Optimized control method of fire-fighting linkage response time applied to energy storage power station

Abstract

This invention relates to the field of energy storage power station operation and maintenance technology, and particularly to an optimized control method for fire-fighting linkage response time applied to energy storage power stations. The method includes: acquiring multi-dimensional operating parameters of the energy storage battery cluster and real-time status parameters of the fire-fighting execution terminal equipment in parallel; determining continuous situation indicators that reflect the evolution of thermal runaway risk in the battery cluster based on the multi-dimensional operating parameters; for battery clusters whose situation indicators exceed warning values, performing time-series simulations of preset fire-fighting linkage logic based on the estimated response delay of the terminal equipment according to the real-time status parameters, and performing pre-activation operations on the terminal equipment on the critical path according to the simulation results; in response to a fire detector alarm, performing cross-validation on the fire detector's associated area; after successful verification, selecting a target logic chain from the completed simulation of the linkage logic and issuing it for execution; during the execution of the target logic chain, monitoring the actual response delay and comparing it with the estimated delay.

Description

Technical Field

[0001] This invention relates to the technical field of operation and maintenance of energy storage power stations, and in particular to an optimized control method for fire-fighting linkage response time applied to energy storage power stations. Background Technology

[0002] Currently, fire protection systems in energy storage power stations generally employ fire detection based on fixed thresholds and linked control logic with preset sequences. Specifically, when the values ​​monitored by sensors such as smoke and temperature exceed set thresholds, the system determines it as a fire alarm and then triggers various fire-fighting execution terminal devices according to a pre-programmed fixed process sequence. For example, first, the alarm is confirmed, then ventilation is shut off, and finally, the extinguishing agent is released.

[0003] The response time of existing control logic is mainly consumed by the cumulative inherent delays of each stage, including sensor signal stabilization and transmission time, controller logic processing time, and the execution time of each terminal device from receiving the command to completing the mechanical action. In actual operation, especially when there are changes in equipment status or minor communication delays, the total time from the first alarm to the initiation of critical fire suppression actions may exceed safety standards. Existing methods lack proactive assessment and dynamic optimization of internal system delays, making it difficult to intervene in potential response bottlenecks in advance, resulting in excessively long overall response times.

[0004] Therefore, there is an urgent need for an optimized control method that can effectively reduce the total system response time from the triggering of a fire alarm to the execution of critical fire-fighting measures, while ensuring the correctness of the fire-fighting action logic. Summary of the Invention

[0005] This invention provides an optimized control method for fire-fighting linkage response time applied to energy storage power stations, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] Optimization control methods for fire alarm response time applied to energy storage power stations include:

[0008] Parallel acquisition of multi-dimensional operating parameters of energy storage battery clusters and real-time status parameters of fire-fighting execution terminal equipment;

[0009] Based on the aforementioned multi-dimensional operating parameters, a continuous trend indicator that can reflect the evolution of the risk of thermal runaway of battery clusters is determined.

[0010] For battery clusters whose status indicators exceed the warning value, the timing of the preset fire linkage logic is simulated based on the terminal device response delay estimated according to the real-time status parameters, and the terminal devices on the critical path are pre-activated according to the simulation results.

[0011] In response to a fire detector alarm, cross-verification is performed on the associated areas of the fire detector; after successful verification, the target logic chain is selected from the completed linkage logic and executed.

[0012] During the execution of the target logic chain, the actual response delay is monitored and compared with the estimated delay. If the difference is found, a preset compensation action is triggered.

[0013] Furthermore, the multi-dimensional operating parameters include: voltage outlier of cells within the battery cluster, average temperature change acceleration within the cluster, inter-cluster circulation intensity, temperature field distribution data obtained by the distributed optical fiber temperature measurement unit, and characteristic gas concentration data obtained by the gas composition analysis unit.

[0014] Furthermore, the real-time status parameters include historical action time records, current command response time, and / or drive signal characteristics of each fire-fighting execution terminal device.

