Greenhouse gas emission early warning equipment based on multi-mode perception

Abstract

The invention provides greenhouse gas emission early warning equipment based on multi-mode perception, and relates to the technical field of data processing, a greenhouse gas emission type determining assembly determines the type of greenhouse gas emitted by a target coal chemical plant, and a gas concentration sensing assembly obtains a gas concentration evaluation factor according to the type of the greenhouse gas and performs early warning according to the gas concentration evaluation factor. The gas particle sensing assembly obtains gas particle evaluation factors, the air pressure environment sensing assembly generates air pressure environment evaluation factors, the gas emission evaluation assembly inputs the received evaluation factors into the greenhouse gas emission evaluation-predictor, the data judgment assembly judges output data, and according to the judgment result, the greenhouse gas emission evaluation-predictor judges the output data. Greenhouse gas emission of the target coal chemical plant is managed. The technical problem that the environment pollution risk is increased due to the fact that a greenhouse gas emission early warning mechanism in the prior art cannot monitor and early warn in time is solved. The technical effects that greenhouse gas emission is monitored in real time, abnormal conditions are early warned in time, the emergency response speed is increased, and the environmental pollution risk is reduced are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a greenhouse gas emission early warning device based on multimodal perception. Background Art

[0002] The traditional greenhouse gas emission early warning mechanism mainly relies on regular monitoring and subsequent data analysis. The inherent time lag of this model makes it difficult for coal-to-chemical plants to capture and respond immediately to sudden abnormalities in emissions. Once greenhouse gas emissions exceed the safety threshold or emissions are abnormal, early warning signals cannot be issued in time, resulting in insufficient emergency response, affecting the timeliness and effectiveness of response measures. This lag also makes it difficult for coal-to-chemical plants to adjust production plans in a timely manner to reduce greenhouse gas emissions, increasing the risk of environmental pollution.

[0003] The greenhouse gas emission early warning mechanism in the existing technology is unable to monitor and warn in a timely manner, resulting in technical problems such as increased environmental pollution risks. Summary of the invention

[0004] The present application provides a greenhouse gas emission warning device based on multimodal perception, which is used to solve the technical problem that the greenhouse gas emission warning mechanism in the prior art cannot monitor and warn in a timely manner, resulting in an increased risk of environmental pollution.

[0005] In view of the above problems, the present application provides a greenhouse gas emission warning device based on multimodal perception, the device comprising: a greenhouse gas emission type determination component, located in the data processing area of ​​the greenhouse gas emission warning device, communicating with the production process management system, and used to determine the type of greenhouse gas emitted by the target coal chemical plant according to the production process characteristics of the target coal chemical plant; a gas concentration sensing component, located at the front end or side of the greenhouse gas emission warning device, communicating with the greenhouse gas emission type determination component, and used to receive and obtain a gas concentration evaluation factor according to the greenhouse gas type; a gas particle sensing component, adjacent to the gas concentration sensing component, and obtaining a gas particle evaluation factor; an air pressure environment sensing component A component, located at the top of the greenhouse gas emission early warning device, is used to generate an air pressure environment evaluation factor; a gas emission assessment component, located in the data processing area of ​​the greenhouse gas emission early warning device, is communicated with the gas concentration sensing component, the gas particle sensing component and the air pressure environment sensing component, and uses the received gas concentration evaluation factor, the gas particle evaluation factor and the air pressure environment evaluation factor as input data, inputs them into a greenhouse gas emission assessment-predictor, and obtains output data; a data judgment component, located in the management center of the greenhouse gas emission early warning device, is used to receive the output data of the gas emission assessment component and judge, and manage the greenhouse gas emissions of the target coal chemical plant according to the judgment result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The greenhouse gas emission type determination component is connected to the production process management system and is used to determine the greenhouse gas type emitted by the target coal chemical plant according to the production process characteristics of the target coal chemical plant; the gas concentration sensing component is connected to the greenhouse gas emission type determination component and obtains the gas concentration evaluation factor according to the greenhouse gas type; the gas particle sensing component is adjacent to the gas concentration sensing component and obtains the gas particle evaluation factor; the air pressure environment sensing component is used to generate the air pressure environment evaluation factor; the gas emission assessment component uses the received gas concentration evaluation factor, gas particle evaluation factor and air pressure environment evaluation factor as input data, inputs the greenhouse gas emission assessment-predictor, and obtains output data; the data judgment component is used to receive the output data of the gas emission assessment component and judge, and manage the greenhouse gas emissions of the target coal chemical plant according to the judgment result. The technical effect of real-time monitoring of greenhouse gas emissions, timely warning of abnormal conditions, improving emergency response speed, and reducing environmental pollution risks has been achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of the structure of the greenhouse gas emission warning device based on multimodal sensing provided in this application;

[0010] Figure 2 A schematic diagram of the execution content of the gas emission assessment component in the greenhouse gas emission warning device based on multimodal perception provided in this application.

