Combustible and toxic gas monitoring method and system

Abstract

The invention provides a combustible and toxic gas monitoring method and system, and the method comprises the steps: dynamically adjusting the density of a sensor based on a historical leakage risk weight, collecting multi-modal environment data, and carrying out the fusion correction, and generating an environment data set; constructing a gas diffusion model based on the environmental data set, predicting future concentration distribution in combination with environmental variables, and identifying a high-risk region; aiming at a high-risk area, an optimization objective function is designed by taking minimization of gas concentration distribution and energy consumption as optimization objectives, an optimal control strategy is generated through the optimization objective function, and temperature, wind speed and ventilation intensity are adjusted to inhibit gas diffusion; and an optimal control strategy is executed and real-time feedback optimization is performed, so that system robustness and energy consumption balance are ensured. According to the invention, the monitoring and control process of gas leakage can be dynamically optimized, more accurate safety management is realized, and the safety and energy efficiency of industrial places are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of gas monitoring, and particularly relates to a method and system for monitoring combustible and toxic gases. Background Art

[0002] In industrial scenarios such as petrochemical, energy storage, laboratories, and underground pipe galleries, the leakage of combustible and toxic gases is a serious safety hazard, which may lead to major accidents such as fires, explosions, and poisoning. Currently, fixed gas sensor networks are mainly used in industrial sites for leakage monitoring. These sensors are distributed at key points (such as around storage tanks, pipe joints, ventilation openings, etc.) to detect the concentration of target gases in the air in real time and trigger an alarm when the safety threshold is exceeded. However, since the diffusion path of the leaked gas is greatly affected by environmental factors such as wind speed, temperature, humidity, and obstacles, the leaked gas may bypass the sensor layout area, resulting in the existence of monitoring blind spots, making the system unable to detect the leakage in time. In addition, existing systems usually rely on fixed thresholds to trigger alarms, and only when the detected gas concentration exceeds the set value will an alarm be triggered, unable to predict the diffusion trend of the gas in advance, resulting in a response lag and increasing the accident risk. For the control of leaked gas, the commonly used method at present is to turn on mechanical ventilation equipment to increase air flow or dilute the gas concentration through a spraying system. However, this passive control strategy often lacks pertinence and cannot accurately match the actual situation of the leakage. In some environments, it may even accelerate the gas diffusion, expand the dangerous range, and at the same time, the continuous operation of the ventilation equipment will also lead to high energy consumption problems.

[0003] Therefore, the existing technology still has problems such as monitoring blind spots, response lag, and extensive control strategies and high energy consumption in gas leakage monitoring and control. There is an urgent need for a more intelligent, accurate, and efficient solution to achieve real-time prediction and precise suppression of leaked gas. Summary of the Invention

[0004] The object of the present invention is to propose a method and system for monitoring combustible and toxic gases, which can predict the gas diffusion trend by analyzing environmental data in real time and intelligently adjust environmental parameters based on the diffusion situation to achieve more accurate and efficient leakage suppression.

[0005] To achieve the above object, in the first aspect of the present invention, a method for monitoring combustible and toxic gases is provided, and the method includes the following steps: Dynamically adjust the sensor density based on the historical leakage risk weight, collect multi-modal environmental data and fuse and correct it to generate an environmental data set; Construct a gas diffusion model based on the environmental data set, combine environmental variables to predict the future concentration distribution and identify high-risk areas; wherein, the determination rule of the high-risk area is based on the future gas concentration and the lower explosion limit; For high-risk areas, an optimization objective function is designed with the goal of minimizing gas concentration distribution and energy consumption. An optimal control strategy is generated through the optimization objective function to adjust temperature, wind speed, and ventilation intensity to inhibit gas diffusion; Execute the optimal control strategy and provide real-time feedback for optimization to ensure system robustness and energy consumption balance.

[0006] Preferably, the sensor density is dynamically adjusted based on the historical leakage risk weight, and the specific steps are as follows: Collect environmental factors, including: historical gas leakage records, wind speed and direction changes, and obstacle positions; Based on the environmental factors, obtain the leakage risk weight through weighted analysis.