[0015] Furthermore, the method for determining the continuous situation indicators includes: inputting the multi-dimensional operating parameters into a trained time-series anomaly detection model;

[0016] The time-series anomaly detection model outputs a risk index, the magnitude of which is positively correlated with the degree of parameter anomaly and the rate of change.

[0017] Furthermore, the time series deduction includes:

[0018] Obtain one or more basic fire alarm linkage logics associated with the target battery cluster;

[0019] The estimated response delay corresponding to each fire-fighting execution terminal device is used as a time variable and substituted into each of the basic fire-fighting linkage logics.

[0020] The simulation calculates the estimated total time for each logic step from initiation to completion and identifies the most time-consuming critical path.

[0021] Furthermore, the pre-activation operation includes at least one of the following:

[0022] Send a pre-positioning command to the gas extinguishing zone valve located on the critical path, causing its drive mechanism to move to the near-open position in advance;

[0023] Perform a pre-release lockout operation on the ventilation isolation devices within the target fire compartment.

[0024] Furthermore, the cross-validation includes:

[0025] Upon receiving an alarm signal, the system simultaneously retrieves a sequence of temperature field changes in the area where the fire detector is located during a set time period prior to the alarm, as well as concentration gradient data of at least one characteristic gas.

[0026] If the temperature field change image shows a temperature rise pattern that matches the characteristics of thermal runaway, and the concentration change gradient exceeds the associated threshold, then the verification is deemed successful.

[0027] Furthermore, the criteria for selecting the target logic chain include: among the completed linkage logic, selecting the one with the shortest estimated total time and the fewest conflicts with other current task resources.

[0028] Furthermore, a target logic chain is selected from the completed linkage logic and executed. After the target logic chain is executed, the receiving status of each terminal device is fed back in real time. If no receiving confirmation signal is received from the device within a set time, the instruction is re-issued and repeated a preset number of times. If no confirmation is received, the instruction is switched to the backup communication channel.

[0029] Furthermore, the backup communication channel adopts a controller area network bus topology and uses a separate communication cable.

[0030] The technical solution of this invention achieves the following technical effects: Through risk assessment and time-series simulation based on real-time multi-dimensional data, the response time of each stage can be accurately predicted, and corresponding pre-activation measures can be taken in advance; thereby shortening the response time of the fire protection system and rapidly initiating fire protection measures within the prescribed safety standards; this method transforms traditional fire linkage control based on fixed thresholds into an adaptive control system based on real-time monitoring data, dynamic optimization, and intelligent simulation; by dynamically adjusting the fire linkage process in real time, it can adapt to complex and uncertain working environments and make more accurate and efficient responses to different types of fires; by actively assessing the system's operating status, this method can predict and address potential response bottlenecks and delays in advance. The problem is that this method ensures critical fire-fighting actions can be completed in the shortest possible time, thereby improving fire response efficiency. Through cross-validation and compensation mechanisms, the risk of false alarms and missed alarms can be reduced, and timely compensation measures can be taken when system anomalies occur, ensuring the high reliability of the linkage control system. It avoids unnecessary downtime and reduces equipment damage caused by misoperation. Through timing simulation and optimized resource allocation, the system can rationally allocate resources to fire-fighting execution terminal equipment, avoiding resource conflicts or excessive consumption. This not only improves overall efficiency but also reduces equipment failure rates. In summary, this method not only improves fire response efficiency but also reduces failure rates, minimizes delays, and enhances the overall reliability of energy storage power stations.

[0031] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating the optimized control method for fire-fighting linkage response time applied to energy storage power stations according to the present invention. Detailed Implementation

[0034] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0036] like Figure 1 As shown, the optimized control method for fire-fighting linkage response time applied to energy storage power stations according to the present invention specifically includes the following steps:

[0037] S1. Parallel acquisition of multi-dimensional operating parameters of energy storage battery clusters and real-time status parameters of fire-fighting execution terminal equipment;

[0038] S2. Based on the aforementioned multi-dimensional operating parameters, determine a continuous trend indicator that can reflect the evolution of the risk of thermal runaway of the battery cluster;

[0039] S3. For battery clusters whose status indicators exceed the warning value, combined with the terminal device response delay estimated based on the real-time status parameters, perform a time-series simulation of the preset fire linkage logic, and perform a pre-activation operation on the terminal devices on the critical path based on the simulation results.

