Gas monitoring and analyzing system with multiple intelligent sensors working cooperatively

Abstract

The invention provides a gas monitoring and analyzing system with multiple intelligent sensors working cooperatively, which belongs to the technical field of sensor monitoring and comprises an area splitting module, an initial monitoring module, a data processing module, a data processing module, a data processing module and a data analysis module, and is characterized in that the area splitting module is used for carrying out atmospheric environment gridding on split areas of a factory according to current area facilities; the control module is used for controlling the fixed sensor and the mobile sensor to cooperatively monitor the current gas information of each gridding unit to obtain an initial environment diagram; the frequency setting module is used for updating the acquisition frequency of each fixed sensor and each mobile sensor in each divided area according to the initial environment map and predicted meteorological data; and the gas quality early warning module is used for receiving the new gas information collected by the fixed sensor and the mobile sensor, constructing a gas quality early warning graph of the target factory and outputting early warning. A monitoring network of static coverage and dynamic tracking is formed, the sampling frequency is adaptively adjusted, control response errors are avoided as far as possible, and the monitoring efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of sensor monitoring technology, and in particular to a gas monitoring and analysis system in which multiple intelligent sensors work in coordination. Background Art

[0002] With the acceleration of industrialization, the impact of various gaseous pollutants emitted by factories (such as volatile organic compounds, nitrogen oxides, and sulfides) on the atmospheric environment is becoming increasingly significant. Traditional factory data collection at fixed frequencies cannot adapt to real-time changes in meteorological conditions (such as wind speed, wind direction, and temperature), resulting in delayed pollution warnings. Atmospheric environmental monitoring also has the following pain points: single or sparse sensor deployments struggle to cover the complex distribution of pollution within factory areas, limiting capture capabilities. Fixed sensors and mobile sensors (such as those mounted on drones and inspection robots) often operate independently, and due to potential control response errors, the collected data is inaccurate, resulting in inefficient gas monitoring.

[0003] Therefore, the present invention proposes a gas monitoring and analysis system in which multiple intelligent sensors work in coordination. Summary of the Invention

[0004] The present invention provides a gas monitoring and analysis system in which multiple intelligent sensors work in coordination, so as to solve the above-mentioned technical problems.

[0005] The present invention provides a gas monitoring and analysis system in which multiple intelligent sensors work in coordination, comprising:

[0006] The regional splitting module is used to refine the functional attributes of the target plant to achieve regional splitting and perform atmospheric environment gridding on the split regions according to the current regional facilities. The atmospheric environment gridding result is the first refined unit of the independent deviation closed contour in the split region and the second refined unit of the remaining area excluding the independent deviation closed contour.

[0007] An initial monitoring module is configured to set fixed sensors and mobile sensors based on the current regional facilities and grid cells of each divided area, and control the fixed sensors and mobile sensors to collaboratively monitor the current gas information of each grid cell to obtain an initial environmental map, wherein the fixed sensors and mobile sensors constitute a multi-intelligent sensor;

[0008] A frequency setting module, configured to update the acquisition frequency of each fixed sensor and mobile sensor in each divided area based on the initial environmental map and the predicted meteorological data;

[0009] The gas quality warning module is used to receive new gas information collected by the fixed sensors and the mobile sensors based on the edge nodes that are in communication with the fixed sensors and the mobile sensors, and to construct a gas quality warning map of the target factory to output warnings.

[0010] Preferably, the region splitting module includes:

[0011] a functional decomposition unit, configured to perform drone monitoring of a target factory to obtain a factory deployment structure, and perform functional structural decomposition of the factory deployment structure based on a construction facility diagram of the target factory to obtain a plurality of decomposition areas, wherein the construction facility diagram includes at least one functional attribute, and the decomposition areas are the areas after the actual decomposition;

[0012] a set determination unit, configured to compare and analyze the split region with the standard region to construct a deviation construction-position set, and determine a plurality of independent deviation position profiles;

[0013] a contour supplement unit, configured to input the position set of each independent deviation position contour into the adaptive contour filling model for adaptive contour supplementation to obtain an independent deviation closed contour, and to match the first refinement unit of the independent deviation position contour from a ratio-coefficient-setting comparison table based on an actual ratio of the contour length of the independent deviation closed contour to the area contour of the corresponding split area and an actual variation coefficient of the facility type within the contour to the facility type within the corresponding standard area;

[0014] a first meshing unit, configured to perform first meshing on the corresponding independent deviation position contours in the split region according to the first refinement unit;

[0015] The second meshing unit is used to match the second refinement unit of the remaining area except the independent deviation position contour in the split area from the function-setting comparison table according to the area function, and perform a second meshing on the remaining area according to the second refinement unit.

[0016] Preferably, the first gridding unit includes:

[0017] a complete judgment subunit, configured to perform a first meshing on the corresponding independent deviation closed contour according to the first refinement unit, and judge whether there are incomplete first refinement units in the edge meshing units of the independent deviation closed contour;

[0018] If it does not exist, the first meshed element corresponding to the independent deviation closed contour is retained unchanged;

[0019] If present, determine the peripheral facility attributes of the incomplete first refinement unit;

[0020] a functional attribute judgment subunit, configured to retain the incomplete first refined unit if the peripheral facility attribute is irrelevant to the functional attribute of the corresponding split area;

[0021] If the peripheral facility attribute is related to the functional attribute of the corresponding split area, and the corresponding peripheral facility belongs to the non-deviation area, then the incomplete first refinement unit is connected to the non-deviation area of ​​the peripheral facility by a line segment;

[0022] an expansion subunit, configured to replace the incomplete first refinement unit with a complete first refinement unit if the connecting line segment is a straight line segment;

[0023] If the connecting line segment is a curved segment, then the outermost horizontal boundary point and the outermost vertical boundary point of the curved segment are determined, and an initial rectangular frame is drawn;

[0024] Expanding the initial rectangular frame as an expansion area and the corresponding incomplete first refinement unit;

[0025] If the peripheral facility attributes are related to the functional attributes of the corresponding split area, and the corresponding peripheral facility belongs to the deviation area, then the incomplete first refined unit is retained.

[0026] Preferably, it also includes:

[0027] a vector construction module, configured to construct an analysis vector based on a first position of each grid unit in the divided area, a correlation with a functional attribute of the divided area, and a unit facility corresponding to the grid unit extracted from the facilities in the current area;

[0028] a pollutant determination module, configured to input the analysis vector into a gas analysis model to obtain a gas pollution set corresponding to a grid cell, wherein the gas pollution set includes gas pollutants and a diffusion factor of each gas pollutant;

[0029] The sensor setting module is used to find the first pollutant whose diffusion factor is less than a preset factor, set a fixed sensor to the corresponding grid unit, and at the same time, set a mobile sensor according to the diffusion path of each remaining pollutant in the unit facility in each grid unit.

