CN117789422A

CN117789422A - Combustible gas alarm control system and method

Info

Publication number: CN117789422A
Application number: CN202410208867.XA
Authority: CN
Inventors: 路通; 瞿岩; 路萍; 蒲清海
Original assignee: Jiangxi Yiai Hongtai Fire Safety Technology Co ltd
Current assignee: Jiangxi Yiai Hongtai Fire Safety Technology Co ltd
Priority date: 2024-02-26
Filing date: 2024-02-26
Publication date: 2024-03-29

Abstract

The invention relates to the technical field of gas detection, in particular to a combustible gas alarm control system and a method, wherein the system comprises an intelligent monitoring module, a risk identification module, a sensor strategy optimization module, an energy efficiency optimization management module, a predictive maintenance module, an emergency response module and a system dynamic reconstruction module. In the invention, environmental data fluctuation is analyzed by adopting a support vector machine, the relevance of risk factors is evaluated by a Bayesian network, the sensor layout and parameters are optimized by a genetic algorithm and a simulated annealing algorithm, the energy consumption distribution is adjusted by a particle swarm optimization algorithm and a dynamic programming algorithm, equipment faults are predicted by combining a long-period memory network with an anomaly detection algorithm, emergency response measures are formulated by a DSS technology and event-driven programming, and the system dynamic reconstruction is carried out by a reinforcement learning and genetic programming algorithm, so that the occurrence probability of accidents is greatly reduced by the system, the maintenance cost is reduced, and the operation safety and economic benefit are improved.

Description

Combustible gas alarm control system and method

Technical Field

The invention relates to the technical field of gas detection, in particular to a combustible gas alarm control system and a method.

Background

The field of gas detection technology has focused on developing and applying various sensors and control systems to identify, monitor and quantify gas components in an environment. These techniques can detect not only combustible gases, such as natural gas and propane, but also hazardous gases, providing real-time monitoring to ensure personnel safety and environmental protection. This technical field encompasses sensor development from basic gas detection devices to complex alarm and control system designs, emphasizing high sensitivity, fast response and long term stability, and the ability to integrate these sensors to create intelligent alarm systems.

The combustible gas alarm control system is a safety device for detecting the concentration of the combustible gas in the environment, and the main purpose of the system is to prevent fire or explosion accidents caused by the leakage of the combustible gas. By timely alarming, personnel safety is protected, property loss is avoided, and safety of work and living environment is ensured. To enable early detection of combustible gas leaks, timely alerting, and automatic activation of safety measures, such as shutting off the gas supply or initiating ventilation, if necessary, to minimize potential safety risks.

The traditional system lacks a highly integrated and intelligent data analysis tool, cannot realize deep mining and dynamic response to environment data, and can not timely or accurately identify potential risks. In the aspect of energy efficiency management, the traditional system lacks effective algorithm support, and is difficult to realize optimal configuration of resources, so that energy waste is often caused. In the aspect of equipment maintenance, the equipment failure cannot be accurately predicted by relying on experience and a traditional maintenance mode of periodic inspection, and the operation and maintenance difficulty and the cost of the system are increased. The formulation and execution of emergency response measures are slow, lack of effective automation and intelligent support, and cannot make effective response in the first time, thus increasing the safety risk. These deficiencies create problems of inefficiency, resource utilization, high operating and maintenance costs, and potential safety hazards during operation, limiting the overall performance and reliability of the system.

Disclosure of Invention

The invention aims to solve the defects in the prior art and provides a combustible gas alarm control system and a combustible gas alarm control method.

In order to achieve the above purpose, the present invention adopts the following technical scheme: the combustible gas alarm control system comprises an intelligent monitoring module, a risk identification module, a sensor strategy optimization module, an energy efficiency optimization management module, a predictive maintenance module, an emergency response module and a system dynamic reconstruction module;

the intelligent monitoring module analyzes data fluctuation by adopting a support vector machine based on environment data acquired in real time, determines whether abnormal conditions exist or not by a threshold analysis method, classifies environment variables and generates an environment state analysis record;

the risk identification module analyzes the relevance of various risk factors by adopting a Bayesian network based on the environmental state analysis record, calculates risk probability according to the relevance and the historical data by utilizing a decision tree, quantitatively evaluates potential risks and generates risk identification information;

the sensor strategy optimization module adopts a genetic algorithm to adjust sensor layout and parameters according to risk levels based on the risk identification information, optimizes sampling frequency and detection threshold value by using a simulated annealing algorithm, and adjusts the strategy to generate a strategy optimization instruction;

The energy efficiency optimization management module evaluates and adjusts energy consumption distribution by adopting a particle swarm optimization algorithm based on a strategy optimization instruction, determines optimal energy consumption and performance balance points by using a dynamic programming algorithm, and reallocates resources to generate an energy efficiency optimization scheme;

the predictive maintenance module adopts a long-short-period memory network based on an energy efficiency optimization scheme, analyzes equipment operation data and performance trend, combines an anomaly detection algorithm to identify potential failure modes, and makes a maintenance plan to generate a maintenance prediction result;

the emergency response module adopts a DSS technology to formulate response measures based on maintenance prediction results and risk identification information, automatically activates an emergency strategy by using event-driven programming, and distributes response resources to generate an emergency response plan;

the system dynamic reconfiguration module analyzes response effects and system states by adopting a reinforcement learning algorithm based on an emergency response plan, adjusts a work flow and system configuration by a genetic programming algorithm, updates operation parameters and generates a system reconfiguration scheme.

As a further scheme of the invention, the environmental state analysis record comprises an abnormal environmental index, a normal environmental index and an environmental parameter fluctuation index, the risk identification information comprises a risk factor association graph, a risk probability score and a risk grade division, the strategy optimization instruction comprises a sensor layout graph, an adjusted sampling frequency value and an adjusted detection threshold value setting, the energy efficiency optimization scheme comprises an energy consumption distribution graph, an energy utilization rate improving measure and an energy efficiency performance balance strategy, the maintenance prediction result comprises a fault mode identification result, a performance trend analysis graph and a maintenance plan list, the emergency response plan comprises a response measure list, an emergency strategy activation scheme and a resource allocation rule, and the system reconstruction scheme comprises a flow adjustment blueprint, a configuration updating list and an operation parameter adjustment record.

