Device, control method and system for quantitative spraying and automatic supply of fire extinguishing agent

Abstract

The invention discloses a quantitative spraying and automatic supplying device for a fire extinguishing agent, a control method and a system, and aims to solve the problems that a fire extinguishing system and a method in the prior art are poor in precision and reaction speed and cannot meet the requirement of efficient fire extinguishing. The control method comprises the following steps: S1, data acquisition; real-time data of a fire scene and the interior of the device are collected through the sensor unit; s2, data processing; the control unit is used for processing the collected data and judging the current fire state and the medicament use condition; s3, based on the processed data, the control unit automatically selects a proper fire extinguishing mode and a proper spraying amount, and the operation of the spraying unit and the supply unit is adjusted; and S4, the control unit continuously receives feedback data of the sensor, the spraying and supplying strategies are adjusted in real time, and efficient utilization and continuous supply of the fire extinguishing agent are ensured. Precise spraying and continuous supply of the fire extinguishing agent can be achieved, the fire extinguishing efficiency is improved, and agent waste is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of fire fighting, and in particular relates to a device, a control method and a system for quantitatively spraying and automatically replenishing fire extinguishing agents. Background Art

[0002] In the field of modern fire fighting technology, the development of fire extinguishing systems has been continuously improving. Traditional fire fighting methods mainly rely on manually controlled spraying of fire extinguishing agents. This method has obvious defects and is difficult to meet the needs of complex fire scenes. Manually controlling the spraying amount of fire extinguishing agents is often difficult to be accurate, which may lead to excessive or insufficient spraying of agents. In this case, not only is the fire extinguishing agent wasted, but the fire may not be effectively extinguished due to insufficient agents. In addition, during the fire extinguishing process, the replenishment of agents often requires manual operation, which increases the complexity and time of the fire extinguishing operation. When the fire is fierce, untimely replenishment may lead to interruption of fire extinguishing, affect the fire extinguishing effect, and even bring greater safety hazards.

[0003] Most of the existing fire extinguishing agent spraying devices can only realize a single function, lack automation and intelligent control, and cannot be flexibly adjusted according to the actual fire extinguishing needs. This single-function device seems to be powerless when facing a complex and changeable fire environment. Although there are some automatic spraying and replenishment technologies, most of them cannot achieve precise quantitative control, and the spraying and replenishment process is also difficult to achieve full automation. The fire extinguishing systems in the prior art mostly use simple liquid level sensors or time controllers. These methods are lacking in accuracy and response speed and cannot meet the needs of efficient fire extinguishing.

[0004] These problems and defects of the prior art often make it difficult to accurately control the amount of fire extinguishing agent sprayed during the actual fire extinguishing process, resulting in low fire extinguishing efficiency; untimely replenishment of fire extinguishing agent may cause the fire extinguishing process to be forced to be interrupted; lack of automation and intelligent control, unable to flexibly adjust the spraying and replenishment strategies according to the fire situation. The existence of these problems and defects makes it particularly important to develop a control method that can achieve quantitative spraying and automatic replenishment of fire extinguishing agent. This method can not only improve the efficiency and effect of fire extinguishing, but also reduce agent waste, ensure the continuous supply of fire extinguishing agent, and effectively solve many problems in the prior art. Summary of the invention

[0005] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a device, a control method and a system for quantitative spraying and automatic replenishment of fire extinguishing agent, through which the precise spraying and continuous supply of fire extinguishing agent can be achieved, the fire extinguishing efficiency can be improved, and the waste of agent can be reduced. In order to solve the above technical problems, the present invention adopts the following technical solutions: The method for controlling the quantitative spraying and automatic replenishment of fire extinguishing agent comprises the following steps: S1. Data collection: collect real-time data from the fire scene and inside the device through the sensor unit; S2, data processing: the control unit processes the collected data to determine the current fire status and the use of the agent; S3. Based on the processed data, the control unit automatically selects the appropriate fire extinguishing mode and spraying amount, and adjusts the operation of the spraying unit and the supply unit; S4. The control unit continuously receives sensor feedback data and adjusts the spraying and replenishment strategies in real time to ensure efficient use and continuous supply of fire extinguishing agents.