[0030] Preferably, the initial monitoring module includes:

[0031] A response set construction unit is used to issue N synchronous working instructions to all sensors under each grid unit based on the controller, and capture the first time point of each instruction issuance and the second time point when each sensor receives the corresponding instruction and starts working, to construct a response set S for each sensor;

[0032] A matrix construction unit is used to regard the time difference between each response time and the standard time in each response set S of the sensor of the same model as the first set, and to sort the time differences in each first set to construct a difference matrix;

[0033] a function determination unit, configured to dynamically divide the difference matrix to obtain a response function and predict a feedback compensation time of a corresponding sensor;

[0034] The environment map construction unit is used to issue the N+1th instruction to the sensor based on the controller-dependent feedback compensation time, and output the current gas information of each grid unit to form an initial environment map.

[0035] Preferably, the function determination unit includes:

[0036] An initial partitioning subunit is used to lock the mutation elements of each row vector in the difference matrix according to a preset time scale window, and perform initial partitioning on the difference matrix to obtain a first matrix and a second matrix, wherein the number of columns of the first matrix is ​​the maximum number of columns in the left row vector in the initial partitioning result, and the number of columns of the second matrix is ​​the maximum number of columns in the right row vector in the initial partitioning result, and when there is a column number of 0 in the left row vector or the right row vector, all are filled with the same value according to the new scale, when there is a column number in the left row vector that is not the maximum number of columns, backward filling with the same value is performed according to the last element in the row vector on the corresponding side until the maximum number of columns is filled, and when there is a column number in the right row vector that is not the maximum number of columns, forward filling with the same value is performed according to the first element in the row vector on the corresponding side until the maximum number of columns is filled;

[0037] A secondary division subunit is configured to determine a decay time of the controller based on the total number of historical uses and the decay time after N synchronous work instructions, and to perform secondary division on the difference matrix to obtain a new scale window by adding the new scale window to a preset time scale window to obtain a third matrix and a fourth matrix;

[0038] The compensation determination subunit is used to solve the characteristic functions of the first matrix, the second matrix, the third matrix and the fourth matrix respectively, and calculate the feedback compensation time of the corresponding sensor based on the corresponding characteristic coefficient of each sensor in all the characteristic functions.

[0039] Preferably, the frequency setting module includes:

[0040] a model analysis unit, configured to input the predicted meteorological data into a gas parameter impact analysis model, and input an impact coefficient of each gas parameter affected by the predicted meteorological data;

[0041] A coefficient judgment unit, configured to keep the original acquisition frequency of the corresponding sensor unchanged if the influence coefficient is less than a preset coefficient, in which case the original acquisition frequency is the first acquisition frequency;

[0042] a frequency updating unit configured to update the original collection frequency to obtain a first collection frequency based on the influence coefficient and the influence level of the preset coefficient, and in combination with the gas collection weight of the corresponding fixed sensor, if the influence coefficient is not less than the preset coefficient;

[0043] At the same time, according to the influence level of the influence coefficient and the preset coefficient, and in combination with the gas collection weight of the corresponding mobile sensor under the moving path of the predicted period corresponding to the predicted meteorological data, the original collection frequency is updated to obtain the second collection frequency.

[0044] Preferably, the gas quality early warning module includes:

[0045] a significant bar construction unit, configured to determine the quality of each gas parameter in each gridded unit based on the new gas information, and draw a quality significant bar based on the corresponding gridded unit, wherein the quality significant bar includes a quality color block for each gas parameter and a comprehensive warning block;

[0046] The early warning map construction unit is used to construct a gas quality early warning map based on the quality significant strips of all gridded units and output it.

[0047] Compared with the prior art, the present invention has the following advantages:

[0048] Based on the functional attributes and pollution source characteristics, the areas are refined, and the "independent deviation closed contours" (such as closed equipment areas) and "remaining areas" are distinguished to achieve differentiated design of monitoring units. Fixed sensors provide basic data, and mobile sensors dynamically fill in the blind spots to form a "static coverage + dynamic tracking" monitoring network. Combined with the initial environmental map and meteorological forecast, the sampling frequency is adaptively adjusted to avoid control response errors as much as possible to improve monitoring efficiency, build a real-time early warning map, and effectively shorten the pollution response time.

[0049] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0050] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] 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:

[0052] Figure 1 This is a structural diagram of a gas monitoring and analysis system in which multiple intelligent sensors work in collaboration according to an embodiment of the present invention;

[0053] Figure 2 A deployment diagram of multiple intelligent sensors in an embodiment of the present invention;

[0054] Figure 3 This is a structural diagram of a quality-significant bar in an embodiment of the present invention;

[0055] Figure 4 This is a structural diagram of an initial rectangular frame in an embodiment of the present invention;

[0056] Figure 5 This is a unit expansion based on a straight line segment in an embodiment of the present invention;

[0057] Figure 6 This is a unit expansion based on curve segments in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The preferred embodiments of the present invention are described below with reference to 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.

[0059] The present invention provides a gas monitoring and analysis system with multiple intelligent sensors working in collaboration, such as Figure 1 Shown, including:

[0060] The regional splitting module is used to refine the functional attributes of the target plant to achieve regional splitting and perform atmospheric environment gridding on the split regions according to the current regional facilities. The atmospheric environment gridding result is the first refined unit of the independent deviation closed contour in the split region and the second refined unit of the remaining area excluding the independent deviation closed contour.

[0061] An initial monitoring module is configured to set fixed sensors and mobile sensors based on the current regional facilities and grid cells of each divided area, and control the fixed sensors and mobile sensors to collaboratively monitor the current gas information of each grid cell to obtain an initial environmental map, wherein the fixed sensors and mobile sensors constitute a multi-intelligent sensor;

[0062] A frequency setting module, configured to update the acquisition frequency of each fixed sensor and mobile sensor in each divided area based on the initial environmental map and the predicted meteorological data;

[0063] The gas quality warning module is used to receive new gas information collected by the fixed sensors and the mobile sensors based on the edge nodes that are in communication with the fixed sensors and the mobile sensors, and to construct a gas quality warning map of the target factory to output warnings.

[0064] In this embodiment, the target factory refers to an oil refinery, and its functional attributes include: high-temperature combustion attributes, chemical reaction attributes, material storage attributes, and wastewater treatment attributes. The high-temperature combustion attributes correspond to the heating furnace and boiler areas of the oil refinery, the chemical reaction attributes correspond to the reaction tower and reactor areas of the oil refinery, the material storage attributes correspond to the crude oil tank and finished oil tank areas of the oil refinery, and the wastewater treatment attributes correspond to the aeration tank and sludge dewatering room areas of the oil refinery, thereby realizing regional splitting.