[0007] Preferably, the multi-modal environmental data is collected, fused, and corrected to generate an environmental data set, and the specific steps are as follows: Collect sensor data; Combined with spatio-temporal correlation, detect and correct outliers; Perform multi-index weighted comprehensive fusion analysis on the corrected data to obtain fused environmental data; Perform a correction operation of dynamic weighted average based on historical trends on the fused environmental data to generate an environmental data set; Among them, if the measured value of a certain sensor deviates from the mean value of neighboring sensors by more than a set threshold, it is considered abnormal, and the data is removed or corrected; Among them, the correction operation of dynamic weighted average based on historical trends is specifically as follows: When the wind speed is maximum, the weight takes the minimum value to enhance the influence of historical data and prevent error accumulation caused by sensor lag response; When the wind speed is minimum, the weight takes the maximum value to enhance the influence of the current measured value and reduce the error caused by historical data.

[0008] Preferably, the gas diffusion model is constructed as follows: Determine the environmental state correction term through weighted sum according to wind speed and direction changes and obstacle positions; Combined with fluid mechanics characteristics, construct an initial gas diffusion model; Based on the environmental state correction term, correct the initial gas diffusion model to generate a gas diffusion model and obtain the gas diffusion rate; among them, the gas diffusion model includes the wind speed at position , the temperature at position , and the air pressure at position .

[0009] Preferably, the specific steps for predicting the future concentration distribution and identifying high-risk areas in combination with environmental variables include: Obtain the gas diffusion rate, and calculate the gas concentration at a future time based on the current gas distribution combined with the environmental state correction term; Conduct a gas leakage hazard area determination and analysis on the gas concentration at the future time to obtain high-risk areas; Among them, the gas leakage hazard area determination and analysis is specifically: Through the proportional relationship between the predicted gas concentration at the future time and the lower explosion limit, screen out the spatial areas where the gas concentration reaches the danger threshold; among them, the proportional relationship is adjusted by a safety factor; Among them, when the gas concentration at a certain area predicted at the future time exceeds , which is the lower explosion limit of the target gas, then determine that area as a high-risk area.

[0010] Preferably, the optimization objective function is: Among them, is an adaptive control strategy, including temperature, wind speed, and ventilation adjustment; is the predicted future gas concentration distribution, in ppm; is the safety concentration threshold to ensure that the gas concentration is far below the explosion limit; is the energy consumption weight factor used to balance the control effect and energy consumption; is the total energy consumption of the control strategy execution, including the energy consumption for temperature regulation , the energy consumption for wind speed adjustment and the energy consumption for ventilation ; Among them, is calculated as follows: Among them, is the coefficient of control energy consumption, determined by the energy efficiency parameters of the industrial system; is the current temperature, wind speed, and ventilation intensity; is the optimized control variable.

[0011] Preferably, generating the optimal control strategy through the optimization objective function, adjusting the temperature, wind speed, and ventilation intensity to inhibit gas diffusion specifically includes: For the local temperature, in the high-risk area, reduce the temperature to reduce the gas volatilization rate, and generate a temperature reduction strategy based on the temperature gradient correction; For the wind speed guidance, in the high-risk area, adjust the wind speed to control the gas diffusion direction, and generate a wind speed guidance strategy based on the wind speed gradient correction; For the ventilation strategy, in high-risk areas, adjust the local ventilation intensity to accelerate gas dilution, and generate a ventilation optimization strategy based on the correction of the ventilation intensity gradient; Generate an optimal control strategy according to the temperature reduction strategy, the wind speed guidance strategy, and the ventilation optimization strategy.

[0012] Preferably, execute the optimal control strategy and provide real-time feedback for optimization to ensure the robustness of the system and the balance of energy consumption. Specifically, it includes: Execute the optimal control strategy, and introduce an adaptive execution correction term for correction to generate the control parameters currently being executed; Re-collect environmental data and obtain the new gas concentration; Based on the re-collected environmental data and the new gas concentration, adopt an adaptive weight adjustment mechanism to dynamically optimize the control strategy according to the control effect; Among them, the adaptive weight adjustment mechanism includes the control parameters currently being executed, the change in gas concentration, and a noise correction term to ensure the balance between the control effect and energy efficiency; the noise correction term is used to prevent instability caused by sudden adjustments.

[0013] Preferably, the adaptive execution correction is based on the control parameters currently being executed, combined with the deviation of the historical execution state, and weighted adjustment is performed through an execution correction coefficient.