[0006] Furthermore, the sensor unit in step S1 includes a flow sensor, a pressure sensor, a liquid level sensor, a temperature sensor and a smoke sensor, which are distributed at various key nodes to collect status data of the fire scene and the inside of the device in real time; The flow sensor is used to monitor the flow of the fire extinguishing agent; the pressure sensor is used to monitor the pressure inside the device; the liquid level sensor is used to monitor the remaining amount of the agent in the storage unit; the temperature sensor is used to monitor the temperature change at the fire scene; the smoke sensor is used to monitor the smoke density at the fire scene; Furthermore, the data processing in step S2 includes: data fusion, the control unit receives and fuses the data from each sensor to form a comprehensive judgment of the fire scene and the system status; data fusion is achieved by a Kalman filter algorithm, and the steps of Kalman filter include state prediction and update; The prediction step involves the formula: Status prediction: (1) in, For the State prediction at step time; is the state transfer matrix, describing the system from the previous moment To the current time Status changes; For the last moment The estimated state at time is the control input matrix, describing the input Impact on status; For the current moment Control input of Error covariance prediction: (2) in, It is the state error covariance matrix prediction at the current moment, describing the distribution of the prediction error; is the error variance matrix of the previous moment; is the state transfer matrix; is the process noise covariance matrix, describing the uncertainty or noise in the system process; The update step involves the formula: Kalman gain calculation: (3) in, is the Kalman gain, which describes how to combine the predicted value with the measured value; is the predicted value of the error covariance matrix; is the measurement matrix, which describes how the state vector is mapped to the measurement space; is the measurement noise covariance matrix, describing the uncertainty in the measurement; Status Update: (4) in, is the updated state estimate; is the measured value; is the predicted value of the measurement model, which represents the measured value calculated based on the predicted state; Error covariance update: (5) in, is the updated error covariance matrix, describing the error of the updated state estimation; is the identity matrix; Among them, the control unit uses the state transfer matrix , measurement matrix , and based on sensor data Estimate the fire state; process noise covariance matrix and the measurement noise covariance matrix Used to correct prediction errors and ensure the accuracy of state judgment; through Kalman filtering, the control unit can adjust the judgment results in real time when receiving sensor data to adapt to the changes in the fire scene and dynamically adjust the spraying and replenishment strategies of the fire extinguishing agent; Data filtering and correction: Data filtering, using moving average filtering or median filtering to smooth sensor data and eliminate random noise or isolated outliers; Data correction, dynamically correcting sensor data through the Kalman filter algorithm, specifically including state prediction and update steps, the prediction step uses the state prediction formula and the error covariance prediction formula; the update step uses the Kalman gain formula and the state update formula; Outlier detection and elimination, according to the set threshold, eliminate abnormal sensor data that exceeds the reasonable range to ensure data accuracy; State assessment: Use the fire model and agent usage model to assess the current fire state and agent consumption; Fire intensity assessment: Based on the data from the temperature sensor and smoke sensor, use the fire intensity formula to assess the intensity of the fire, and use the fire intensity change rate to assess the fire intensity. Determine the spread trend of the fire; Agent consumption assessment: According to the data of the flow sensor and liquid level sensor, the agent consumption rate and the remaining amount are calculated through the agent consumption rate formula and the agent remaining amount formula; The fire state assessment formula is combined with the fire intensity and agent consumption to determine the degree of fire control; Fire intensity assessment involves the formula: Fire intensity formula: Assume fire intensity A comprehensive evaluation value based on temperature sensor data and smoke concentration sensor data , the fire intensity assessment formula is: (6) in: and is a weight coefficient that reflects the different effects of temperature and smoke on fire intensity and can be determined through experiments or empirical data; Spread trend assessment: The spread trend of a fire can be assessed by the rate of change of fire intensity. If the fire intensity increases over time, it indicates that the fire has a tendency to spread; the rate of change of fire intensity Indicates the fire spreading trend: (7) like , indicating that the fire intensity is increasing and the possibility of spreading is greater; on the contrary, if , then the fire may be weakening; The evaluation of drug consumption involves the formula: Formula for the rate of consumption of medicine: Assume that the flow rate of medicine is , the unit is , then the consumption rate of the drug It can be calculated from the flow sensor data: (8) Among them, the flow Measured in real time by flow sensor; Remaining amount of medicine formula: Remaining amount of medicine Data from the level sensor Calculation; Assume that the initial total amount of the agent is , then at a certain moment The remaining amount of medicine is: (9) This formula represents the integral of the total consumption of the drug from the initial time to the current time, and the flow rate measured by the flow sensor In time The integral value on will give the total consumption; Status assessment involves the formula: Fire status evaluation formula: Considering the fire intensity and the agent consumption rate, the fire status evaluation formula is: (10) in: is the fire status assessment value; yes Empirical coefficients are used to adjust the relative influence of fire intensity and agent consumption rate; if , indicating that the fire situation is still serious; like , indicating that the fire may have been brought under control.

[0007] Furthermore, the fire extinguishing mode selection in step S3 specifically includes: the control unit automatically selects a suitable fire extinguishing mode according to the fire state and on-site requirements; the fire extinguishing mode library presets multiple fire extinguishing modes, each mode corresponds to a different fire scene; mode matching, selects the optimal fire extinguishing mode according to the fire intensity, spreading trend and remaining amount of agent; The calculation of the spraying amount in step S3 specifically includes: based on the fire intensity and fire extinguishing mode demand factor , calculate the required amount of extinguishing agent spray , the formula is: (11) in, is the adjustment constant, The remaining amount of medicine; Adjustment of the operations of the spraying unit and the replenishment unit include: spraying volume optimization, using optimization algorithms to adjust the spraying parameters to ensure maximum utilization of the reagent; Replenishment strategy formulation: formulate medicine replenishment strategy based on medicine consumption rate and remaining amount; replenishment trigger conditions: set replenishment trigger conditions to ensure the continuity of medicine supply.

[0008] Furthermore, the real-time adjustment in S4 includes: a feedback mechanism, the control unit continuously receives sensor feedback data, and uses PID control formula 11 to adjust the spraying and replenishment parameters in real time; Closed-loop control: Through the closed-loop control system, the spraying and replenishment parameters are continuously corrected according to the feedback data to ensure the stable operation of the system; dynamic adjustment is carried out according to the change rate of fire intensity. and potion consumption rate , use formula 12 to dynamically adjust the spraying volume; respond to changes in fire status, when the fire intensity changes, use formula 13 to automatically adjust the spraying volume and fire extinguishing mode; optimize agent consumption, according to the current agent consumption rate and forecast demand Formula 14 is used to optimize the replenishment amount; Intelligent prediction: using machine learning models to predict fire development and agent demand, and predicting fire development based on formula 15; agent demand prediction: based on the current consumption rate and fire prediction, agent demand is calculated in advance through formula 16 to optimize the replenishment plan; Closed-loop control formula: The closed-loop control system can adjust the spray volume and replenishment parameters through proportional-integral-differential control (PID control); the formula for PID control is as follows: (12) in, It is a control signal, indicating the adjustment of the spraying amount or the supply amount; is the error value, is the set value (target fire intensity or agent consumption rate), is feedback data (actual fire intensity or agent consumption rate); is the proportionality coefficient, is the integration coefficient, is the differential coefficient; Dynamic adjustment formula: dynamic release amount of fire extinguishing agent Based on the rate of change of fire intensity And the potion consumption rate is adjusted to: (13) in, is the adjusted spray volume; and It is the adjustment coefficient, which controls the influence of changes in fire intensity and agent consumption on the spraying volume; Fire state response formula: When the fire intensity changes significantly, the system automatically adjusts the fire extinguishing mode and spray volume according to the new fire state: (14) in, For the new fire intensity, It is the newly selected fire extinguishing mode.

[0009] Replenishment Optimization Formula: Replenishment Frequency and Replenishment Amount of Medicine Can be determined by the current potion consumption rate and predicted future demand The calculation results in: (15) in, is the adjustment coefficient, It is the future agent demand based on the current consumption rate and fire development forecast; Fire prediction model formula: Based on historical data and real-time data, the machine learning model predicts future changes in fire intensity To adjust the fire extinguishing strategy in advance. The prediction formula can be expressed as: (16) in, It is the basic prediction function of machine learning models (such as decision trees, support vector machines, etc.); is the weight of each model; is the input data (historical fire data, real-time sensor data, etc.); Drug demand prediction formula: Based on the current drug consumption rate and fire development prediction , the future demand for pharmaceuticals It can be calculated by the following formula: (17).

[0010] Furthermore, it also includes safety and exception handling, specifically including: fault detection and handling, real-time monitoring of the device status, and timely handling of abnormalities; automatic diagnosis, the device has automatic diagnosis function, can identify problems and issue alarms; emergency measures, in the event of a fault, the device automatically activates the emergency plan to ensure that the fire-fighting process is not affected.