[0065] In this embodiment, the current regional facilities refer to the facility conditions of the corresponding split area, including: facility layout and facility products. Taking the crude oil tank and finished oil tank areas as an example, the facilities existing therein include: fixed roof storage tanks, floating roof storage tanks, loading and unloading piers, and oil and gas recovery devices.

[0066] In this embodiment, the independent deviation closed contour is determined based on the difference between the actual factory facilities and the pre-planned factory facilities, and the first refinement unit and the second refinement unit are both divided in a grid form.

[0067] In this embodiment, fixed sensors such as electrochemical sensors (monitoring H2S, SO2), PID photoionization detectors (monitoring VOC S ), laser scattering instrument (monitoring PM 2.5 、PM 1.0 ).

[0068] Mobile sensors, for example, the multi-gas detectors carried by inspection robots monitor oxygen, carbon monoxide, etc. The training path of each mobile sensor is pre-planned.

[0069] In this embodiment, collaborative monitoring refers to ensuring that the gas data collected by the sensors are collected at the same time as much as possible, so as to facilitate a global analysis of the overall gas situation in the factory at a certain moment.

[0070] In this embodiment, the initial environment map includes gas data collected by each sensor in each grid unit and is displayed on a geographical location map corresponding to the factory.

[0071] In this embodiment, the forecasted weather data is obtained based on the weather station.

[0072] In this embodiment, the frequency update is to ensure the comprehensiveness of gas monitoring based on the actual situation of the factory on the basis of synchronous data collection by sensors of the same model, and to avoid monitoring omissions due to abnormalities.

[0073] In this embodiment, the edge node adopts an industrial-grade edge computing gateway.

[0074] In this embodiment, the early warning map is constructed based on the quality significance bars of all gridded cells.

[0075] The beneficial effects of the above technical solution are: refining areas based on functional attributes and pollution source characteristics, distinguishing "independent deviation closed contours" (such as closed equipment areas) from "remaining areas", realizing differentiated design of monitoring units, fixed sensors providing basic data, and mobile sensors dynamically filling in blind spots, forming a "static coverage + dynamic tracking" monitoring network, combining the initial environmental map with meteorological forecasts, adaptively adjusting the sampling frequency, avoiding control response errors as much as possible to improve monitoring efficiency, building real-time early warning maps, and effectively shortening pollution response time.

[0076] The present invention provides a gas monitoring and analysis system in which multiple intelligent sensors work in collaboration, wherein the region splitting module comprises:

[0077] a functional decomposition unit, configured to perform drone monitoring of a target factory to obtain a factory deployment structure, and perform functional structural decomposition of the factory deployment structure based on a construction facility diagram of the target factory to obtain a plurality of decomposition areas, wherein the construction facility diagram includes at least one functional attribute, and the decomposition areas are the areas after the actual decomposition;

[0078] a set determination unit, configured to compare and analyze the split region with the standard region to construct a deviation construction-position set, and determine a plurality of independent deviation position profiles;

[0079] a contour supplement unit, configured to input the position set of each independent deviation position contour into the adaptive contour filling model for adaptive contour supplementation to obtain an independent deviation closed contour, and to match the first refinement unit of the independent deviation position contour from a ratio-coefficient-setting comparison table based on an actual ratio of the contour length of the independent deviation closed contour to the area contour of the corresponding split area and an actual variation coefficient of the facility type within the contour to the facility type within the corresponding standard area;

[0080] a first meshing unit, configured to perform first meshing on the corresponding independent deviation position contours in the split region according to the first refinement unit;

[0081] The second meshing unit is used to match the second refinement unit of the remaining area except the independent deviation position contour in the split area from the function-setting comparison table according to the area function, and perform a second meshing on the remaining area according to the second refinement unit.

[0082] In this embodiment, the plant deployment structure is the physical layout structure of the refinery obtained through drone aerial photography and three-dimensional modeling technology, the construction facility map is the CAD drawing of the refinery design stage, and the actual split area is realized based on the plant deployment structure.

[0083] In this embodiment, the standard area is an area corresponding to a function determined based on a CAD drawing.

[0084] In this embodiment, the deviation construction-position set is a set of areas and their location coordinates where the actual construction differs from the standard design. For example, operations such as adding temporary loading and unloading ports and expanding the tank area result in the actual factory deployment structure being different from the structure planned on the CAD drawing. Therefore, it is necessary to determine the existing deviation positions to provide a basis for subsequent gridding.

[0085] In this embodiment, the independent deviation position contour is a geometric contour of the deviation area with a clear boundary obtained by sequentially connecting the deviation construction-position sets, and the contour can be an unclosed line segment. Therefore, in order to perform gridding, it is more reasonable to implement it based on the closed area, and it is necessary to construct an independent deviation closed contour based on the independent deviation position contour.

[0086] In this embodiment, the adaptive contour filling model is based on a machine learning algorithm and is a model that predicts a complete closed contour based on known contour points. It is implemented using a U-Net network.

[0087] In this embodiment, the first refinement unit is a refined monitoring unit designed for the independent deviation area, and the second refinement unit is a conventional monitoring unit set for the remaining area (standard area).

[0088] In this embodiment, the ratio-coefficient-setting comparison table is as follows:

[0089]

[0090]

[0091] For example, the contour length ratio is 0.9 and the facility coefficient change is 0.8. At this time, the size of the first refined unit is: 8 meters × 8 meters. After determining the grid unit, the sensors can be directly installed according to the factory gas monitoring requirements. Generally, each grid is installed with a group of solid sensors (solid sensors that monitor different gas parameters) and a mobile sensor.

[0092] In this embodiment, the function-setting comparison table stores the functional attributes of the regional functions and the regional division unit size (second refinement unit) matching the functional attributes, which can be directly obtained. For example, 10 meters × 10 meters is used as the second refinement unit to grid the corresponding remaining area.

[0093] In this embodiment, the independent deviation areas are divided into grids to ensure comprehensive monitoring.

[0094] In this embodiment, the facility variation coefficient is implemented based on the complexity of the facility layout of the factory.

[0095] In this embodiment, the square root of the product of (the actual number of facility types within the outline / the number of facility types within the standard area) and (the total number of facilities involved in the actual number of types / the total number of facilities involved in the quasi-area) is the facility variation coefficient.

[0096] The beneficial effect of the above technical solution is: through refined regional segmentation and differentiated sensor configuration, the reliability and comprehensive monitoring of gas information of the target factory are guaranteed.