[0014] In another aspect of the present invention, a flammable and toxic gas monitoring system is provided. The system includes: A dynamic perception module, which is used to dynamically adjust the sensor density based on the historical leakage risk weight, collect multi-modal environmental data, fuse and correct it, and generate an environmental data set; A diffusion prediction module, which is used to construct a gas diffusion model based on the environmental data set, combine environmental variables to predict the future concentration distribution, and identify high-risk areas; among them, the determination rule for the high-risk areas is based on the future gas concentration and the lower explosion limit; An intelligent control module, which is used to design an optimization objective function with the goal of minimizing the gas concentration distribution and energy consumption for high-risk areas, and generate an optimal control strategy through the optimization objective function to adjust the temperature, wind speed, and ventilation intensity to inhibit gas diffusion; An execution feedback module, which is used to execute the optimal control strategy and provide real-time feedback for optimization to ensure the robustness of the system and the balance of energy consumption.

[0015] The beneficial technical effects of the present invention are at least as follows: In view of the deficiencies of the prior art, the present invention proposes an intelligent adaptive control system based on gas diffusion prediction. By analyzing environmental data in real time, predicting the gas diffusion trend, and intelligently adjusting environmental parameters based on the diffusion situation, more accurate and efficient leakage suppression can be achieved. First, the present invention adopts a dynamic gas diffusion prediction method. By analyzing various environmental variables (such as wind speed, temperature, air pressure, etc.) in real time, the diffusion path of the leaked gas is calculated, so that an early warning can be triggered before the gas concentration reaches a dangerous level, improving the monitoring coverage rate and reducing the response lag problem. Second, the present invention adopts an intelligent adaptive control strategy. Based on the gas diffusion prediction results, the environmental parameters in a local area (such as temperature, air flow direction, ventilation intensity, etc.) are automatically adjusted to accurately suppress gas leakage, avoid the high energy consumption problem caused by simply relying on mechanical ventilation, and prevent unreasonable air flow from exacerbating gas diffusion. Compared with the traditional fixed monitoring + mechanical ventilation method, the present invention can dynamically optimize the monitoring and control process of gas leakage, achieve more accurate safety management, and effectively improve the safety and energy efficiency of industrial sites. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the following drawings.

[0017] Figure 1 It is a flowchart of a method for monitoring combustible and toxic gases disclosed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as a limitation to the present invention.

[0019] Embodiment 1 As Figure 1 shown, a method for monitoring combustible and toxic gases provided by an embodiment of the present invention includes the following steps: S1. Dynamically adjust the sensor density based on the historical leakage risk weight, collect multi-modal environmental data, fuse and correct it to generate an environmental data set.

[0020] Specifically, the goal of this step is to establish a high-precision environmental data set to ensure that the gas diffusion prediction (step 2) is based on high-quality input data, thereby improving the accuracy of leakage monitoring and adaptive control.

[0021] Preferably, in an industrial environment, the distribution of gas leakage points is uneven. The traditional regular layout method may result in too low monitoring density in some areas, forming blind spots, while too high monitoring density in some areas, increasing redundant costs. Therefore, the present invention proposes a dynamic sensor optimization layout strategy based on leakage risk, adaptively increasing the sensor density in high-risk areas to improve the monitoring coverage rate.

[0022] To optimize the distribution of sensors, the present invention defines the leakage risk weight , and its calculation method combines environmental factors such as historical gas leakage records, wind speed and direction changes, and obstacle positions. The calculation is as follows: Where: : Historical gas concentration, a larger value means a higher leakage risk; : Wind speed gradient, areas with large wind speed changes are prone to form high-concentration gas masses; : Obstacle influence factor, in areas close to obstacles, the leaked gas is not easily diffused, and the sensor density needs to be increased; : Weight coefficient, determined based on experiments.

[0023] After optimization, the sensor density is dynamically adjusted to be denser in high-risk areas, ensuring comprehensive monitoring, reducing blind spots, and improving data quality.

[0024] Preferably, industrial sensors may have problems such as noise interference, measurement errors, and equipment failures during actual operation. Directly using the original data may lead to incorrect diffusion predictions. Therefore, this step adopts a multi-sensor data fusion algorithm, combines spatio-temporal correlation, and detects and corrects outliers.

