Chemical plant safety supervision system and method based on combustible gas concentration detection
By acquiring samples of chemical workshop characteristics and gas types, and using a strategy network model to train a sensor deployment model, the problem of inaccurate monitoring of flammable gas leaks in chemical plants was solved, the sensor deployment scheme was optimized, and the safety monitoring effect of chemical workshops was improved.
Patent Information
- Application Number
- CN202511513009.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-22
- Publication Date
- 2026-02-10
AI Technical Summary
In existing technologies, the monitoring of flammable gas leaks in chemical plants is inaccurate, and it is difficult to reasonably deploy sensors to improve detection effectiveness.
By acquiring environmental characteristics of chemical workshops, types of combustible gases, and sensor deployment scheme samples, a strategy network model is used to train a sensor deployment model, and the optimal deployment scheme is output to improve detection performance.
A sensor deployment scheme with good detection effect was realized based on the characteristics of chemical workshops and gas types, which improved the real-time concentration safety monitoring effect of flammable gases in chemical workshops.
Smart Images
Figure CN121506281A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of combustible gas detection, specifically to a chemical plant safety monitoring system and method based on combustible gas concentration detection. Background Technology
[0002] Chemical plants are factories engaged in the production of chemical products, using chemical reactions and physical processing to produce various chemical products, covering multiple fields such as basic raw materials, fine chemicals, and synthetic materials. During chemical production, many chemical reactions produce flammable gases with high combustion capacity and calorific value. Leaks of these flammable gases can cause serious hazards; for example, if a leaked flammable gas comes into contact with an open flame, electrical spark, or high temperature, it can easily trigger an explosion. While existing technologies use sensors to monitor for flammable gas leaks in chemical workshops, different environmental characteristics and sensor placement can affect the accuracy of these monitoring efforts. Therefore, how to rationally deploy sensors to improve the detection effectiveness of flammable gas concentrations is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0003] The purpose of this application is to overcome the shortcomings and deficiencies in the prior art and provide a chemical plant safety monitoring system and method based on the detection of combustible gas concentration.
[0004] The first aspect of this application provides a chemical plant safety monitoring system based on combustible gas concentration detection, including:
[0005] The sample acquisition module is used to acquire environmental characteristic samples of the chemical workshop, samples of the types of combustible gases to be detected, samples of chemical equipment characteristics, and samples of multiple sensor deployment schemes.
[0006] The prediction action score acquisition module is used to input the environmental characteristic samples of the sample chemical workshop, the samples of combustible gas types to be detected, the chemical equipment characteristic samples, and multiple sensor deployment scheme samples into the strategy network model to obtain the deployment action prediction score output by the strategy network model.
[0007] The model training module is used to train the strategy network model based on the deployment action target scores of multiple sensor deployment scheme samples in various sample chemical workshops, and the corresponding deployment action prediction scores, to obtain a sensor deployment model; wherein, the better the detection effect of the sensor deployment scheme samples, the higher the deployment action target score.
[0008] The target deployment scheme acquisition module is used to input the environmental characteristics of the physical chemical workshop of the target chemical plant, the types of combustible gases to be detected, and the characteristics of chemical equipment into the sensor deployment model to obtain the target deployment scheme with the highest deployment action prediction score.
[0009] The gas concentration safety monitoring module is used to monitor the real-time concentration of flammable gases in the physical chemical workshop through several sensors deployed based on the target deployment scheme.
[0010] As one implementation, it also includes a scheme parameter acquisition module, used to acquire the target cost, actual cost, and detection result parameters of the sensors corresponding to the multiple sensor deployment scheme samples.
[0011] The target action score acquisition module is used to obtain the deployment action target score of each sensor deployment scheme sample based on the target cost, the actual cost and the detection result parameters.
[0012] As one implementation method, the step of obtaining the deployment action target score for each sensor deployment scheme sample based on the target cost, the actual cost, and the detection result parameters includes:
[0013] Based on the target cost and the actual cost, a deployment cost score is obtained;
[0014] Based on the detection result parameters, a detection score is obtained;
[0015] The deployment action target score is obtained based on the deployment action cost score and the detection score.
