A VOCs infrared imaging gas leak detection system

Abstract

The invention discloses a VOCs infrared imaging gas leak detection system, comprising: a gas key data acquisition unit for acquiring key parameter data of VOCs gas, the key parameter data including thermal radiation image data of a target area and VOCs gas concentration data; a gas key data analysis unit for processing the key parameter data, acquiring stable parameter data, analyzing the stable parameter data, and acquiring analysis results; a data management unit for acquiring current leakage source information data based on the analysis results, the leakage source information data including the location and leakage degree of the leakage source, and taking corresponding management measures according to the leakage source information data. Thus, the VOCs leakage source in the environment can be detected and located quickly and accurately, and the VOCs leakage source can be monitored in real time to facilitate timely response and take corresponding management measures, thereby improving the emergency handling capability of leakage events.

Description

Technical Field

[0001] The invention relates to the technical field of gas detection, and in particular to a VOCs infrared imaging gas leak detection system. Background Art

[0002] VOCs (volatile organic compounds) are a class of organic compounds widely present in industrial production, chemical processes and indoor environments. They are volatile and potentially harmful. Therefore, timely detection of VOCs leakage is crucial for environmental protection and personnel safety.

[0003] Traditional VOCs detection methods mainly rely on portable gas detection instruments, which are cumbersome to operate and cannot provide the exact location of the leak source; the traditional VOCs gas detection range may be limited, and some detection technologies can only effectively detect VOCs of specific types or specific concentration ranges, but may not be applicable to other types of VOCs; for certain low-concentration VOCs gas leaks, traditional VOCs gas detection equipment has sensitivity limitations, especially in complex environments, such as high humidity, high temperature or other gas interference conditions, it is challenging to detect low-concentration VOCs.

[0004] Therefore, there is an urgent need for a VOCs infrared imaging gas leak detection system. Summary of the invention

[0005] The present invention provides a VOCs infrared imaging gas leak detection system to solve the problems existing in the prior art, namely, that the traditional VOCs detection method mainly relies on portable gas detection instruments, which are cumbersome to operate and cannot provide the accurate location of the leakage source; the traditional VOCs gas detection range may be limited, and some detection technologies can only effectively detect VOCs of a specific type or a specific concentration range, but may not be applicable to other types of VOCs; for certain low-concentration VOCs gas leaks, traditional VOCs gas detection equipment has sensitivity limitations, especially in complex environments, such as high humidity, high temperature or other gas interference conditions, the detection of low-concentration VOCs is challenging.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A VOCs infrared imaging gas leak detection system, comprising:

[0008] The gas key data acquisition unit is used to acquire key parameter data of VOCs gas, and the key parameter data includes thermal radiation image data of the target area and VOCs gas concentration data;

[0009] The gas key data analysis unit is used to process key parameter data, obtain stable parameter data, analyze the stable parameter data, and obtain analysis results;

[0010] The data management unit is used to obtain current leakage source information data based on the analysis results, and the leakage source information data includes the location and leakage degree of the leakage source, and take corresponding management measures according to the leakage source information data.

[0011] Among them, the gas key data acquisition unit includes: thermal image acquisition module and VOCs gas acquisition module;

[0012] The thermal image acquisition module is used to capture infrared radiation in a specified environment through an infrared detector and then convert the infrared radiation into a thermal image;

[0013] The VOCs gas collection module is used to collect the VOCs gas concentration in a specified environment in real time through several gas sensors.

[0014] Among them, the gas key data analysis unit includes: data management library, threshold setting module and data intelligent analysis module;

[0015] The data management library is used to store the thermal image data and VOCs gas concentration data collected in the monitoring area;

[0016] The threshold setting module is used to retrieve several VOCs gas concentration data and thermal image data in the data management library as training sets, take the impact of VOCs gas concentration on human body and environment as variables, input deep learning model, output deep learning results, and set thermal signal threshold and VOCs gas concentration threshold based on deep learning results;

[0017] The data intelligent analysis module is used to start the data analysis operation when the VOCs gas concentration exceeds the set threshold, retrieve the currently collected thermal image data, extract the thermal signal features through image processing technology, classify and identify the thermal signal features, and determine the location and type of the current VOCs gas leakage source.

[0018] Among them, the data management unit includes: terminal, early warning module and measure formulation module;

[0019] The terminal is used to display the collected thermal images, VOCs gas concentration data and analysis results in real time. The analysis results are displayed in the form of images and text descriptions. When the VOCs gas concentration data exceeds the threshold, the VOCs gas concentration after exceeding the threshold and the corresponding time are displayed;

[0020] The early warning module is used to determine the location and type of the VOCs leakage source based on the analysis results when the VOCs concentration exceeds the threshold, automatically stop the operation related to the leakage source location, and then issue an alarm to the staff;

[0021] The measures formulation module is used to formulate the current measures to be taken based on the current VOCs concentration and the location of the VOCs leakage source when the early warning module is activated. The measures include notifying relevant departments, controlling the leakage source, limiting the spread of VOCs, and evacuating staff.

[0022] Among them, the thermal image acquisition module includes: an infrared detector, a signal conversion submodule and a thermal image generation submodule;

[0023] The infrared detector is used to receive infrared radiation signals in the environment through an infrared detector array;

[0024] The signal conversion submodule is used to convert the infrared radiation signal into a corresponding electrical signal when the infrared radiation enters the infrared detector array, and then amplify the electrical signal;

[0025] The thermal image generation submodule is used to convert the amplified electrical signal into a thermal image based on image processing technology.