[0025] Core idea: Spatial consistency correction: The measured value of a sensor should be consistent with the measured values of its neighboring sensors, otherwise there may be errors. Environmental factor compensation: Gas diffusion is affected by wind speed and temperature, and these factors should be considered during data fusion to improve the correction accuracy. Dynamic credibility weighting: If a certain sensor has had more drift or abnormal data in the past, then reduce the weight of its current data to reduce its interference with the overall data.

[0026] The fusion calculation is as follows: Where: : Sensor 's set of neighboring sensors; : The weight of adjacent sensors, dynamically adjusted based on measurement stability; : An environmental compensation factor that takes into account the effects of wind speed and temperature on gas diffusion to prevent abnormal measurements of individual sensors from affecting global data.

[0027] In addition, if the measured value of a certain sensor deviates from the mean value of adjacent sensors by more than a set threshold, it is considered abnormal, and the data is excluded or corrected to avoid the influence of incorrect data on diffusion prediction.

[0028] Preferably, in an industrial environment, sensors may experience short-term data loss due to reasons such as faults, signal interference, and data loss. If the missing data is directly used, it will affect the stability of gas diffusion prediction. Therefore, this step introduces a time series correction mechanism to infer the missing data based on historical trends, improving the robustness of the system. The basic method of data correction is dynamic weighted average based on historical trends: where is dynamically adjusted by environmental parameters (such as wind speed): When the wind speed is high, take a smaller value to enhance the influence of historical data and prevent error accumulation caused by sensor lag response; When the wind speed is low, take a larger value to enhance the influence of the current measured value to reduce the error caused by historical data.

[0029] Finally, after all data has undergone optimized sensor layout, anomaly detection and correction, and time series completion, a corrected environmental data set is obtained: S2. Construct a gas diffusion model based on the environmental data set, predict the future concentration distribution in combination with environmental variables, and identify high-risk areas; among them, the determination rule for the high-risk area is based on the future gas concentration and the lower explosion limit.

[0030] Specifically, the goal of this step is to predict the future gas concentration distribution and identify high-risk areas based on the high-quality environmental data generated in step 1 to support the calculation of intelligent control strategies. Gas leakage in an industrial environment is affected by complex factors such as wind speed, temperature and humidity, air pressure, and obstacles, and has the characteristics of spatio-temporal non-uniformity and strong dynamic changes. Traditional fixed-threshold monitoring methods cannot predict leakage trends in advance, but can only trigger alarms when the gas concentration exceeds a set value, resulting in a lag in response. Therefore, this step models the gas diffusion process in real time, adjusts the prediction model in combination with environmental variables, and evaluates potential dangerous areas to ensure that the control system can take measures before a danger occurs.

[0031] Preferably, construct a gas diffusion model: The diffusion behavior of gas is affected by environmental variables, and the most important influencing factor among them is wind speed , temperature and air pressure . To accurately model gas diffusion, the gas diffusion rate is introduced in this step. Its calculation method combines hydrodynamic characteristics and introduces a dynamic correction term based on the environmental state: Where: : Wind speed at position , unit m / s; : Temperature at position , unit °C; : Air pressure at position , unit hPa; : Weight used to adjust the influence of different environmental factors on the diffusion rate, optimized by experimental data; : Environmental state correction term, used to correct the diffusion error caused by factors such as obstacles and environmental turbulence.

[0032] The calculation is as follows: Where: : Obstacle influence factor, diffusion is restricted in the area close to the obstacle; : Wind speed gradient, high-concentration air masses are likely to form in areas where the wind speed changes violently; : Temperature gradient, when the temperature difference is large, the gas diffusion behavior is more complex; : Parameter for adjusting the weight of the correction term, determined by experiments.

[0033] The feature of this model is to dynamically adjust the diffusion rate to adapt to different environmental conditions, making the prediction more accurate.

[0034] Preferably, after obtaining the gas diffusion rate , the present invention calculates the gas concentration at the future time based on the current gas distribution . Traditional gas diffusion equations usually calculate based on a simple diffusion rate, while this step further combines the environmental impact term to enhance the adaptive ability of the model: Where: : Current position The current gas concentration at , in ppm; : Gas concentration gradient, indicating the diffusion direction; : Predicted time step, in s; : External environmental disturbance term, simulating unstable factors such as turbulence and airflow fluctuations.