[0011] Furthermore, it also includes self-learning and optimization of the device, specifically including: data recording and analysis, the device records all data during each fire extinguishing process to form a historical database; data analysis, regularly analyze historical data to discover and improve deficiencies in fire extinguishing strategies; self-learning algorithms, using self-learning algorithms to continuously optimize fire extinguishing modes and spraying strategies, and improve the intelligence level of the device; parameter adjustment, based on historical data and real-time feedback, autonomously adjust control parameters to improve fire extinguishing efficiency and reliability.

[0012] The present invention also relates to a control system for quantitative spraying and automatic replenishment of fire extinguishing agents, comprising: Data acquisition module, used to collect real-time status data of the fire scene and the system; The data processing module is used to process the data collected by the data acquisition module and determine the current fire status and the use of the reagent; The decision-making module automatically selects the appropriate fire extinguishing mode and spraying volume based on the data processed by the data processing module, and adjusts the spraying and replenishment operations; The real-time adjustment module adjusts the spraying and replenishment strategies in real time according to the data collected by the continuously received data acquisition module to ensure the efficient use and continuous supply of fire extinguishing agents.

[0013] Furthermore, it also includes a security and exception handling module and a self-learning and optimization module; The safety and exception handling module is used to monitor the device status in real time, handle exceptions in a timely manner, automatically diagnose and identify problems, and issue alarms; The self-learning and optimization module is used to regularly analyze the data of historical fire-fighting processes, discover and improve deficiencies in fire-fighting strategies, continuously optimize fire-fighting modes and spraying strategies, and autonomously adjust control parameters based on historical data and real-time feedback.

[0014] The present invention also relates to a device for quantitatively spraying and automatically replenishing a fire extinguishing agent, comprising: a fire extinguishing agent storage unit, a fire extinguishing agent spraying unit, a sensor unit, a control unit and a replenishing unit; The fire extinguishing agent storage unit is in the form of an agent tank, which is used to store the fire extinguishing agent; the fire extinguishing agent spraying unit sprays the agent through a nozzle, and the switch of the nozzle is controlled by the control unit; the sensor unit includes a liquid level sensor, a flow sensor, a pressure sensor, a temperature sensor and a smoke sensor, which are used to monitor the amount of agent, the spraying state and the fire situation; the control unit has a built-in computer-readable storage medium, which is used to adjust the spraying amount and the supply strategy in real time according to the sensor data; the supply unit is used to automatically replenish the fire extinguishing agent to the storage unit, and includes an agent delivery pipeline and a pump. The beneficial effects of the present invention are: 1. Precise and quantitative spraying: The disadvantage of the prior art is that the traditional fire extinguishing system mostly relies on manual control, which makes it difficult to achieve accurate fire extinguishing agent spraying, resulting in excessive or insufficient agent spraying, affecting the efficiency and effect of fire extinguishing. The advantage of the present invention is that the agent spraying amount is monitored in real time by flow sensors and pressure sensors, combined with PID control algorithms, to achieve precise and quantitative spraying of fire extinguishing agents. The control unit dynamically adjusts the spraying amount according to real-time data to ensure efficient use of fire extinguishing agents and reduce waste.

[0015] 2. Automation and intelligent control: The shortcomings of the prior art are that the existing fire extinguishing systems mostly use simple liquid level sensors or time controllers, lack automation and intelligent control, and cannot flexibly adjust the spraying and replenishment strategies according to the fire situation. The advantages of the present invention are that it uses a variety of sensors to monitor the fire scene and system status in real time, and uses intelligent control algorithms to automatically select the fire extinguishing mode and spraying volume. The system has self-learning and optimization capabilities, continuously improves fire extinguishing efficiency and reliability, and can quickly respond and effectively extinguish fires in complex fire environments.

[0016] 3. Continuous supply of reagents: The disadvantage of the prior art is that the replenishment of reagents in traditional fire extinguishing systems often requires manual operation, which increases the complexity and time of fire extinguishing. Untimely replenishment of reagents may lead to interruption of fire extinguishing, affecting the fire extinguishing effect. The advantage of the present invention is that the liquid level sensor monitors the remaining amount of reagents in the storage unit in real time, and the control unit automatically starts and stops the replenishment unit according to the monitoring data to ensure the continuous supply of reagents. During the replenishment process, the system monitors the replenishment status in real time to ensure the accuracy and timeliness of the replenishment, and avoids the interruption of fire extinguishing caused by insufficient reagents.

[0017] 4. Efficient fire extinguishing effect: The shortcomings of the existing technology are that the spraying and replenishment process of the existing fire extinguishing system lacks coordination, and the spraying and replenishment amounts are difficult to dynamically adjust according to the development of the fire, resulting in poor fire extinguishing effect. The advantage of the present invention is that the control algorithm can adjust the spraying and replenishment strategy in real time according to the development of the fire and the consumption of the agent. The system ensures the uniformity and effective coverage of the agent spraying through multi-point monitoring and coordinated control, improves the efficiency and effect of fire extinguishing, and is suitable for a variety of complex fire scenes.

[0018] 5. Safety and reliability: The disadvantage of the existing technology is that the fault detection and processing capabilities of the traditional fire extinguishing system are limited. System failures may cause the fire extinguishing process to be blocked, posing a major safety hazard. The advantage of the present invention is that the built-in fault detection and processing mechanism can monitor the system status in real time and handle abnormalities in a timely manner. The system has automatic diagnosis and emergency measures functions to ensure the continuity and safety of the fire extinguishing process, and improve the reliability and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 This is a flow chart of a control method for quantitative spraying and automatic replenishment of fire extinguishing agent provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1 like Figure 1 As shown, the control method for quantitative spraying and automatic replenishment of fire extinguishing agent includes the following steps: S1. Data collection: collect real-time data from the fire scene and inside the device through the sensor unit; The sensor unit includes a flow sensor, a pressure sensor, a liquid level sensor, a temperature sensor and a smoke sensor, which are distributed at various key nodes to collect status data of the fire scene and the inside of the device in real time; The flow sensor is used to monitor the flow of the fire extinguishing agent; the pressure sensor is used to monitor the pressure inside the device; the liquid level sensor is used to monitor the remaining amount of the agent in the storage unit; the temperature sensor is used to monitor the temperature change at the fire scene; the smoke sensor is used to monitor the smoke density at the fire scene; In this embodiment, the flow sensor monitors the flow rate of the sprayed agent in real time, the flow sensor and the pressure sensor monitor the flow rate of the agent and the system pressure in real time, and the environmental sensor (such as temperature and smoke sensor) monitors the changes of the fire scene in real time and transmits the data to the control unit. The pressure sensor monitors the pressure in the spraying system to ensure the stability during the spraying process. The environmental sensors such as temperature and smoke sensors monitor the changes of the fire scene and provide data support for the adjustment of the spraying strategy.