[0026] The data management library includes: a data stratification submodule and a data retrieval submodule;

[0027] The data stratification submodule is used to formulate data stratification strategies according to the requirements for retrieval of thermal image data and VOCs gas concentration data and application scenarios. The stratification strategies include time stratification, space stratification, VOCs type stratification, event stratification and data type stratification.

[0028] The data retrieval submodule is used to retrieve the corresponding data in the data management library according to the demand for historical data in the data intelligent analysis module to provide support for data analysis.

[0029] Among them, the threshold setting module includes: a deep learning model;

[0030] Several thermal image data and corresponding VOCs gas concentration data in the data management library are retrieved as a data set, and the data set is annotated. The annotated data set is input into the deep learning model, and the impact of VOCs gas concentration on human body and environment is used as a variable. The output results are the threshold of thermal signal and the threshold of VOCs gas concentration. The deep learning model is trained with the annotated data set. During the training process, the gap between the predicted value of the deep learning model and the actual threshold is shortened by optimizing the algorithm and the loss function.

[0031] Among them, the data intelligent analysis module includes: a leakage source location determination submodule;

[0032] In the process of determining the location of the current VOCs gas leakage source, the reference temperature is first determined. The reference temperature refers to the thermal image data under normal working conditions. The temperature changes in the monitoring area are monitored in real time. The hot spot distribution in the thermal image is obtained based on the temperature changes. The leakage source location determination submodule determines the location of the current leakage source based on the hot spot distribution. The VOCs gas acquisition module performs further gas sampling and analysis on the location area of ​​the leakage source to confirm the existence and concentration level of VOCs gas at the current location.

[0033] Wherein, the terminal includes: a visual display submodule;

[0034] The detection data is connected to the terminal through wireless communication technology, and the visualization display submodule displays the VOCs gas concentration data and analysis result data in an intuitive way. The analysis result data includes the statistical information, trends and abnormal conditions of the VOCs gas concentration. The display methods include charts, graphs and maps. When VOCs gas leakage is detected, the visualization display submodule displays an alarm notification to remind the staff to take corresponding management measures.

[0035] Among them, the early warning module includes: prediction model;

[0036] The data feature values ​​of the collected thermal images are extracted, and the historical data and data feature values ​​are divided into training sets and test sets through the support vector machine algorithm. Based on machine learning technology, the training set is input into the prediction model for training. During the training process, the performance of the prediction model is optimized through cross-validation, parameter adjustment and feature selection techniques. The prediction model is trained and optimized to obtain an optimized training model. The currently collected VOCs gas concentration data is predicted and analyzed through the optimized training model. The leakage source is located according to the output results of the optimized training model. According to the results of the leakage source location, corresponding repair measures are taken. After the repair, the VOCs gas concentration continues to be monitored, and the location of the repaired leakage source is verified. Through verification, it is determined that the VOCs gas leakage problem at the current location has been solved.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] A VOCs infrared imaging gas leak detection system includes: a gas key data acquisition unit for acquiring key parameter data of VOCs gas, the key parameter data including thermal radiation image data of the target area and VOCs gas concentration data; a gas key data analysis unit for processing the key parameter data, acquiring stable parameter data, analyzing the stable parameter data, and acquiring analysis results; a data management unit for acquiring current leakage source information data based on the analysis results, the leakage source information data including the location and leakage degree of the leakage source, and taking corresponding management measures according to the leakage source information data. Thus, the VOCs leakage source in the environment can be detected and located quickly and accurately, and the VOCs leakage source can be monitored in real time to facilitate timely response and take corresponding management measures, thereby improving the emergency handling capability of leakage events.

[0039] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention.

[0040] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0042] Figure 1 This is a structural diagram of a VOCs infrared imaging gas leak detection system in an embodiment of the present invention;

[0043] Figure 2 This is a flow chart of a VOCs infrared imaging gas leak detection system according to an embodiment of the present invention;

[0044] Figure 3 This is a structural diagram of a gas key data analysis unit in an embodiment of the present invention;

[0045] Figure 4 4 is a structural diagram of a data management unit in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0047] The embodiment of the present invention provides a VOCs infrared imaging gas leak detection system, comprising:

[0048] The gas key data acquisition unit is used to acquire key parameter data of VOCs gas, and the key parameter data includes thermal radiation image data of the target area and VOCs gas concentration data;

[0049] The gas key data analysis unit is used to process key parameter data, obtain stable parameter data, analyze the stable parameter data, and obtain analysis results;

[0050] The data management unit is used to obtain current leakage source information data based on the analysis results, and the leakage source information data includes the location and leakage degree of the leakage source, and take corresponding management measures according to the leakage source information data.

