A safety and environmental protection emergency system based on industrial interconnection and digital panorama

Abstract

The present invention discloses a safety and environmental protection emergency system based on industrial interconnection and digital panorama, relates to the technical field of safety and environmental protection emergency system, and solves the technical problem that the prior art does not consider that the exhaust gas may be liquefied due to low-temperature air when detecting exhaust gas, resulting in inaccurate detection results; the present invention obtains a correction factor through the inversely proportional relationship between gas concentration and temperature, constructs a correction model based on the correction factor to correct the collected exhaust gas concentration, so as to improve the accuracy of exhaust gas detection, and constructs a panoramic image through digital panorama technology, and at the same time, displays the real-time monitored data on a visual interface through visualization processing, so that the staff can obtain more intuitive information and more judgment basis, thereby improving the accuracy of pre-judgment of the cause of the problem.

Description

Technical Field

[0001] The present invention belongs to the field of emergency systems, and relates to industrial interconnection and digital panoramic technology, and specifically is a safety and environmental protection emergency system based on industrial interconnection and digital panoramic technology. Background Art

[0002] With the acceleration of industrialization, environmental pollution problems have become increasingly prominent, especially industrial pollution, whose impact on the environment and human health and safety cannot be ignored. Various pollutants will be generated during the industrial production process. How to effectively monitor these pollutants in real time is a major challenge facing the fields of environmental protection and industrial production.

[0003] In the existing technology for monitoring industrial pollutants, when collecting waste gas, it is not considered that the waste gas will be affected by temperature. When the waste gas encounters low-temperature cold air, the gas may liquefy, so that the gas data collected by the sensor is different from the actual data; and in the existing technical solution, when an unexpected situation is found during the waste gas treatment process, such as the safety index after the waste gas treatment does not meet the standard, in order to investigate the cause, the staff analyzes the possible abnormal causes through the collected data. Because there is little basis for analysis and the data changes cannot be observed intuitively, the accuracy of pre-judgment of the abnormal cause is not high, so that the cause of the problem cannot be found in the first time, affecting the processing progress;

[0004] The present invention proposes a safety and environmental protection emergency system based on industrial interconnection and digital panorama to solve the above problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a safety and environmental emergency system based on industrial interconnection and digital panorama, which is used to solve the problem that the prior art monitors industrial pollution. When collecting exhaust gas, it is not taken into account that the exhaust gas will be affected by temperature, so that the gas data collected by the sensor is different from the actual data; and when unexpected situations are found during the treatment of exhaust gas, such as the safety indicators after exhaust gas treatment do not meet the standards, the accident cannot be handled in the first time.

[0006] To achieve the above-mentioned object, a first aspect of the present invention provides a safety and environmental protection emergency system based on industrial interconnection and digital panorama, comprising: a data analysis module, and a data acquisition module and an emergency processing module connected to the data analysis module;

[0007] Data acquisition module: using a panoramic acquisition device to collect image data of a target area at several acquisition angles; wherein the panoramic acquisition device includes a panoramic camera or a VR camera, and the target area is an exhaust gas treatment area; and,

[0008] The monitoring data is collected by data sensors arranged in the target area; wherein the data sensors include air quality sensors and temperature sensors;

[0009] Data analysis module: synthesizes image data from several acquisition angles based on a panoramic stitching tool and a stitching algorithm to obtain a panoramic image; wherein the panoramic stitching tool includes PTGui, Hugin or Adobe Photoshop software, and the stitching algorithm includes a SIFT algorithm or a SURF algorithm; and,

[0010] Extract air quality data from the monitoring data, correct the air quality data, and obtain corrected data; identify whether the corrected data is abnormal through the deep analysis model; if so, send a warning signal to the emergency processing module; wherein the deep analysis model is built based on the artificial intelligence model;

[0011] Emergency processing module: after receiving the warning signal, locate the abnormal position on the panoramic image based on the correction data and mark it as the accident position; initiate the emergency plan and take emergency measures at the accident position; and,

[0012] Use visualization tools to visualize the accident location and monitoring data; visualization tools include Tableau, Power BI, D3.js or matplotlib.