As a further scheme of the invention, the intelligent monitoring module comprises an environment data acquisition sub-module, an abnormality detection sub-module and a state evaluation sub-module;

the environment data acquisition submodule gathers the collected temperature, humidity and gas concentration parameters based on the environment data acquired in real time, and verifies the real-time performance and accuracy of the data by utilizing a data verification algorithm to generate an environment parameter set;

the abnormality detection submodule analyzes fluctuation conditions of multiple parameters based on an environment parameter set by using a support vector machine, identifies data points exceeding a normal range by setting a threshold value, marks abnormal fluctuation and generates an abnormal parameter index;

the state evaluation submodule evaluates the environmental state by adopting a local abnormality factor algorithm based on the abnormal parameter index and combining the monitoring parameters, classifies the environmental parameters into normal or abnormal categories, and generates an environmental state analysis record.

As a further scheme of the invention, the risk identification module comprises a potential risk analysis sub-module, a risk factor evaluation sub-module and a risk level classification sub-module;

the potential risk analysis submodule is used for identifying and analyzing potential risk factors based on environmental state analysis records by applying a data mining technology, wherein the potential risk factors comprise key points which form threat to safety, and a potential risk point analysis result is generated;

The risk factor evaluation sub-module analyzes relevance and influence degree of the identified risk points by adopting a Bayesian network based on the analysis result of the potential risk points, evaluates potential influence of a plurality of risk factors on environmental safety, and generates a risk influence evaluation record;

the risk level classification sub-module classifies risks according to the influence degree and occurrence probability of multiple risk factors by adopting a decision tree model based on the risk influence evaluation record, determines the priority of the risks and emergency treatment requirements, and generates risk identification information.

As a further scheme of the invention, the sensor strategy optimization module comprises a sensor deployment optimization sub-module, a strategy adjustment sub-module and a sensitivity adjustment sub-module;

the sensor deployment optimization submodule analyzes the sensor layout by adopting a genetic algorithm based on the risk identification information, adjusts the sensor deployment aiming at the region of the differentiated risk level, captures the optimal sensor layout configuration by simulating multi-generation evolution, and generates a sensor layout optimization scheme;

the strategy adjustment submodule refines the sampling frequency and the detection threshold of the sensor by adopting a simulated annealing algorithm based on a sensor layout optimization scheme, adjusts working parameters to be matched with environmental monitoring requirements, and generates parameter adjustment instructions;

The sensitivity adjustment submodule adopts a dynamic adjustment algorithm to adjust the sensitivity of the sensor based on the parameter adjustment instruction, optimizes the sensitivity and response performance of the sensor to environmental changes, and generates a strategy optimization instruction.

As a further scheme of the invention, the energy efficiency optimization management module comprises an energy consumption analysis sub-module, an optimization strategy sub-module and an execution measure sub-module;

the energy consumption analysis submodule analyzes the energy consumption mode of the system based on the strategy optimization instruction by applying a particle swarm optimization algorithm, identifies key links for improving the energy efficiency, evaluates the current energy efficiency use condition and generates an energy consumption analysis record;

the optimization strategy submodule adopts a dynamic programming algorithm to mine a differentiated energy distribution scheme based on the energy consumption analysis record, and captures the balance point between the energy consumption and the system performance by constructing an optimal decision sequence to generate an energy efficiency optimization strategy;

the execution measure submodule optimizes the working mode of the sensor and adjusts the data processing flow by utilizing a genetic algorithm based on an energy efficiency optimization strategy, reconfigures system resources and achieves optimal energy efficiency, and an energy efficiency optimization scheme is generated.

As a further scheme of the invention, the predictive maintenance module comprises a performance data analysis sub-module, a fault prediction sub-module and a maintenance strategy planning sub-module;

The performance data analysis submodule collects and sorts the operation data of the equipment by adopting a time sequence analysis method based on an energy efficiency optimization scheme, analyzes the data and analyzes the long-term trend and the periodic change of the performance of the equipment to generate a performance trend analysis record;

the fault prediction submodule is used for analyzing the time sequence characteristics of equipment operation data by applying a long-term and short-term memory network based on the performance trend analysis record, combining an anomaly detection algorithm, identifying a potential fault mode and an anomaly behavior, predicting faults of the equipment and generating a fault prediction analysis record;

the maintenance strategy planning submodule analyzes the cost benefit of differentiated maintenance measures based on fault prediction analysis records by utilizing a DSS technology, plans optimal maintenance time points and resource configuration, optimizes maintenance cost and equipment downtime and generates maintenance prediction results.

As a further scheme of the invention, the emergency response module comprises an emergency measure design sub-module, an automatic response sub-module and a coordination and notification sub-module;

the emergency measure design submodule adopts a DSS technology to analyze the severity and urgency of emergency conditions based on maintenance prediction results and risk identification information, and makes targeted emergency measures and coping strategies to generate an emergency measure design scheme;

The automatic response submodule automatically triggers a predefined emergency response flow by using event-driven programming based on an emergency measure design scheme, and comprises automatic power-off and starting of standby measures, so that the influence of faults is reduced, and an automatic response flow is generated;

the coordination and notification sub-module is used for sending an emergency notification and response instruction by adopting an instant messaging protocol based on an automatic response flow, coordinating implementation of emergency measures and resource allocation and generating an emergency response plan.

As a further scheme of the invention, the system dynamic reconfiguration module comprises a learning feedback analysis sub-module, a configuration optimization sub-module and a workflow adjustment sub-module;

the learning feedback analysis sub-module analyzes the response effect and the current state by adopting a reinforcement learning algorithm based on an emergency response plan, automatically adjusts the response strategy through a continuous test and error correction process, and generates a response effect and a state analysis result;

the configuration optimization submodule performs optimization analysis on a system workflow and configuration parameters by using a genetic programming algorithm based on a response effect and a state analysis result, captures a configuration solution meeting the system requirement and generates a refined configuration scheme;

The workflow adjustment submodule adjusts the workflow based on the refined configuration scheme by utilizing a genetic programming algorithm, and comprises the steps of updating operation parameters, optimizing data flow and processing logic, determining that the system can operate correctly under the adjusted configuration and working environment, and generating a system reconstruction scheme.