[0011] Explanation of the reference numerals: greenhouse gas emission type determination component 11, gas concentration sensing component 12, gas particle sensing component 13, air pressure environment sensing component 14, gas emission assessment component 15, data judgment component 16. DETAILED DESCRIPTION

[0012] This application provides a greenhouse gas emission warning device based on multimodal perception, which is used to solve the technical problem that the greenhouse gas emission warning mechanism in the prior art cannot monitor and warn in time, resulting in increased environmental pollution risks. It achieves the technical effect of real-time monitoring of greenhouse gas emissions, timely warning of abnormal situations, improving emergency response speed, and reducing environmental pollution risks.

[0013] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0014] like Figure 1 As shown, the present application provides a greenhouse gas emission early warning device based on multimodal perception, the device comprising:

[0015] The greenhouse gas emission type determination component is located in the data processing area of ​​the greenhouse gas emission early warning device, is communicated with the production process management system, and is used to determine the type of greenhouse gas emitted by the target coal chemical plant based on the production process characteristics of the target coal chemical plant.

[0016] Specifically, the greenhouse gas emission type determination component is located in the data processing area of ​​the greenhouse gas emission early warning device, which can ensure that the data can be effectively identified and communicated with the production process management system. The production process management system is a system used in the factory to monitor and control the production process. It will continuously monitor the entire production process of the target coal chemical plant, including specific production process stages, raw materials used, and combustion process related content. The greenhouse gas emission type determination component obtains these process data in real time, analyzes the production process characteristics, and determines the main greenhouse gas types emitted in the current production stage, including carbon dioxide, methane, etc. For example, when the production process management system monitors that carbon-containing raw materials are used in the production process and the combustion process is involved in the reaction process, it can be identified that the greenhouse gases emitted mainly contain carbon dioxide. When the process of organic matter decomposition or anaerobic fermentation is found, methane is judged as the main emission gas. By communicating with the production process management system, the greenhouse gas emission type determination component can also dynamically adjust the determination of greenhouse gas types according to real-time data. For example, when the process changes, such as from a combustion process to a cooling or chemical reaction stage, the greenhouse gas type is automatically updated to monitor the new gas emission type. Through close integration with the production process management system, the greenhouse gas emission type determination component can monitor and determine the types of greenhouse gases emitted by the target coal chemical plant at different stages in real time, achieving efficient and accurate gas emission monitoring.

[0017] The gas concentration sensing component is located at the front end or side of the greenhouse gas emission warning device, and is connected to the greenhouse gas emission type determination component for receiving and obtaining a gas concentration evaluation factor based on the greenhouse gas type.

[0018] Furthermore, the gas concentration sensing component obtains a gas concentration evaluation factor according to the greenhouse gas type, including: determining an environmental gas detection sensor according to the greenhouse gas type, the environmental gas detection sensor including a carbon dioxide sensor and a methane sensor; installing the environmental gas detection sensor according to the geographical location of the target coal chemical plant in combination with wind direction, wind speed and pollution source distribution; collecting gas concentration data of the target greenhouse gas through the environmental gas detection sensor to obtain greenhouse gas concentration data; and performing statistical analysis based on the greenhouse gas concentration data to obtain the gas concentration evaluation factor.