[0040] S4. In response to a fire detector alarm, perform cross-verification on the associated area of ​​the fire detector; after successful verification, select the target logic chain from the completed linkage logic and send it for execution.

[0041] S5. During the execution of the target logic chain, monitor the actual response delay and compare it with the estimated delay. If the difference is found, trigger a preset compensation action.

[0042] In this embodiment, by conducting risk assessment and time-series simulation based on real-time multi-dimensional data, the response time of each link can be accurately predicted, and corresponding pre-activation measures can be taken in advance. This shortens the response time of the fire protection system and enables rapid activation of fire protection measures within the prescribed safety standards. This method transforms the traditional fire linkage control based on fixed thresholds into an adaptive control system based on real-time monitoring data, dynamic optimization, and intelligent simulation. By dynamically adjusting the fire linkage process in real time, it can adapt to complex and uncertain working environments and make more accurate and efficient responses to different types of fires. By proactively assessing the system's operating status, this method can predict and address potential response bottlenecks and delays in advance, ensuring critical... Firefighting actions can be completed in the shortest possible time, thereby improving the efficiency of fire response. Cross-validation and compensation mechanisms reduce the risk of false alarms and missed alarms, while timely compensation measures are taken when system anomalies occur, ensuring the high reliability of the linkage control system. This avoids unnecessary downtime and reduces equipment damage caused by misoperation. Through timing simulation and optimized resource allocation, the system can rationally allocate resources to firefighting execution terminal equipment, avoiding resource conflicts or excessive consumption. This not only improves overall efficiency but also reduces equipment failure rates. In summary, this method not only improves fire response efficiency but also reduces failure rates, minimizes delays, and enhances the overall reliability of energy storage power stations.

[0043] In some embodiments of the present invention, for step S1, multi-dimensional operating parameters of the energy storage battery cluster and real-time status parameters of the fire-fighting execution terminal equipment are acquired in parallel.

[0044] A hierarchical data acquisition architecture is established, which consists of a parameter acquisition master station and several sub-acquisition units. The sub-acquisition units are divided into battery parameter acquisition units and terminal status acquisition units according to their functions. Each sub-acquisition unit establishes a communication connection with the parameter acquisition master station through an industrial Ethernet.

[0045] The battery parameter acquisition unit is equipped with a multi-channel data acquisition module. Each channel corresponds to the functions of cell voltage detection, temperature sensor signal reception, circulating current sampling, distributed fiber optic temperature measurement signal demodulation, and characteristic gas concentration signal conversion. Each channel is independently configured with a signal conditioning circuit and an A / D converter, and is connected to the output terminal of the corresponding type of sensor. Among them, the cell voltage detection channel is connected in parallel to the positive and negative terminals of each cell in the battery cluster through a voltage sampling chip; the temperature sensor signal reception channel is connected to the signal output interface of the NTC temperature sensor arranged in the cluster and the distributed fiber optic temperature measurement unit; the circulating current sampling channel is connected in series with the secondary coil of the current transformer; and the characteristic gas concentration signal conversion channel is connected to the analog signal output terminal of the gas component analysis unit.

[0046] The terminal status acquisition unit is configured with a protocol parsing module and an IO interface expansion module. The protocol parsing module has a built-in general protocol conversion chip and pre-stores parsing algorithms for mainstream industrial communication protocols. The IO interface expansion module is connected to the status feedback interface and drive signal detection interface of each fire-fighting execution terminal device. The status feedback interface collects the device's operating status code and historical action time records, while the drive signal detection interface collects characteristic parameters such as the electrical signal pulse width and drive current amplitude corresponding to the device's current command response time.