[0013] Preferably, the method of collecting image data of a target area at several collection angles using a panoramic collection device includes:

[0014] By selecting a reference point in the target area, a panoramic acquisition device is used to capture a 360° full coverage of the target area based on the reference point.

[0015] Preferably, the data sensors arranged in the target area collect monitoring data, including:

[0016] The actual exhaust concentration and the actual exhaust temperature at the corresponding collection time are collected in real time by the temperature sensor and air quality sensor installed in the exhaust port; and

[0017] The ambient temperature is collected by a temperature sensor installed outside the exhaust port; wherein the ambient temperature is not affected by the exhaust port.

[0018] Preferably, the method of synthesizing image data from several acquisition angles based on a panoramic stitching tool and a stitching algorithm to obtain a panoramic image includes:

[0019] Detect and match the image data through the panoramic stitching tool to obtain feature points; calculate the feature points based on the stitching algorithm to obtain descriptors; match the descriptors in the image data to obtain feature point pairs; and,

[0020] The image transformation relationship of the feature point pairs is obtained by using a geometric transformation model; wherein the geometric transformation model includes an affine transformation or a projection transformation; and a panoramic image is constructed based on the image data and the image transformation relationship.

[0021] Preferably, the correcting the air quality data to obtain the corrected data includes:

[0022] The actual concentration of the collected exhaust gas is marked as C, the actual temperature of the exhaust gas at the time of collection is marked as T, and the ambient temperature is marked as T0, and the correction model is obtained: Cx = C×F, where Cx is the corrected exhaust gas concentration and F is the correction factor; and

[0023] Based on the formula PV=nRT and the relationship between gas concentration and volume C=n / V, the formula: C=P / RT is derived, and it is concluded that the gas concentration is inversely proportional to the temperature, thus obtaining the correction factor F=A×(T0 / T), that is, the correction model Cx=C×A×(T0 / T), where P represents pressure, V represents gas volume, n represents the number of moles, R is the ideal gas constant, and A is the proportionality coefficient.

[0024] The existing technical solution does not consider the effect of temperature on gas concentration when collecting exhaust gas concentration, resulting in a difference between the collected exhaust gas concentration and the actual exhaust gas concentration. The difference mainly comes from the fact that when the exhaust gas encounters low-temperature cold air, the gas may liquefy, resulting in a smaller collected exhaust gas concentration.

[0025] The present invention corrects the collected exhaust gas concentration by constructing a correction model, so that the corrected gas concentration is closer to the actual gas concentration, thereby improving the accuracy of the collected data.

[0026] Preferably, the identifying whether the correction data is abnormal by using a deep analysis model includes:

[0027] Generate standard input data based on the actual concentration of exhaust gas and the actual temperature of exhaust gas corresponding to the historical exhaust gas data; obtain corresponding historical correction data by inputting the standard input data into the deep analysis model, wherein the deep analysis model is obtained based on artificial intelligence model training; and

[0028] Based on the comparison between the historical correction data and the exhaust gas safety emission index, if the safety emission index is exceeded, the exhaust gas quality is judged to be abnormal and a warning signal is sent to the warning processing module; the exhaust gas historical data is the historical data of the target area for collection.

[0029] Preferably, locating the abnormal position on the panoramic image based on the correction data and marking it as the accident position includes:

[0030] Based on the position information of the abnormal sensor corresponding to the correction data, the accident position is located on the panoramic image; based on the accident position, the image information of the corresponding abnormal sensor in the panoramic image is retrieved; the position information includes: longitude and latitude and pixel position relative to the panoramic image; the image information includes: the specific location of the abnormal sensor and the surrounding environment and building facilities.