[0019] Specifically, the gas concentration sensing component is located at the front end or side of the greenhouse gas emission warning device, which can ensure that the component is close to the emission source area of ​​the target coal chemical plant, and can collect the target greenhouse gas concentration data in the atmosphere in a timely and accurate manner. The gas concentration sensing component is connected to the greenhouse gas emission type determination component in communication, and can select a suitable environmental gas detection sensor according to the gas type determined in the production process. First, the gas concentration sensing component receives the greenhouse gas type provided by the greenhouse gas emission type determination component, and then determines the environmental gas detection sensor according to the greenhouse gas type. The environmental gas detection sensor is used to detect the greenhouse gas concentration, including a carbon dioxide sensor, a methane sensor, etc. Then, according to the geographical location of the target coal chemical plant, the distribution of emission sources, and environmental characteristics such as wind direction and wind speed, the deployment location of the environmental gas detection sensor is determined to ensure the detection accuracy. The environmental detection sensor is installed according to the determined deployment location. For example, according to the geographical location of the target coal chemical plant, combined with the wind direction, wind speed and emission source distribution, it is determined that the environmental gas detection sensor is installed in the emission chimney, vent or downwind area to ensure that the actual gas concentration emitted by the factory can be obtained. Through these sensors, the concentration data of greenhouse gases in the atmosphere can be collected in real time. After data collection, the gas concentration sensing component performs statistical analysis on the collected greenhouse gas concentration data and calculates the gas concentration evaluation factor of the greenhouse gas. The gas concentration evaluation factor is used to represent the concentration level of the emission gas and is obtained by calculating the average and maximum values ​​of the greenhouse gas concentration data. The gas concentration sensing component collects and analyzes gas concentration data in real time through linkage with the greenhouse gas emission type determination component, and finally generates a gas concentration evaluation factor, providing accurate and reliable basic data for greenhouse gas emission assessment.

[0020] The gas particle sensing component is adjacent to the gas concentration sensing component and obtains a gas particle evaluation factor.

[0021] Furthermore, a gas particle sensing component obtains a gas particle evaluation factor, including: installing a multi-spectral scattering sensor and a miniature air pressure sensor at key positions of a target coal chemical plant, wherein the key positions of the target coal chemical plant include emission chimneys, vents or downwind areas; obtaining light scattering intensity data of multiple wavelengths according to the multi-spectral scattering sensor; generating gas particle characteristic parameters based on the light scattering intensity data, wherein the gas particle characteristic parameters include shape, density and refractive index; and obtaining the gas particle evaluation factor according to the characteristic parameters.

[0022] Specifically, the gas particle sensing component is located adjacent to the gas concentration sensing component. The gas particle sensing component includes a multi-spectral scattering sensor. By installing the gas particle sensing component adjacent to the gas concentration sensing component, that is, the key position of the target coal chemical plant, such as the emission chimney, vents or downwind area, the multi-spectral scattering sensor in the gas particle sensing component can be used to collect the light scattering intensity data generated when multi-wavelength light passes through particulate matter. The multi-spectral scattering sensor can emit light of different wavelengths. When these lights encounter particulate matter in the gas, they will scatter. The scattering intensities of light of different wavelengths are different, and light scattering intensity data of multiple wavelengths are collected. Furthermore, by analyzing the light scattering intensity data, characteristic parameters of gas particles in terms of shape, density, and refractive index, that is, characteristic parameters of gas particles, are obtained. Then, the gas particle characteristic parameters are comprehensively analyzed, and the gas particle sensing component can generate a gas particle evaluation factor. For example, weights are assigned to different characteristic parameters. The weight assignment is determined according to the degree of influence of the particle characteristics on the environment, that is, the shape, density and refractive index are weighted according to the actual situation, and then the standardized characteristic parameters are weighted according to the assigned weights to generate the weighted value of each characteristic parameter. The weighted characteristic parameter values ​​will be further integrated into a gas particle evaluation factor to intuitively display the degree of influence of particulate matter in greenhouse gases on the environment. It provides important data support for subsequent greenhouse gas emission assessment and early warning.

[0023] Furthermore, based on the light scattering intensity data, characteristic parameters of gas particles are generated, and the characteristic parameters of gas particles include shape, density and refractive index, including: obtaining shape information of particles by analyzing the angular distribution of the light scattering intensity data; obtaining size information of particles by analyzing the intensity of the light scattering intensity data; obtaining the refractive index of particles by analyzing the changes in light intensity of different wavelengths and combining the refractive index range of industrial particles; and normalizing the shape information, size information and refractive index to obtain the characteristic parameters of gas particles.