[0051] The working principle of the above technical solution is: the harm of VOCs (volatile organic compounds) gas to the human body is closely related to the exposure time and concentration. Therefore, in the fields of industrial production, chemical plants, indoor air quality monitoring, etc., it is necessary to monitor the concentration, leakage time and location of VOCs gas leakage in real time to quickly and accurately detect and locate the VOCs leakage source in the environment;

[0052] First, the gas key data acquisition unit acquires the thermal radiation image data and VOCs gas concentration data of the target area. The thermal radiation image data is acquired through infrared thermal imaging technology, which can capture the temperature distribution of the target area, and the VOCs gas concentration data is measured by a gas sensor; then, the gas key data analysis unit processes these key parameter data and extracts stable parameter data; the stable parameter data includes the characteristics of temperature distribution and the average value of VOCs gas concentration; next, the data management unit uses the analysis results to obtain the leakage source information data, that is, to determine the location and leakage degree of the leakage source; finally, according to the leakage source information data, take corresponding management measures;

[0053] Taking a chemical plant as an example, this method is used to monitor and manage VOCs gas: the gas key data acquisition unit uses an infrared thermal imager to acquire thermal radiation image data of the target area, showing the temperature distribution of various locations in the target area. At the same time, a gas sensor is used to acquire VOCs gas concentration data and associate it with the thermal radiation image data. The gas key data analysis unit performs image processing on the thermal radiation image data to extract stable parameter data of the target area, such as the characteristics of temperature distribution; statistical analysis is performed on the VOCs gas concentration data to calculate the average concentration value of the VOCs gas. The data management unit determines the area where the leakage exists based on the analysis results of the stable parameter data and the VOCs gas concentration data, and calculates the location and degree of the leakage source; based on the leakage source information data, corresponding management measures are formulated, and the corresponding management measures include repairing the leaking pipes and improving the efficiency of the ventilation system.

[0054] The beneficial effects of the above technical solution are: it can monitor the thermal radiation image data and VOCs gas concentration data of the target area in real time, and extract stable parameter data. By analyzing the stable parameter data, the location and degree of leakage source can be accurately determined, which helps to quickly take corresponding management measures; this can effectively reduce the impact of VOCs gas leakage on the environment and human health, and improve safety and environmental protection levels; at the same time, the method can quickly discover potential leakage problems, help to carry out maintenance and repair in a timely manner, reduce the probability of accidents, and improve production efficiency.

[0055] In another embodiment, the gas key data acquisition unit includes: a thermal image acquisition module and a VOCs gas acquisition module;

[0056] The thermal image acquisition module is used to capture infrared radiation in a specified environment through an infrared detector and convert the infrared radiation into a thermal image;

[0057] The VOCs gas collection module is used to collect the VOCs gas concentration in a specified environment in real time through several gas sensors.

[0058] The working principle of the above technical solution is as follows: the thermal image acquisition module includes a number of infrared detectors, and a number of cameras are installed at designated locations in the monitoring area. The system sets the triggering conditions of the infrared detectors according to a preset threshold value. When the camera detects a heat signal exceeding the threshold value, the system will start data processing and analysis;

[0059] The VOCs gas collection module includes several gas sensors, which are composed of a gas sampling submodule and a gas concentration measurement submodule. Several gas sensors and several infrared detectors are installed at the same time at the designated position of the monitoring area. The designated position ensures that the gas sensor is in full contact with the monitoring environment. The gas sampling submodule collects gas samples in the environment, and then the gas concentration measurement submodule analyzes the collected gas samples to obtain the concentration value of the VOCs gas.

[0060] The beneficial effects of the above technical solution are: thermal images and VOCs gas concentration data in a specified environment can be obtained in real time. Thermal images can provide intuitive information on the ambient temperature distribution, which is helpful for detecting abnormal temperature areas. VOCs gas concentration data can provide information on the degree of VOCs pollution in the environment. By analyzing thermal images and VOCs gas concentration data, potential problems such as temperature anomalies and VOCs leakage can be discovered in time, and corresponding management measures can be taken; this can ensure the safety of the production environment and the health of employees, reduce the probability of accidents, and improve production efficiency.

[0061] In another embodiment, the gas key data analysis unit includes: a data management library, a threshold setting module and a data intelligent analysis module;

[0062] The data management library is used to store the thermal image data and VOCs gas concentration data collected in the monitoring area;

[0063] The threshold setting module is used to retrieve several VOCs gas concentration data in the data management library as a training set, take the impact of VOCs gas concentration on the human body and the environment as a variable, input the deep learning model, output the deep learning results, and set the thermal signal threshold and VOCs gas concentration threshold based on the deep learning results;

[0064] The data intelligent analysis module is used to start the data analysis operation when the VOCs gas concentration exceeds the set threshold, retrieve the currently collected thermal image data, extract the thermal signal features through image processing technology, classify and identify the thermal signal features, and determine the location and type of the current VOCs gas leakage source.

[0065] The working principle of the above technical solution is as follows: the data management library is used to store the thermal image data and VOCs gas concentration data collected in the monitoring area; the threshold setting module is used to retrieve several VOCs gas concentration data in the data management library as a training set, and take the impact of VOCs gas concentration on the human body and the environment as a variable, input the deep learning model, output the deep learning results, and set the thermal signal threshold and VOCs gas concentration threshold based on the deep learning results; the data intelligent analysis module is used to start the data analysis operation when the VOCs gas concentration exceeds the set threshold, retrieve the currently collected thermal image data, extract the thermal signal features through image processing technology, classify and identify the thermal signal features, and also identify and quantitatively analyze the leakage source in combination with the VOCs gas concentration to determine the location and type of the current VOCs gas leakage source.

[0066] Among them, excessive VOCs gas concentration will have adverse effects on human health, such as respiratory irritation, allergic reactions, poisoning, etc.; at the same time, VOCs gas emissions will also have negative impacts on the environment, such as ozone generation, greenhouse effect, etc.; therefore, when setting VOCs concentration thresholds, data related to human health and data on environmental impacts will be considered to ensure that workers exposed to VOCs will not suffer serious health hazards and to ensure the stability of the air quality and ecosystem.