[0097] The present invention provides a gas monitoring and analysis system in which multiple intelligent sensors work in collaboration, wherein the first gridding unit comprises:

[0098] a complete judgment subunit, configured to perform a first meshing on the corresponding independent deviation closed contour according to the first refinement unit, and judge whether there are incomplete first refinement units in the edge meshing units of the independent deviation closed contour;

[0099] If it does not exist, the first meshed element corresponding to the independent deviation closed contour is retained unchanged;

[0100] If present, determine the peripheral facility attributes of the incomplete first refinement unit;

[0101] a functional attribute judgment subunit, configured to retain the incomplete first refined unit if the peripheral facility attribute is irrelevant to the functional attribute of the corresponding split area;

[0102] If the peripheral facility attribute is related to the functional attribute of the corresponding split area, and the corresponding peripheral facility belongs to the non-deviation area, then the incomplete first refinement unit is connected to the non-deviation area of ​​the peripheral facility by a line segment;

[0103] an expansion subunit, configured to replace the incomplete first refinement unit with a complete first refinement unit if the connecting line segment is a straight line segment;

[0104] If the connecting line segment is a curved segment, then the outermost horizontal boundary point and the outermost vertical boundary point of the curved segment are determined, and an initial rectangular frame is drawn;

[0105] Expanding the initial rectangular frame as an expansion area and the corresponding incomplete first refinement unit;

[0106] If the peripheral facility attributes are related to the functional attributes of the corresponding split area, and the corresponding peripheral facility belongs to the deviation area, then the incomplete first refined unit is retained.

[0107] In this embodiment, the independent deviation closed contour is gridded according to the requirements of the first refinement unit to more accurately monitor the gas information in the area. For example, the small storage tank area is divided into square grids with a side length of 5 meters, and each grid is a first gridding unit.

[0108] In this embodiment, an incomplete first-level refined cell is a cell within the edge meshing process whose shape, size, or contained facilities do not fully meet the initial design criteria for the first-level refined cell due to its location at the edge of the outline. For example, at the edge of a tank area, a grid cell may be blocked by a wall, with an actual area of ​​only half the standard grid size. This makes it impossible to deploy the full number of sensors originally planned. This is an incomplete first-level refined cell.

[0109] In this embodiment, the peripheral facility attributes are the functions, types, risk levels, and other characteristics of the facilities surrounding the incomplete first refined unit. For example, there is an oil pipeline (for transporting crude oil) and a small oil pump house (an auxiliary oil transportation facility with a certain risk of oil and gas leakage) adjacent to the incomplete unit. These are the peripheral facility attributes.

[0110] In this embodiment, the non-deviation area refers to an area that complies with the factory design standards and functional planning and has no construction deviations.

[0111] In this embodiment, the connecting line segment is formed by the connection between the incomplete first refined unit and the surrounding non-deviation area. Its shape can be straight or curved. When the incomplete unit is adjacent to a regular non-deviation tank area, the boundary between them is the connecting line segment. If the boundary line is a straight wall, it is a straight line segment; if the boundary line is a curved pipeline, it is a curved segment.

[0112] In this embodiment, for the connecting line segments of the curve segments, the outermost points in the horizontal and vertical directions are the outermost transverse boundary points and the outermost longitudinal boundary points, such as Figure 4 As shown in FIG. 1 , the curve segment is A1. At this time, the outermost horizontal boundary points are r1 and r2, and the outermost vertical boundary points are r3 and r4. After connecting the horizontal and vertical boundaries, the initial rectangular frame J is obtained.

[0113] In this embodiment, Figure 5 As shown, B1 is an incomplete first refinement unit, and B2 is a portion where the incomplete first refinement unit is replaced with a complete first refinement unit, that is, replacement is achieved.

[0114] In this embodiment, Figure 6 As shown, C1 is an incomplete first refinement unit, and J is an initial rectangular frame. At this time, the position and size of J are the expansion of the incomplete first refinement unit.

[0115] The beneficial effects of the above technical solution are: for incomplete units that are not related to the functional attributes of the split area, their status quo is retained to avoid excessive deployment of sensors in unnecessary areas. When the incomplete unit is connected to the relevant non-deviation area and the connecting line segment is a straight line segment or can be expanded by drawing an initial rectangular frame, the sensor can be reasonably deployed to the key area. After the incomplete unit is reasonably expanded, it meets the first refined unit standard, ensuring the consistency and standardization of sensor deployment, sampling and other operations in each monitoring unit, reducing data deviations caused by incomplete units, and expanding by drawing an initial rectangular frame for the connection of curved segments, which can better adapt to the complex facility layout in the refinery. According to the actual situation of the incomplete unit and its peripheral facility attributes, it can flexibly decide whether to expand, so that the monitoring system can better adapt to various construction deviations in the refinery.

[0116] The present invention provides a gas monitoring and analysis system in which multiple intelligent sensors work in coordination, further comprising:

[0117] a vector construction module, configured to construct an analysis vector based on a first position of each grid unit in the divided area, a correlation with a functional attribute of the divided area, and a unit facility corresponding to the grid unit extracted from the facilities in the current area;

[0118] a pollutant determination module, configured to input the analysis vector into a gas analysis model to obtain a gas pollution set corresponding to a grid cell, wherein the gas pollution set includes gas pollutants and a diffusion factor of each gas pollutant;

[0119] The sensor setting module is used to find the first pollutant whose diffusion factor is less than a preset factor, set a fixed sensor to the corresponding grid unit, and at the same time, set a mobile sensor according to the diffusion path of each remaining pollutant in the unit facility in each grid unit.

[0120] In this embodiment, the first position is the specific spatial position coordinates of the grid unit in the divided area to which it belongs. For example, the coordinates of the grid unit are (X=100 meters, Y=200 meters), which is the first position.

[0121] In this embodiment, the current regional facilities are the various production equipment and auxiliary facilities currently available in the divided area. The unit facilities are the specific facilities contained in each grid unit, which may include a catalyst delivery pipeline, a small valve, etc. These are the unit facilities of the unit.

[0122] In this example, the analysis vector is a multidimensional vector consisting of the first position of the grid cell, its correlation with the functional attributes of the divided area, and information about the cell's facilities. This vector is used as input into the model for analysis. The first position coordinates are (X = 150 meters, Y = 180 meters), and the correlation with the functional attributes of the area (catalytic cracking reaction, sulfur-containing waste gas generation, etc.) is 0.6. The cell facilities are a section of reaction pipe and a control valve. Therefore, the analysis vector can be expressed as [150, 180, correlation coefficient, reaction pipe, control valve].

[0123] In this embodiment, the correlation coefficient is directly obtained based on the matching degree of the facilities and functions, specifically: the number of matching facilities / the total number of facilities in the corresponding functional area.

[0124] In this embodiment, the gas analysis model utilizes a neural network model constructed using a deep learning algorithm. The neural network model is trained by combining the refinery's historical gas monitoring data, facility layout, production process, etc. in the required dimensions to obtain a vector as the model's input sample, and using the pollution conditions corresponding to the corresponding vector as the output sample.