[0024] Specifically, when light passes through particles, it will produce different scattering effects at different angles according to the shape of the particles. By analyzing the angular distribution of the light scattering intensity data, the shape information of the particles can be obtained. More symmetrical particles (such as spherical particles) will produce consistent scattering distribution at specific angles, while irregularly shaped particles will cause fluctuations in the angular distribution. Then, since the light intensity is closely related to the diameter and volume of the particles. By analyzing the intensity of the light scattering intensity data, the size information of the particles can be obtained. For example, larger particles will produce stronger light scattering signals, while smaller particles will have lower scattering intensity. By measuring the light scattering intensity at different wavelengths, the size information of the particles can be obtained. The settling speed of the particles in the atmosphere and the impact on the environment can be evaluated by the size of the particles. Then, the refractive index of the particles is calculated for the changes in the light intensity of different wavelengths of light. The refractive index reflects the ability of the particles to refract or reflect light, and the refractive index of particles of different materials is different. By comparing the changing trends of light scattering at different wavelengths, combined with the known refractive index range of industrial particles, the refractive index of the particles can be calculated, and the composition of the particles can be judged according to the refractive index. Finally, the shape information, size information and refractive index obtained by the above calculations are normalized and integrated into unified gas particle characteristic parameters, which provide a basis for the subsequent generation of gas particle evaluation factors. The purpose of normalization is to convert characteristic parameters of different scales and different physical quantities into unified standard values ​​so that they can be compared and analyzed in the same calculation model. This process ensures that the characteristics of particulate matter can be accurately identified and quantified, thereby providing effective support for environmental monitoring and gas emission assessment.

[0025] The air pressure environment sensing component, located on top of the greenhouse gas emission warning device, is used to generate the air pressure environment evaluation factor.

[0026] Furthermore, the air pressure environment sensing component generates an air pressure environment evaluation factor, including: collecting atmospheric pressure data according to a micro air pressure sensor; performing time series analysis on the atmospheric pressure data to construct an air pressure change curve; obtaining an air pressure mean, an air pressure change rate and an air pressure anomaly value according to the air pressure change curve; and obtaining the air pressure environment evaluation factor based on the air pressure mean, the air pressure change rate and the air pressure anomaly value.

[0027] Specifically, the air pressure environment sensing component is located at the top of the greenhouse gas emission warning device, ensuring that the air pressure environment sensing component can accurately monitor the air pressure changes in the atmospheric environment around the coal chemical plant. The air pressure environment sensing component includes a micro air pressure sensor. By installing the micro air pressure sensor at a high place, it can ensure that the air pressure sensor is not interfered by the ground, and can accurately collect the atmospheric pressure data around the target coal chemical plant in real time. After collecting continuous atmospheric pressure data, the atmospheric pressure data is subjected to time series analysis. Time series analysis is a technology for processing and analyzing data that changes over time. By mining the long-term change pattern of the data, the fluctuation trend of air pressure over time can be identified. Using the data analysis platform, according to the results of the time series analysis, an air pressure change curve is constructed to show the changes in atmospheric pressure in different periods of time. Furthermore, based on the air pressure change curve, key parameters are extracted. The key parameters include the mean air pressure, the air pressure change rate and the air pressure anomaly. The mean air pressure reflects the overall level of atmospheric pressure during the period, which can help evaluate the basic impact of air pressure on greenhouse gas emissions under normal circumstances. By analyzing the speed of change of air pressure between different time periods, the air pressure change rate is calculated. A higher change rate indicates rapid fluctuations in atmospheric pressure, which may accelerate the diffusion of greenhouse gases or affect their residence time in the atmosphere. Through time series analysis, it is determined whether there are outliers in the air pressure data, and outliers also have a direct impact on the diffusion of greenhouse gases. Finally, based on the above-extracted air pressure mean, air pressure change rate, and air pressure anomalies, a comprehensive air pressure environment evaluation factor is generated. The air pressure environment evaluation factor reflects the overall impact of the current atmospheric pressure on greenhouse gas emissions and diffusion, ensuring that the greenhouse gas emission early warning equipment not only monitors greenhouse gas emissions themselves, but also fully considers the impact of atmospheric pressure on emissions, thereby improving the accuracy and reliability of subsequent greenhouse gas assessments.

[0028] The gas emission assessment component is located in the data processing area of ​​the greenhouse gas emission warning device, is communicated with the gas concentration sensing component, the gas particle sensing component and the air pressure environment sensing component, and receives the gas concentration evaluation factor, the gas particle evaluation factor and the air pressure environment evaluation factor as input data, inputs them into the greenhouse gas emission assessment-predictor, and obtains output data.