[0016] As one implementation method, the detection result parameters include detection accuracy and detection efficiency;
[0017] The steps for obtaining a detection score based on the detection result parameters include:
[0018] An accuracy score is obtained based on the accuracy of the detection.
[0019] An efficiency score is obtained based on the detection efficiency.
[0020] The detection score is obtained based on the accuracy score and the efficiency score.
[0021] As one implementation method, the step of training the strategy network model based on the deployment action target scores of multiple sensor deployment scheme samples from various sample chemical workshops, and the corresponding deployment action prediction scores, to obtain the sensor deployment model includes:
[0022] A loss function is constructed based on the deployment action target scores and corresponding deployment action prediction scores of various sample chemical workshops, and the function output of the loss function is obtained.
[0023] The network parameters of the policy network model are updated according to the output of the function to obtain the trained policy network model.
[0024] Compared to related technologies, the chemical plant safety monitoring system based on combustible gas concentration detection in this application inputs environmental characteristic samples of a sample chemical workshop, samples of combustible gas types to be detected, chemical equipment characteristic samples, and multiple sensor deployment scheme samples into a strategy network model to obtain a deployment action prediction score output by the strategy network model. Based on the deployment action target scores of multiple sensor deployment scheme samples from various sample chemical workshops, and the corresponding deployment action prediction scores, the strategy network model is trained to obtain a sensor deployment model. Then, the environmental characteristics of the actual chemical workshop of the target chemical plant, the required combustible gas types, chemical equipment characteristic samples, and multiple sensor deployment scheme samples are input into a strategy network model to obtain a deployment action prediction score. The types of flammable gases to be detected and the characteristics of chemical equipment are input into the sensor deployment model to obtain the target deployment scheme with the highest deployment action prediction score. Since the better the detection effect of the sensor deployment scheme sample, the higher the target score of the deployment action, the sensor deployment model trained can output a target deployment scheme with good detection effect based on the environmental characteristics of the actual chemical workshop, the types of flammable gases to be detected, and the characteristics of chemical equipment. This allows users to deploy sensors in the actual chemical workshop according to the target deployment scheme, thereby improving the real-time concentration safety monitoring effect of flammable gases in the actual chemical workshop.
[0025] The second aspect of this application provides a method for safety supervision of chemical plants based on the detection of combustible gas concentrations, including:
[0026] Obtain environmental characteristic samples of the sample chemical workshop, samples of the types of combustible gases to be detected, samples of chemical equipment characteristics, and samples of multiple sensor deployment schemes;
[0027] The environmental characteristics of the sample chemical workshop, the types of combustible gases to be detected, the characteristics of chemical equipment, and multiple sensor deployment schemes are input into the strategy network model to obtain the deployment action prediction score output by the strategy network model.
[0028] The strategy network model is trained based on the deployment action target scores of multiple sensor deployment scheme samples in various chemical workshops and the corresponding deployment action prediction scores to obtain the sensor deployment model; wherein, the better the detection effect of the sensor deployment scheme samples, the higher the deployment action target score.
[0029] The environmental characteristics of the actual chemical workshop of the target chemical plant, the types of combustible gases to be detected, and the characteristics of the chemical equipment are input into the sensor deployment model to obtain the target deployment scheme with the highest deployment action prediction score.
[0030] By deploying several sensors based on the target deployment scheme, the real-time concentration safety monitoring of flammable gases in the physical chemical workshop is carried out.
[0031] As one implementation method, the deployment action target score is obtained through the following steps:
[0032] Obtain the target cost, actual cost, and detection result parameters of the sensors corresponding to the multiple sensor deployment scheme samples;
[0033] Based on the target cost, the actual cost, and the detection result parameters, the deployment action target score for each sensor deployment scheme sample is obtained.
[0034] As one implementation method, the step of obtaining the deployment action target score for each sensor deployment scheme sample based on the target cost, the actual cost, and the detection result parameters includes:
[0035] Based on the target cost and the actual cost, a deployment cost score is obtained;
[0036] Based on the detection result parameters, a detection score is obtained;
[0037] The deployment action target score is obtained based on the deployment action cost score and the detection score.
[0038] As one implementation method, the detection result parameters include detection accuracy and detection efficiency;
[0039] The steps for obtaining a detection score based on the detection result parameters include:
[0040] An accuracy score is obtained based on the accuracy of the detection.