[0125] In this example, the gas pollution set includes the types of gaseous pollutants within a grid cell and the diffusion factors for each pollutant. For example, in a grid cell, the gas pollution set is {[sulfur dioxide, 0.3], [catalyst dust, 0.5]}, indicating that sulfur dioxide and catalyst dust are present in the cell, with diffusion factors of 0.3 and 0.5, respectively. The diffusion factor reflects characteristics such as the ease or speed of diffusion of pollutants within the area.

[0126] The diffusion factor is a parameter that measures the diffusion characteristics of gaseous pollutants within a grid unit. The smaller the value, the more difficult or slower the diffusion. For example, the diffusion factor of sulfur dioxide, a concentrated gas pollutant, is 0.3, indicating that it is relatively difficult to diffuse within the grid unit; while the diffusion factor of catalyst dust is 0.5, which means it is relatively easier to diffuse.

[0127] In this example, the preset factor is a manually set critical value for determining the diffusion characteristics of a pollutant. Based on the refinery's actual monitoring experience and needs, the preset factor is set to 0.4. If a pollutant's diffusion factor is less than 0.4, it is considered relatively difficult to diffuse. For example, if the diffusion factor of sulfur dioxide is 0.3, which is less than the preset factor of 0.4, sulfur dioxide is the primary pollutant in that grid cell.

[0128] In this embodiment, the diffusion path is the route followed by the pollutants to diffuse and move in the environment. The catalyst dust may diffuse from the reaction tower to the surrounding area along the direction of the airflow in the workshop. Its specific movement route in the corresponding grid unit is the diffusion path, which can be determined through airflow simulation, actual monitoring and tracking, etc.

[0129] like Figure 2 As shown, it is a structural diagram of multiple intelligent sensors, for example, including: grid unit 1, grid unit 2, grid unit 3, and each unit includes a fixed sensor and a mobile sensor.

[0130] The beneficial effects of the above technical solution are: by determining the first pollutant, setting up fixed sensors in areas where substances are relatively difficult to diffuse but may cause serious pollution, and setting up mobile sensors according to the diffusion paths of the remaining pollutants, so that the mobile sensors can perform dynamic monitoring along the actual diffusion paths of the pollutants. Based on the gas pollution set output by the gas analysis model, the specific pollution situation in each grid unit can be understood, thereby improving the targeted monitoring and avoiding blind monitoring.

[0131] The present invention provides a gas monitoring and analysis system in which multiple intelligent sensors work in collaboration, wherein the initial monitoring module comprises:

[0132] A response set construction unit is used to issue N synchronous working instructions to all sensors under each grid unit based on the controller, and capture the first time point of each instruction issuance and the second time point when each sensor receives the corresponding instruction and starts working, to construct a response set S for each sensor;

[0133] A matrix construction unit is used to regard the time difference between each response time and the standard time in each response set S of the sensor of the same model as the first set, and to sort the time differences in each first set to construct a difference matrix;

[0134] a function determination unit, configured to dynamically divide the difference matrix to obtain a response function and predict a feedback compensation time of a corresponding sensor;

[0135] The environment map construction unit is used to issue the N+1th instruction to the sensor based on the controller-dependent feedback compensation time, and output the current gas information of each grid unit to form an initial environment map.

[0136] In this embodiment, since there may be a delay when the sensor receives the instructions issued by the controller, but the gas information to be measured needs to be executed synchronously (the gas measurement results of the target factory at the same time point need to be obtained synchronously), it is necessary to adjust the time of the instructions received by the sensor to ensure that sensors of the same model can work at the same time at the same time point as much as possible, and ensure the accuracy of the measurement of the same gas parameters of the target factory.

[0137] In this embodiment, the controller is used to manage and control the equipment or system of the sensor, which can send instructions, collect data and process it. In an oil refinery, an industrial-grade computer system installed in the central control room serves as the controller. It is connected to the sensors of each grid unit through a wired or wireless network to coordinate the operation of the sensors.

[0138] In this embodiment, the first time point is the specific moment when the controller issues the synchronous working instruction, and the second time point is the moment when each sensor receives the synchronous working instruction and starts working.

[0139] In this embodiment, the response set S is a set consisting of the first time point and the second time point each time each sensor receives an instruction, and is used to record the response time of the sensor.

[0140] The sulfur dioxide sensor is tested with three synchronous working instructions. The corresponding first time points are 8:00:00, 12:00:00, and 16:00:00, and the second time points are 8:00:03, 12:00:02, and 16:00:04. Then the response set S of the sensor is S = {(8:00:00, 8:00:03), (12:00:00, 12:00:02), (16:00:00, 16:00:04)}.

[0141] In this embodiment, the response duration is the time interval from the first time point (instruction issuance) to the second time point (start of operation) of the sensor.

[0142] In this embodiment, the standard time is a preset response time of the sensor under ideal conditions. For example, based on the performance parameters of the sensor and the requirements of the refinery, the standard time of this model of sensor is set to 2 seconds.

[0143] In this embodiment, in the response set of sensors of the same model, the set consisting of the time difference between each response duration and the standard duration is the first set. The difference matrix is ​​a matrix formed by sorting the time differences in the first set from small to large. The first set contains the time differences {3, 1, 0, 2}. After sorting, the difference matrix obtained is [0, 1, 2, 3].

[0144] In this embodiment, the feedback compensation time is predicted based on the response function. To enable the sensor to work more accurately and synchronously, the time required to advance or delay the next instruction is to be issued. The response time difference of a certain sensor is in the range of (1-2). According to the response function, its feedback compensation time is 1 second. This means that the next time a command is issued, the controller needs to send the command to the sensor 1 second in advance to compensate for its response delay.

[0145] In this embodiment, after the controller issues the N+1th instruction according to the feedback compensation time, a graph is drawn by collecting the current gas information output by the sensor in each grid unit to intuitively display the gas environment conditions in the refinery.

[0146] The beneficial effects of the above technical solution are: by constructing a response set, analyzing the time difference and performing feedback compensation, sensors of the same model can start working more simultaneously after receiving the command. The improvement of synchronization enables multiple sensors to work together more effectively, and the instruction issuance time is adjusted according to the feedback compensation time, avoiding monitoring confusion caused by delayed or early sensor response.