[0035] Calculation method: : Wind speed change rate. When the airflow changes violently, the gas concentration fluctuation intensifies; : Air pressure change rate. Unstable air pressure may lead to abnormal gas flow; : Obstacle influence factor. High-concentration gas may accumulate near obstacles; : Adjustment weight, used to balance the influence of various factors.

[0036] Compared with the traditional diffusion equation, this model adds the influence factors of the meteorological environment, making the prediction more in line with the actual leakage scenario.

[0037] Preferably, in order to ensure that the monitoring system can detect dangerous areas in advance, the present invention defines a high-risk area , and its determination rule is based on the future gas concentration and the lower explosion limit (LEL). The determination and analysis of the gas leakage dangerous area are as follows: Where: : Lower explosion limit of the target gas, in ppm; : Safety factor, usually taking , indicating that measures are taken before reaching the lower explosion limit.

[0038] When the predicted gas concentration at a certain area exceeds , then it is determined that this area is a high-risk area and output to step 3 (intelligent adaptive control strategy calculation) for calculating the optimal control scheme.

[0039] S3. For the high-risk area, design an optimization objective function with minimizing the gas concentration distribution and energy consumption as the optimization objective, and generate an optimal control strategy through the optimization objective function to adjust the temperature, wind speed and ventilation intensity to inhibit gas diffusion.

[0040] Specifically, the core goal of this step is to predict the future gas concentration distribution based on step 2. and high risk areas , calculate the optimal adaptive control strategy , so that gas diffusion can be precisely regulated and control measures can dynamically adapt to complex industrial environments. Gas leakage in industrial scenarios often leads to high-concentration air masses due to factors such as high temperature, complex airflow, and partially closed environment. Traditional ventilation, spraying or cooling methods are difficult to accurately control and often require a lot of energy to achieve the desired effect. Therefore, the goal of this step is to build an intelligent optimization control strategy that enables the control system to adapt to different working conditions and dynamically adjust the temperature, wind speed, and ventilation intensity, thereby achieving the best leakage suppression effect with the lowest energy consumption.

[0041] Preferably, the essential goal of gas leakage control is to reduce gas concentration while reducing the energy consumption of the control strategy. Therefore, the present invention designs an optimization objective function so that the system can achieve a safe gas concentration with minimal resource consumption: in: : Adaptive control strategy, including temperature, wind speed, and ventilation adjustment; : predicted future gas concentration distribution, unit: ppm; : Safety concentration threshold, usually set to , to ensure that the gas concentration is well below the explosion limit; : Energy consumption weight factor, used to balance control effect and energy consumption; : Total energy consumption of control strategy execution, including temperature regulation energy consumption , Wind speed adjustment energy consumption and ventilation energy consumption .

[0042] in, The calculation is as follows: : The coefficient for controlling energy consumption is determined by the energy efficiency parameters of the industrial system; : Current temperature, wind speed, ventilation intensity; : Optimized control variables.

[0043] This objective function ensures that the control strategy can both reduce gas concentration and avoid unnecessary energy waste.

[0044] Preferably, calculate the local temperature adjustment strategy: The volatility of gas is greatly affected by temperature. Especially in high-temperature industrial environments (such as chemical plants and storage tank areas), high temperature will exacerbate gas evaporation and increase the leakage risk. Therefore, in high-risk areas , it is necessary to reduce the temperature to reduce the gas evaporation rate: Where: : Temperature adjustment coefficient, which determines the cooling amplitude; : Temperature stability correction term, which prevents large temperature fluctuations from affecting production safety.

[0045] The calculation is as follows: Where: : Temperature gradient, the cooling demand is greater in high-temperature areas; : Adjustment weight, which makes the cooling amplitude adapt to environmental changes.

[0046] This strategy ensures that the temperature is appropriately reduced in high-risk areas, thereby reducing the volatilization of combustible gases.

[0047] Preferably, calculate the wind speed guidance strategy: Gas diffusion is mainly affected by wind speed. Reasonably adjusting the wind speed can control the gas diffusion direction and make the gas move away from high-risk areas. The wind speed adjustment strategy is calculated as follows: Where: : Wind speed adjustment coefficient, which makes the wind speed change adapt to the gas concentration change; : Wind speed stability correction term, which prevents excessive wind speed fluctuations from affecting equipment operation.

[0048] The calculation is as follows: Where: : Wind speed gradient, no further increase in wind speed is required in strong wind areas; : Adjustment coefficient, which ensures smooth wind speed adjustment.