[0067] The beneficial effects of the above technical solution are as follows: by storing thermal image data and VOCs gas concentration data in the data management library, real-time monitoring of thermal signals and gas concentrations in the monitoring area can be achieved; by using the threshold setting module and the data intelligent analysis module, deep learning and image processing technology can be applied to achieve intelligent identification and positioning of VOCs gas leakage sources; when the VOCs gas concentration exceeds the set threshold, the system will automatically start the data analysis operation, extract the thermal signal characteristics through image processing technology, and determine the location and type of the leakage source, so that gas leakage problems can be discovered and handled in time, and corresponding management measures can be taken to ensure the safety of the production environment and the health of employees; through intelligent analysis and early warning functions, gas leakage problems can be quickly discovered, the probability of accidents can be reduced, and production efficiency can be improved. When setting the VOCs concentration threshold, data related to human health and data on environmental impact will be considered to ensure that workers exposed to VOCs will not suffer serious health hazards and to ensure the stability of air quality and ecosystems.

[0068] In another embodiment, the data management unit includes: a terminal, an early warning module, and a measure formulation module;

[0069] The terminal is used to display the collected thermal images, VOCs gas concentration data and analysis results in real time. The analysis results are displayed in the form of images and text descriptions. When the VOCs gas concentration data exceeds the threshold, the VOCs gas concentration after exceeding the threshold and the corresponding time are displayed;

[0070] The early warning module is used to determine the location and type of the VOCs leakage source based on the analysis results when the VOCs concentration exceeds the threshold, automatically stop the operation related to the leakage source location, and then issue an alarm to the staff;

[0071] The measures formulation module is used to formulate the current measures to be taken based on the current VOCs concentration and the location of the VOCs leakage source when the early warning module is activated. The measures include notifying relevant departments, controlling the leakage source, limiting the spread of VOCs, and evacuating staff.

[0072] The working principle of the above technical solution is as follows: the terminal is used to display the collected thermal images, VOCs gas concentration data and analysis results in real time. The analysis results are displayed in the form of images and text descriptions. When the VOCs gas concentration data exceeds the threshold, the VOCs gas concentration after exceeding the threshold and the corresponding time are displayed; the early warning module is used to determine the location and type of the VOCs leakage source according to the analysis results when the VOCs concentration exceeds the threshold, automatically stop the operation related to the leakage source location, and then issue an alarm to the staff; the measures formulation module is used to formulate the current measures to be taken based on the current VOCs concentration and the location of the VOCs leakage source after the early warning module is started. The measures include notifying relevant departments, controlling the leakage source, limiting the spread of VOCs, and evacuating staff. The specific response measures for VOCs leakage will vary depending on the nature, scale and environmental conditions of the leakage. Different measures are taken according to different analysis results to ensure rapid response and alarm, and improve the emergency response capabilities of leakage incidents.

[0073] The beneficial effects of the above technical solution are as follows: the terminal is used to display the collected thermal images, VOCs gas concentration data and analysis results in real time. The analysis results are displayed in the form of images and text descriptions. When the VOCs gas concentration data exceeds the threshold, the VOCs gas concentration after exceeding the threshold and the corresponding time are displayed; the early warning module is used to determine the location and type of the VOCs leakage source according to the analysis results when the VOCs concentration exceeds the threshold, automatically stop the operation related to the leakage source location, and then issue an alarm to the staff; the measure formulation module is used to formulate the current measures to be taken based on the current VOCs concentration and the location of the VOCs leakage source after the early warning module is started. The measures include notifying relevant departments, controlling the leakage source, limiting the spread of VOCs, and evacuating staff. Ensure rapid response and alarm, and improve the emergency handling capabilities of leakage incidents.

[0074] In another embodiment, the thermal image acquisition module includes: an infrared detector, a signal conversion submodule, and a thermal image generation submodule;

[0075] The infrared detector is used to receive infrared radiation signals in the environment through an infrared detector array;

[0076] The signal conversion submodule is used to convert the infrared radiation signal into a corresponding electrical signal when the infrared radiation enters the infrared detector array, and then amplify the electrical signal;

[0077] The thermal image generation submodule is used to convert the amplified electrical signal into a thermal image based on image processing technology.

[0078] The working principle of the above technical solution is as follows: the main component used by the thermal image acquisition module is the infrared detector array, which is an array composed of many tiny detectors, each of which can sense infrared radiation and generate corresponding electrical signals. Infrared radiation is infrared energy emitted by an object, and its intensity and distribution are related to the temperature and surface characteristics of the object. The infrared radiation from the object is guided to the detector array through the lens or lens of the thermal image acquisition module; when the infrared radiation enters the detector array, each infrared detector converts the sensed infrared energy into a corresponding electrical signal, which is amplified and processed to provide visual thermal image data; after signal processing and image processing technology, the thermal image generation submodule converts the electrical signal into a thermal image; in the thermal image, each pixel represents the infrared radiation intensity at the corresponding position on the infrared detector array, thus forming an image composed of many pixels, showing the temperature distribution of the object being measured; the generated thermal image can be displayed in real time on the terminal, so that the staff can intuitively observe and analyze the thermal distribution of the object being measured. Thermal images usually use pseudo-color or grayscale to represent different temperature areas, making temperature differences easier to identify and understand.

[0079] The beneficial effects of the above technical solution are as follows: the infrared detector is used to receive infrared radiation signals in the environment through the infrared detector array; the signal conversion submodule is used to convert the infrared radiation signal into a corresponding electrical signal when the infrared radiation enters the infrared detector array, and then amplify the electrical signal; the thermal image generation submodule is used to convert the amplified electrical signal into a thermal image based on image processing technology. The generated thermal image makes temperature differences easier to identify and understand.