[0147] The present invention provides a gas monitoring and analysis system in which multiple intelligent sensors work in collaboration, wherein the function determination unit comprises:

[0148] An initial partitioning subunit is used to lock the mutation elements of each row vector in the difference matrix according to a preset time scale window, and perform initial partitioning on the difference matrix to obtain a first matrix and a second matrix, wherein the number of columns of the first matrix is ​​the maximum number of columns in the left row vector in the initial partitioning result, and the number of columns of the second matrix is ​​the maximum number of columns in the right row vector in the initial partitioning result, and when there is a column number of 0 in the left row vector or the right row vector, all are filled with the same value according to the new scale, when there is a column number in the left row vector that is not the maximum number of columns, backward filling with the same value is performed according to the last element in the row vector on the corresponding side until the maximum number of columns is filled, and when there is a column number in the right row vector that is not the maximum number of columns, forward filling with the same value is performed according to the first element in the row vector on the corresponding side until the maximum number of columns is filled;

[0149] A secondary division subunit is configured to determine a decay time of the controller based on the total number of historical uses and the decay time after N synchronous work instructions, and to perform secondary division on the difference matrix to obtain a new scale window by adding the new scale window to a preset time scale window to obtain a third matrix and a fourth matrix;

[0150] The compensation determination subunit is used to solve the characteristic functions of the first matrix, the second matrix, the third matrix and the fourth matrix respectively, and calculate the feedback compensation time of the corresponding sensor based on the corresponding characteristic coefficient of each sensor in all the characteristic functions.

[0151] In this embodiment, the preset time scale window is 1.5 seconds.

[0152] In this embodiment, N is set to 5 for simple calculation. It is assumed that there are 3 sensors of the same model. It should be noted that the actual number of tests N is greater than 10, and each line corresponds to the test result of one sensor.

[0153] Since the preset time scale window is 1.5 seconds, after splitting the difference matrix, we get At this time, the maximum number of columns for the first matrix is ​​5, and the maximum number of columns for the second matrix is ​​5.

[0154] In this embodiment, the decay time is obtained by matching the total number of historical uses of the controller since its inception + the sum of N synchronization instructions from the times-duration comparison table. At this time, the decay coefficient obtained is 0.5, and the new scale window is 2 seconds. It should be noted that the times-duration comparison table contains different times of use and the decay time of the corresponding instruction issuance time, which is set before leaving the factory and can be used directly. At this time, the obtained It should be noted that the acquisition of the third matrix and the fourth matrix is ​​similar to the acquisition principle of the first matrix and the second matrix, and will not be repeated here.

[0155] In this embodiment, the characteristic functions of the four matrices obtained by solving the characteristic function of each matrix are:

[0156] T1=a1x1+a2x2+...+anxn

[0157] T2=b1x1+b2x2+...+bnxn

[0158] T3=c1x1+c2x2+...+cnxn

[0159] T4=d1x1+d2x2+...+dnxn

[0160] Among them, T1, T2, T3, and T4 are characteristic functions of the first matrix, the second matrix, the third matrix, and the fourth matrix, respectively, which are obtained by solving the characteristics of the matrices and are common knowledge in mathematics, and x1, x2...xn are each sensor of the same model; a1, a2...an are characteristic coefficients corresponding to each sensor based on the characteristic function of the first matrix; b1, b2...bn are characteristic coefficients corresponding to each sensor based on the characteristic function of the second matrix; c1, c2...cn are characteristic coefficients corresponding to each sensor based on the characteristic function of the third matrix; d1, d2...dn are characteristic coefficients corresponding to each sensor based on the characteristic function of the fourth matrix;

[0161] The feedback compensation time Bi of the i-th sensor of the same model is calculated according to the following formula:

[0162]

[0163] Among them, m0 represents the number of columns of the first matrix; m2 represents the number of columns of the third matrix; M1 represents the total number of sensors of the same model; ai, ci, bi, and di represent the characteristic coefficients of the i-th sensor of the same model based on the first matrix, the third matrix, the second matrix, and the fourth matrix, respectively.

[0164] The above characteristic coefficients are all calculated in time units, so the basic unit of the matrix is ​​the time unit.

[0165] In this embodiment, the characteristic coefficients of the four matrices are combined in a certain ratio so that the influence of each factor on the feedback compensation time is balanced. The weights of the characteristic coefficients of the first and third matrices are determined respectively, The weights of the characteristic coefficients of the second matrix and the fourth matrix are determined, which avoids excessive amplification or reduction of the influence of a certain factor and ensures that the calculation results can comprehensively and balancedly reflect the joint effect of multiple factors.

[0166] The beneficial effect of this technical solution is that it accurately calculates the feedback compensation time for each sensor based on the multifaceted information reflected by multiple matrices. This ensures that the sensors can more accurately synchronize their operations when receiving instructions, reduces data deviations and monitoring errors caused by inconsistent response times, and improves the monitoring accuracy and efficiency of the entire sensor network.

[0167] The present invention provides a gas monitoring and analysis system in which multiple intelligent sensors work in collaboration, wherein the frequency setting module comprises:

[0168] a model analysis unit, configured to input the predicted meteorological data into a gas parameter impact analysis model, and input an impact coefficient of each gas parameter affected by the predicted meteorological data;

[0169] A coefficient judgment unit, configured to keep the original acquisition frequency of the corresponding sensor unchanged if the influence coefficient is less than a preset coefficient, in which case the original acquisition frequency is the first acquisition frequency;

[0170] a frequency updating unit configured to update the original collection frequency to obtain a first collection frequency based on the influence coefficient and the influence level of the preset coefficient, and in combination with the gas collection weight of the corresponding fixed sensor, if the influence coefficient is not less than the preset coefficient;

[0171] At the same time, according to the influence level of the influence coefficient and the preset coefficient, and in combination with the gas collection weight of the corresponding mobile sensor under the moving path of the predicted period corresponding to the predicted meteorological data, the original collection frequency is updated to obtain the second collection frequency.

[0172] In this embodiment, the gas parameter impact analysis model is a mathematical model established based on the relationship between meteorological data and gas parameters, which is used to analyze the impact of predicted meteorological data on various gas parameters (such as gas concentration, diffusion rate, etc.). After determining the predicted meteorological data, the diffusion of the corresponding gas parameters can be directly obtained, and then the influence coefficient can be obtained. For example, the model can analyze the impact of these meteorological conditions on the concentration and diffusion of gases such as sulfur dioxide and volatile organic compounds (VOCs) emitted by the refinery. Specifically, through calculations using the gas parameter impact analysis model, it is found that under the above-mentioned predicted meteorological conditions, the influence coefficient of wind speed on the diffusion rate of VOCs emitted by the refinery is 0.8.

[0173] In this embodiment, the gas parameter is a description of the gas type that the sensor type needs to measure.

[0174] In this embodiment, the preset coefficient is a threshold value set based on experience or actual needs. Based on historical monitoring data and actual operating experience of the refinery, the preset coefficient for the effect of wind speed on gas diffusion is set to 0.6. When the calculated influence coefficient is compared with the preset coefficient, a decision can be made as to whether to adjust the sensor acquisition frequency.