The combustible gas alarm control method is executed based on the combustible gas alarm control system and comprises the following steps of:

s1, analyzing data fluctuation by adopting a support vector machine based on environment data acquired in real time, determining whether abnormal conditions exist or not by a threshold analysis method, classifying environment variables, and generating an environment state analysis record;

s2, analyzing the relevance of various risk factors by adopting a Bayesian network based on the environmental state analysis record, calculating the risk probability according to the relevance and the historical data by utilizing a decision tree, quantitatively evaluating the potential risk, and generating risk identification information;

s3, based on risk identification information, adopting a genetic algorithm to adjust sensor layout and parameters according to risk levels, optimizing sampling frequency and detection threshold value by using a simulated annealing algorithm, and adjusting strategies to generate strategy optimization instructions;

S4, based on a strategy optimization instruction, evaluating and adjusting energy consumption distribution by adopting a particle swarm optimization algorithm, determining an optimal energy consumption and performance balance point by adopting a dynamic programming algorithm, and reallocating resources to generate an energy efficiency optimization scheme;

s5, based on an energy efficiency optimization scheme, analyzing equipment operation data and performance trend by adopting a long-period memory network, identifying potential failure modes by combining an anomaly detection algorithm, and making a maintenance plan to generate a maintenance prediction result;

s6, based on maintenance prediction results and risk identification information, adopting a DSS technology to formulate response measures, automatically activating an emergency strategy by using event-driven programming, and allocating response resources to generate an emergency response plan;

s7, based on an emergency response plan, analyzing response effects and system states by adopting a reinforcement learning algorithm, adjusting a work flow and system configuration by using a genetic programming algorithm, updating operation parameters, and generating a system reconstruction scheme.

Compared with the prior art, the invention has the advantages and positive effects that:

according to the invention, environmental data fluctuation is analyzed by adopting a support vector machine, the relevance of risk factors is evaluated by a Bayesian network, the sensor layout and parameters are optimized by a genetic algorithm and a simulated annealing algorithm, the energy consumption distribution is adjusted by a particle swarm optimization algorithm and a dynamic programming algorithm, equipment faults are predicted by combining a long-term memory network with an anomaly detection algorithm, emergency response measures are formulated by a DSS technology and event-driven programming, and the dynamic reconfiguration of the system is performed by a reinforcement learning and genetic programming algorithm, so that the monitoring accuracy of the system, the accuracy of risk evaluation, the optimization level of energy efficiency management, the prospectivity of predictive maintenance, the timeliness of emergency response and the overall adaptability of the system are effectively improved. Through the comprehensive application of the technology, the system can display higher efficiency and higher flexibility than the traditional system in the aspects of real-time monitoring, risk assessment, resource optimal configuration, fault prevention and emergency management, thereby greatly reducing the occurrence probability of accidents, lowering the maintenance cost and improving the operation safety and economic benefit.

Drawings

FIG. 1 is a system flow diagram of the present invention;

FIG. 2 is a schematic diagram of a system framework of the present invention;

FIG. 3 is a flow chart of the intelligent monitoring module of the present invention;

FIG. 4 is a flow chart of a risk identification module of the present invention;

FIG. 5 is a flow chart of a sensor strategy optimization module of the present invention;

FIG. 6 is a flow chart of an energy efficiency optimization management module of the present invention;

FIG. 7 is a flow chart of a predictive maintenance module of the present invention;

FIG. 8 is a flow chart of an emergency response module of the present invention;

FIG. 9 is a flow chart of a system dynamic reconfiguration module according to the present invention.

Detailed Description

The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

Example 1

Referring to fig. 1, the present invention provides a technical solution: the combustible gas alarm control system comprises an intelligent monitoring module, a risk identification module, a sensor strategy optimization module, an energy efficiency optimization management module, a predictive maintenance module, an emergency response module and a system dynamic reconstruction module;

the risk identification module analyzes the relevance of various risk factors by adopting a Bayesian network based on the environmental state analysis record, calculates the risk probability according to the relevance and the historical data by utilizing a decision tree, quantitatively evaluates the potential risk and generates risk identification information;

the sensor strategy optimization module adopts a genetic algorithm to adjust the sensor layout and parameters according to the risk level based on the risk identification information, optimizes the sampling frequency and the detection threshold value by using a simulated annealing algorithm, and adjusts the strategy to generate a strategy optimization instruction;

The predictive maintenance module adopts a long-period memory network based on an energy efficiency optimization scheme, analyzes equipment operation data and performance trend, combines an anomaly detection algorithm to identify potential failure modes, makes a maintenance plan and generates a maintenance prediction result;

the emergency response module adopts a DSS technology to formulate response measures based on maintenance prediction results and risk identification information, automatically activates an emergency strategy by using event-driven programming, allocates response resources and generates an emergency response plan;

the system dynamic reconfiguration module adopts a reinforcement learning algorithm to analyze response effects and system states based on an emergency response plan, adjusts a work flow and system configuration through a genetic programming algorithm, updates operation parameters and generates a system reconfiguration scheme.

The environment state analysis records comprise abnormal environment indexes, normal environment indexes and environment parameter fluctuation indexes, the risk identification information comprises a risk factor association graph, a risk probability score and a risk grade division, the strategy optimization instructions comprise a sensor layout graph, an adjusted sampling frequency value and an adjusted detection threshold value setting, the energy efficiency optimization scheme comprises an energy consumption distribution graph, an energy utilization rate improving measure and an energy efficiency performance balance strategy, the maintenance prediction result comprises a fault mode identification result, a performance trend analysis graph and a maintenance plan list, the emergency response plan comprises a response measure list, an emergency strategy activation scheme and a resource allocation rule, and the system reconstruction scheme comprises a flow adjustment blueprint, a configuration updating list and an operation parameter adjustment record.

In the intelligent monitoring module, environmental data, particularly indexes such as temperature, humidity and combustible gas concentration, are collected in real time, a Support Vector Machine (SVM) algorithm is adopted to carry out data fluctuation analysis, the SVM classifies environmental variables by constructing one or more hyperplanes, the distinction between abnormal and normal states is realized, the real-time data and historical data are compared by setting a threshold analysis method, whether the data exceeds a normal range is determined, whether abnormal conditions exist or not is determined, environmental state analysis record generation comprises abnormal environmental indexes, normal environmental indexes and environmental parameter fluctuation indexes, basic data is provided for a subsequent module by recording, the process not only realizes real-time monitoring of the environmental states, but also effectively improves the response speed and accuracy of a monitoring system by accurately classifying.