[0029] Specifically, the gas emission assessment component is located in the data processing area of ​​the greenhouse gas emission warning device. It is the core analysis and calculation unit, responsible for comprehensive processing of the evaluation factors obtained from different sensing components (gas concentration sensing component, gas particle sensing component, and air pressure environment sensing component), and passing these data as input to the greenhouse gas emission assessment-predictor for further evaluation and prediction. The gas emission assessment component first receives data from these components through communication connections with the gas concentration sensing component, the gas particle sensing component, and the air pressure environment sensing component. The data includes gas concentration evaluation factors, gas particle evaluation factors, and air pressure environment evaluation factors, which reflect the status of greenhouse gas emissions in different dimensions. The gas concentration evaluation factor, gas particle evaluation factor, and air pressure environment evaluation factor are input into the greenhouse gas emission assessment-predictor, which includes two parallel channels: an evaluation channel and a prediction channel. The two channels of the greenhouse gas emission assessment-predictor analyze and process the input data and generate output data. The gas emission assessment component receives, integrates and processes the evaluation factors collected by different sensors, and inputs these data into the greenhouse gas emission assessment-predictor for analysis. It can accurately assess the current emission level and predict future emission trends, ensure the accuracy of greenhouse gas emission early warning equipment, and provide coal chemical plants with accurate emission management basis.

[0030] In one embodiment, Figure 2 As shown, the gas emission assessment component receives the gas concentration evaluation factor, the gas particle evaluation factor and the air pressure environment evaluation factor as input data, inputs them into a greenhouse gas emission assessment-predictor, and obtains output data, including: collecting historical emission data of the target coal chemical plant, the historical emission data including gas concentration, particulate matter characteristics and air pressure data; obtaining historical evaluation factors based on the historical emission data, the historical evaluation factors including historical gas concentration evaluation factors, historical gas particle evaluation factors and historical air pressure environment evaluation factors; training a linear regression model based on the historical gas concentration evaluation factors, historical gas particle evaluation factors and historical air pressure environment evaluation factors to obtain an evaluation channel; training a time series model based on the historical gas concentration evaluation factors, historical gas particle evaluation factors and historical air pressure environment evaluation factors to obtain a prediction channel; configuring the evaluation channel and the prediction channel in parallel to construct a greenhouse gas emission assessment-predictor.

[0031] Specifically, the historical emission data of the target coal chemical plant are extracted from the environmental monitoring record library. These data come from multiple sensing components, including gas concentration sensing components, gas particle sensing components and air pressure environment sensing components. The specific historical emission data include gas concentration data, particulate matter characteristic data and air pressure data obtained from long-term monitoring. The historical emission data are processed and analyzed to obtain different historical evaluation factors, including historical gas concentration evaluation factors that reflect the trend of historical greenhouse gas concentration changes and emission characteristics, historical gas particle evaluation factors that describe the physical properties of historical particulate matter, and historical air pressure environment evaluation factors that reflect the impact of atmospheric pressure changes on gas diffusion and emissions.

[0032] Next, the linear regression model is used to establish the evaluation channel. The linear regression model evaluates the greenhouse gas emission level by inputting historical evaluation factors (including historical gas concentration evaluation, historical gas particle evaluation factor and historical air pressure environment evaluation factor). Based on the characteristics of historical data, the linear regression model calculates the contribution of each evaluation factor to the current emission, so that the current emission level of the coal chemical plant can be evaluated in real time. Based on the historical evaluation factors, the time series model is trained. Time series models such as ARIMA and LSTM can learn the changing laws of past data through the analysis of historical gas concentration evaluation, historical gas particle evaluation factor and historical air pressure environment evaluation factor, and make predictions for future emissions based on these laws to obtain the prediction channel. After the evaluation channel and the prediction channel are established, the two channels are configured in parallel to build a complete greenhouse gas emission assessment-predictor. Through parallel configuration, the current emission level can be evaluated and the future emission trend can be predicted at the same time, ensuring that the coal chemical plant can not only cope with the current emission situation, but also prepare in advance for the possible risk of excessive emissions in the future. The parallel operation of the evaluation channel and the prediction channel improves the response speed and prediction accuracy of the equipment, thereby enabling the coal chemical plant to provide efficient and accurate emission warning and management.