[0031] When the existing technical solution finds abnormal sensor data, it uses data analysis and field survey to find the cause of the abnormality, which not only increases the maintenance time and prevents the factory from operating normally as soon as possible, but also during the field survey, the long stay may affect the safety of the staff;

[0032] The present invention establishes a panoramic image, utilizes the position information of the abnormal sensor, retrieves the image information of the corresponding abnormal sensor in the panoramic image, provides the staff with the position of the abnormal sensor and the surrounding environment and building facilities, enables the staff to observe the abnormal area intuitively, and is conducive to making accurate decisions and taking countermeasures in the first time.

[0033] Preferably, the initiation of the emergency plan to take emergency measures at the accident location includes:

[0034] Based on wireless communication technology, the emergency processing module is connected to the exhaust gas treatment equipment. When an early warning signal is received, the emergency processing module sends instructions to the exhaust gas treatment equipment to take emergency measures at the accident location; the emergency measures include: stopping the production process, reducing the generation of exhaust gas, closing the exhaust port, alerting the staff, and real-time monitoring of the exhaust gas data for the staff's reference.

[0035] Preferably, the use of a visualization tool to visualize the accident location and monitoring data includes:

[0036] Visual data is obtained by preprocessing the corrected data, wherein the preprocessing includes: data cleaning, aggregation, calculation of derived indicators and statistical analysis processing; based on the visual data, image data is generated to associate the visual data with charts; based on the image data, real-time monitoring data and early warning information are displayed through charts; and,

[0037] Based on real-time monitoring data, data curves are generated through visualization tools to reflect data change trends.

[0038] The existing technical solutions analyze the causes of abnormalities by collecting data, which may lead to the inaccuracy of the causes of abnormalities judged in advance by the staff. The main reason for this problem is that a single data cannot provide too much information to the staff, and the analyzed data are all past data, which leads to the staff's judgment basis being very limited, and thus the accuracy of judging the causes of abnormalities is not high.

[0039] The present invention visualizes the collected data and displays it on a visual interface, which can not only provide staff with more reliable and easy-to-observe chart data, but also reflect the trend of data changes through real-time monitoring, so that staff can not only obtain more intuitive information, but also obtain more basis for judgment, thereby improving the accuracy of judgment.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The existing technology for monitoring industrial pollutants does not take into account the fact that exhaust gas will be affected by temperature when collecting exhaust gas. When the exhaust gas encounters low-temperature cold air, the gas may liquefy, causing the gas data collected by the sensor to be different from the actual data. The present invention obtains a correction factor through the inversely proportional relationship between gas concentration and temperature, constructs a correction model based on the correction factor, and corrects the collected gas concentration, so that the corrected gas concentration is closer to the actual gas concentration, thereby improving the accuracy of the collected data.

[0042] 2. In the prior art, the data collected by the sensor during the treatment of exhaust gas is abnormal. In order to investigate the cause, the staff analyzes the possible causes of the abnormality through the collected data. However, due to the lack of basis for analysis and the inability to visually observe the changes in the data, the accuracy of pre-judgment of the causes of the abnormality is not high. The present invention visualizes the collected data, displays the chart data on a visual interface, and reflects the trend of data changes through real-time monitoring, which not only enables the staff to obtain more intuitive information, but also obtains more basis for judgment, thereby improving the accuracy of judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0044] Figure 1 It is a schematic diagram of the overall framework of an embodiment of the present invention;

[0045] Figure 2 The figure is a schematic diagram of a specific process of an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. 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.