[0049] The function of this strategy is to increase the wind speed in high-risk areas, guide the gas to diffuse to safe areas, and thus reduce the accumulation of locally high-concentration gases.

[0050] Preferably, calculate the ventilation optimization strategy: In some cases, simply adjusting the temperature and wind speed may not effectively control the gas concentration, so it is necessary to adjust the local ventilation intensity , to accelerate gas dilution: Where: : Ventilation adjustment coefficient, controlling the ventilation adjustment range; : Ventilation stability correction term, preventing sudden wind changes.

[0051] The calculation is as follows: Where: : Ventilation intensity gradient, optimizing ventilation resource allocation; : Adjustment parameter, ensuring that the ventilation adjustment does not affect the overall airflow stability.

[0052] This strategy ensures appropriate ventilation increase in high-risk areas to accelerate the dilution of leaked gas.

[0053] S4. Execute the optimal control strategy and provide real-time feedback for optimization to ensure system robustness and energy consumption balance.

[0054] Specifically, the core objective of this step is to execute intelligent regulation operations based on the optimal control strategy calculated in step 3 , and optimize the control strategy through real-time monitoring feedback, enabling the system to have adaptive capabilities. Gas leakage control in industrial environments is not a one-time decision-making problem but a dynamic optimization process. Due to the influence of uncertain factors such as airflow changes, temperature fluctuations, and gas source changes, the control strategy needs to be continuously adjusted to adapt to new working conditions. Therefore, the core task of this step is to execute the control strategy and build a closed-loop feedback mechanism, enabling the control system to self-learn and optimize, improving long-term stability.

[0055] Preferably, based on the control strategy calculated in step 3 , this step executes temperature regulation, wind speed guidance, and ventilation optimization through an industrial control system: Temperature regulation execution: Adjust the temperature control equipment in the high-risk area , so that the actual temperature approaches the optimal target ; Wind speed regulation execution: Adjust the local air supply system so that the actual wind speed approaches the optimal target ; Ventilation optimization execution: Adjust the regional ventilation equipment to make the actual ventilation intensity approach 。

[0056] During the execution process, there are problems such as equipment response delay, external environmental interference, and energy consumption limitation. Therefore, the present invention introduces an adaptive execution correction term: Wherein: : The control parameter currently being executed; : The control parameter executed at the previous moment; : The execution correction coefficient, used to smooth the control execution and prevent mutations.

[0057] This strategy ensures the stability of the control execution and avoids system overshoot or oscillation.

[0058] Preferably, in order to evaluate the actual effect of the control strategy, it is necessary to re-collect environmental data after execution and calculate the new gas concentration 。

[0059] During the monitoring process, the following indicators need to be focused on: Gas concentration change: Calculate the change rate of relative to ; Control response time: Calculate the time required for the environmental variables to reach a stable state after executing ; ; Equipment energy consumption: Calculate the total energy consumed by the execution control 。

[0060] The new environmental data is calculated as follows: This data will be input into Step 2 (gas diffusion prediction) for optimizing the next round of control strategies.

[0061] Preferably, due to the continuous changes in air flow and temperature in the industrial environment, the control system needs to adjust and optimize the strategy according to real-time feedback. The present invention adopts an adaptive weight adjustment mechanism to dynamically optimize the control strategy according to the control effect : Wherein: : The adaptive weight adjustment coefficient, which determines the strategy update amplitude; : Noise correction term to prevent instability caused by sudden adjustment.

[0062] It is calculated as follows: Where: : Current gas concentration gradient; : Smoothing adjustment coefficient to prevent error amplification.

[0063] This feedback mechanism ensures that the control system can adaptively adjust the control strategy for the next moment according to the real-time monitoring results.

[0064] Preferably, in order to prevent the control system from failing due to sudden environmental changes, the present invention introduces stability constraint conditions to keep the optimization process robust: Where: : Maximum allowable adjustment amplitude to prevent the system from becoming unstable due to too fast adjustment.

[0065] This constraint condition ensures that the control system will not experience violent fluctuations during the continuous optimization process, guaranteeing the stable operation of the entire system.