[0080] In another embodiment, the data management library includes: a data layering submodule and a data retrieval submodule;

[0081] The data stratification submodule is used to formulate data stratification strategies according to the requirements for retrieval of thermal image data and VOCs gas concentration data and application scenarios. The stratification strategies include time stratification, space stratification, VOCs type stratification, event stratification and data type stratification.

[0082] The data retrieval submodule is used to retrieve the corresponding data in the data management library according to the demand for historical data in the data intelligent analysis module to provide support for data analysis.

[0083] The working principle of the above technical solution is: the VOCs gas concentration data is stored in layers according to the timestamp of the data. The data can be organized and archived by hour, day, week, month or year to meet the needs of long-term monitoring and trend analysis. This storage method makes it easy to review historical data and conduct long-term data analysis and comparison. Because there are multiple VOCs gas concentration monitoring points, the VOCs gas concentration data is stored in layers according to the spatial dimension, for example, according to different areas, rooms or locations, so that the data of a specific area or location can be managed and analyzed independently; involving a variety of different VOCs substances, the data is stored in layers according to different types of VOCs, which can make data management clearer and can independently analyze and compare the concentration changes of different VOCs; VOCs gas concentration data is stored in layers according to events. For example, for a sudden leakage event, the data related to the event can be stored in a specific event layer for subsequent analysis and investigation; the data management library stores different types of thermal image data, which are managed in layers according to the data type. For example, the original thermal image data and the result data obtained after processing and analysis can be stored separately for subsequent analysis and comparison;

[0084] Among them, a data stratification strategy is formulated according to the requirements for retrieval of thermal image data and VOCs gas concentration data and application scenarios, including:

[0085] Obtain preset thermal image data and VOCs gas concentration data retrieval requirements, the retrieval requirements including: multiple first data retrieval requirements;

[0086] Obtaining an application scenario corresponding to the first data retrieval requirement, and at the same time, obtaining a priority of the application scenario;

[0087] Based on thermal image data and VOCs gas concentration data, formulate data stratification strategies, including time stratification, space stratification, VOCs type stratification, event stratification and data type stratification;

[0088] Parsing the data stratification strategy to obtain a plurality of first data strata;

[0089] Obtaining data retrieval parameters corresponding to the first data layer, the data retrieval parameters including: data retrieval time, data retrieval location, VOCs type, related events and data type;

[0090] Based on the data retrieval parameter, a first data set corresponding to the first data layer is obtained;

[0091] Obtaining a preset data retrieval optimization strategy, the data retrieval optimization strategy including: a plurality of first data retrieval optimization strategies;

[0092] Splitting the first data retrieval optimization strategy into a plurality of first optimization strategy items;

[0093] Obtaining an execution scenario corresponding to the first optimization strategy item, and at the same time, obtaining the credibility of the execution scenario;

[0094] Performing feature extraction on the first optimization strategy item to obtain a plurality of second features;

[0095] Obtaining a preset feature-benefit value library, determining a benefit value corresponding to the second feature based on the feature-benefit value library, and associating the benefit value with the corresponding first optimization strategy item;

[0096] Accumulating and calculating the benefit values ​​associated with the first optimization strategy item to obtain a benefit value sum;

[0097] If the credibility is greater than or equal to the preset credibility threshold and / or the benefit value and the benefit value and the threshold are greater than or equal to the preset benefit value and the threshold, the corresponding first optimization strategy item is used as the second optimization strategy item;

[0098] Obtaining the benefit weight of the second optimization strategy item corresponding to the first data retrieval optimization strategy, and if the benefit weight is greater than or equal to a benefit weight threshold, retaining the corresponding first data retrieval optimization strategy;

[0099] When all the first data retrieval optimization strategies that need to be retained are retained, the remaining first data retrieval optimization strategies are retained as the second data retrieval optimization strategies;

[0100] Retrieving the optimization strategy based on the second data, obtaining the optimization data corresponding to the first data set;

[0101] storing the optimized data in a data storage unit;

[0102] When all the optimized data that need to be stored in the data storage unit are stored, the data retrieval optimization is completed.

[0103] The beneficial effects of the above technical solution are as follows: the data stratification submodule is used to formulate data stratification strategies according to the retrieval requirements and application scenarios of thermal image data and VOCs gas concentration data, and the stratification strategies include time stratification, space stratification, VOCs type stratification, event stratification and data type stratification; the data retrieval submodule is used to retrieve the corresponding data in the data management library according to the demand for historical data in the data intelligent analysis module, and provide support for data analysis. To meet the requirements of data management and analysis.

[0104] In another embodiment, the threshold setting module includes: a deep learning model;

[0105] Several thermal image data and corresponding VOCs gas concentration data in the data management library are retrieved as a data set, and the data set is annotated. The annotated data set is input into the deep learning model, and the impact of VOCs gas concentration on human body and environment is used as a variable. The output results are the threshold of thermal signal and the threshold of VOCs gas concentration. The deep learning model is trained with the annotated data set. During the training process, the gap between the predicted value of the deep learning model and the actual threshold is shortened by optimizing the algorithm and the loss function.