[0047] See also Figure 1 - Figure 2 , the first aspect of the present invention provides a safety and environmental protection emergency system based on industrial interconnection and digital panorama, including: a data analysis module, and a data acquisition module and an emergency processing module connected to the data analysis module;

[0048] Data acquisition module: using a panoramic acquisition device to collect image data of a target area at several acquisition angles; wherein the panoramic acquisition device includes a panoramic camera or a VR camera, and the target area is an exhaust gas treatment area; and,

[0049] The monitoring data is collected by data sensors arranged in the target area; wherein the data sensors include air quality sensors and temperature sensors;

[0050] A reference point is selected in the target area, and based on the reference point, a panoramic acquisition device is used to capture a 360° full coverage of the target area. The actual exhaust gas concentration and the actual exhaust gas temperature at the corresponding acquisition time are acquired in real time through the temperature sensor and air quality sensor installed in the exhaust port. The ambient temperature is acquired through the temperature sensor installed outside the exhaust port. The ambient temperature will not be affected by the exhaust port.

[0051] For example: there is an exhaust gas treatment area where the concentration and temperature of the exhaust gas need to be monitored and the temperature conditions of the environment need to be understood; a panoramic camera is used as a panoramic acquisition device, as well as a temperature sensor and an air quality sensor for data acquisition.

[0052] First, a reference point is selected in the target area, such as the center of the area; then, based on the reference point, a panoramic camera is used to shoot a 360° full coverage of the target area; in this way, image data of the target area in all directions can be obtained;

[0053] At the same time, a temperature sensor and an air quality sensor are installed in the exhaust port; these sensors will collect the actual concentration of the exhaust gas and the actual temperature of the exhaust gas at the corresponding collection time in real time; in addition, a temperature sensor is installed outside the exhaust port, which collects the ambient temperature; since it is not affected by the exhaust port, it can provide accurate ambient temperature data.

[0054] Data analysis module: synthesizes image data from several acquisition angles based on a panoramic stitching tool and a stitching algorithm to obtain a panoramic image; wherein the panoramic stitching tool includes PTGui, Hugin or Adobe Photoshop software, and the stitching algorithm includes a SIFT algorithm or a SURF algorithm; and,

[0055] Extract air quality data from the monitoring data, correct the air quality data, and obtain corrected data; identify whether the corrected data is abnormal through the deep analysis model; if so, send a warning signal to the emergency processing module; wherein the deep analysis model is built based on the artificial intelligence model;

[0056] The image data is detected and matched by a panoramic stitching tool to obtain feature points; the feature points are calculated based on a stitching algorithm to obtain descriptors; the descriptors are matched in the image data to obtain feature point pairs; and the image transformation relationship of the feature point pairs is obtained using a geometric transformation model; wherein the geometric transformation model includes an affine transformation or a projection transformation; and a panoramic image is constructed based on the image data and the image transformation relationship.

[0057] For example, to perform panoramic stitching of Image 1, Image 2, and Image 3, use PTGui as the panoramic stitching tool and select the SIFT algorithm for feature point detection and matching.

[0058] First, import the image data into the PTGui software; PTGui will automatically detect and match feature points to generate feature point pairs; then, use the stitching algorithm to calculate the descriptors of the feature points; the descriptors represent the unique characteristics of the feature points and can be used for matching feature points; next, match the descriptors in the image data to obtain feature point pairs; feature point pairs represent feature points at corresponding positions in different images.

[0059] In this example, assume that PTGui generates the following feature point pairs:

[0060] Feature point pairs of image 1: (p1, p2), (p3, p4)

[0061] Feature point pairs of image 2: (p5, p6), (p7, p8)

[0062] Feature point pairs of image 3: (p9, p10), (p11, p12)

[0063] Then, a geometric transformation model (such as affine transformation or projection transformation) is used to calculate the image transformation relationship between feature point pairs; these transformation relationships describe how to move feature points on one image to corresponding positions on another image; finally, a panoramic image is constructed based on the image data and the image transformation relationship; the final panoramic image can be obtained by overlapping and fusing each image according to its corresponding transformation relationship.