[0041] An efficiency score is obtained based on the detection efficiency.
[0042] The detection score is obtained based on the accuracy score and the efficiency score.
[0043] As one implementation method, the step of training the strategy network model based on the deployment action target scores of multiple sensor deployment scheme samples from various sample chemical workshops, and the corresponding deployment action prediction scores, to obtain the sensor deployment model includes:
[0044] A loss function is constructed based on the deployment action target scores and corresponding deployment action prediction scores of various sample chemical workshops, and the function output of the loss function is obtained.
[0045] The network parameters of the policy network model are updated according to the output of the function to obtain the trained policy network model.
[0046] Compared to related technologies, the chemical plant safety supervision method based on combustible gas concentration detection in this application involves inputting environmental characteristic samples of a sample chemical workshop, samples of combustible gas types to be detected, chemical equipment characteristic samples, and multiple sensor deployment scheme samples into a strategy network model to obtain a deployment action prediction score output by the strategy network model. The strategy network model is then trained based on the deployment action target scores of multiple sensor deployment scheme samples from various sample chemical workshops, and the corresponding deployment action prediction scores, to obtain a sensor deployment model. Finally, the environmental characteristics of the actual chemical workshop of the target chemical plant, the required combustible gas types, chemical equipment characteristic samples, and multiple sensor deployment scheme samples are input into a strategy network model to obtain a deployment action prediction score. The types of flammable gases to be detected and the characteristics of chemical equipment are input into the sensor deployment model to obtain the target deployment scheme with the highest deployment action prediction score. Since the better the detection effect of the sensor deployment scheme sample, the higher the target score of the deployment action, the sensor deployment model trained can output a target deployment scheme with good detection effect based on the environmental characteristics of the actual chemical workshop, the types of flammable gases to be detected, and the characteristics of chemical equipment. This allows users to deploy sensors in the actual chemical workshop according to the target deployment scheme, thereby improving the real-time concentration safety monitoring effect of flammable gases in the actual chemical workshop.
[0047] To provide a clearer understanding of this application, the specific embodiments of this application will be described below in conjunction with the accompanying drawings. Attached Figure Description
[0048] Figure 1 This is a first schematic diagram of the module connections of a chemical plant safety monitoring system based on combustible gas concentration detection according to an embodiment of this application.
[0049] Figure 2 This is a second schematic diagram showing the module connections of a chemical plant safety monitoring system based on combustible gas concentration detection, according to one embodiment of this application.
[0050] Figure 3 This is a flowchart illustrating a chemical plant safety monitoring method based on combustible gas concentration detection, according to one embodiment of this application.
[0051] 100. Chemical Plant Safety Supervision System; 101. Sample Acquisition Module; 102. Predicted Action Score Acquisition Module; 103. Model Training Module; 104. Target Deployment Scheme Acquisition Module; 105. Gas Concentration Safety Supervision Module; 106. Scheme Parameter Acquisition Module; 107. Target Action Score Acquisition Module. Detailed Implementation
[0052] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.
[0053] It should be understood that the described embodiments are merely some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.
[0054] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, it should be understood that the terms "target," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. The singular forms "a," "the," and "the" used in this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. The word "if" as used herein can be interpreted as "when," "when," or "in response to determination."
[0055] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.
[0056] Please see Figure 1 This is a first schematic diagram of the module connections of the chemical plant safety monitoring system based on combustible gas concentration detection according to the first embodiment of this application. The chemical plant safety monitoring system based on combustible gas concentration detection disclosed in the first embodiment of this application includes:
[0057] The sample acquisition module is used to acquire environmental characteristic samples of the chemical workshop, samples of the types of combustible gases to be detected, samples of chemical equipment characteristics, and samples of multiple sensor deployment schemes.
[0058] The prediction action score acquisition module is used to input the environmental characteristic samples of the sample chemical workshop, the samples of combustible gas types to be detected, the chemical equipment characteristic samples, and multiple sensor deployment scheme samples into the strategy network model to obtain the deployment action prediction score output by the strategy network model.