[0106] The working principle of the above technical solution is as follows: retrieve the thermal image and the corresponding VOCs gas concentration data set in the data management library, and annotate the data. For thermal images, set different thresholds as labels as needed, for example, mark pixels above a certain temperature threshold as positive samples, and pixels below the threshold as negative samples. For VOCs gas concentration, annotate according to the known concentration value; preprocess the data, including image enhancement, denoising, and normalization steps to improve data quality and model training effects; select the corresponding deep learning model to perform the threshold prediction task of thermal signals and gas concentrations. The input of the model is thermal image data, and the output is the threshold of the thermal signal and the threshold of the VOCs gas concentration; use the annotated data set to train the deep learning model. During the training process, the gap between the model prediction value and the true threshold is minimized by optimizing the algorithm and the loss function, so that the model can learn the threshold prediction ability of thermal signals and gas concentrations; use the reserved test set to evaluate the trained model, and tune it according to the evaluation results. Use the trained model to predict new thermal images to obtain the threshold prediction results of thermal signals and VOCs gas concentrations.

[0107] The beneficial effects of the above technical solution are: retrieve several thermal image data and corresponding VOCs gas concentration data in the data management library as a data set, annotate the data set, input the annotated data set into the deep learning model, take the impact of VOCs gas concentration on the human body and the environment as variables, output the threshold of the thermal signal and the threshold of VOCs gas concentration, train the deep learning model through the annotated data set, and in the training process, shorten the gap between the predicted value of the deep learning model and the actual threshold through the optimization algorithm and loss function, so that the deep learning model can learn the threshold prediction ability of thermal signals and gas concentrations.

[0108] In another embodiment, the data intelligent analysis module includes: a leakage source location determination submodule;

[0109] In the process of determining the location of the current VOCs gas leakage source, the reference temperature is first determined. The reference temperature refers to the thermal image data under normal working conditions. The temperature changes in the monitoring area are monitored in real time. The hot spot distribution in the thermal image is obtained based on the temperature changes. The leakage source location determination submodule determines the location of the current leakage source based on the hot spot distribution. The VOCs gas acquisition module performs further gas sampling and analysis on the location area of ​​the leakage source to confirm the existence and concentration level of VOCs gas at the current location.

[0110] The working principle of the above technical solution is: by comparing the temperature differences and hot spot distribution in different areas, the potential leakage source location can be identified, and then the type and concentration of VOCs gas can be determined through further analysis;

[0111] Before starting infrared thermal imaging, first determine a reference temperature, that is, the ambient temperature when no leakage occurs. This can be done by performing a comprehensive scan of the area to be tested and recording the thermal map under normal working conditions; under the monitoring of the thermal image acquisition module, monitor the temperature changes in the area to be tested. VOCs gas leakage will cause the surrounding environment temperature to rise, thus forming a temperature difference; find hot spots, that is, areas with significantly higher temperatures relative to the reference temperature; obtain the hot spot distribution in the thermal map. VOCs gas leakage will lead to uneven hot spot distribution, that is, higher temperatures appear in specific areas around the leakage source. By comparing the temperature differences and hot spot locations in different areas, the possible location range of the leakage source can be gradually narrowed; in addition to temperature differences and hot spot distribution, the information of the characteristic data of the monitoring area should also be comprehensively considered. Among them, the characteristic data of the monitoring area includes equipment layout, pipeline direction, and gas diffusion characteristics. Considering the characteristic data of the monitoring area can help further determine the location of the leakage source; once the possible leakage source location is determined, the current leakage source location is verified and confirmed. For example, use the VOCs gas acquisition module to further sample and analyze the gas in the area to confirm the presence and concentration level of VOCs gas.

[0112] The beneficial effects of the above technical solution are as follows: in the process of determining the location of the current VOCs gas leakage source, the reference temperature is first determined, which refers to the thermal image data under normal working conditions, and the temperature changes in the monitoring area are monitored in real time. The hot spot distribution in the thermal image is obtained based on the temperature change, and the leakage source location determination submodule determines the location of the current leakage source according to the hot spot distribution. Determining the location of the VOCs gas leakage source based on temperature differences and hot spot distribution is a complex process. The accuracy and reliability of the VOCs gas leakage source location determination are improved through the leakage source location determination submodule.

[0113] In another embodiment, the terminal includes: a visual display submodule;

[0114] The detection data is connected to the terminal through wireless communication technology, and the visualization display submodule displays the VOCs gas concentration data and analysis result data in an intuitive way. The analysis result data includes the statistical information, trends and abnormal conditions of the VOCs gas concentration. The display methods include charts, graphs and maps. When VOCs gas leakage is detected, the visualization display submodule displays an alarm notification to remind the staff to take corresponding management measures.

[0115] The working principle of the above technical solution is as follows: by analyzing and processing the collected data, statistical information, trends and abnormal conditions of VOCs gas concentration are obtained, and the statistical information includes average concentration, peak concentration and concentration change trend; using wireless communication technology, the detection data is connected to the terminal or staff to realize remote monitoring and data transmission, so that the operator can monitor the status and data of the terminal in real time, and respond and handle leakage incidents in a timely manner; using intelligent data visualization technology, the VOCs gas concentration data is displayed in an intuitive manner, and the concentration conditions of different areas or equipment are displayed in different forms, including charts, graphs or maps, so that users can easily observe and compare the concentration changes in different locations or time periods; combining the results of data analysis and processing, a real-time early warning module is established to detect abnormal VOCs concentration levels according to preset thresholds or model algorithms, and issue alarms or notifications, and the notification methods include mobile phone applications, emails, and text messages, so that relevant personnel can take necessary measures in a timely manner.