[0047] The parameter acquisition master station has a built-in high-precision clock synchronization module, which sends synchronization clock signals to each sub-acquisition unit via the PTP protocol. After receiving the synchronization signal, each sub-acquisition unit calibrates its local timing module to ensure that the sampling start time of all acquisition channels remains synchronized. At the same time, the parameter acquisition master station is configured with a data buffer module and a fixed data frame format. Each sub-acquisition unit triggers sampling according to the synchronization clock, encapsulates the acquired parameter data into data frames according to the preset format, and uploads them in parallel to the data buffer module via industrial Ethernet. The data buffer module performs frame header verification and integrity verification on the received data frames. After successful verification, the data frames are stored in the designated storage area.

[0048] Among them, the multi-dimensional operating parameters include: the voltage outlier of the cells in the battery cluster, the average temperature change acceleration within the cluster, the inter-cluster circulation intensity, the temperature field distribution data obtained by the distributed optical fiber temperature measurement unit, and the characteristic gas concentration data obtained by the gas composition analysis unit.

[0049] Real-time status parameters include historical action time records, current command response time, and / or drive signal characteristics of each fire-fighting execution terminal device.

[0050] In this embodiment, by collecting multi-dimensional operating parameters of the energy storage battery cluster and real-time status parameters of the fire-fighting execution terminal equipment in parallel, the real-time operating status of the system can be comprehensively and accurately reflected. The collection of multi-dimensional parameters enables real-time monitoring of the battery cluster's health status and timely detection of potential safety hazards. Real-time monitoring of the status parameters of the fire-fighting execution terminal equipment effectively assesses its working status, determines whether it is in optimal working condition, and thus optimizes the equipment's response efficiency and improves the fire-fighting system's response capability. A high-precision clock synchronization module is used, employing the PTP protocol to achieve time synchronization of each sub-collection unit, ensuring that each data acquisition channel collects data at the same time point, thereby avoiding data errors or deviations caused by timing discrepancies and ensuring data accuracy. By setting a fixed data frame format, frame header verification and integrity verification, and designing a data caching module, the system can effectively manage large amounts of collected data, ensuring reliable data storage and preventing data loss or corruption. The multi-dimensional operating parameters help to detect potential anomalies in the energy storage battery cluster in advance, providing the system with stronger early warning capabilities and ensuring the safety of equipment and personnel.

[0051] In a specific implementation, as one example, for step S2, based on the multi-dimensional operating parameters, a continuous trend indicator that can reflect the evolution of the risk of thermal runaway of the battery cluster is determined.

[0052] Multi-dimensional operating parameters are preprocessed to remove outlier data points; voltage outlier data are categorized and organized by cell number, and the fluctuation range of voltage deviation from the mean is calculated every five minutes; intra-cluster average temperature change acceleration data are sorted by timestamp to form a continuous time series; inter-cluster circulation intensity data are filtered to remove sampling noise and retain effective current change signals; temperature field distribution data are used to extract temperature values ​​from each monitoring point to generate a regional temperature matrix; characteristic gas concentration data are categorized by gas type, and the change in concentration over time is recorded.

[0053] A temporal anomaly detection model is constructed, which includes a feature fusion layer, a temporal convolutional layer, and a risk output layer. The feature fusion layer adopts an attention mechanism to assign weights to the operating parameters of each dimension. The weight values ​​are determined based on the contribution of each parameter to the risk warning in historical thermal runaway cases. The temporal convolutional layer is set with three convolutional kernels to extract the variation features of each parameter in different time windows. The risk output layer adopts the sigmoid activation function to map the fused features to a risk index in the [0,1] interval.

[0054] The training dataset for the timing anomaly detection model consists of several sets of data on normal operation of battery clusters, data on the early stage of thermal runaway, and data on the development stage of thermal runaway. All data are derived from actual operation records and simulation tests of similar energy storage power stations. During training, the model parameters are adjusted through backpropagation using the thermal runaway risk level as a label to control the fitting error between the risk index output by the model and the actual risk level.

