis module is as follows: T1: Retrieve dynamic multidimensional data of the target site from the real-time virtual 3D model, and simultaneously call the pre-set fire risk model. After preprocessing the dynamic multidimensional data, input it into the pre-set fire risk model to determine whether there are potential fault characteristics in the target site. T2: When the pre-set fire risk model identifies potential fault characteristics, it extracts the physical location information corresponding to the fault characteristics and transmits the location information to the real-time virtual 3D model to obtain the early warning virtual 3D model.

4. The fire alarm linkage device based on new energy smart power stations according to claim 3, characterized in that, It also includes T3: Simultaneously retrieve dynamic multidimensional data under different scenarios of the target site, match the dynamic multidimensional data under the current scenario of the target site with the retrieved dynamic multidimensional data under different scenarios, and set the scenario type with the highest matching degree as the target scenario.

5. The fire alarm linkage device based on new energy smart power stations according to claim 1, characterized in that, The analysis process of the linkage selection and control module is as follows: Based on the target scenario, all alternative linkage schemes corresponding to the target scenario are extracted from the preset linkage control scheme library. The execution process of each alternative linkage scheme is simulated to obtain the positive and negative evaluation indicators of each alternative linkage scheme. The extreme value standardization method was used to convert positive and negative evaluation indicators into standard scores of 0-100. Based on the formula: Scheme Optimization Score = ∑ (Indicator Standard Score Zi × Corresponding Weight Coefficient ai), the scheme optimization score of each candidate linkage scheme is obtained; All alternative linkage schemes are sorted from highest to lowest according to their optimal scheme score, and the alternative linkage scheme with the highest score is the optimal linkage control scheme for the target fault scenario.

6. The fire alarm linkage device based on new energy smart power stations according to claim 5, characterized in that, Based on the optimal linkage control scheme, the controlled equipment is controlled, and the execution information (success / failure) of each controlled equipment within the preset time threshold is obtained. The controlled equipment that is executed successfully in the execution information is marked in green in the early warning virtual 3D model, and the controlled equipment that is executed unsuccessfully in the execution information is marked in yellow in the early warning virtual 3D model, thus obtaining the execution 3D model diagram; Execution analysis is performed based on the 3D model diagram. If all controlled devices execute successfully, a collaborative feedback instruction is generated. If a single device fails to execute, the "backup device" instruction is automatically invoked, and an alarm message is sent to the operation and maintenance terminal. If multiple devices fail to execute, the "emergency manual intervention" process is triggered.

7. The fire alarm linkage device based on new energy smart power stations according to claim 1, characterized in that, The analysis process of the linkage division module is as follows: The execution process of the optimal linkage control scheme is divided into the fault suppression start stage, the fault recovery and stabilization stage, and the post-recovery observation stage (representing the preset duration observation stage after the fault recurrence rate = 0). For each stage, obtain the expected benchmark value of the optimal linkage solution; collect multi-dimensional target data for each stage in real time, including load recovery rate and equipment protection success rate; Based on the multidimensional target data at each stage, calculate the deviation rate P between the actual value and the expected benchmark value of each quantitative indicator in the multidimensional target data at each stage: The deviation rate P of each stage is processed to determine the deviation stage or the target stage. When a deviation stage exists, the quantitative indicator corresponding to the deviation rate P being less than zero is marked as the target indicator. A dynamic deviation list is constructed based on the target indicator and the deviation stage.

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