[0175] In this embodiment, the original collection frequency is the frequency at which the sensor collects data in advance without considering the influence of meteorological data. For example, a fixed sensor is used to monitor the sulfur dioxide concentration in the catalytic cracking unit area of ​​a refinery, and its original collection frequency is set to collect data every 10 minutes; the original collection frequency of a mobile sensor (such as a sensor carried by a drone) is to collect data every 30 minutes during an inspection of a designated area.

[0176] In this embodiment, the first acquisition frequency is determined for a fixed sensor by updating the original acquisition frequency based on the comparison of the impact coefficient with the preset coefficient and related conditions. For example, based on the impact level and the gas acquisition weight of the sensor, the original acquisition frequency is updated from once every 10 minutes to once every 5 minutes. This once every 5 minutes is the first acquisition frequency. Different levels, divided according to the difference between the impact coefficient and the preset coefficient, are used to more precisely determine the adjustment range of the acquisition frequency. The difference range between the impact coefficient and the preset coefficient is divided into three levels: a low impact level for a difference between 0 and 0.2, a medium impact level for a difference between 0.2 and 0.5, and a high impact level for a difference greater than 0.5. Different impact levels correspond to different acquisition frequency adjustment strategies, such as a larger acquisition frequency adjustment range for a high impact level.

[0177] In this embodiment, the gas collection weight reflects the importance or contribution of the sensor in collecting certain gas data, and is pre-set and can be used directly.

[0178] In this embodiment, the second acquisition frequency is for mobile sensors. For a mobile sensor carried by a drone, the original acquisition frequency is once every 30 minutes. Based on the forecasted meteorological data, after analysis, its influence coefficient on the monitored gas parameters is greater than the preset coefficient. Combined with the gas acquisition weight and influence level of the mobile sensor under the corresponding moving path, its acquisition frequency is updated to once every 15 minutes. This once every 15 minutes is the second acquisition frequency.

[0179] The beneficial effects of the above technical solution are: when changes in meteorological data cause the impact coefficient to be greater than the preset coefficient, the sensor acquisition frequency is promptly increased; when the impact coefficient is less than the preset coefficient, the original sensor acquisition frequency is maintained unchanged, avoiding unnecessary high-frequency acquisition when meteorological conditions have little impact on gas parameters, reducing the data transmission volume and processing pressure of the sensor, and adjusting the acquisition frequency in combination with the gas acquisition weight, so that sensors in key locations can collect data at a higher frequency when meteorological conditions change, while sensors in relatively less important locations are adjusted according to actual conditions. This can concentrate monitoring resources in more important areas, improve the accuracy and efficiency of monitoring key pollution sources, and adjust the acquisition frequency according to the gas acquisition weight of the mobile sensor's movement path in different forecast periods, so that the mobile sensor can more reasonably plan the monitoring path and acquisition timing.

[0180] The present invention provides a gas monitoring and analysis system in which multiple intelligent sensors work in collaboration, wherein the gas quality early warning module comprises:

[0181] a significant bar construction unit, configured to determine the quality of each gas parameter in each gridded unit based on the new gas information, and draw a quality significant bar based on the corresponding gridded unit, wherein the quality significant bar includes a quality color block for each gas parameter and a comprehensive warning block;

[0182] The early warning map construction unit is used to construct a gas quality early warning map based on the quality significant strips of all gridded units and output it.

[0183] In this embodiment, it is assumed that the oil refinery stipulates that the sulfur dioxide concentration threshold is 4ppm. When the sulfur dioxide concentration in a grid unit is 5ppm, its quality is considered to be slightly exceeded. If the nitrogen oxide concentration threshold is 2ppm, the nitrogen oxide concentration in a unit is 3ppm, its quality is also considered to be slightly exceeded. It is assumed that if it exceeds 2 times the corresponding threshold, it is considered to be moderately exceeded, and if it exceeds 3 times the threshold, it is considered to be severely exceeded.

[0184] In this embodiment, the comprehensive warning block gives an overall warning mark after comprehensive judgment based on the quality of all gas parameters in a grid unit, which is used to quickly reflect the overall status of the gas quality of the unit. The display result of the comprehensive warning block is based on the combination of whether the gas parameters meet the standards or not and matches the combination-comprehensive comparison table. The table contains the result combinations of different gas parameters and the comprehensive results for the combinations, which are all pre-stored and can be used directly. It is assumed that green indicates that the gas parameters meet the standards, yellow indicates that the gas parameters are slightly exceeded, orange indicates that the gas parameters are moderately exceeded, and red indicates that the gas parameters are severely exceeded.

[0185] For example, quality-significant items such as Figure 3 As shown in the figure, if sulfur dioxide in a grid cell slightly exceeds the standard (yellow), nitrogen oxides seriously exceed the standard (red), and VOCs meet the standard (green), a black exclamation mark will be displayed in the comprehensive warning block after comprehensive judgment, indicating that the cell is in a light warning state;

[0186] If sulfur dioxide exceeds the standard seriously (red), nitrogen oxide exceeds the standard moderately (orange), and VOCs meet the standard (green), the comprehensive warning block will display a black cross, indicating that the unit is in a serious warning state.

[0187] The beneficial effects of the above technical solution are: through the gas quality warning map, the gas quality of each grid unit is displayed in an intuitive graphic. The comprehensive warning block in the quality significance bar provides overall warning information for each unit, helping decision makers to quickly judge the severity of pollution and fully understand the status of various pollutants in an area, thereby more accurately evaluating the overall gas environment quality of the refinery and providing strong data support for the formulation of more scientific and reasonable environmental protection measures and production scheduling strategies.

[0188] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A gas monitoring and analysis system with multiple intelligent sensors working in collaboration, characterized in that: include: The regional splitting module is used to refine the functional attributes of the target plant to achieve regional splitting and perform atmospheric environment gridding on the split regions according to the current regional facilities. The atmospheric environment gridding result is the first refined unit of the independent deviation closed contour in the split region and the second refined unit of the remaining area excluding the independent deviation closed contour. An initial monitoring module is configured to set fixed sensors and mobile sensors based on the current regional facilities and grid cells of each divided area, and control the fixed sensors and mobile sensors to collaboratively monitor the current gas information of each grid cell to obtain an initial environmental map, wherein the fixed sensors and mobile sensors constitute a multi-intelligent sensor; A frequency setting module, configured to update the acquisition frequency of each fixed sensor and mobile sensor in each divided area based on the initial environmental map and the predicted meteorological data; The gas quality warning module is used to receive new gas information collected by the fixed sensors and the mobile sensors based on the edge nodes that are in communication with the fixed sensors and the mobile sensors, and to construct a gas quality warning map of the target factory to output warnings.