In the risk identification module, the relevance of various risk factors is analyzed through an environmental state analysis record by adopting a Bayesian network, the risk probability is calculated according to the relevance and historical data by utilizing a decision tree algorithm, the Bayesian network models the dependency relationship among the risk factors through probabilistic reasoning, the decision tree carries out risk assessment according to condition judgment through a path from a root to a leaf, the potential risk is quantitatively assessed through the process, risk identification information is generated, the risk identification information comprises a risk factor relevance graph, a risk probability score and risk grade division, and the core contribution of the module is that the complex risk factor relevance is converted into an operable risk assessment result, so that a quantitative basis is provided for risk management.

In the sensor strategy optimization module, based on risk identification information, a genetic algorithm is adopted to adjust sensor layout and parameters, meanwhile, a simulated annealing algorithm is utilized to optimize sampling frequency and detection threshold, the genetic algorithm is used for searching optimal sensor layout and parameter setting through simulating natural selection and genetic mechanism, the simulated annealing algorithm is used for optimizing the sampling frequency and detection threshold through simulating annealing process of metallurgy, false alarm and false alarm are reduced, and generated strategy optimization instructions comprise sensor layout diagrams, adjusted sampling frequency values and detection threshold setting.

In the energy efficiency optimization management module, a Particle Swarm Optimization (PSO) algorithm and a dynamic programming algorithm are adopted to work together to evaluate and adjust energy consumption distribution, the particle swarm optimization algorithm is used for searching an energy consumption optimization solution by simulating the flying behavior of a bird swarm, the dynamic programming algorithm is used for determining the balance point of optimal energy consumption and performance through a staged decision process to generate an energy efficiency optimization scheme, the energy efficiency optimization scheme comprises an energy consumption distribution diagram, energy utilization rate lifting measures and an energy efficiency performance balance strategy, the energy efficiency optimization module optimizes the energy utilization efficiency, ensures the system performance and provides support for sustainable operation.

In the predictive maintenance module, equipment operation data and performance trends are analyzed through a long-short-term memory (LSTM) network, a latent fault mode is identified by combining an anomaly detection algorithm, the LSTM network can process and predict long-term dependence problems in time sequence data, the anomaly detection algorithm identifies data modes which are inconsistent with normal performance trends, and the generated maintenance prediction results comprise fault mode identification results, a performance trend analysis chart and a maintenance plan list.

In the emergency response module, based on maintenance prediction results and risk identification information, decision Support System (DSS) technology and event-driven programming are adopted to formulate response measures, the DSS technology provides a decision making framework based on data, the event-driven programming ensures that emergency strategies can be automatically activated and response resources can be allocated when specific conditions are triggered, the generated emergency response plan comprises a response measure list, an emergency strategy activation scheme and resource allocation rules, and the module ensures that the system can respond quickly and effectively in emergency situations and reduces the influence of accidents to the greatest extent.

In the system dynamic reconfiguration module, based on an emergency response plan, a reinforcement learning and genetic programming algorithm is adopted to analyze response effects and system states, work flow and system configuration are adjusted, the reinforcement learning optimizes a decision process through a rewarding mechanism, the genetic programming algorithm adjusts and optimizes the system configuration through simulating a natural genetic mechanism, the generated system reconfiguration scheme comprises a flow adjustment blueprint, a configuration update list and an operation parameter adjustment record, and the module can maintain optimal performance and efficiency when facing new challenges and environmental changes through continuous learning and adaptation.

Referring to fig. 2 and 3, the intelligent monitoring module includes an environmental data acquisition sub-module, an anomaly detection sub-module, and a state evaluation sub-module;

the environmental data acquisition submodule gathers the collected temperature, humidity and gas concentration parameters based on the environmental data acquired in real time, and verifies the instantaneity and accuracy of the data by utilizing a data verification algorithm to generate an environmental parameter set;

the abnormality detection submodule analyzes fluctuation conditions of multiple parameters by using a support vector machine based on an environment parameter set, identifies data points exceeding a normal range by setting a threshold value, marks abnormal fluctuation and generates an abnormal parameter index;

The state evaluation sub-module evaluates the environmental state by adopting a local abnormality factor algorithm based on the abnormal parameter index and the monitoring parameter, classifies the environmental parameter into a normal or abnormal category, and generates an environmental state analysis record.

In an environmental data acquisition sub-module, environmental parameters including temperature, humidity, gas concentration and the like are collected through a real-time monitoring device, an adopted data format is time sequence data, each parameter is attached with a time stamp to ensure the real-time performance of the data, the sub-module firstly performs format and range verification on the collected data by utilizing a data verification algorithm to ensure the validity and rationality of the data, and in the data verification process, an outlier detection technology is adopted to identify and reject data points which obviously deviate from a normal range. For example, the average value and standard deviation of each parameter are calculated by using a statistical analysis method, and the data falling outside the normal range are treated as abnormal values for processing.

In the anomaly detection submodule, an environment parameter set is processed through a Support Vector Machine (SVM) algorithm, fluctuation conditions among multiple parameters are analyzed, the SVM algorithm is used as a classifier, a segmentation hyperplane is established to divide data points into two types of normal and anomaly, the process relates to selection of a kernel function, a Gaussian kernel function (RBF) is generally adopted to process nonlinear separable data, the SVM can identify the data points beyond a normal fluctuation range by setting a proper threshold value, the setting of the threshold value is based on historical data analysis, the average fluctuation amplitude and standard deviation of historical data are calculated to determine the limit of anomaly fluctuation, after anomaly fluctuation is marked, an anomaly parameter index is generated, the parameter name, anomaly value, time stamp and anomaly degree of each anomaly data point are recorded in detail by the index, and the process not only improves the monitoring capability of environmental emergencies, but also provides a key basis for state evaluation, so that subsequent processing can be more targeted and effective.