[0023] S2, data processing: the control unit processes the collected data to determine the current fire status and the use of the agent; The data processing in step S2 includes: data fusion, the control unit receives and fuses the data from each sensor to form a comprehensive judgment of the fire scene and system status; data fusion is achieved through the Kalman filter algorithm, and the steps of Kalman filter include state prediction and update; the state prediction formula is ,in is the state transfer matrix. The error covariance prediction is The control unit uses these formulas to predict the state of the fire scene, and after receiving the sensor data, it uses the Kalman gain formula Make corrections, and ultimately form a comprehensive judgment on the fire status, and dynamically adjust the fire-fighting strategy.

[0024] The prediction step involves the formula: Status prediction: (1) in, For the State prediction at step time; is the state transfer matrix, describing the system from the previous moment To the current time Status changes; For the last moment The estimated state at time is the control input matrix, describing the input Impact on status; For the current moment Control input of Error covariance prediction: (2) in, It is the state error covariance matrix prediction at the current moment, describing the distribution of the prediction error; is the error variance matrix of the previous moment; is the state transfer matrix; is the process noise covariance matrix, describing the uncertainty or noise in the system process; The update step involves the formula: Kalman gain calculation: (3) in, is the Kalman gain, which describes how to combine the predicted value with the measured value; is the predicted value of the error covariance matrix; is the measurement matrix, which describes how the state vector is mapped to the measurement space; is the measurement noise covariance matrix, describing the uncertainty in the measurement; Status Update: (4) in, is the updated state estimate; is the measured value; is the predicted value of the measurement model, which represents the measured value calculated based on the predicted state; Error covariance update: (5) in, is the updated error covariance matrix, describing the error of the updated state estimation; is the identity matrix; Among them, the control unit uses the state transfer matrix , measurement matrix , and based on sensor data Estimate the fire state; process noise covariance matrix and the measurement noise covariance matrix Used to correct prediction errors and ensure the accuracy of state judgment; through Kalman filtering, the control unit can adjust the judgment results in real time when receiving sensor data to adapt to the changes in the fire scene and dynamically adjust the spraying and replenishment strategies of the fire extinguishing agent; Data filtering and correction: Data filtering, using moving average filtering or median filtering to smooth sensor data and eliminate random noise or isolated outliers; Data correction, dynamically correcting sensor data through the Kalman filter algorithm, specifically including state prediction and update steps, the prediction step uses the state prediction formula and the error covariance prediction formula; the update step uses the Kalman gain formula and the state update formula; Outlier detection and elimination, according to the set threshold, eliminate abnormal sensor data that exceeds the reasonable range to ensure data accuracy; State assessment: Use the fire model and agent usage model to assess the current fire state and agent consumption; Fire intensity assessment: Based on the data from the temperature sensor and smoke sensor, use the fire intensity formula to assess the intensity of the fire, and use the fire intensity change rate to assess the fire intensity. Determine the spread trend of the fire; Agent consumption assessment: According to the data of the flow sensor and liquid level sensor, the agent consumption rate and the remaining amount are calculated through the agent consumption rate formula and the agent remaining amount formula; The fire state assessment formula is combined with the fire intensity and agent consumption to determine the degree of fire control; Fire intensity assessment involves the formula: Fire intensity formula: Assume fire intensity A comprehensive evaluation value based on temperature sensor data and smoke concentration sensor data , the fire intensity assessment formula is: (6) in: and is a weight coefficient that reflects the different effects of temperature and smoke on fire intensity and can be determined through experiments or empirical data; Spread trend assessment: The spread trend of a fire can be assessed by the rate of change of fire intensity. If the fire intensity increases over time, it indicates that the fire has a tendency to spread; the rate of change of fire intensity Indicates the fire spreading trend: (7) like , indicating that the fire intensity is increasing and the possibility of spreading is greater; on the contrary, if , then the fire may be weakening; The evaluation of drug consumption involves the formula: Formula for the rate of consumption of medicine: Assume that the flow rate of medicine is , the unit is , then the consumption rate of the drug The following can be calculated from the flow sensor data: (8) Among them, the flow Measured in real time by flow sensor; Remaining amount of medicine formula: Remaining amount of medicine Data from the level sensor Calculation; Assume that the initial total amount of the agent is , then at a certain moment The remaining amount of medicine is: (9) This formula represents the integral of the total consumption of the drug from the initial time to the current time, and the flow rate measured by the flow sensor In time The integral value on will give the total consumption; Status assessment involves the formula: Fire status evaluation formula: Considering the fire intensity and the agent consumption rate, the fire status evaluation formula is: (10) in: is the fire status assessment value; yes Empirical coefficients are used to adjust the relative influence of fire intensity and agent consumption rate; if , indicating that the fire situation is still serious; like , indicating that the fire may have been brought under control.

[0025] S3. Based on the processed data, the control unit automatically selects the appropriate fire extinguishing mode and spraying amount, and adjusts the operation of the spraying unit and the supply unit; The fire extinguishing mode selection in step S3 specifically includes: the control unit automatically selects a suitable fire extinguishing mode according to the fire state and on-site requirements; the fire extinguishing mode library presets multiple fire extinguishing modes, each mode corresponds to a different fire scene; mode matching, selects the optimal fire extinguishing mode according to the fire intensity, spread trend and remaining amount of agent; The calculation of the spraying amount in step S3 specifically includes: based on the fire intensity and fire extinguishing mode demand factor , calculate the required amount of extinguishing agent spray , the formula is: (11) in, is the adjustment constant, The remaining amount of medicine; Adjustment of the operations of the spraying unit and the replenishment unit include: spraying volume optimization, using optimization algorithms to adjust the spraying parameters to ensure maximum utilization of the reagent; Replenishment strategy formulation: formulate medicine replenishment strategy based on medicine consumption rate and remaining amount; replenishment trigger conditions: set replenishment trigger conditions to ensure the continuity of medicine supply.

[0026] The mass automatic replenishment control function of the present invention includes a liquid level sensor function through the liquid level detection in the mass automatic replenishment control to monitor the remaining amount of fire extinguishing agent in the storage unit in real time and transmit the data to the control unit; data processing, so that the control unit determines whether the current amount of agent is lower than the preset safe liquid level according to the data of the liquid level sensor; multi-point monitoring, in a large storage unit, multiple liquid level sensors are distributed in different positions to ensure the comprehensiveness and accuracy of agent monitoring.