[0116] The beneficial effects of the above technical solution are: the detection data is connected to the terminal through wireless communication technology, and the visualization display submodule displays the VOCs gas concentration data and analysis result data in an intuitive way. The analysis result data includes statistical information, trends and abnormal conditions of VOCs gas concentration, and the display methods include charts, graphs and maps. When VOCs gas leakage is detected, the visualization display submodule displays an alarm notification to remind the staff to take corresponding management measures. Providing data support to the staff through visual display helps the staff to monitor and manage the concentration of VOCs, and take appropriate measures in advance when VOCs gas leakage occurs to protect the health and safety of personnel and the environment.

[0117] In another embodiment, the early warning module includes: a prediction model;

[0118] The data feature values ​​of the collected thermal images are extracted, and the historical data and data feature values ​​are divided into training sets and test sets through the support vector machine algorithm. Based on machine learning technology, the training set is input into the prediction model for training. During the training process, the performance of the prediction model is optimized through cross-validation, parameter adjustment and feature selection techniques. The prediction model is trained and optimized to obtain an optimized training model. The currently collected VOCs gas concentration data is predicted and analyzed through the optimized training model. The leakage source is located according to the output results of the optimized training model. According to the results of the leakage source location, corresponding repair measures are taken. After the repair, the VOCs gas concentration continues to be monitored, and the location of the repaired leakage source is verified. Through verification, it is determined that the VOCs gas leakage problem at the current location has been solved.

[0119] The working principle of the above technical solution is as follows: extract the data feature values ​​of the thermal image, which can help identify the source of VOCs gas leakage, and select the most relevant features through machine learning methods; use machine learning technology to establish a prediction model, select the support vector machine algorithm based on historical data and features, divide the data into a training set and a test set, use the training set to train the model, and use the test set to evaluate the performance of the model; train the established model and optimize it as needed, and improve the performance and generalization ability of the model by using cross-validation, parameter adjustment and feature selection techniques; use the trained and optimized model to predict and analyze new VOCs gas concentration data, and locate the possible leakage source according to the output results of the model;

[0120] The prediction model can identify equipment, pipelines or areas where leakage may occur based on the matching of features and patterns; take appropriate repair measures based on the results of leak source location, including repairing or replacing equipment, improving pipeline systems, and strengthening protective measures; after repair, continue to monitor VOCs gas concentrations and conduct verification to ensure that the VOCs gas leakage problem is effectively resolved;

[0121] Among them, locating the leakage source according to the output result of the optimized training model includes:

[0122] Use the gas key data acquisition unit to obtain the thermal image data and VOCs gas concentration data of the target area, and store the data in the data management library; retrieve a part of the VOCs gas concentration data from the data management library as a training set, use the VOCs gas concentration as a variable, and the impact of the human body and the environment as output, train the deep learning network, adjust the network parameters, and optimize the model; connect the trained deep learning model to the threshold setting module, and when the VOCs gas concentration is detected to exceed the set threshold, proceed to the next step; start the data intelligent analysis module, retrieve the currently collected thermal image data, extract the thermal signal features through image processing technology, and input them into the deep learning model to perform Classify and locate the leakage source, and determine the location of the leakage source based on the results of the model output; take corresponding repair measures based on the results of the leakage source location to repair the leakage source; after the repair, continue to monitor the VOCs gas concentration, and verify the location of the repaired leakage source, re-collect thermal image data and VOCs gas concentration data through the gas key data acquisition unit, and perform corresponding data processing and analysis to verify the effectiveness of the repair measures; through data analysis and post-repair verification, determine whether the VOCs gas leakage problem at the current location has been solved. If the leakage problem has been solved, feedback relevant information. If the problem still exists, further investigate the cause and take measures to solve it;

[0123] The location of the leakage source can be determined based on the output results of the optimized training model, and the leakage problem can be solved through repair measures, and finally the repair effect can be verified. This can effectively improve the accuracy and efficiency of leakage source positioning and repair, and ensure the safety of the production environment and the health of employees.

[0124] The beneficial effects of the above technical solution are: extracting data feature values ​​from the collected thermal images, dividing historical data and data feature values ​​into training sets and test sets through the support vector machine algorithm, inputting the training set into the prediction model for training based on machine learning technology, optimizing the performance of the prediction model through cross-validation, parameter adjustment and feature selection technology during the training process, obtaining an optimized training model by training and optimizing the prediction model, predicting and analyzing the currently collected VOCs gas concentration data through the optimized training model, locating the leakage source according to the output results of the optimized training model, taking corresponding repair measures according to the results of the leakage source location, and continuing to monitor the VOCs gas concentration after repair, and verifying the location of the repaired leakage source, and determining that the VOCs gas leakage problem at the current location has been solved through verification. This provides more accurate prediction results and suggestions for staff.

[0125] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A VOCs infrared imaging gas leak detection system, characterized in that: include: The gas key data acquisition unit is used to acquire key parameter data of VOCs gas, and the key parameter data includes thermal radiation image data of the target area and VOCs gas concentration data; The gas key data analysis unit is used to process key parameter data and obtain stable parameter data, which includes the characteristics of temperature distribution and the average value of VOCs gas concentration, and analyze the stable parameter data to obtain analysis results; The data management unit is used to obtain the current leakage source information data based on the analysis result, and the leakage source information data includes the location and leakage degree of the leakage source, and take corresponding management measures according to the leakage source information data; The gas key data analysis unit includes: data management library, threshold setting module and data intelligent analysis module; The data management library is used to store the thermal image data and VOCs gas concentration data collected in the monitoring area; The threshold setting module is used to retrieve several VOCs gas concentration data and thermal image data in the data management library as training sets, take the impact of VOCs gas concentration on human body and environment as variables, input deep learning model, output deep learning results, and set thermal signal threshold and VOCs gas concentration threshold based on deep learning results; The data intelligent analysis module is used to start the data analysis operation when the VOCs gas concentration exceeds the set threshold, retrieve the currently collected thermal image data, extract the thermal signal features through image processing technology, classify and identify the thermal signal features, and determine the location and degree of the current VOCs gas leakage source.