[0055] The preprocessed multi-dimensional operating parameters are input into the trained time-series anomaly detection model in chronological order, and the model outputs a risk index. This index is updated in real time with the changes in the multi-dimensional operating parameters, forming a continuous situation indicator curve. This index can reflect the current thermal runaway trend of the battery cluster. Specifically, the magnitude of the risk index is positively correlated with the degree of anomaly and the rate of change of various parameters within the battery cluster.

[0056] In this embodiment, by preprocessing multiple operating parameters of the battery cluster and extracting key features, the working status of the battery cluster can be accurately reflected, thereby improving the accuracy of prediction. The removal of abnormal data points and filtering of noise signals in the preprocessing step can avoid interference from data noise on the risk detection model, ensuring the quality of input data and thus improving the model's accuracy. By converting various operating data of the battery cluster into a continuous time series and combining it with a temporal convolutional layer to extract change features within a time window, the dynamic changes of the battery cluster at different time periods during operation can be captured, thereby timely detection of potential risks. Through the constructed temporal anomaly detection model… The system can calculate and update risk indices in real time, forming continuous situation indicator curves. These curves reflect the current thermal runaway trend of the battery cluster, helping operators to promptly identify risks and take corresponding preventative measures to avoid thermal runaway. The feature fusion layer in the model employs an attention mechanism, assigning weights to each parameter based on its contribution to historical thermal runaway cases. This ensures that the contribution of different parameters to risk prediction is reasonably reflected, thereby improving the accuracy of risk prediction. In summary, this step, through comprehensive analysis of the multi-dimensional operating parameters of the battery cluster and combined with a time-series anomaly detection model, outputs indices reflecting thermal runaway risk in real time, thus enhancing system safety.

[0057] In some embodiments of the present invention, for step S3, for battery clusters whose status indicators exceed the warning value, the preset fire linkage logic is time-series deduced based on the terminal device response delay estimated according to the real-time status parameters, and the terminal devices on the critical path are pre-activated according to the deduction results.

[0058] Establish a basic fire-fighting linkage logic library, which stores multiple basic logics corresponding to different battery cluster areas. Each logic includes execution steps, involved fire-fighting terminal equipment and equipment action sequence. The logic library supports updates based on the layout and equipment configuration of the energy storage power station.

[0059] Based on real-time status parameters, the response delay of terminal devices is estimated. The average value of the historical action time records of each device and the current command response time are extracted. Combined with the drive signal characteristics, weights are assigned to the historical time, the current command response time and the drive signal characteristics. The estimated response delay of a single device is obtained through weighted calculation.

[0060] For target battery clusters whose situation indicators exceed the warning value, all associated basic fire linkage logics are retrieved from the logic library. The estimated response delay of the equipment involved in each logic is used as a time variable and substituted into the execution steps corresponding to the logic. The total time consumption of each logic is calculated through discrete event simulation algorithm, and the cumulative time consumption of each step is recorded. The path with the longest total time consumption is identified as the critical path.

[0061] For gas extinguishing zone valves on the critical path, a pre-positioning command is sent to control their drive mechanism to move to the near-open position and maintain this state; an independent communication frequency band is allocated to high-priority linkage logic through the communication scheduling module to avoid communication resource occupation; for ventilation isolation devices in the target fire compartment, a pre-unlocking command is sent to release the jamming constraint of the mechanical locking mechanism, so that the device can quickly respond to subsequent action commands.

[0062] After the pre-activation operation is executed, the status feedback signal of the activated device is monitored in real time to confirm whether the position of the drive mechanism, the usage of communication resources, and the locking status meet the preset requirements. If the feedback is abnormal, the pre-activation command is resent to ensure that the device is in a valid ready-to-start state.