In the state evaluation sub-module, a local anomaly factor (LOF) algorithm is adopted to comprehensively evaluate the environment state by combining monitoring parameters and anomaly parameter indexes, the LOF algorithm is used for identifying the anomaly points by calculating the local density deviation of each data point and adjacent points thereof, the LOF algorithm is particularly suitable for identifying the anomaly points in different density areas, in the process, each environment parameter point is endowed with a local anomaly factor score to reflect the anomaly degree of the environment parameter point relative to the surrounding environment, the environment parameter is classified into normal or anomaly categories by comparing the LOF score with a preset anomaly threshold, the generated environment state analysis record details the state, the anomaly score and the timestamp of each parameter, an accurate state evaluation result is provided for an environment monitoring system, the analysis record not only provides basis for early warning and response mechanisms of the system, but also provides data support for maintaining and optimizing the environment monitoring strategy, and the self-adaption capability and the potential risk prevention capability of the system are enhanced.

Referring to fig. 2 and fig. 4, the risk identification module includes a potential risk analysis sub-module, a risk factor evaluation sub-module, and a risk level classification sub-module;

the potential risk analysis submodule is used for identifying and analyzing potential risk factors based on the environmental state analysis record by applying a data mining technology, wherein the potential risk factors comprise key points which form threat to safety, and a potential risk point analysis result is generated;

the risk factor evaluation sub-module adopts a Bayesian network to analyze relevance and influence degree of the identified risk points based on the analysis result of the potential risk points, evaluates potential influence of a plurality of risk factors on environmental safety, and generates a risk influence evaluation record;

In the potential risk analysis submodule, environmental state analysis records are analyzed by applying a data mining technology, real-time data including environmental parameters such as temperature, humidity, gas concentration and the like and abnormal state indexes of the parameters are recorded, an association rule learning and cluster analysis method is adopted in the data mining technology and is used for identifying and analyzing key environmental parameters which form potential threats to safety, association rule learning is used for identifying frequently occurring parameter combinations by analyzing the relation among the parameters, the combination predicts specific risk conditions, cluster analysis is used for identifying parameter groups with similar abnormal modes by dividing the environmental parameters into a plurality of groups, the groups represent potential risk points, and a potential risk point analysis result is generated by refining operation, wherein the result details all the identified potential risk points including characteristics, occurrence frequencies and the affiliated parameter groups, and provides a basis for further risk assessment and management, so that risk management measures can be designed and implemented more specifically.

In the risk factor evaluation submodule, based on a potential risk point analysis result, a Bayesian network is adopted to analyze relevance and influence degree of the identified risk points, the Bayesian network is a probability graph model, dependence relation among variables can be described, influence of the risk factors is evaluated through probability reasoning, in the process, firstly, a structure of the Bayesian network is built according to the potential risk point analysis result, then, historical data and expert knowledge are utilized to evaluate conditional probability distribution in the network, in this way, probability of occurrence of other risk factors and potential influence of the risk factors on environmental safety when given certain risk factors occur can be calculated, and the generated risk influence evaluation record details relevance, influence degree and occurrence probability of each risk factor, so that important basis is provided for formulating targeted risk management and relief measures.

In the risk level classification submodule, based on risk influence evaluation records, a decision tree model is adopted to classify risks according to influence degrees and occurrence probabilities of multiple risk factors, a decision tree is a decision support tool of a tree structure, classification decisions are made by generating a series of rules through induction learning of data, in the process, classification standards of the risk levels, such as low risk, medium risk and high risk, are defined firstly, then a decision tree model is constructed according to the influence degrees and occurrence probabilities of the risk factors, the risk levels are automatically classified by analyzing interaction and combined effects among the risk factors, the generated risk identification information lists the level, priority and emergency processing requirement of each identification risk in detail, clear guidance is provided for risk response and resource allocation, and by the method, the fact that risk management measures can effectively aim at most important risk points is ensured, the use of resources is optimized, and the efficiency and effect of risk management are improved.

Referring to fig. 2 and 5, the sensor policy optimization module includes a sensor deployment optimization sub-module, a policy adjustment sub-module, and a sensitivity adjustment sub-module;

In the sensor deployment optimization sub-module, based on risk identification information, a genetic algorithm is used for analyzing and optimizing the sensor layout, the sub-module firstly receives the area data with clear risk level distinction, the format is usually that an area marked by longitude and latitude is used as an initial population with corresponding risk level value, the genetic algorithm defines a fitness function to reflect the optimization degree of the layout, such as coverage rate and effectiveness of risk monitoring, the fitness function evaluates the coverage efficiency of each layout scheme on the differentiated risk level area, the algorithm is caused to take full monitoring of the high risk area preferentially, the algorithm iterates to optimize the sensor layout through selection, crossover and mutation operation simulation evolution process, the optimal layout configuration in each generation population is selected according to the fitness score and used for generating the next generation, after multiple generations of iteration, the captured optimal layout configuration is used as the sensor layout optimization scheme to form a specific sensor deployment instruction file, the file specifies the position and expected working parameters of each sensor in detail, and the optimization scheme remarkably improves the efficiency and accuracy of the sensor network on the specific risk area.

In the strategy adjustment submodule, a sensor layout optimization scheme is adopted, a simulated annealing algorithm is adopted to refine the sampling frequency and the detection threshold value of the sensor, the submodule takes the optimized sensor layout scheme as input, the format is sensor position and layout configuration, the aim is to adjust working parameters to match environment monitoring requirements, initial temperature is set when the simulated annealing algorithm is started, the exploration degree of freedom of a parameter searching space is represented, the searching process gradually turns from global exploration to local refined adjustment along with gradual cooling of an iterative process, at each step, the algorithm tries to adjust the sampling frequency and the detection threshold value, the influence of adjustment on the monitoring effect is evaluated through an energy function, the energy function consideration factors comprise accuracy of monitoring data and energy consumption of the sensor, the parameter configuration with minimized energy is found through iteration, the algorithm generates a final parameter adjustment instruction, the sampling frequency and the detection threshold value of each sensor are listed in detail, and the optimal balance between the energy consumption and the monitoring accuracy of the sensor network is ensured.