2. A VOCs infrared imaging gas leak detection system according to claim 1, characterized in that: The gas key data acquisition unit includes: thermal image acquisition module and VOCs gas acquisition module; The thermal image acquisition module is used to capture infrared radiation in a specified environment through an infrared detector and then convert the infrared radiation into a thermal image; The VOCs gas collection module is used to collect the VOCs gas concentration in a specified environment in real time through several gas sensors.

3. A VOCs infrared imaging gas leak detection system according to claim 1, characterized in that: The data management unit includes: terminal, early warning module and measure formulation module; The terminal is used to display the collected thermal images, VOCs gas concentration data and analysis results in real time. The analysis results are displayed in the form of images and text descriptions. When the VOCs gas concentration data exceeds the threshold, the VOCs gas concentration after exceeding the threshold and the corresponding time are displayed; The early warning module is used to determine the location and extent of the VOCs leakage source based on the analysis results when the VOCs concentration exceeds the threshold, automatically stop the operation related to the leakage source location, and then issue an alarm to the staff; The measures formulation module is used to formulate the current measures to be taken based on the current VOCs concentration and the location of the VOCs leakage source when the early warning module is activated. The measures include notifying relevant departments, controlling the leakage source, limiting the spread of VOCs, and evacuating staff.

4. A VOCs infrared imaging gas leak detection system according to claim 2, characterized in that: The thermal image acquisition module includes: an infrared detector, a signal conversion submodule and a thermal image generation submodule; The infrared detector is used to receive infrared radiation signals in the environment through an infrared detector array; The signal conversion submodule is used to convert the infrared radiation signal into a corresponding electrical signal when the infrared radiation enters the infrared detector array, and then amplify the electrical signal; The thermal image generation submodule is used to convert the amplified electrical signal into a thermal image based on image processing technology.

5. The VOCs infrared imaging gas leak detection system according to claim 1, characterized in that: The data management library includes: data stratification submodule and data retrieval submodule; The data stratification submodule is used to formulate data stratification strategies based on the requirements for retrieval of thermal image data and VOCs gas concentration data and application scenarios. The stratification strategies include time stratification, space stratification, VOCs type stratification, event stratification and leakage degree stratification. The data retrieval submodule is used to retrieve the corresponding data in the data management library according to the demand for historical data in the data intelligent analysis module to provide support for data analysis.

6. A VOCs infrared imaging gas leak detection system according to claim 1, characterized in that: The threshold setting module includes: deep learning model; Several thermal image data and corresponding VOCs gas concentration data in the data management library are retrieved as a data set, and the data set is annotated. The annotated data set is input into the deep learning model, and the impact of VOCs gas concentration on human body and environment is used as a variable. The output results are the threshold of thermal signal and the threshold of VOCs gas concentration. The deep learning model is trained with the annotated data set. During the training process, the gap between the predicted value of the deep learning model and the actual threshold is shortened by optimizing the algorithm and the loss function.

7. A VOCs infrared imaging gas leak detection system according to claim 1, characterized in that: The data intelligent analysis module includes: a leakage source location determination submodule; In the process of determining the location of the current VOCs gas leakage source, the reference temperature is first determined. The reference temperature refers to the thermal image data under normal working conditions. The temperature changes in the monitoring area are monitored in real time. The hot spot distribution in the thermal image is obtained based on the temperature changes. The leakage source location determination submodule determines the location of the current leakage source based on the hot spot distribution. The VOCs gas acquisition module performs further gas sampling and analysis on the location area of ​​the leakage source to confirm the existence and concentration level of VOCs gas at the current location.

8. The VOCs infrared imaging gas leak detection system according to claim 3, characterized in that: The terminal includes: Visual display submodules; The detection data is connected to the terminal through wireless communication technology, and the visualization display submodule displays the VOCs gas concentration data and analysis result data in an intuitive way. The analysis result data includes the statistical information, trends and abnormal conditions of the VOCs gas concentration. The display methods include charts, graphs and maps. When VOCs gas leakage is detected, the visualization display submodule displays an alarm notification to remind the staff to take corresponding management measures.

9. A VOCs infrared imaging gas leak detection system according to claim 3, characterized in that: The early warning module includes: prediction model; The data feature values ​​of the collected thermal images are extracted, and the historical data and data feature values ​​are divided into training sets and test sets through the support vector machine algorithm. Based on machine learning technology, the training set is input into the prediction model for training. During the training process, the performance of the prediction model is optimized through cross-validation, parameter adjustment and feature selection techniques. The prediction model is trained and optimized to obtain an optimized training model. The currently collected VOCs gas concentration data is predicted and analyzed through the optimized training model. The leakage source is located according to the output results of the optimized training model. According to the results of the leakage source location, corresponding repair measures are taken. After the repair, the VOCs gas concentration continues to be monitored, and the location of the repaired leakage source is verified. Through verification, it is determined that the VOCs gas leakage problem at the current location has been solved.

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