[0063] In this embodiment, by weighting the historical action time of the equipment, the current command response time, and the characteristics of the drive signal, the response delay of each terminal device can be accurately estimated, avoiding the drawbacks of relying solely on fixed timing and improving the system's adaptability. Through timing deduction and discrete event simulation algorithms, critical paths can be identified, ensuring that the fire protection system executes the optimal response operation in the shortest time, reducing response delay and improving the overall system efficiency. Pre-activation of equipment on the critical path ensures that it can be quickly activated when needed, reducing on-site operation response time and improving emergency response capabilities. By monitoring equipment status feedback in real time, it is ensured that the pre-activated equipment can operate normally upon startup, avoiding delays or failures caused by equipment not being in a ready-to-start state or communication resource occupation. By allocating independent communication frequency bands to high-priority linkage logic, communication resource conflicts can be avoided, ensuring that the system can still operate stably in emergency situations.

[0064] In some embodiments of the present invention, for step S4, in response to a fire detector alarm, cross-verification is performed on the associated area of ​​the fire detector; after verification, a target logic chain is selected from the completed linkage logic and executed.

[0065] Preset cross-validation parameter thresholds, based on test data of thermal runaway characteristics of energy storage power station batteries, determine the criteria for judging temperature rise modes that meet thermal runaway characteristics, and clarify the requirements for temperature change at multiple consecutive time nodes in the same monitoring area within the temperature field distribution data during the set period before alarm; set concentration change gradient thresholds according to characteristic gas types, and the thresholds can be adjusted according to battery type;

[0066] After receiving the alarm signal from the fire detector, the area identification information carried by the alarm signal is extracted. Through the parameter acquisition master station data cache module, the temperature field change image sequence and the concentration time series data of at least one characteristic gas within the set period before the alarm in that area are retrieved simultaneously to generate a concentration change gradient curve.

[0067] The image recognition algorithm is used to analyze the temperature field change image sequence to determine whether there is a temperature rise pattern that meets the preset standard; the gradient of characteristic gas concentration change is calculated and compared with the corresponding threshold; if both conditions are met, the verification is deemed to be successful; if either condition is not met, the verification is deemed to be unsuccessful, and only the alarm information is recorded without triggering the linkage logic.

[0068] After verification, retrieve all the linkage logic related to the target area that has been simulated.

[0069] Establish a logical screening and evaluation system. The first evaluation dimension is the estimated total time, sorted by value from smallest to largest. The second evaluation dimension is the degree of resource conflict. By querying the current occupancy status of each fire-fighting execution terminal equipment and the communication bandwidth usage, the number of conflict items between resources and existing tasks involved in each logic is counted. The fewer the number of conflict items, the higher the priority.

[0070] The target logic chain is selected according to the evaluation system, and the logic with the shortest estimated total time is selected first. If there are multiple logics with the same time, the one with the fewest resource conflict items is selected. The target logic chain instructions are sent to the corresponding fire execution terminal equipment step by step through industrial Ethernet.

[0071] After the command is issued, the reception status of each terminal device is fed back in real time. If no confirmation signal is received from the device within the set time, the command is reissued, repeating up to a preset number of times. If no confirmation is received, the command is switched to the backup communication channel.

[0072] In this embodiment, by cross-validating the alarm signals of fire detectors and combining thermal runaway characteristics with changes in characteristic gas concentrations, the actual situation of a fire can be accurately determined, avoiding false alarms or missed alarms. Utilizing thermal runaway test data from energy storage power station batteries and the criteria for determining temperature rise patterns, the alarm thresholds and conditions can be dynamically adjusted based on different battery types and gas characteristics. Through a logical screening and evaluation system, the linkage logic is optimized, considering both execution efficiency and resource conflict levels, thereby achieving optimal linkage response, avoiding resource waste and conflicts, and improving the timeliness of fire response. A backup communication channel and a mechanism for repeatedly issuing instructions are set up to ensure that instructions are ultimately successfully transmitted in the event of communication failure or device non-response, guaranteeing the reliability of the fire execution terminal.