[0033] Furthermore, the gas emission assessment component receives the received gas concentration evaluation factor, the gas particle evaluation factor and the air pressure environment evaluation factor as input data, inputs them into a greenhouse gas emission assessment-predictor, and obtains output data, and also includes: preprocessing the gas concentration evaluation factor, the gas particle evaluation factor and the air pressure environment evaluation factor, the preprocessing including denoising, de-noising and normalization; inputting the preprocessed gas concentration evaluation factor, the gas particle evaluation factor and the air pressure environment evaluation factor into the greenhouse gas emission assessment-predictor; the assessment channel of the greenhouse gas emission assessment-predictor obtains a greenhouse gas emission assessment result based on the input data, and the prediction channel of the greenhouse gas emission assessment-predictor obtains a greenhouse gas emission prediction result based on the input data; and the output data is obtained based on the greenhouse gas emission assessment result and the greenhouse gas emission prediction result.

[0034] Specifically, the data obtained from the gas concentration sensing component, the gas particle sensing component and the air pressure environment sensing component are first preprocessed, including denoising, de-noising and normalizing the gas concentration evaluation factor, the gas particle evaluation factor and the air pressure environment evaluation factor. The denoising refers to eliminating random noise in the data, which can be achieved by moving average method or filtering technology. De-noising refers to removing the existing outliers or extreme data points during the preprocessing process, which can be performed by Z score method. Normalization is to convert the values ​​of each factor to the same scale so that they can be fairly compared and analyzed in the model calculation. The preprocessed gas concentration evaluation factor, gas particle evaluation factor and air pressure environment evaluation factor are input into the greenhouse gas emission assessment-predictor. The two parallel channels of the greenhouse gas emission assessment-predictor analyze the input data respectively. The evaluation channel generates a greenhouse gas emission evaluation result by analyzing the current input data, reflecting the greenhouse gas emission level of the coal chemical plant at the current moment. The prediction channel uses a time series model to process the input data to generate a greenhouse gas emission prediction result to help the plant foresee possible excessive emission risks in the future. Finally, the greenhouse gas emission assessment-predictor integrates the output results of the two channels to generate output data, which consists of two parts: greenhouse gas emission assessment results and greenhouse gas emission prediction results. The quality of the input data can be ensured by denoising, removing noise and normalizing the environmental evaluation factors of gas concentration, particulate matter and air pressure. On this basis, after processing by the greenhouse gas emission assessment-predictor, accurate assessment results and prediction results are generated, which can not only realize real-time monitoring of current emission levels, but also effectively predict future emission trends, provide early warnings and support coal chemical plants to take preventive management measures.

[0035] The data judgment component is located in the management center of the greenhouse gas emission early warning device, and is used to receive and judge the output data of the gas emission assessment component, and manage the greenhouse gas emissions of the target coal chemical plant according to the judgment result.

[0036] Furthermore, the data judgment component manages the greenhouse gas emissions of the target coal chemical plant according to the judgment result, including: if the greenhouse gas emission assessment result is greater than or equal to the preset greenhouse gas emission threshold, a first-level alarm signal is triggered, and the first-level alarm signal includes a siren, a red light, and a text message reminder; if the greenhouse gas emission prediction result is greater than or equal to the preset greenhouse gas emission threshold, a second-level alarm signal is triggered, and the second-level alarm signal includes a blue light and an email reminder; if the greenhouse gas emission assessment result and the greenhouse gas emission prediction result are both greater than or equal to the preset greenhouse gas emission threshold, a first-level alarm signal is triggered; according to the alarm signal, the greenhouse gas emissions of the target coal chemical plant are managed.

[0037] Specifically, the data judgment component is located in the management center of the greenhouse gas emission early warning equipment. Its main function is to receive the output data from the gas emission assessment component, make real-time judgments based on these data, promptly warn of abnormal situations, and improve emergency response speed, thereby effectively managing and controlling the greenhouse gas emissions of the target coal chemical plant and reducing environmental pollution risks.