[0059] The model training module is used to train the strategy network model based on the deployment action target scores of multiple sensor deployment scheme samples in various sample chemical workshops, and the corresponding deployment action prediction scores, to obtain a sensor deployment model; wherein, the better the detection effect of the sensor deployment scheme samples, the higher the deployment action target score.
[0060] The target deployment scheme acquisition module is used to input the environmental characteristics of the physical chemical workshop of the target chemical plant, the types of combustible gases to be detected, and the characteristics of chemical equipment into the sensor deployment model to obtain the target deployment scheme with the highest deployment action prediction score.
[0061] The gas concentration safety monitoring module is used to monitor the real-time concentration of flammable gases in the physical chemical workshop through several sensors deployed based on the target deployment scheme.
[0062] The environmental characteristics sample includes the layout, dimensions, and construction materials of the sample chemical workshop. The sample of flammable gas types to be tested includes the types of flammable gases to be tested in the sample chemical workshop. The chemical equipment characteristics sample includes the parameters of the chemical equipment used in the sample chemical workshop, as well as the layout of the chemical equipment.
[0063] The sample acquisition module acquires multiple sensor deployment scheme samples, which are multiple deployment schemes for a single sample chemical workshop. This means that multiple sample chemical workshops with the same environmental characteristics, the same types of combustible gases to be detected, and the same chemical equipment characteristics belong to the same type of sample chemical workshop. For example, five sample chemical workshops with the same environmental characteristics, the same types of combustible gases to be detected, and the same chemical equipment characteristics are considered five identical sample chemical workshops. The five sample chemical workshops have different sensor deployment schemes, which constitute five sensor deployment scheme samples for the sample chemical workshop.
[0064] When the model training module performs module training, it uses multiple sensor deployment scheme samples from various sample chemical workshops. For example, there are three sample chemical workshops: the first sample workshop has 5 sensor deployment scheme samples, the second sample workshop has 8 sensor deployment scheme samples, and the third sample workshop has 12 sensor deployment scheme samples. In this case, the total number of sensor deployment scheme samples across the three sample chemical workshops is 5 + 8 + 12 = 25. That is, the model training module uses the deployment action target scores and corresponding deployment action prediction scores from these 25 sensor deployment scheme samples during module training.
[0065] The sensor deployment scheme sample includes characteristics such as sensor type, quantity, deployment location, and scheme cost.
[0066] Compared to related technologies, the chemical plant safety monitoring system based on combustible gas concentration detection in this application inputs environmental characteristic samples of a sample chemical workshop, samples of combustible gas types to be detected, chemical equipment characteristic samples, and multiple sensor deployment scheme samples into a strategy network model to obtain a deployment action prediction score output by the strategy network model. Based on the deployment action target scores of multiple sensor deployment scheme samples from various sample chemical workshops, and the corresponding deployment action prediction scores, the strategy network model is trained to obtain a sensor deployment model. Then, the environmental characteristics of the actual chemical workshop of the target chemical plant, the required combustible gas types, chemical equipment characteristic samples, and multiple sensor deployment scheme samples are input into a strategy network model to obtain a deployment action prediction score. The types of flammable gases to be detected and the characteristics of chemical equipment are input into the sensor deployment model to obtain the target deployment scheme with the highest deployment action prediction score. Since the better the detection effect of the sensor deployment scheme sample, the higher the target score of the deployment action, the sensor deployment model trained can output a target deployment scheme with good detection effect based on the environmental characteristics of the actual chemical workshop, the types of flammable gases to be detected, and the characteristics of chemical equipment. This allows users to deploy sensors in the actual chemical workshop according to the target deployment scheme, thereby improving the real-time concentration safety monitoring effect of flammable gases in the actual chemical workshop.
[0067] Please see Figure 2 In one feasible embodiment, it further includes:
[0068] The scheme parameter acquisition module is used to acquire the target cost, actual cost, and detection result parameters of the sensors corresponding to the multiple sensor deployment scheme samples.
[0069] The target action score acquisition module is used to obtain the deployment action target score of each sensor deployment scheme sample based on the target cost, the actual cost and the detection result parameters.