In the sensitivity adjustment submodule, a dynamic adjustment algorithm is adopted to adjust the sensitivity of the sensor according to a parameter adjustment instruction, the input format is working parameters of the sensor, the working parameters comprise sampling frequency and detection threshold, the dynamic adjustment algorithm adjusts the sensitivity of the sensor in real time according to environmental changes and monitoring requirements, the sensor can rapidly and accurately respond to the environmental changes, the sensitivity parameter is automatically adjusted by analyzing the relation between environmental monitoring data and a preset threshold, the response speed and accuracy of the monitoring data are improved, in the sensitivity adjustment process, the algorithm evaluates the influence of different sensitivity settings on the quality of the monitoring data, the sensitivity configuration which is most suitable for the current environmental conditions is found by an optimization algorithm, and after adjustment is completed, a strategy optimization instruction is generated, wherein the instruction comprises the sensitivity settings optimized for each sensor, so that the sensor network can more sensitively and effectively respond to the environmental changes.

Referring to fig. 2 and 6, the energy efficiency optimization management module includes an energy consumption analysis sub-module, an optimization strategy sub-module, and an execution measure sub-module;

the optimization strategy submodule adopts a dynamic planning algorithm to mine a differentiated energy distribution scheme based on the energy consumption analysis record, and captures the balance point between the energy consumption and the system performance by constructing an optimal decision sequence to generate an energy efficiency optimization strategy;

In the energy consumption analysis submodule, deep analysis is carried out on the energy consumption mode of the system by applying a particle swarm optimization algorithm, the process is based on a strategy optimization instruction, the instruction comprises detailed description on the current energy consumption condition and performance requirement of the system, a data format generally comprises time series energy consumption data, a system running state and related performance indexes, the comprehensiveness and accuracy of analysis are ensured, each particle represents an energy consumption optimization solution when the particle swarm optimization algorithm is started, the particle searches for an optimal solution in a solution space by simulating social behaviors of a bird swarm, and in the iterative process of the algorithm, the speed and the position of the particle are updated according to individual experience and the optimal position of the swarm, so that the particle continuously approaches to the optimal energy consumption mode, in this way, the key links of energy efficiency improvement can be identified, the current energy efficiency use condition can be accurately estimated, the generated energy consumption analysis record details the identified key links, the current energy efficiency use condition and the potential improvement scheme, scientific basis and direction guidance are provided for energy efficiency optimization, and the pertinence and effectiveness of energy efficiency optimization measures are ensured.

In the optimization strategy sub-module, a differential energy distribution scheme is mined by adopting a dynamic programming algorithm based on energy consumption analysis records, the dynamic programming algorithm decomposes a complex problem into a series of interrelated sub-problems and solves the problems one by one, an optimal decision sequence is finally constructed, in the context of energy efficiency optimization, the algorithm evaluates the influence of different energy distribution schemes on the system performance, meanwhile considers the balance between energy consumption and performance, represents the performance and energy consumption level of the system under different energy distribution by defining states, the optimal solution of each state is calculated by the dynamic programming algorithm, and the decision sequence which leads to the balance of the optimal performance and the energy consumption is determined by a backtracking method.

In the execution measure submodule, the sensor working mode is optimized by utilizing a genetic algorithm and the data processing flow is adjusted based on an energy efficiency optimization strategy, the genetic algorithm is a search algorithm simulating a natural evolution process, an optimal solution is found in a solution space through selection, intersection and variation operation, each individual represents a configuration scheme of the sensor working mode and the data processing flow in the execution measure submodule, the algorithm guides the evolution process by evaluating the fitness of each scheme, namely the energy efficiency and the performance index of the sensor working mode and the data processing flow, the algorithm can find the optimal system configuration scheme through iterative evolution, the energy consumption is minimized, the system performance is simultaneously maintained or improved, the generated energy efficiency optimization scheme is used for recording the optimized sensor working mode, the data processing flow and the system resource configuration in detail, the energy efficiency of the system is remarkably improved, the system is ensured to operate in the optimal energy consumption state on the premise of meeting the performance requirement, and a foundation is provided for long-term stable operation of the system.

Referring to fig. 2 and 7, the predictive maintenance module includes a performance data analysis sub-module, a fault prediction sub-module, and a maintenance policy planning sub-module;

the fault prediction submodule is used for analyzing the time sequence characteristics of the equipment operation data by applying a long-term memory network based on the performance trend analysis record, combining an anomaly detection algorithm, identifying a potential fault mode and an anomaly behavior, and predicting faults of the equipment to generate a fault prediction analysis record;

the maintenance strategy planning submodule analyzes the cost benefit of differentiated maintenance measures based on the fault prediction analysis record by utilizing a DSS technology, plans optimal maintenance time points and resource configuration, optimizes maintenance cost and equipment downtime and generates maintenance prediction results.

In a performance data analysis submodule, collecting and sorting equipment operation data through a time series analysis method, aiming at a data format comprising operation state data, energy consumption data and other relevant performance index data of equipment, each item of data is provided with a time stamp so as to carry out time series analysis, the submodule firstly adopts a data preprocessing technology such as denoising and data interpolation to ensure the accuracy of analysis, then adopts an autoregressive moving average (ARMA) model or a seasonal decomposition time series analysis technology to analyze long-term trend and periodical change of the data, the analysis method can reveal the law of the change of the performance of the equipment along with time, identify the enhancement or decline trend of the performance of the equipment and periodical maintenance requirement, and generates a performance trend analysis record through thinning operation, and the long-term trend, periodical change and any potential abnormal fluctuation of the performance of the equipment are recorded in detail, so that basic data is provided for fault prediction, and the aim of providing basis for future performance improvement and maintenance decision through deep analysis of past and present performance data.

In the fault prediction submodule, based on performance trend analysis records, a long-term memory (LSTM) network is used for analyzing the time sequence characteristics of equipment operation data, the LSTM network is particularly suitable for processing and predicting long-term dependence problems in the time sequence data, long-term trends and modes in the data can be learned, and by combining an anomaly detection algorithm such as an isolated forest or Gaussian mixture model, the submodule can identify potential fault modes and anomaly behaviors which are obviously different from the historical performance trends, the LSTM network model predicts the time points of performance degradation or faults occurring in the future by analyzing the time sequence characteristics of the equipment performance data, the generated fault prediction analysis records describe the potential fault modes, the anomaly behaviors and the possible occurrence time of the potential fault modes and the anomaly behaviors in detail, accurate early warning information is provided for maintenance strategy planning, and the core function of the process is that the faults estimated by the equipment can be found in advance, so that preventive measures are taken, and the influence caused by the equipment faults is reduced or avoided.