[0038] The data judgment component receives the output data of the evaluation and prediction in real time through the communication connection with the gas emission assessment component. After obtaining the output data, the data judgment component compares and judges the received evaluation results and prediction results according to the preset greenhouse gas emission threshold, that is, the maximum emission level allowed. If the greenhouse gas emission assessment result is greater than or equal to the preset emission threshold, it means that the current emission has exceeded the standard, and the first-level alarm signal is immediately triggered. In addition, if the greenhouse gas emission assessment result and the greenhouse gas emission prediction result are both greater than or equal to the preset greenhouse gas emission threshold, it means that both current and future emissions exceed the standard, and the first-level alarm signal is also triggered. If the greenhouse gas emission prediction result is greater than or equal to the preset greenhouse gas emission threshold, it means that there may be a risk of exceeding the emission standard in the future, and the second-level alarm signal is triggered. Among them, the first-level alarm signal represents an urgent and serious emission exceeding the standard, which needs to be handled immediately, including sirens, red lights, and text message reminders, and the second-level alarm signal is used to warn of potential risks in the future, including blue lights and email reminders. Finally, according to the alarm signal, the greenhouse gas emissions of the target coal chemical plant are managed. For example, for the first-level alarm, emergency response measures are taken, such as adjusting the production plan, reducing or suspending high-emission processes, and opening spare emission control devices, such as adsorption devices and filtration systems; for the second-level alarm, preventive measures are taken, such as optimizing the production plan, fine-tuning process parameters, and reducing emission intensity. All alarm events, treatment measures and results are recorded to form a complete log for subsequent analysis and regulatory review, and continuous monitoring is carried out. The data judgment component continuously receives new evaluation and prediction data, and executes judgment logic in a loop to ensure real-time monitoring of emissions. The data judgment component triggers the corresponding level of alarm signals in time through intelligent analysis of the evaluation and prediction results, and guides the coal chemical plant to take appropriate management measures. By distinguishing different levels of alarms and improving the emergency response speed, it can effectively respond to current emergencies and potential risks in the future, ensure that greenhouse gas emissions are always controlled within a reasonable range, and reduce environmental pollution risks.

[0039] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0040] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A greenhouse gas emission early warning device based on multimodal sensing, characterized in that: The device comprises: A greenhouse gas emission type determination component is located in the data processing area of ​​the greenhouse gas emission early warning device, is connected to the production process management system, and is used to determine the type of greenhouse gas emitted by the target coal chemical plant according to the production process characteristics of the target coal chemical plant; A gas concentration sensing component is located at the front end or side of the greenhouse gas emission warning device, and is connected to the greenhouse gas emission type determination component for receiving and obtaining a gas concentration evaluation factor according to the greenhouse gas type; A gas particle sensing component, adjacent to the gas concentration sensing component, obtains a gas particle evaluation factor; The air pressure environment sensing component is located on top of the greenhouse gas emission warning device and is used to generate the air pressure environment evaluation factor; The gas emission assessment component is located in the data processing area of ​​the greenhouse gas emission early warning device, is in communication with the gas concentration sensing component, the gas particle sensing component and the air pressure environment sensing component, and uses the received gas concentration evaluation factor, the gas particle evaluation factor and the air pressure environment evaluation factor as input data, and inputs them into a greenhouse gas emission assessment-predictor to obtain output data; The data judgment component is located in the management center of the greenhouse gas emission early warning device, and is used to receive and judge the output data of the gas emission assessment component, and manage the greenhouse gas emissions of the target coal chemical plant according to the judgment result.

2. The greenhouse gas emission warning device based on multimodal sensing according to claim 1, characterized in that: The gas concentration sensing component obtains a gas concentration evaluation factor according to the greenhouse gas type, including: Determine an environmental gas detection sensor according to the type of greenhouse gas, wherein the environmental gas detection sensor includes a carbon dioxide sensor and a methane sensor; According to the geographical location of the target coal chemical plant, combined with wind direction, wind speed and emission source distribution, the environmental gas detection sensor is installed; Collecting gas concentration data of target greenhouse gas through the environmental gas detection sensor to obtain greenhouse gas concentration data; Based on the greenhouse gas concentration data, statistical analysis is performed to obtain the gas concentration evaluation factor.

3. The greenhouse gas emission warning device based on multimodal sensing according to claim 1, characterized in that: Gas particle sensing component, obtains gas particle evaluation factors, including: Installing a multi-spectral scattering sensor and a micro air pressure sensor at key locations of a target coal chemical plant, wherein the key locations of the target coal chemical plant include an emission chimney, a vent or a downwind area; According to the multi-spectral scattering sensor, light scattering intensity data of multiple wavelengths are obtained; generating gas particle characteristic parameters based on the light scattering intensity data, wherein the gas particle characteristic parameters include shape, density and refractive index; The gas particle evaluation factor is obtained according to the gas particle characteristic parameter.