[0070] In a feasible embodiment, the step of obtaining the deployment action target score for each sensor deployment scheme sample based on the target cost, the actual cost, and the detection result parameters includes:
[0071] Based on the target cost and the actual cost, a deployment cost score is obtained;
[0072] Based on the detection result parameters, a detection score is obtained;
[0073] The deployment action target score is obtained based on the deployment action cost score and the detection score.
[0074] Among them, the smaller the difference between the actual cost and the target cost, the higher the deployment cost score; the better the detection result parameters, the higher the corresponding detection score.
[0075] In this embodiment, the deployment action target score can be obtained by weighted summation of the deployment action cost score and the detection score.
[0076] In one feasible embodiment, the detection result parameters include detection accuracy and detection efficiency;
[0077] The steps for obtaining a detection score based on the detection result parameters include:
[0078] An accuracy score is obtained based on the accuracy of the detection.
[0079] An efficiency score is obtained based on the detection efficiency.
[0080] The detection score is obtained based on the accuracy score and the efficiency score.
[0081] In this embodiment, the detection score can be obtained by weighted summation of the accuracy score and the efficiency score.
[0082] In a feasible embodiment, the step of training the strategy network model to obtain the sensor deployment model based on the deployment action target scores of multiple sensor deployment scheme samples from various sample chemical workshops and the corresponding deployment action prediction scores includes:
[0083] A loss function is constructed based on the deployment action target scores and corresponding deployment action prediction scores of various sample chemical workshops, and the function output of the loss function is obtained.
[0084] The loss function can be obtained using the following formula:
[0085]
[0086] δ is the output of the loss function, y t ′ represents the prediction score for the deployment action, y t Scoring is given for the action targets set up.
[0087] The network parameters of the policy network model are updated according to the output of the function to obtain the trained policy network model.
[0088] Specifically, based on the function output, the network parameters of the policy network model are updated using a gradient descent algorithm to obtain a trained policy network model where the function output is less than or equal to a preset function threshold. The function threshold is set by the user.
[0089] Please see Figure 3 The second embodiment of this application provides a method for safety supervision of chemical plants based on the detection of combustible gas concentration, including:
[0090] S1: Obtain environmental characteristic samples of the sample chemical workshop, samples of the types of combustible gases to be detected, samples of chemical equipment characteristics, and samples of multiple sensor deployment schemes;
[0091] S2: Input the environmental characteristic samples of the sample chemical workshop, the samples of combustible gas types to be detected, the chemical equipment characteristic samples, and the samples of multiple sensor deployment schemes into the strategy network model to obtain the deployment action prediction score output by the strategy network model.
[0092] S3: The strategy network model is trained based on the deployment action target scores of multiple sensor deployment scheme samples in various sample chemical workshops and the corresponding deployment action prediction scores to obtain the sensor deployment model; wherein, the better the detection effect of the sensor deployment scheme samples, the higher the deployment action target score.
[0093] S4: Input the environmental characteristics of the actual chemical workshop of the target chemical plant, the types of combustible gases to be detected, and the characteristics of the chemical equipment into the sensor deployment model to obtain the target deployment scheme with the highest deployment action prediction score.
[0094] S5: Real-time safety monitoring of the concentration of flammable gases in the physical chemical workshop is carried out using several sensors deployed based on the target deployment scheme.
[0095] Compared to related technologies, the chemical plant safety supervision method based on combustible gas concentration detection in this application involves inputting environmental characteristic samples of a sample chemical workshop, samples of combustible gas types to be detected, chemical equipment characteristic samples, and multiple sensor deployment scheme samples into a strategy network model to obtain a deployment action prediction score output by the strategy network model. The strategy network model is then trained based on the deployment action target scores of multiple sensor deployment scheme samples from various sample chemical workshops, and the corresponding deployment action prediction scores, to obtain a sensor deployment model. Finally, the environmental characteristics of the actual chemical workshop of the target chemical plant, the required combustible gas types, chemical equipment characteristic samples, and multiple sensor deployment scheme samples are input into a strategy network model to obtain a deployment action prediction score. The types of flammable gases to be detected and the characteristics of chemical equipment are input into the sensor deployment model to obtain the target deployment scheme with the highest deployment action prediction score. Since the better the detection effect of the sensor deployment scheme sample, the higher the target score of the deployment action, the sensor deployment model trained can output a target deployment scheme with good detection effect based on the environmental characteristics of the actual chemical workshop, the types of flammable gases to be detected, and the characteristics of chemical equipment. This allows users to deploy sensors in the actual chemical workshop according to the target deployment scheme, thereby improving the real-time concentration safety monitoring effect of flammable gases in the actual chemical workshop.