[0064] The actual concentration of the collected exhaust gas is marked as C, the actual temperature of the exhaust gas at the time of collection is marked as T, and the ambient temperature is marked as T0, and the correction model is obtained: Cx=C×F, where Cx is the corrected exhaust gas concentration and F is the correction factor; and, based on the formula PV=nRT and the relationship between gas concentration and volume C=n / V, the formula is deduced: C=P / RT, and it is concluded that the gas concentration is inversely proportional to the temperature, thereby obtaining the correction factor F=A×(T0 / T), that is, the correction model Cx=C×A×(T0 / T), where P represents pressure, V represents gas volume, n represents the number of moles, R is the ideal gas constant, and A is the proportionality coefficient; based on the exhaust gas historical data, the actual concentration of the exhaust gas and the actual temperature of the exhaust gas are generated according to the standard input data; the corresponding historical correction data is obtained by inputting the standard input data into the deep analysis model, wherein the deep analysis model is obtained based on the training of the artificial intelligence model; and, based on the comparison between the historical correction data and the exhaust gas safety emission index, if the safety emission index is exceeded, it is judged that the exhaust gas quality is abnormal, and an early warning signal is sent to the early warning processing module; the exhaust gas historical data is the historical data of the collection target area.

[0065] For example: an exhaust gas collection system, which can collect exhaust gas concentration and exhaust gas temperature in the target area; assuming that the following exhaust gas historical data are collected: actual exhaust gas concentration C = 100ppm, actual exhaust gas temperature T = 300K, ambient temperature T0 = 298K, proportional coefficient A = 2.

[0066] According to the correction model Cx = C × F, we can calculate the correction factor F:

[0067] F=A×(T0 / T)=2×(298K / 300K)=1.9866

[0068] Apply the correction factor to the exhaust gas concentration to obtain the corrected exhaust gas concentration:

[0069] Cx=C×A×(T0 / T)=100×1.9866=198.66

[0070] The actual concentration and actual temperature of the exhaust gas are used as standard input data and input into the deep analysis model. Based on the trained artificial intelligence model, the deep analysis model can analyze and process the input data and generate historical correction data; the historical correction data is compared with the exhaust gas safety emission index. If the corrected exhaust gas concentration exceeds the safety emission index, that is, Cx>safety emission index, the exhaust gas quality is judged to be abnormal, and an early warning signal is sent to the early warning processing module.

[0071] Emergency processing module: after receiving the warning signal, locate the abnormal position on the panoramic image based on the correction data and mark it as the accident position; initiate the emergency plan and take emergency measures at the accident position; and,

[0072] Use visualization tools to visualize the accident location and monitoring data; visualization tools include Tableau, Power BI, D3.js or matplotlib.

[0073] Based on the position information of the abnormal sensor corresponding to the correction data, the accident position is located on the panoramic image; based on the accident position, the image information of the corresponding abnormal sensor in the panoramic image is retrieved; the position information includes: longitude and latitude and pixel position relative to the panoramic image; the image information includes: the specific location of the abnormal sensor and the surrounding environment and building facilities; based on wireless communication technology, the emergency treatment module is connected to the exhaust gas treatment equipment, and when the warning signal is received, the emergency treatment module sends instructions to the exhaust gas treatment equipment to take emergency measures for the accident position; the emergency measures include: stopping the production process, reducing the generation of exhaust gas, and closing the exhaust port, alerting the staff, and real-time monitoring of the exhaust gas data for the staff's reference. Visual data is obtained by preprocessing the correction data, wherein the preprocessing includes: data cleaning, aggregation, calculation of derived indicators and statistical analysis processing; based on the visual data, image data that associates the visual data with charts is generated; based on the image data, real-time monitoring data and warning information are displayed through charts; and, based on the real-time monitoring data, data curves are generated through visualization tools to reflect data change trends.

[0074] For example, the correction data of exhaust gas concentration was obtained through the correction model and the deep analysis model, and the early warning signal of abnormal exhaust gas quality was received. The following is an example of how to locate the accident location on a panoramic image based on the correction data and visualize it:

[0075] First, the accident location is located on the panoramic image according to the location information of the abnormal sensor corresponding to the correction data. Assume that the pixel coordinates of the accident location in the panoramic image have been determined to be (x, y).