[0027] The automatic replenishment control in the mass automatic replenishment control includes: starting the replenishment unit, when the liquid level is lower than the preset value, the control unit sends a command to start the replenishment unit; the delivery pipeline and the pump, the supply unit includes the delivery pipeline and the pump, which are responsible for transporting the fire extinguishing agent from the reserve tank to the storage unit; the control unit applies a control algorithm to dynamically adjust the working state and delivery speed of the pump according to the data of the liquid level sensor to ensure the stability and efficiency of the agent replenishment; the control unit controls the replenishment amount, the control unit accurately controls the replenishment amount according to the real-time liquid level data to avoid overdose or underdose; the flow sensor, the flow sensor is installed on the supply pipeline to monitor the flow of the replenishment agent in real time to ensure the controllability of the replenishment process.

[0028] The feedback confirmation in the automatic quality replenishment control includes: continuous monitoring, the liquid level sensor continuously monitors the liquid level of the medicine in the storage unit during the replenishment process; data feedback, the data of the liquid level sensor is continuously fed back to the control unit, and the control unit calculates the replenishment progress in real time; automatic stop, when the liquid level reaches the preset liquid level, the control unit issues a command to immediately stop the operation of the replenishment unit; valve control, closing the control valve of the delivery pipeline to ensure the safety and accuracy of the replenishment process; pump shutdown, stopping the operation of the pump to avoid excessive replenishment of medicine.

[0029] S4. The control unit continuously receives sensor feedback data and adjusts the spraying and replenishment strategies in real time to ensure efficient use and continuous supply of fire extinguishing agents.

[0030] The real-time adjustment in S4 includes: a feedback mechanism, the control unit continuously receives sensor feedback data, and uses PID control formula 11 to adjust the spraying and replenishment parameters in real time; Closed-loop control: Through the closed-loop control system, the spraying and replenishment parameters are continuously corrected according to the feedback data to ensure the stable operation of the system; dynamic adjustment is carried out according to the change rate of fire intensity. and potion consumption rate , using formula 12 to dynamically adjust the spraying volume; responding to changes in fire status, when the fire intensity changes, using formula 13 to automatically adjust the spraying volume and fire extinguishing mode; optimizing agent consumption, based on the current agent consumption rate and forecast demand Formula 14 is used to optimize the replenishment amount; Intelligent prediction: using machine learning models to predict fire development and agent demand, and predicting fire development based on formula 15; agent demand prediction: based on the current consumption rate and fire prediction, agent demand is calculated in advance through formula 16 to optimize the supply plan; Closed-loop control formula: The closed-loop control system can adjust the spray volume and replenishment parameters through proportional-integral-differential control (PID control); the formula for PID control is as follows: (12) in, It is a control signal, indicating the adjustment of the spraying amount or the supply amount; is the error value, is the set value (target fire intensity or agent consumption rate), is feedback data (actual fire intensity or agent consumption rate); is the proportionality coefficient, is the integration coefficient, is the differential coefficient; Dynamic adjustment formula: dynamic release amount of fire extinguishing agent Based on the rate of change of fire intensity And the potion consumption rate is adjusted to: (13) in, is the adjusted spray volume; and It is the adjustment coefficient, which controls the influence of changes in fire intensity and agent consumption on the spraying volume; Fire state response formula: When the fire intensity changes significantly, the system automatically adjusts the fire extinguishing mode and spray volume according to the new fire state: (14) in, For the new fire intensity, It is the newly selected fire extinguishing mode.

[0031] Replenishment Optimization Formula: Replenishment Frequency and Replenishment Amount of Medicine Can be determined by the current potion consumption rate and predicted future demand The calculations show that: (15) in, is the adjustment coefficient, It is the future agent demand based on the current consumption rate and fire development forecast; Fire prediction model formula: Based on historical data and real-time data, the machine learning model predicts future changes in fire intensity To adjust the fire extinguishing strategy in advance. The prediction formula can be expressed as: (16) in, It is the basic prediction function of machine learning models (such as decision trees, support vector machines, etc.); is the weight of each model; is the input data (historical fire data, real-time sensor data, etc.); Drug demand prediction formula: Based on the current drug consumption rate and fire development prediction , the future demand for pharmaceuticals It can be calculated by the following formula: (17).

[0032] The feedback control data acquisition of the quantitative spray control includes: the control unit continuously receives data from the flow sensor and the pressure sensor, and the built-in PID control algorithm is used to compare and analyze the preset spray volume and real-time data to calculate the error. The opening of the control valve is adjusted according to the error to accurately control the spray volume of the agent. Data processing includes the algorithm built into the control unit to compare and analyze the real-time data and the preset spray volume. The error calculation includes calculating the error between the actual spray volume and the preset spray volume. The control algorithm in the feedback mechanism adopts the proportional-integral-differential (PID) control algorithm to adjust the opening of the control valve according to the error to achieve accurate control of the spray volume of the agent. Among them, P (proportional) control: according to the current error, the valve opening is adjusted immediately. I (integral) control: accumulate past errors and adjust the spray strategy to eliminate long-term deviations. D (differential) control: predict the error change trend, make adjustments in advance, and reduce future deviations. On the basis of traditional PID control, an adaptive control algorithm is introduced to dynamically adjust the control parameters according to historical data and real-time feedback to improve control accuracy.

[0033] Intelligent prediction uses machine learning algorithms to predict fire development and agent consumption and make adjustments in advance. Fire prediction models predict fire development trends based on historical data and real-time data and adjust fire-fighting strategies in advance. Agent demand prediction calculates future agent demand in advance based on current consumption rates and fire development forecasts to optimize replenishment plans.

[0034] The dynamic adjustment of the quantitative spray control fire scene data analysis includes: the control unit combines the data of the environmental sensors (such as fire intensity, temperature, smoke concentration, etc.) to dynamically analyze the development of the fire. Adjustment of the spraying strategy includes increasing the spraying volume. When the fire increases, the control unit increases the valve opening to increase the spraying volume to quickly control the fire; reducing the spraying volume. When the fire weakens or the fire extinguishing effect is significant, the control unit reduces the valve opening and reduces the spraying volume to avoid waste of reagents; spraying mode switching. According to the type of fire and the changes on the scene, the control unit can switch different spraying modes (such as continuous spraying, intermittent spraying, etc.) to achieve the best fire extinguishing effect; multi-point monitoring and coordination: at large fire scenes, the system can work together through multiple spraying units and sensors to ensure the uniformity and effective coverage of the spraying of the reagents; regional control, for different fire areas, spraying strategies are set separately to accurately control the spraying volume of each area; synchronous adjustment, multiple spraying units share data and synchronize adjustments to ensure overall fire extinguishing efficiency.