[0073] In some embodiments of the present invention, for step S5, during the execution of the target logic chain, the actual response delay is monitored and compared with the estimated delay. If the difference is found, a preset compensation action is triggered.

[0074] Deploy a delay monitoring module and establish a communication connection with the parameter acquisition master station. The module has a built-in timestamp recording unit and data transmission interface. For each fire-fighting execution terminal device involved in the target logic chain, set an action trigger flag and an action completion flag respectively. The action trigger flag is the time when the device receives the instruction, which is captured by the instruction issuance feedback signal. The action completion flag is the time when the device executes the action and reaches the preset state, which is captured by the device status feedback sensor signal.

[0075] Set out the criteria for judging out-of-tolerance. Based on the calculation error range of the estimated response delay of the terminal equipment and combined with the safety operation requirements of the fire protection system of the energy storage power station, determine the absolute out-of-tolerance threshold and the relative out-of-tolerance threshold. The absolute out-of-tolerance threshold is a fixed time value, and the relative out-of-tolerance threshold is a percentage value of the estimated response delay. If either of the two thresholds is met, it is judged as out of tolerance.

[0076] During the execution of the target logic chain, the delay monitoring module captures the action trigger time and action completion time of each terminal device in real time, calculates the time difference between the two as the actual response delay, and synchronously retrieves the estimated terminal device response delay of the corresponding device and compares them according to a preset period.

[0077] A pre-defined compensation action library is provided, with compensation actions corresponding to the types of errors. For errors caused by communication transmission, a backup communication channel is activated to transmit subsequent instructions, while communication resources occupied by non-critical tasks are released. For errors caused by mechanical jamming of equipment, a secondary drive instruction is sent to the corresponding equipment to increase the output power of the drive signal to a preset ratio of the rated power. For errors caused by the accumulation of delays in multi-device collaboration, non-critical auxiliary action steps are skipped, and the core fire extinguishing action is executed first.

[0078] After detecting an error, the delay monitoring module analyzes the cause of the error, matches the corresponding compensation action from the compensation action library and triggers its execution. At the same time, it records the error data, the type of compensation action and the execution result, and feeds it back to the parameter acquisition master station.

[0079] In this embodiment, the delay monitoring module captures and calculates the response delay of each fire-fighting device in the target logic chain in real time, and compares it with the estimated delay. This allows for timely detection of out-of-tolerance phenomena, preventing delays from causing slow system response or failure. When an out-of-tolerance delay is detected, the system automatically triggers a preset compensation action library. Different compensation measures are taken for different types of out-of-tolerance to ensure the system returns to normal as quickly as possible and avoids delays in fire response. Through dynamic compensation and optimization, strategies can be automatically adjusted according to different types of out-of-tolerance causes, avoiding system performance degradation caused by communication, equipment lag, or multi-device coordination problems. In the fire protection system, the compensation mechanism ensures that the overall system can still operate stably even when some devices experience delays, thereby improving system safety and ensuring the successful completion of fire-fighting tasks. By recording out-of-tolerance data, compensation action types, and execution results, valuable data support can be provided for subsequent system optimization and troubleshooting, improving the long-term stability of the system.

[0080] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. An optimized control method for fire-fighting linkage response time applied to energy storage power stations, characterized in that, include: Parallel acquisition of multi-dimensional operating parameters of energy storage battery clusters and real-time status parameters of fire-fighting execution terminal equipment; Based on the aforementioned multi-dimensional operating parameters, a continuous trend indicator that can reflect the evolution of the risk of thermal runaway of battery clusters is determined. For battery clusters whose status indicators exceed warning values, the system performs a time-series simulation of the preset fire-fighting linkage logic based on the estimated response delay of terminal devices according to the real-time status parameters. Based on the simulation results, pre-activation operations are performed on terminal devices on the critical path. The preset fire-fighting linkage logic is stored in a basic fire-fighting linkage logic library, which contains multiple basic linkage logics corresponding one-to-one with different battery cluster areas within the energy storage power station. Each basic linkage logic includes execution steps, involved fire-fighting execution terminal devices, and the sequence of device actions. The time-series simulation and critical path identification method is as follows: All basic linkage logics associated with the target battery cluster are retrieved from the basic fire-fighting linkage logic library. The estimated response delay of the devices involved in each basic linkage logic is used as a time variable and substituted into the execution steps of the corresponding basic linkage logic. The total execution time of each basic linkage logic is calculated using a discrete event simulation algorithm, and the cumulative execution time of each execution step is recorded. The path with the longest total execution time in a single basic linkage logic is identified as the critical path. In response to a fire detector alarm, cross-validation is performed on the associated areas of the fire detector; After verification, the target logic chain is selected from the completed linkage logic and executed. During the execution of the target logic chain, the actual response delay is monitored and compared with the estimated delay. If the difference is found, a preset compensation action is triggered.