[0066] Embodiment 2 In another embodiment of the present invention, a flammable and toxic gas monitoring system is disclosed, and the system includes: A dynamic perception module for dynamically adjusting the sensor density based on the historical leakage risk weight, collecting multi-modal environmental data and fusing and correcting it to generate an environmental data set; A diffusion prediction module for constructing a gas diffusion model based on the environmental data set, predicting the future concentration distribution in combination with environmental variables and identifying high-risk areas; wherein, the determination rule for the high-risk areas is based on the future gas concentration and the lower explosion limit; An intelligent control module for designing an optimization objective function with the goal of minimizing the gas concentration distribution and energy consumption for high-risk areas, generating an optimal control strategy through the optimization objective function, and adjusting the temperature, wind speed and ventilation intensity to inhibit gas diffusion; An execution feedback module for executing the optimal control strategy and providing real-time feedback optimization to ensure the robustness and energy consumption balance of the system.

[0067] The above description is of specific embodiments of this specification, and other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures need not necessarily be performed in the particular order shown or in a sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing are also possible or may be advantageous.

[0068] The systems, devices, modules, or units illustrated in the above embodiments may be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0069] For convenience of description, the above devices are described by dividing their functions into various units. Of course, when implementing this specification, the functions of each unit may be implemented in the same or multiple software and / or hardware.

[0070] Those skilled in the art should understand that the embodiments of this specification may be provided as a method, a system, or a computer program product. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0071] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of this specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0072] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in the flowchart Figure 1 a flowchart or multiple flowcharts and / or boxes Figure 1 a box or multiple boxes.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart Figure 1 a flowchart or multiple flowcharts and / or boxes Figure 1 a box or multiple boxes.

[0074] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0075] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0076] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0077] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0078] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0079] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference may be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For the relevant parts, reference may be made to the description of the method embodiment.

[0080] Finally, it should be noted that what is disclosed in an embodiment of a lithium battery pack chip equalization control platform of the present invention is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring combustible and toxic gases, characterized in that, The method includes the following steps: Dynamically adjust the sensor density based on the historical leakage risk weight, collect multi-modal environmental data, fuse and correct it to generate an environmental data set; Construct a gas diffusion model based on the environmental data set, combine environmental variables to predict the future concentration distribution and identify high-risk areas; among them, the determination rule of the high-risk area is based on the future gas concentration and the lower explosion limit; For high-risk areas, design an optimization objective function with the goal of minimizing the gas concentration distribution and energy consumption, generate an optimal control strategy through the optimization objective function, and adjust the temperature, wind speed, and ventilation intensity to inhibit gas diffusion; Execute the optimal control strategy and provide real-time feedback for optimization to ensure the system robustness and energy consumption balance.

2. The combustible and toxic gas monitoring method according to claim 1, characterized in that The specific steps for dynamically adjusting the sensor density based on the historical leakage risk weight are as follows: Collect environmental factors, including: historical gas leakage records, wind speed and direction changes, and obstacle positions; Based on the environmental factors, obtain the leakage risk weight through weighted analysis.

3. The combustible and toxic gas monitoring method according to claim 2, wherein, The specific steps for collecting multi-modal environmental data, fusing and correcting it to generate an environmental data set are as follows: Collect sensor data; Combined with spatio-temporal correlation, detect and correct outliers; Perform multi-index weighted comprehensive fusion analysis on the corrected data to obtain fused environmental data; Perform a correction operation of dynamic weighted average based on historical trends on the fused environmental data to generate an environmental data set; Among them, if the measurement value of a certain sensor deviates from the mean value of adjacent sensors by more than a set threshold, it is considered abnormal, and the data is excluded or corrected; Among them, the correction operation of dynamic weighted average based on historical trends is specifically as follows: When the wind speed is the maximum, the weight takes the minimum value to enhance the influence of historical data and prevent error accumulation caused by the lag response of sensors; When the wind speed is the minimum, the weight takes the maximum value to enhance the influence of the current measurement value to reduce the error caused by historical data.

4. A method for monitoring combustible and toxic gases according to claim 1, characterized in that, The gas diffusion model is constructed as follows: Determine the environmental state correction term through weighted sum according to wind speed and direction changes and obstacle positions; Combined with fluid mechanics characteristics, construct an initial gas diffusion model; Modify the initial gas diffusion model based on the environmental state correction term to generate a gas diffusion model, and obtain the gas diffusion rate; wherein, the gas diffusion model includes the wind speed at position , the temperature at position , and the air pressure at position .