In the maintenance strategy planning submodule, based on fault prediction analysis records, the cost benefits of differentiated maintenance measures are analyzed by utilizing a Decision Support System (DSS) technology, the submodule calculates the cost benefit ratio of different maintenance strategies by integrating operation data, fault prediction results and maintenance history records of equipment and applying an optimization algorithm such as linear programming or integer programming, so as to determine optimal maintenance time points and resource configuration.

Referring to fig. 2 and 8, the emergency response module includes an emergency measure design sub-module, an automation response sub-module, and a coordination and notification sub-module;

the coordination and notification sub-module is used for sending an emergency notification and response instruction by adopting an instant messaging protocol based on an automatic response flow, and coordinating implementation of emergency measures and resource allocation to generate an emergency response plan.

In the emergency measure design sub-module, a Decision Support System (DSS) technology analyzes the severity and urgency of an emergency according to a maintenance prediction result and risk identification information, a data format received by the sub-module comprises an operation state of equipment, a historical maintenance record, real-time monitoring data and a risk assessment report, the data format comprises structural data, such as JSON or XML, the DSS technology evaluates the potential risk level reflected in the data and the influence of the emergency through an integrated analysis model, and further determines the priority of the emergency response, in the analysis process, the DSS utilizes an algorithm to weight and sort the risk data so as to determine which conditions need to be preferentially processed, then, according to the analysis result, the system designs targeted emergency measures and coping strategies, such as equipment shutdown, emergency maintenance or safety pre-warning release, each measure specifies an execution step and an expected effect, and the generated emergency measure design scheme is output in the form of a document, and comprises an emergency response flow, a resource allocation proposal and an expected release effect, and aims to reduce the influence brought by the risk to the maximum extent.

In the automatic response sub-module, a predefined emergency response flow is automatically triggered based on an emergency measure design scheme through event-driven programming, the sub-module takes the emergency measure in the design scheme as a trigger condition, an event monitoring and processing mechanism is adopted to monitor the state change of the system, once a matched risk event is detected, the system immediately executes the corresponding emergency measure, such as automatic power-off, standby system starting or safety locking program starting, in the automatic response flow, the trigger condition, the execution command and the expected target of each emergency measure are accurately configured, the rapid reduction of the fault influence when the emergency condition occurs is ensured, the generated automatic response flow is recorded in a system log, the basis is provided for subsequent audit and optimization, meanwhile, the output flow file details the operation and effect of each step, and the transparency and the traceability of the response measure are enhanced.

The coordination and notification submodule utilizes an instant communication protocol, sends emergency notification and response instructions based on an automatic response flow, and coordinates implementation of emergency measures and resource allocation, the submodule determines target groups and resource allocation requirements to be notified by analyzing operation and state changes in the automatic response flow, adopts the instant communication protocol, such as MQTT or AMQP, ensures instant transmission and high reliability of messages, ensures that all relevant personnel and systems can receive the emergency instructions and the notifications immediately, ensures accurate transmission and easy understanding of information through optimizing a message format, comprises emergency summaries, specific response instructions and resource allocation information, and the generated emergency response plan document details notification flow, instruction execution sequence and resource allocation scheme, and aims to ensure efficient execution and team cooperation of the emergency measures and improve overall response capacity and recovery of the system.

Referring to fig. 2 and 9, the system dynamic reconfiguration module includes a learning feedback analysis sub-module, a configuration optimization sub-module, and a workflow adjustment sub-module;

the workflow adjustment submodule adjusts the workflow by utilizing a genetic programming algorithm based on the refined configuration scheme, and comprises the steps of updating operation parameters, optimizing data flow and processing logic, determining that the system can correctly operate under the adjusted configuration and working environment, and generating a system reconstruction scheme.

In a learning feedback analysis sub-module, the execution effect of an emergency response plan and the current system state are comprehensively analyzed through a reinforcement learning algorithm, the data format adopted by the process is usually time series data, the system state change before and after the emergency response plan is executed, the effect evaluation of response measures and related environment and operation parameters are recorded, the reinforcement learning algorithm plays the role of an intelligent agent in the process, the aim is to improve the response strategy through interactive learning with the environment so as to maximize long-term rewards, specifically, the algorithm firstly establishes an initial strategy model based on historical data, then optimizes the model through continuous test and error correction process, namely, specific response measures are adopted, the observation result and then the adjustment strategy, in each iteration, the influence of the action adopted by the algorithm evaluation on the system state is recorded, the generated response effect and the state analysis result are recorded in detail according to the action selection, the result evaluation and the strategy adjustment of each iteration in the optimization process, continuous optimization and self-adjustment capability are provided for the system, and the process enables the system to automatically learn and adapt to the unknown or changing environment, and the response efficiency is improved.

In the configuration optimization sub-module, based on response effect and state analysis result, the genetic programming algorithm is used for carrying out deep optimization analysis on system workflow and configuration parameters, the genetic programming algorithm simulates the evolution process in the nature, the structure and parameters of a program are searched through selection, intersection, variation and other operations to find the optimal or near optimal solution, in the sub-module, the genetic programming algorithm is used for exploring different system configuration and workflow settings to find configuration schemes capable of improving system performance and enhancing response capability, the algorithm starts from a set of randomly generated initial solutions, the simulation evolution process is continuously iterated and improved, the evaluation criteria are based on comprehensive consideration of system performance indexes and response effects, the generated refined configuration schemes describe the optimized system workflow, configuration parameters and expected performance improvement thereof in detail, the scheme provides configuration options optimized for specific requirements and environmental conditions for the system, and the flexibility and performance of the system are improved.

In the workflow adjustment submodule, based on a refined configuration scheme, the genetic programming algorithm is utilized again to adjust and optimize the workflow, the aim of the stage is to ensure that the system can operate correctly under the adjusted configuration and working environment, and maximize the efficiency and effect of the system, the algorithm is focused on optimizing data flow, processing logic and operation parameters to adapt to the new configuration scheme at the stage, new workflow variants are continuously generated through the crossover and mutation operation of the genetic programming algorithm, the performance of the new workflow variants is evaluated through simulation execution, so that the optimal workflow configuration is identified, the generated system reconfiguration scheme lists the adjusted workflow, the updated operation parameters and the expected system performance improvement in detail, the efficient operation of the system under the new configuration is ensured, the adaptability and flexibility of the system are enhanced, the resource utilization and operation efficiency are optimized, and a dynamic and self-adaptive solution strategy is provided for continuous optimization and improvement of the system.