[0035] It also includes safety and exception handling steps, including: fault detection and handling, real-time monitoring of the device status, and timely handling of abnormalities; automatic diagnosis, the device has automatic diagnosis function, can identify problems and issue alarms; emergency measures, in the event of a fault, the device automatically activates the emergency plan to ensure that the fire-fighting process is not affected.

[0036] The safety and abnormality handling in the automatic quality replenishment control includes: safety valves and pressure relief devices. Safety valves and pressure relief devices are set in the supply system to prevent system failure or leakage caused by excessive pressure; fault detection and alarm. The control unit has a built-in fault detection mechanism to monitor the operating status of the supply unit in real time; abnormality handling. When a fault or abnormality is detected in the supply unit, the control unit immediately issues an alarm and starts a backup supply system or takes other emergency measures; backup supply system. Backup supply pipelines and pumps are designed to be able to switch in time when the main supply system fails to ensure uninterrupted supply of medicines.

[0037] The fault detection and alarm in the quantitative spray control include: a built-in fault detection mechanism in the system, when the sensor or control unit is abnormal, an alarm is issued in time and self-repair is performed or switching to a backup plan; emergency shutdown, in extreme cases (such as excessive spraying of the agent, system failure, etc.), the control unit can immediately shut down the spray unit to ensure safety.

[0038] It also includes the self-learning and optimization steps of the device, including: data recording and analysis, the device records all data during each fire-fighting process to form a historical database; data analysis, regularly analyze historical data to discover and improve deficiencies in fire-fighting strategies; self-learning algorithms, using self-learning algorithms to continuously optimize fire-fighting modes and spraying strategies to improve the intelligence level of the device; parameter adjustment, based on historical data and real-time feedback, autonomously adjust control parameters to improve fire-fighting efficiency and reliability.

[0039] The intelligent optimization and historical data analysis in the quality automatic replenishment control include: control optimization, the control unit adopts a machine learning algorithm to optimize the replenishment strategy based on historical replenishment data and fire scene data, and improve the replenishment efficiency and reliability; historical data analysis, stores data during the replenishment process, regularly analyzes the replenishment situation, discovers and solves potential problems, and continuously improves system performance.

[0040] This embodiment also relates to a control system for quantitative spraying and automatic replenishment of fire extinguishing agents, including: Data acquisition module, used to collect real-time status data of the fire scene and the system; The data processing module is used to process the data collected by the data acquisition module and determine the current fire status and the use of the reagent; The decision-making module automatically selects the appropriate fire extinguishing mode and spraying volume based on the data processed by the data processing module, and adjusts the spraying and replenishment operations; The real-time adjustment module adjusts the spraying and replenishment strategies in real time according to the data collected by the continuously received data acquisition module to ensure the efficient use and continuous supply of fire extinguishing agents.

[0041] The safety and exception handling module is used to monitor the device status in real time, handle exceptions in a timely manner, automatically diagnose and identify problems, and issue alarms; The self-learning and optimization module is used to regularly analyze the data of historical fire-fighting processes, discover and improve deficiencies in fire-fighting strategies, continuously optimize fire-fighting modes and spraying strategies, and autonomously adjust control parameters based on historical data and real-time feedback.

[0042] The quantitative spraying and automatic replenishment device of the fire extinguishing agent of this embodiment includes a fire extinguishing agent storage unit, a fire extinguishing agent spraying unit, a sensor unit, a control unit and a replenishment unit. The fire extinguishing agent storage unit is used to store the fire extinguishing agent, usually a plurality of agent tanks, and is equipped with a liquid level sensor to monitor the remaining amount of the agent. The fire extinguishing agent spraying unit includes a nozzle and a control valve, and the spraying state is monitored by a flow sensor and a pressure sensor. The sensor unit is installed at a key node, including a flow sensor, a pressure sensor, a liquid level sensor, a temperature sensor and a smoke sensor, to collect the status data of the fire scene and the system in real time. The control unit is the core control system with a built-in control algorithm, which is responsible for data processing and decision making. The replenishment unit includes an agent delivery pipeline and a pump, which is used to automatically replenish the fire extinguishing agent.

[0043] In terms of quantitative spraying control, the device is implemented through the following steps: First, the flow sensor and pressure sensor are installed in the spraying unit to monitor the flow and pressure of the fire extinguishing agent in real time, while environmental sensors (such as temperature and smoke sensors) monitor changes at the fire scene. The control unit continuously receives sensor data and has a built-in PID control algorithm. It calculates the error based on the preset spraying volume and real-time data comparison, and accurately controls the spraying volume of the agent by adjusting the opening of the control valve. The control unit dynamically adjusts the spraying strategy based on the fire scene data. When the fire increases, the control unit increases the valve opening and increases the spraying volume; when the fire decreases, the control unit reduces the valve opening and reduces the spraying volume.

[0044] In terms of automatic quality replenishment control, the system is implemented through the following steps: First, the liquid level sensor is installed in the storage unit to monitor the remaining amount of the agent in real time. When the liquid level is lower than the preset value, the control unit issues a command to start the replenishment unit, and the replenishment unit replenishes the agent to the storage unit through the delivery pipe and pump. During the replenishment process, the flow sensor monitors the flow rate of the replenished agent to ensure the stability and efficiency of the replenishment process. At the same time, the liquid level sensor continuously monitors the agent level in the storage unit. When the liquid level reaches the preset value, the control unit issues a command to stop the operation of the replenishment unit.

[0045] The control method of the quantitative spraying and automatic replenishment device of the fire extinguishing agent mainly includes the following contents: First, the real-time data of the fire scene and the system are collected through the sensor unit. The control unit processes the collected data to determine the current fire status and the use of the agent. Based on the processed data, the control unit automatically selects the appropriate fire extinguishing mode and spraying volume, and adjusts the operation of the spraying unit and the replenishment unit. The control unit continuously receives sensor feedback data, adjusts the spraying and replenishment strategies in real time, and ensures the efficient use and continuous supply of the fire extinguishing agent.

[0046] Example 2 Take the fire extinguishing device of an industrial plant as an example. The device is equipped with multiple agent storage tanks, each tank is equipped with a liquid level sensor, and the spraying units are installed in different areas of the plant to cover the entire plant. The control unit receives sensor data and automatically adjusts the spraying volume of each area according to the fire situation. When the agent in a certain area is consumed too quickly, the system automatically starts the supply unit to replenish the agent.