[0096] In a feasible embodiment, the deployment action target score is obtained through the following steps:
[0097] S301: Obtain the target cost, actual cost, and detection result parameters of the sensors corresponding to the multiple sensor deployment scheme samples;
[0098] S302: Based on the target cost, the actual cost, and the detection result parameters, obtain the deployment action target score for each sensor deployment scheme sample.
[0099] In a feasible embodiment, S302: the step of obtaining the deployment action target score of each sensor deployment scheme sample based on the target cost, the actual cost, and the detection result parameters includes:
[0100] S3021: Based on the target cost and the actual cost, obtain the deployment action cost score;
[0101] S3022: Obtain the detection score based on the detection result parameters;
[0102] S3023: Based on the deployment action cost score and the detection score, obtain the deployment action target score.
[0103] In one feasible embodiment, the detection result parameters include detection accuracy and detection efficiency;
[0104] S3022: The step of obtaining a detection score based on the detection result parameters includes:
[0105] S30221: Obtain an accuracy score based on the aforementioned detection accuracy;
[0106] S30222: Obtain an efficiency score based on the detection efficiency;
[0107] S30223: The detection score is obtained based on the accuracy score and the efficiency score.
[0108] In a feasible embodiment, step S3: training the strategy network model to obtain the sensor deployment model based on the deployment action target scores of multiple sensor deployment scheme samples from various sample chemical workshops and the corresponding deployment action prediction scores, includes:
[0109] S31: Construct a loss function based on the deployment action target scores of various sample chemical workshops and the corresponding deployment action prediction scores, and obtain the function output of the loss function;
[0110] S32: Update the network parameters of the policy network model according to the output of the function to obtain the trained policy network model.
[0111] It should be noted that the chemical plant safety supervision method based on combustible gas concentration detection provided in the second embodiment of this application and the chemical plant safety supervision system based on combustible gas concentration detection in the first embodiment of this application belong to the same concept. The implementation process is detailed in the first embodiment and will not be repeated here.
[0112] The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.
[0113] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0114] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function selected in one or more boxes.
[0115] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function selected in one or more boxes.
[0116] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0117] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0118] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0119] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0120] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A chemical plant safety monitoring system based on combustible gas concentration detection, characterized in that, include: The sample acquisition module is used to acquire environmental characteristic samples of the chemical workshop, samples of the types of combustible gases to be detected, samples of chemical equipment characteristics, and samples of multiple sensor deployment schemes. The prediction action score acquisition module is used to input the environmental characteristic samples of the sample chemical workshop, the samples of combustible gas types to be detected, the chemical equipment characteristic samples, and multiple sensor deployment scheme samples into the strategy network model to obtain the deployment action prediction score output by the strategy network model. The model training module is used to train the strategy network model based on the deployment action target scores of multiple sensor deployment scheme samples in various sample chemical workshops, and the corresponding deployment action prediction scores, to obtain a sensor deployment model; wherein, the better the detection effect of the sensor deployment scheme samples, the higher the deployment action target score. The target deployment scheme acquisition module is used to input the environmental characteristics of the physical chemical workshop of the target chemical plant, the types of combustible gases to be detected, and the characteristics of chemical equipment into the sensor deployment model to obtain the target deployment scheme with the highest deployment action prediction score. The gas concentration safety monitoring module is used to monitor the real-time concentration of flammable gases in the physical chemical workshop through several sensors deployed based on the target deployment scheme.
2. The chemical plant safety monitoring system based on combustible gas concentration detection according to claim 1, characterized in that, Also includes: The scheme parameter acquisition module is used to acquire the target cost, actual cost, and detection result parameters of the sensors corresponding to the multiple sensor deployment scheme samples. The target action score acquisition module is used to obtain the deployment action target score of each sensor deployment scheme sample based on the target cost, the actual cost and the detection result parameters.