The invention also provides a flammable gas alarm control method, which is executed based on the flammable gas alarm control system and comprises the following steps:

Through data feature classification and abnormal fluctuation identification of a support vector machine and environmental state judgment of threshold analysis, the method can timely and accurately identify abnormal changes in the environment, thereby early warning in advance, reducing the risk of safety accidents caused by combustible gas leakage, carrying out deep probability analysis and evaluation on risk factors by using a Bayesian network and a decision tree model, enabling risk management to be more scientific and accurate, making precautionary measures in a targeted manner, and greatly improving the efficiency and effect of safety management.

The genetic algorithm and the simulated annealing algorithm are applied to optimization of sensor layout and monitoring parameters, so that the coverage range and accuracy of a monitoring system are improved, the adaptability and flexibility of the system in a complex environment are ensured, the particle swarm optimization algorithm is combined with the dynamic programming method to optimize energy consumption distribution, balance points of energy efficiency and system performance are found, the running cost of the system is obviously reduced, and meanwhile, the long-term stable running of the monitoring system is ensured.

The long-term and short-term memory network is applied to analysis and fault prediction of equipment performance trend, and combines abnormal detection of time sequence data, so that not only is time for fault discovery greatly advanced, but also the unscheduled downtime of the system is reduced, scientific basis is provided for maintenance decision, maintenance cost and resource allocation are optimized, finally, the quick response capability of the system under emergency is ensured through implementation and optimization of emergency response measures of event-driven programming and reinforcement learning algorithms, and dynamic adjustment of system workflow and allocation, and meanwhile, the intelligent level and self-adaptive capability of the system are improved through continuous learning.

The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.

Claims

1. A flammable gas alarm control system, characterized by: the system comprises an intelligent monitoring module, a risk identification module, a sensor strategy optimization module, an energy efficiency optimization management module, a predictive maintenance module, an emergency response module and a system dynamic reconstruction module;

2. A combustible gas alarm control system in accordance with claim 1 wherein: the environment state analysis records comprise abnormal environment indexes, normal environment indexes and environment parameter fluctuation indexes, the risk identification information comprises a risk factor association graph, a risk probability score and risk grade division, the strategy optimization instructions comprise a sensor layout graph, an adjusted sampling frequency value and an adjusted detection threshold value setting, the energy efficiency optimization scheme comprises an energy consumption distribution graph, energy utilization rate lifting measures and an energy efficiency performance balance strategy, the maintenance prediction results comprise a fault mode identification result, a performance trend analysis graph and a maintenance plan list, the emergency response plan comprises a response measure list, an emergency strategy activation scheme and a resource allocation rule, and the system reconstruction scheme comprises a flow adjustment blueprint, a configuration update list and an operation parameter adjustment record.

3. A combustible gas alarm control system in accordance with claim 1 wherein: the intelligent monitoring module comprises an environment data acquisition sub-module, an abnormality detection sub-module and a state evaluation sub-module;

4. A combustible gas alarm control system in accordance with claim 1 wherein: the risk identification module comprises a potential risk analysis sub-module, a risk factor evaluation sub-module and a risk level classification sub-module;

5. A combustible gas alarm control system in accordance with claim 1 wherein: the sensor strategy optimization module comprises a sensor deployment optimization sub-module, a strategy adjustment sub-module and a sensitivity adjustment sub-module;

6. A combustible gas alarm control system in accordance with claim 1 wherein: the energy efficiency optimization management module comprises an energy consumption analysis sub-module, an optimization strategy sub-module and an execution measure sub-module;

7. A combustible gas alarm control system in accordance with claim 1 wherein: the predictive maintenance module comprises a performance data analysis sub-module, a fault prediction sub-module and a maintenance strategy planning sub-module;

8. A combustible gas alarm control system in accordance with claim 1 wherein: the emergency response module comprises an emergency measure design sub-module, an automatic response sub-module and a coordination and notification sub-module;

9. A combustible gas alarm control system in accordance with claim 1 wherein: the system dynamic reconfiguration module comprises a learning feedback analysis sub-module, a configuration optimization sub-module and a workflow adjustment sub-module;

10. A method of flammable gas alarm control, characterized in that a flammable gas alarm control system according to any of claims 1-9 is implemented, comprising the steps of:

based on environment data acquired in real time, a support vector machine is adopted to analyze data fluctuation, whether abnormal conditions exist or not is determined through a threshold analysis method, and environment variables are classified to generate an environment state analysis record;

Based on the environmental state analysis record, adopting a Bayesian network to analyze the relevance of various risk factors, calculating risk probability according to the relevance and historical data by utilizing a decision tree, quantitatively evaluating potential risks, and generating risk identification information;

based on the risk identification information, adopting a genetic algorithm to adjust the layout and parameters of the sensor according to the risk level, optimizing the sampling frequency and the detection threshold value by using a simulated annealing algorithm, and adjusting the strategy to generate a strategy optimization instruction;

based on the strategy optimization instruction, a particle swarm optimization algorithm is adopted to evaluate and adjust energy consumption distribution, a dynamic programming algorithm is used to determine optimal energy consumption and performance balance points, resources are redistributed, and an energy efficiency optimization scheme is generated;

based on an energy efficiency optimization scheme, a long-term and short-term memory network is adopted, equipment operation data and performance trend are analyzed, potential fault modes are identified by combining an anomaly detection algorithm, a maintenance plan is formulated, and a maintenance prediction result is generated;

based on the maintenance prediction result and the risk identification information, adopting a DSS technology to formulate a response measure, automatically activating an emergency strategy by using event-driven programming, and distributing response resources to generate an emergency response plan;

Based on the emergency response plan, a reinforcement learning algorithm is adopted to analyze the response effect and the system state, and the operation parameters are updated by adjusting the workflow and the system configuration through a genetic programming algorithm to generate a system reconstruction scheme.