4. The greenhouse gas emission warning device based on multimodal sensing according to claim 3 is characterized in that: Based on the light scattering intensity data, gas particle characteristic parameters are generated, wherein the gas particle characteristic parameters include shape, density and refractive index, including: By analyzing the angular distribution of the light scattering intensity data, the shape information of the particles is obtained; By analyzing the intensity of the light scattering intensity data, the size information of the particles is obtained; By analyzing the changes in light intensity at different wavelengths and combining the refractive index range of industrial particles, the refractive index of particles can be obtained; The shape information, size information and refractive index are normalized to obtain the characteristic parameters of the gas particles.

5. The greenhouse gas emission warning device based on multimodal sensing according to claim 1, characterized in that: The air pressure environment perception component generates air pressure environment evaluation factors, including: Collect atmospheric pressure data based on micro air pressure sensor; Performing time series analysis on the atmospheric pressure data to construct an atmospheric pressure change curve; According to the air pressure change curve, an air pressure mean value, an air pressure change rate and an air pressure abnormal value are obtained; The air pressure environment evaluation factor is obtained based on the air pressure mean value, the air pressure change rate and the air pressure anomaly value.

6. The greenhouse gas emission warning device based on multimodal sensing according to claim 1, characterized in that: The gas emission assessment component receives the received gas concentration assessment factor, the gas particle assessment factor and the air pressure environment assessment factor as input data, inputs them into a greenhouse gas emission assessment-predictor, and obtains output data, including: Collect historical emission data of the target coal chemical plant, wherein the historical emission data includes gas concentration, particulate matter characteristics and air pressure data; According to the historical emission data, a historical evaluation factor is obtained, wherein the historical evaluation factor includes a historical gas concentration evaluation factor, a historical gas particle evaluation factor and a historical air pressure environment evaluation factor; The linear regression model is trained according to the historical gas concentration evaluation factor, the historical gas particle evaluation factor and the historical air pressure environment evaluation factor to obtain an evaluation channel; The time series model is trained according to the historical gas concentration evaluation factor, the historical gas particle evaluation factor and the historical air pressure environment evaluation factor to obtain a prediction channel; The assessment channel and prediction channel are configured in parallel to construct a greenhouse gas emission assessment-predictor.

7. The greenhouse gas emission warning device based on multimodal sensing according to claim 6, characterized in that: The gas emission assessment component receives the received gas concentration assessment factor, the gas particle assessment factor and the air pressure environment assessment factor as input data, inputs them into a greenhouse gas emission assessment-predictor, and obtains output data, and further includes: Preprocessing the gas concentration evaluation factor, the gas particle evaluation factor and the air pressure environment evaluation factor, wherein the preprocessing includes denoising, de-noising and normalization; Inputting the pre-processed gas concentration evaluation factor, the gas particle evaluation factor and the air pressure environment evaluation factor into the greenhouse gas emission assessment-predictor; The evaluation channel of the greenhouse gas emission evaluation-predictor obtains a greenhouse gas emission evaluation result based on the input data, and the prediction channel of the greenhouse gas emission evaluation-predictor obtains a greenhouse gas emission prediction result based on the input data; The output data is obtained according to the greenhouse gas emission assessment result and the greenhouse gas emission prediction result.

8. The greenhouse gas emission warning device based on multimodal sensing according to claim 7, characterized in that: The data judgment component manages the greenhouse gas emissions of the target coal chemical plant based on the judgment results, including: If the greenhouse gas emission assessment result is greater than or equal to the preset greenhouse gas emission threshold, a first-level alarm signal is triggered, and the first-level alarm signal includes a siren, a red light, and a text message reminder; If the greenhouse gas emission prediction result is greater than or equal to the preset greenhouse gas emission threshold, a secondary alarm signal is triggered, and the secondary alarm signal includes a blue light and an email reminder; If the greenhouse gas emission assessment result and the greenhouse gas emission prediction result are both greater than or equal to the preset greenhouse gas emission threshold, a first-level alarm signal is triggered; Based on the alarm signals, greenhouse gas emissions from the target coal chemical plant are managed.

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