[0076] Next, the image information corresponding to the accident location in the panoramic image is retrieved; this includes the specific location of the abnormal sensor as well as the surrounding environment and building facilities; it can be displayed by extracting the area in the image or marking the accident location.

[0077] Use visualization tools (such as Tableau, Power BI, D3.js, or matplotlib) to visualize the accident location and monitoring data. For example, you can mark the accident location on a panoramic image and use data charts to display the corrected data of exhaust gas concentration and other related monitoring data (such as temperature, pressure, etc.).

[0078] Based on wireless communication technology, the emergency treatment module is connected to the exhaust gas treatment equipment. When receiving the warning signal, the emergency treatment module can send instructions to the exhaust gas treatment equipment to take emergency measures at the accident location. These emergency measures may include stopping the production process, reducing the generation of exhaust gas, and closing the exhaust port. At the same time, the staff is warned and the exhaust gas data is monitored in real time for reference by the staff.

[0079] Visual data can be obtained by preprocessing the corrected data, which includes steps such as data cleaning, aggregation, calculation of derived indicators, and statistical analysis.

[0080] Based on the visualization data, image data is generated that associates the visualization data with the chart. You can select appropriate visualization tools to operate according to actual needs to display real-time monitoring data and warning information.

[0081] Finally, based on the real-time monitoring data, a data curve is generated through a visualization tool to reflect the data change trend. This can help staff understand the exhaust gas monitoring data more intuitively and take corresponding measures in a timely manner.

[0082] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0083] Working principle of the present invention:

[0084] Use panoramic acquisition equipment to collect image data of several acquisition angles of the target area; and, obtain monitoring data through data sensors arranged in the target area; synthesize the image data of several acquisition angles based on panoramic stitching tools and stitching algorithms to obtain a panoramic image; extract air quality data from the monitoring data, and correct the air quality data to obtain corrected data; identify whether the corrected data is abnormal through a deep analysis model; if so, send a warning signal to the emergency processing module; wherein the deep analysis model is constructed based on an artificial intelligence model; after receiving the warning signal, locate the abnormal position on the panoramic image based on the corrected data and mark it as the accident position; initiate the emergency plan to take emergency measures at the accident position; and, use visualization tools to visualize the accident position and monitoring data.

[0085] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A safety and environmental protection emergency system based on industrial interconnection and digital panorama, characterized in that: include: A data analysis module, and a data acquisition module and an emergency processing module connected to the data analysis module; Data acquisition module: using a panoramic acquisition device to collect image data of a target area at several acquisition angles; wherein the panoramic acquisition device includes a panoramic camera or a VR camera, and the target area is an exhaust gas treatment area; and, The monitoring data is collected by data sensors arranged in the target area; wherein the data sensors include air quality sensors and temperature sensors; Data analysis module: synthesizes image data from several acquisition angles based on a panoramic stitching tool to obtain a panoramic image; wherein the panoramic stitching tool includes PTGui, Hugin or Adobe Photoshop software; and, Extract air quality data from the monitoring data, correct the air quality data, and obtain corrected data; identify whether the corrected data is abnormal through the deep analysis model; if so, send a warning signal to the emergency processing module; wherein the deep analysis model is built based on the artificial intelligence model; Emergency processing module: after receiving the warning signal, locate the abnormal position on the panoramic image based on the correction data and mark it as the accident position; initiate the emergency plan and take emergency measures at the accident position; and, Use visualization tools to visualize the accident location and monitoring data; visualization tools include Tableau, Power BI, D3.js or matplotlib; The correcting of the air quality data to obtain the corrected data includes: The actual concentration of the collected exhaust gas is marked as C, the actual temperature of the exhaust gas at the time of collection is marked as T, and the ambient temperature is marked as T0, and the correction model is obtained: Cx=C×F, where Cx is the corrected exhaust gas concentration and F is the correction factor; and Based on the formula PV=nRT and the relationship between gas concentration and volume C=n / V, the formula: C=P / RT is derived, and it is concluded that gas concentration is inversely proportional to temperature, thus obtaining the correction factor F=A×(T0 / T), that is, the correction model Cx=C×A×(T0 / T), where P represents pressure, V represents gas volume, n represents the number of moles, R is the ideal gas constant, and A is the proportionality coefficient; The identifying whether the correction data is abnormal by using a deep analysis model includes: Generate standard input data based on the actual concentration of exhaust gas and the actual temperature of exhaust gas corresponding to the historical exhaust data; generate standard output data based on whether the exhaust gas quality of the historical exhaust data is abnormal, and train the deep analysis model through the standard input data and the standard output data; wherein the determination of whether the exhaust gas quality of the historical exhaust data is abnormal includes: based on the actual concentration of exhaust gas and the actual temperature of exhaust gas corresponding to the historical data, correct the historical data through the correction model to obtain historical correction data; and, Based on the comparison between the historical correction data and the exhaust gas safety emission index, if the safety emission index is exceeded, the exhaust gas quality is judged to be abnormal and a warning signal is sent to the warning processing module; the exhaust gas historical data is the historical data of the target area for collection.