[0047] Example 3 A smart fire extinguishing device in a family, which includes a small agent storage tank, and a liquid level sensor to monitor the amount of agent in real time. The spraying unit is installed in high-risk areas such as the kitchen and living room, and uses an intelligent control valve to control the spraying amount. When a fire is detected, the control unit automatically adjusts the spraying amount according to the fire, and starts the supply unit to ensure the supply of agents. The device can be linked with smart home devices, such as smoke alarms and home monitoring systems, to provide comprehensive safety protection. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for controlling the quantitative spraying and automatic replenishment of fire extinguishing agents, characterized in that: The steps include: S1. Data collection: Collect real-time data from the fire scene and inside the device through the sensor unit; S2, data processing: the control unit processes the collected data to determine the current fire status and agent usage; S3. Based on the processed data, the control unit automatically selects the appropriate fire extinguishing mode and spraying volume, and adjusts the operation of the spraying unit and the supply unit; S4. The control unit continuously receives sensor feedback data and adjusts the spraying and replenishment strategies in real time to ensure efficient use and continuous supply of fire extinguishing agents.

2. The method for controlling the quantitative spraying and automatic replenishment of fire extinguishing agent according to claim 1, characterized in that: The sensor units in step S1 include flow sensors, pressure sensors, liquid level sensors, temperature sensors, and smoke sensors, which are distributed at various key nodes to collect real-time status data of the fire scene and the inside of the device; The flow sensor is used to monitor the flow of fire extinguishing agent; the pressure sensor is used to monitor the pressure inside the device; the liquid level sensor is used to monitor the remaining amount of agent in the storage unit; the temperature sensor is used to monitor the temperature changes at the fire scene; and the smoke sensor is used to monitor the smoke concentration at the fire scene.

3. The method for controlling the quantitative spraying and automatic replenishment of fire extinguishing agent according to claim 1 or 2, characterized in that: The data processing in step S2 includes: data fusion, where the control unit receives and fuses data from various sensors to form a comprehensive judgment of the fire scene and system status; data fusion is achieved through a Kalman filter algorithm, and the steps of Kalman filtering include state prediction and update; The prediction step involves the formula: Status prediction: (1) in, For the State prediction at step time; is the state transfer matrix, describing the system from the previous moment To the current moment Status changes; For the last moment The estimated state at time ; is the control input matrix, describing the input Impact on status; For the current moment Control input; Error covariance prediction: (2) in, is the state error covariance matrix prediction at the current moment, describing the distribution of the prediction error; is the error variance matrix of the previous moment; is the state transfer matrix; is the process noise covariance matrix, which describes the uncertainty or noise in the system process; The update step involves the formula: Kalman gain calculation: (3) in, is the Kalman gain, which describes how to combine the predicted value with the measured value; is the error covariance matrix predicted value; is the measurement matrix, which describes how the state vector is mapped to the measurement space; is the measurement noise covariance matrix, describing the uncertainty in the measurement; Status Update: (4) in, is the updated state estimate; is the measured value; is the predicted value of the measurement model, which represents the measurement value calculated based on the predicted state; Error covariance update: (5) in, is the updated error covariance matrix, describing the error of the updated state estimation; is the identity matrix; Among them, the control unit uses the state transfer matrix , measurement matrix , and based on sensor data Estimate the fire state; process noise covariance matrix and the measurement noise covariance matrix Used to correct prediction errors and ensure the accuracy of state judgment; through Kalman filtering, the control unit can adjust the judgment results in real time when receiving sensor data to adapt to the changes in the fire scene and dynamically adjust the spraying and supply strategies of the fire extinguishing agent; Data filtering and correction: Data filtering uses moving average filtering or median filtering to smooth sensor data and eliminate random noise or isolated outliers. Data correction uses the Kalman filter algorithm to dynamically correct sensor data, specifically including state prediction and update steps. The prediction step uses the state prediction formula and the error covariance prediction formula; the update step uses the Kalman gain formula and the state update formula. Outlier detection and elimination: Based on the set threshold, abnormal sensor data that exceeds the reasonable range is eliminated to ensure data accuracy. State assessment: Use fire models and agent usage models to assess the current fire state and agent consumption; Fire intensity assessment: Based on the data from temperature sensors and smoke sensors, use the fire intensity formula to assess the intensity of the fire, and use the fire intensity change rate to assess the fire intensity. Determine the spread trend of the fire; Agent consumption assessment: Based on the data from the flow sensor and liquid level sensor, calculate the agent consumption rate and remaining amount using the agent consumption rate formula and the agent remaining amount formula; Determine the degree of fire control using the fire state assessment formula combined with the fire intensity and agent consumption; Fire intensity assessment involves the formula: Fire intensity formula: Assume fire intensity A comprehensive evaluation value based on temperature sensor data and smoke concentration sensor data , the fire intensity assessment formula is: (6) in: and is a weight coefficient that reflects the different effects of temperature and smoke on fire intensity and can be determined through experiments or empirical data; Spread trend assessment: The spread trend of a fire can be assessed by the rate of change of fire intensity. If the fire intensity increases over time, it indicates that the fire has a tendency to spread; the rate of change of fire intensity Indicates the fire spreading trend: (7) like , indicating that the fire intensity is increasing and the possibility of spreading is greater; on the contrary, if , then the fire may be weakening; The formula for evaluating drug consumption is: Formula for the rate of drug consumption: Assume the flow rate of the drug is , the unit is , the consumption rate of the drug The following can be calculated from the flow sensor data: (8） Among them, traffic Measured in real time by flow sensor; Remaining amount of medicine formula: Remaining amount of medicine Data from the liquid level sensor Calculation; Assume that the initial total amount of the drug is , then at some point The remaining amount of medicine is: （9） This formula represents the integral of the total consumption of the drug from the initial time to the current moment, and the flow rate measured by the flow sensor In time The integral value on will give the total consumption; Status assessment involves the formula: Fire status evaluation formula: Considering the fire intensity and the agent consumption rate, the fire status evaluation formula is: （10） in: is the fire status assessment value; yes Empirical coefficients are used to adjust the relative influence of fire intensity and agent consumption rate; if , indicating that the fire situation is still serious; like , indicating the fire may have been brought under control.