5. A method for monitoring combustible and toxic gases according to claim 4, characterized in that, The specific steps for combining environmental variables to predict the future concentration distribution and identify high-risk areas include: Obtain the gas diffusion rate, and calculate the gas concentration at the future moment based on the current gas distribution and the environmental state correction term; Perform gas leakage hazard area determination analysis on the gas concentration at the future moment to obtain high-risk areas; Among them, the gas leakage hazard area determination analysis is specifically as follows: Through the proportional relationship between the predicted gas concentration at the future moment and the lower explosion limit, screen out the spatial areas where the gas concentration reaches the dangerous threshold; among them, the proportional relationship is adjusted by a safety factor; Among them, when the predicted gas concentration at a certain future moment in a certain area exceeds , which is the lower explosion limit of the target gas, it is determined that the area is a high-risk area.

6. The combustible and toxic gas monitoring method according to claim 1, wherein The optimization objective function is: Among them, is an adaptive control strategy, including temperature, wind speed, and ventilation adjustment; is the predicted future gas concentration distribution, in ppm; is the safety concentration threshold to ensure that the gas concentration is far below the explosion limit; is the energy consumption weight factor used to balance the control effect and energy consumption; is the total energy consumption for implementing the control strategy, including the energy consumption for temperature regulation , the energy consumption for wind speed adjustment and the energy consumption for ventilation ; Among them, The calculation is as follows: Among them, is the coefficient for controlling energy consumption, which is determined by the energy efficiency parameters of the industrial system; is the current temperature, wind speed, and ventilation intensity; is the optimized control variable.

7. A method for monitoring combustible and toxic gases according to claim 6, characterized in that, The specific steps for generating an optimal control strategy through the optimization objective function to adjust the temperature, wind speed, and ventilation intensity to inhibit gas diffusion include: For local temperature, in high-risk areas, reduce the temperature to reduce the gas volatilization rate, and generate a temperature reduction strategy based on temperature gradient correction; For wind speed guidance, in high-risk areas, adjust the wind speed to control the gas diffusion direction, and generate a wind speed guidance strategy based on wind speed gradient correction; For the ventilation strategy, in high-risk areas, adjust the local ventilation intensity to accelerate gas dilution, and generate a ventilation optimization strategy based on the correction of the ventilation intensity gradient; Generate an optimal control strategy according to the temperature reduction strategy, wind speed guidance strategy, and ventilation optimization strategy.

8. A method for monitoring combustible and toxic gases according to claim 1, characterized in that, Execute the optimal control strategy and provide real-time feedback for optimization to ensure the system robustness and energy consumption balance, specifically including: Execute the optimal control strategy and introduce an adaptive execution correction term for correction to generate the currently executed control parameters; Re-collect environmental data and obtain the new gas concentration; Based on the re-collected environmental data and the new gas concentration, adopt an adaptive weight adjustment mechanism to dynamically optimize the control strategy according to the control effect; Among them, the adaptive weight adjustment mechanism includes the currently executed control parameters, gas concentration change, and noise correction term to ensure the balance between control effect and energy efficiency; the noise correction term is used to prevent instability caused by sudden adjustment.

9. A method for monitoring combustible and toxic gases according to claim 8, characterized in that, The adaptive execution correction is based on the currently executed control parameters, combined with the deviation of the historical execution state, and weighted adjustment is performed through the execution correction coefficient.

10. A flammable and toxic gas monitoring system, characterized in that, The system includes: A dynamic perception module, which is used to dynamically adjust the sensor density based on the historical leakage risk weight, collect and fuse multi-modal environmental data for correction, and generate an environmental data set; A diffusion prediction module, which is used to construct a gas diffusion model based on the environmental data set, predict the future concentration distribution in combination with environmental variables, and identify high-risk areas; among them, the determination rule of the high-risk area is based on the future gas concentration and the lower explosion limit; An intelligent control module, which is used to design an optimization objective function with the goal of minimizing the gas concentration distribution and energy consumption for high-risk areas, generate an optimal control strategy through the optimization objective function, and adjust the temperature, wind speed, and ventilation intensity to inhibit gas diffusion; An execution feedback module, which is used to execute the optimal control strategy and provide real-time feedback for optimization to ensure the system robustness and energy consumption balance.

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