2. According to claim 1, a safety and environmental protection emergency system based on industrial interconnection and digital panorama is characterized in that: The data sensors arranged in the target area collect monitoring data, including: The actual exhaust concentration and the actual exhaust temperature at the corresponding collection time are collected in real time by the temperature sensor and air quality sensor installed in the exhaust port; and The ambient temperature is collected by a temperature sensor installed outside the exhaust port; wherein the ambient temperature is not affected by the exhaust port.

3. According to claim 1, a safety and environmental protection emergency system based on industrial interconnection and digital panorama is characterized in that: The method of synthesizing image data from several acquisition angles based on the panoramic stitching tool to obtain a panoramic image includes: Detect and match the image data through the panoramic stitching tool to obtain feature points; calculate the feature points based on the stitching algorithm to obtain descriptors; match the descriptors in the image data to obtain feature point pairs; and, The image transformation relationship of the feature point pairs is obtained by using a geometric transformation model; wherein the geometric transformation model includes an affine transformation or a projection transformation; and a panoramic image is constructed based on the image data and the image transformation relationship.

4. According to claim 1, a safety and environmental protection emergency system based on industrial interconnection and digital panorama is characterized in that: The method of locating the abnormal position on the panoramic image based on the correction data and marking it as the accident position includes: Based on the position information of the abnormal sensor corresponding to the correction data, the accident position is located on the panoramic image; based on the accident position, the image information of the corresponding abnormal sensor in the panoramic image is retrieved; the position information includes: longitude and latitude and pixel position relative to the panoramic image; the image information includes: the specific location of the abnormal sensor and the surrounding environment and building facilities.

5. According to claim 1, a safety and environmental protection emergency system based on industrial interconnection and digital panorama is characterized in that: The emergency plan is activated to take emergency measures at the accident location, including: Based on wireless communication technology, the emergency processing module is connected to the exhaust gas treatment equipment. When an early warning signal is received, the emergency processing module sends instructions to the exhaust gas treatment equipment to take emergency measures at the accident location; the emergency measures include: stopping the production process, reducing the generation of exhaust gas, closing the exhaust port, alerting the staff, and real-time monitoring of the exhaust gas data for the staff's reference.

6. According to claim 1, a safety and environmental protection emergency system based on industrial interconnection and digital panorama is characterized in that: The visualization tool is used to visualize the accident location and monitoring data, including: Visual data is obtained by preprocessing the corrected data, wherein the preprocessing includes: data cleaning, aggregation, calculation of derived indicators and statistical analysis processing; based on the visual data, image data is generated to associate the visual data with charts; based on the image data, real-time monitoring data and early warning information are displayed through charts; and, Based on real-time monitoring data, data curves are generated through visualization tools to reflect data change trends.

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