4. The method for controlling the quantitative spraying and automatic replenishment of fire extinguishing agent according to claim 3, characterized in that: The fire extinguishing mode selection in step S3 specifically includes: the control unit automatically selects the appropriate fire extinguishing mode according to the fire status and on-site requirements; the fire extinguishing mode library presets multiple fire extinguishing modes, each mode corresponding to a different fire scenario; mode matching, selects the optimal fire extinguishing mode according to the fire intensity, spread trend and remaining amount of fire agent; The calculation of the discharge amount in step S3 specifically includes: and fire extinguishing mode demand factor , calculate the required amount of fire extinguishing agent spray , the formula is: （11） in, is the adjustment constant, The remaining amount of medicine; Adjustment of the spraying unit and the replenishment unit operations include: spraying volume optimization, using optimization algorithms to adjust the spraying parameters to ensure maximum utilization of the agent; Replenishment strategy formulation: formulate the medicine replenishment strategy based on the medicine consumption rate and remaining amount; replenishment trigger conditions: set the replenishment trigger conditions to ensure the continuity of medicine supply.

5. The method for controlling the quantitative spraying and automatic replenishment of fire extinguishing agent according to claim 4, characterized in that: The real-time adjustment in S4 includes: a feedback mechanism, where the control unit continuously receives sensor feedback data and uses PID control formula 11 to adjust the spraying and replenishment parameters in real time; Closed-loop control: Through the closed-loop control system, the spraying and supply parameters are continuously corrected according to the feedback data to ensure the stable operation of the system; dynamic adjustment is carried out according to the change rate of fire intensity. and potion consumption rate , use formula 12 to dynamically adjust the spraying volume; respond to changes in fire status, when the fire intensity changes, use formula 13 to automatically adjust the spraying volume and fire extinguishing mode; optimize agent consumption, according to the current agent consumption rate and forecast demand Use formula 14 to optimize the supply amount; Intelligent prediction uses machine learning models to predict fire development and agent demand, using Formula 15 to predict fire development. Agent demand prediction uses Formula 16 to calculate agent demand in advance based on current consumption rates and fire forecasts, optimizing supply plans. Closed-loop control formula: The closed-loop control system uses PID control to adjust the spray volume and supply parameters; the PID control formula is as follows: (11) in, It is a control signal, indicating the adjustment of the spraying amount or the supply amount; is the error value, is the set value, It is feedback data; is the proportionality coefficient, is the integration coefficient, is the differential coefficient; Dynamic adjustment formula: dynamic release rate of fire extinguishing agent Can be based on the rate of change of fire intensity The potion consumption rate is adjusted to: (12) in, is the adjusted spray volume; and It is the adjustment coefficient, which controls the effect of changes in fire intensity and agent consumption on the spraying volume; Fire state response formula: When the fire intensity changes significantly, the system automatically adjusts the fire extinguishing mode and spray volume according to the new fire state: (13) in, For the new fire intensity, For the newly selected fire extinguishing mode; Replenishment Optimization Formula: Replenishment Frequency and Replenishment Amount of Medicine Can be determined by the current potion consumption rate and predicted future demand The calculation shows that: (14) in, is the adjustment coefficient, It is the future agent demand based on the current consumption rate and the fire development forecast; Fire prediction model formula: Based on historical data and real-time data, the machine learning model predicts future changes in fire intensity To adjust the fire extinguishing strategy in advance. The prediction formula can be expressed as: (15) in, It is the basic prediction function of the machine learning model; is the weight of each model; is the input data; Formula for predicting drug demand: Based on the current drug consumption rate and fire development prediction , the future demand for pharmaceuticals It can be calculated by the following formula: (16)。 6. The method for controlling the quantitative spraying and automatic replenishment of fire extinguishing agent according to claim 5, characterized in that: It also includes safety and exception handling, specifically including: fault detection and handling, real-time monitoring of device status, and timely handling of abnormalities; automatic diagnosis, the device has automatic diagnosis function, can identify problems and issue alarms; emergency measures, in the event of a fault, the device automatically activates the emergency plan to ensure that the fire extinguishing process is not affected.

7. The method for controlling the quantitative spraying and automatic replenishment of fire extinguishing agent according to claim 1, 4, 5 or 6, characterized in that: It also includes device self-learning and optimization, specifically including: data recording and analysis, the device records all data during each fire extinguishing process to form a historical database; data analysis, regular analysis of historical data to discover and improve deficiencies in fire extinguishing strategies; self-learning algorithm, using self-learning algorithm to continuously optimize fire extinguishing modes and spraying strategies, and improve the intelligence level of the device; parameter adjustment, based on historical data and real-time feedback, autonomously adjust control parameters to improve fire extinguishing efficiency and reliability.

8. A control system for quantitative spraying and automatic replenishment of fire extinguishing agents, characterized in that: include: Data acquisition module, used to collect real-time status data of the fire scene and the system; The data processing module is used to process the data collected by the data acquisition module and determine the current fire status and the use of the reagents; The decision-making module automatically selects the appropriate fire extinguishing mode and spraying volume based on the data processed by the data processing module, and adjusts the spraying and replenishment operations; The real-time adjustment module adjusts the spraying and replenishment strategies in real time according to the data collected by the continuously received data acquisition module to ensure the efficient use and continuous supply of fire extinguishing agents.

9. The control system for quantitative spraying and automatic replenishment of fire extinguishing agent according to claim 8, characterized in that: It also includes a security and exception handling module and a self-learning and optimization module; The safety and exception handling module is used to monitor the status of the device in real time, handle exceptions promptly, and automatically diagnose and identify problems and issue alarms; The self-learning and optimization module is used to regularly analyze data from historical fire extinguishing processes, identify and improve deficiencies in fire extinguishing strategies, continuously optimize fire extinguishing modes and spraying strategies, and autonomously adjust control parameters based on historical data and real-time feedback.

10. A device for quantitatively dispensing and automatically replenishing a fire extinguishing agent, comprising: Fire extinguishing agent storage unit, fire extinguishing agent spraying unit, sensor unit, control unit and supply unit; The fire extinguishing agent storage unit is in the form of an agent tank, which is used to store fire extinguishing agent; the fire extinguishing agent spraying unit sprays the agent through a nozzle, and the switch of the nozzle is controlled by a control unit; the sensor unit includes a liquid level sensor, a flow sensor, a pressure sensor, a temperature sensor and a smoke sensor, which are used to monitor the agent dosage, spraying status and fire situation; the control unit has a built-in computer-readable storage medium, which is used to adjust the spraying volume and supply strategy in real time according to sensor data; the supply unit is used to automatically replenish the fire extinguishing agent to the storage unit, and includes an agent delivery pipeline and a pump.

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