3. The chemical plant safety monitoring system based on combustible gas concentration detection according to claim 2, characterized in that, The steps for obtaining the deployment action target score for each sensor deployment scheme sample based on the target cost, the actual cost, and the detection result parameters include: Based on the target cost and the actual cost, a deployment cost score is obtained; Based on the detection result parameters, a detection score is obtained; The deployment action target score is obtained based on the deployment action cost score and the detection score.
4. The chemical plant safety monitoring system based on combustible gas concentration detection according to claim 3, characterized in that, The detection result parameters include detection accuracy and detection efficiency; The steps for obtaining a detection score based on the detection result parameters include: An accuracy score is obtained based on the accuracy of the detection. An efficiency score is obtained based on the detection efficiency. The detection score is obtained based on the accuracy score and the efficiency score.
5. The chemical plant safety monitoring system based on combustible gas concentration detection according to claim 1, characterized in that, The step of training the strategy network model to obtain the sensor deployment model based on the deployment action target scores of multiple sensor deployment scheme samples from various sample chemical workshops and the corresponding deployment action prediction scores includes: A loss function is constructed based on the target scores of deployment actions in various sample chemical workshops and the corresponding predicted scores of deployment actions, and the output of the loss function is obtained. The network parameters of the policy network model are updated according to the output of the function to obtain the trained policy network model.
6. A method for safety supervision of chemical plants based on the detection of combustible gas concentration, characterized in that, include: Obtain environmental characteristic samples of the sample chemical workshop, samples of the types of combustible gases to be detected, samples of chemical equipment characteristics, and samples of multiple sensor deployment schemes; The environmental characteristics of the sample chemical workshop, the types of combustible gases to be detected, the characteristics of chemical equipment, and multiple sensor deployment schemes are input into the strategy network model to obtain the deployment action prediction score output by the strategy network model. The strategy network model is trained based on the deployment action target scores of multiple sensor deployment scheme samples in various chemical workshops and the corresponding deployment action prediction scores to obtain the sensor deployment model; wherein, the better the detection effect of the sensor deployment scheme samples, the higher the deployment action target score. The environmental characteristics of the actual chemical workshop of the target chemical plant, the types of combustible gases to be detected, and the characteristics of the chemical equipment are input into the sensor deployment model to obtain the target deployment scheme with the highest deployment action prediction score. By deploying several sensors based on the target deployment scheme, the real-time concentration safety monitoring of flammable gases in the physical chemical workshop is carried out.
7. The method for safety supervision of chemical plants based on the detection of combustible gas concentration according to claim 6, characterized in that, The target score for the deployment action is obtained through the following steps: Obtain the target cost, actual cost, and detection result parameters of the sensors corresponding to the multiple sensor deployment scheme samples; Based on the target cost, the actual cost, and the detection result parameters, the deployment action target score for each sensor deployment scheme sample is obtained.
8. The method for safety supervision of chemical plants based on the detection of combustible gas concentration according to claim 7, characterized in that, The steps for obtaining the deployment action target score for each sensor deployment scheme sample based on the target cost, the actual cost, and the detection result parameters include: Based on the target cost and the actual cost, a deployment cost score is obtained; Based on the detection result parameters, a detection score is obtained; The deployment action target score is obtained based on the deployment action cost score and the detection score.
9. The method for safety supervision of chemical plants based on the detection of combustible gas concentration according to claim 8, characterized in that, The detection result parameters include detection accuracy and detection efficiency; The steps for obtaining a detection score based on the detection result parameters include: An accuracy score is obtained based on the accuracy of the detection. An efficiency score is obtained based on the detection efficiency. The detection score is obtained based on the accuracy score and the efficiency score.
10. The method for safety supervision of chemical plants based on the detection of combustible gas concentration according to claim 6, characterized in that, The step of training the strategy network model to obtain the sensor deployment model based on the deployment action target scores of multiple sensor deployment scheme samples from various sample chemical workshops and the corresponding deployment action prediction scores includes: A loss function is constructed based on the target scores of deployment actions in various sample chemical workshops and the corresponding predicted scores of deployment actions, and the output of the loss function is obtained. The network parameters of the policy network model are updated according to the output of the function to obtain the trained policy network model.