2. The optimized control method for fire-fighting linkage response time applied to energy storage power stations according to claim 1, characterized in that, The multi-dimensional operating parameters include: voltage outlier of cells within the battery cluster, average temperature change acceleration within the cluster, inter-cluster circulation intensity, temperature field distribution data obtained by the distributed optical fiber temperature measurement unit, and characteristic gas concentration data obtained by the gas composition analysis unit.

3. The optimized control method for fire-fighting linkage response time applied to energy storage power stations according to claim 1, characterized in that, The real-time status parameters include historical action time records, current command response time, and / or drive signal characteristics of each fire-fighting execution terminal device.

4. The optimized control method for fire-fighting linkage response time applied to energy storage power stations according to claim 2, characterized in that, The method for determining the continuous situation indicators includes: inputting the multi-dimensional operating parameters into a trained time-series anomaly detection model; The time-series anomaly detection model outputs a risk index, the magnitude of which is positively correlated with the degree of parameter anomaly and the rate of change.

5. The optimized control method for fire-fighting linkage response time applied to energy storage power stations according to claim 1, characterized in that, The time series deduction includes: Obtain one or more basic fire alarm linkage logics associated with the target battery cluster; The estimated response delay of each fire-fighting execution terminal device is used as a time variable and substituted into each of the basic fire-fighting linkage logics. The simulation calculates the estimated total time for each logic step from initiation to completion and identifies the most time-consuming critical path.

6. The optimized control method for fire-fighting linkage response time applied to energy storage power stations according to claim 1, characterized in that, The pre-activation operation includes at least one of the following: Send a pre-positioning command to the gas extinguishing zone valve located on the critical path, causing its drive mechanism to move to the near-open position in advance; Perform a pre-release lockout operation on the ventilation isolation devices within the target fire compartment.

7. The optimized control method for fire-fighting linkage response time applied to energy storage power stations according to claim 1, characterized in that, The cross-validation includes: Upon receiving an alarm signal, the system simultaneously retrieves a sequence of temperature field changes in the area where the fire detector is located during a set time period prior to the alarm, as well as concentration gradient data of at least one characteristic gas. If the temperature field change image shows a temperature rise pattern that matches the characteristics of thermal runaway, and the concentration change gradient exceeds the associated threshold, then the verification is deemed successful.

8. The optimized control method for fire-fighting linkage response time applied to energy storage power stations according to claim 1, characterized in that, The criteria for selecting the target logic chain include: among the linked logic chains that have been simulated, select the one with the shortest estimated total time and the fewest conflicts with other current task resources.

9. The optimized control method for fire-fighting linkage response time applied to energy storage power stations according to claim 1, characterized in that, Select the target logic chain from the completed linkage logic and send it for execution. After the target logic chain is sent, the receiving status of each terminal device is fed back in real time. If no receiving confirmation signal is received from the device within a set time, the instruction is resent and repeated a preset number of times. If no confirmation is received, the instruction is switched to the backup communication channel for sending.

10. The optimized control method for fire-fighting linkage response time applied to energy storage power stations according to claim 9, characterized in that, The backup communication channel adopts a controller area network bus topology and uses a separate communication cable.

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