2. The gas monitoring and analysis system with multiple intelligent sensors working in collaboration according to claim 1 is characterized in that: The region splitting module includes: a functional decomposition unit, configured to perform drone monitoring of a target factory to obtain a factory deployment structure, and perform functional structural decomposition of the factory deployment structure based on a construction facility diagram of the target factory to obtain a plurality of decomposition areas, wherein the construction facility diagram includes at least one functional attribute, and the decomposition areas are the areas after the actual decomposition; a set determination unit, configured to compare and analyze the split region with the standard region to construct a deviation construction-position set, and determine a plurality of independent deviation position profiles; a contour supplement unit, configured to input the position set of each independent deviation position contour into the adaptive contour filling model for adaptive contour supplementation to obtain an independent deviation closed contour, and to match the first refinement unit of the independent deviation position contour from a ratio-coefficient-setting comparison table based on an actual ratio of the contour length of the independent deviation closed contour to the area contour of the corresponding split area and an actual variation coefficient of the facility type within the contour to the facility type within the corresponding standard area; a first meshing unit, configured to perform first meshing on the corresponding independent deviation position contours in the split region according to the first refinement unit; The second meshing unit is used to match the second refinement unit of the remaining area except the independent deviation position contour in the split area from the function-setting comparison table according to the area function, and perform a second meshing on the remaining area according to the second refinement unit.

3. The gas monitoring and analysis system with multiple intelligent sensors working in collaboration according to claim 2 is characterized in that: The first gridding unit includes: a complete judgment subunit, configured to perform a first meshing on the corresponding independent deviation closed contour according to the first refinement unit, and judge whether there are incomplete first refinement units in the edge meshing units of the independent deviation closed contour; If it does not exist, the first meshed element corresponding to the independent deviation closed contour is retained unchanged; If present, determine the peripheral facility attributes of the incomplete first refinement unit; a functional attribute judgment subunit, configured to retain the incomplete first refined unit if the peripheral facility attribute is irrelevant to the functional attribute of the corresponding split area; If the peripheral facility attribute is related to the functional attribute of the corresponding split area, and the corresponding peripheral facility belongs to the non-deviation area, then the incomplete first refinement unit is connected to the non-deviation area of ​​the peripheral facility by a line segment; an expansion subunit, configured to replace the incomplete first refinement unit with a complete first refinement unit if the connecting line segment is a straight line segment; If the connecting line segment is a curved segment, then the outermost horizontal boundary point and the outermost vertical boundary point of the curved segment are determined, and an initial rectangular frame is drawn; Expanding the initial rectangular frame as an expansion area and the corresponding incomplete first refinement unit; If the peripheral facility attributes are related to the functional attributes of the corresponding split area, and the corresponding peripheral facility belongs to the deviation area, then the incomplete first refined unit is retained.

4. The gas monitoring and analysis system with multiple intelligent sensors working in collaboration according to claim 1 is characterized in that: Also includes: a vector construction module, configured to construct an analysis vector based on a first position of each grid unit in the divided area, a correlation with a functional attribute of the divided area, and a unit facility corresponding to the grid unit extracted from the facilities in the current area; a pollutant determination module, configured to input the analysis vector into a gas analysis model to obtain a gas pollution set corresponding to a grid cell, wherein the gas pollution set includes gas pollutants and a diffusion factor of each gas pollutant; The sensor setting module is used to find the first pollutant whose diffusion factor is less than a preset factor, set a fixed sensor to the corresponding grid unit, and at the same time, set a mobile sensor according to the diffusion path of each remaining pollutant in the unit facility in each grid unit.

5. The gas monitoring and analysis system with multiple intelligent sensors working in collaboration according to claim 4 is characterized in that: The initial monitoring module includes: A response set construction unit is used to issue N synchronous working instructions to all sensors under each grid unit based on the controller, and capture the first time point of each instruction issuance and the second time point when each sensor receives the corresponding instruction and starts working, to construct a response set S for each sensor; A matrix construction unit is used to regard the time difference between each response time and the standard time in each response set S of the sensor of the same model as the first set, and to sort the time differences in each first set to construct a difference matrix; a function determination unit, configured to dynamically divide the difference matrix to obtain a response function and predict a feedback compensation time of a corresponding sensor; The environment map construction unit is used to issue the N+1th instruction to the sensor based on the controller-dependent feedback compensation time, and output the current gas information of each grid unit to form an initial environment map.

6. The gas monitoring and analysis system with multiple intelligent sensors working in collaboration according to claim 5 is characterized in that: The function determination unit includes: An initial partitioning subunit is used to lock the mutation elements of each row vector in the difference matrix according to a preset time scale window, and perform initial partitioning on the difference matrix to obtain a first matrix and a second matrix, wherein the number of columns of the first matrix is ​​the maximum number of columns in the left row vector in the initial partitioning result, and the number of columns of the second matrix is ​​the maximum number of columns in the right row vector in the initial partitioning result, and when there is a column number of 0 in the left row vector or the right row vector, all are filled with the same value according to the new scale, when there is a column number in the left row vector that is not the maximum number of columns, backward filling with the same value is performed according to the last element in the row vector on the corresponding side until the maximum number of columns is filled, and when there is a column number in the right row vector that is not the maximum number of columns, forward filling with the same value is performed according to the first element in the row vector on the corresponding side until the maximum number of columns is filled; A secondary division subunit is configured to determine a decay time of the controller based on the total number of historical uses and the decay time after N synchronous work instructions, and to perform secondary division on the difference matrix to obtain a new scale window by adding the new scale window to a preset time scale window to obtain a third matrix and a fourth matrix; The compensation determination subunit is used to solve the characteristic functions of the first matrix, the second matrix, the third matrix and the fourth matrix respectively, and calculate the feedback compensation time of the corresponding sensor based on the corresponding characteristic coefficient of each sensor in all the characteristic functions.

7. The gas monitoring and analysis system with multiple intelligent sensors working in collaboration according to claim 1 is characterized in that: The frequency setting module includes: a model analysis unit, configured to input the predicted meteorological data into a gas parameter impact analysis model, and input an impact coefficient of each gas parameter affected by the predicted meteorological data; A coefficient judgment unit, configured to keep the original acquisition frequency of the corresponding sensor unchanged if the influence coefficient is less than a preset coefficient, in which case the original acquisition frequency is the first acquisition frequency; a frequency updating unit configured to update the original collection frequency to obtain a first collection frequency based on the influence coefficient and the influence level of the preset coefficient, and in combination with the gas collection weight of the corresponding fixed sensor, if the influence coefficient is not less than the preset coefficient; At the same time, according to the influence level of the influence coefficient and the preset coefficient, and in combination with the gas collection weight of the corresponding mobile sensor under the moving path of the predicted period corresponding to the predicted meteorological data, the original collection frequency is updated to obtain the second collection frequency.

8. The gas monitoring and analysis system with multiple intelligent sensors working in collaboration according to claim 1 is characterized in that: The gas quality early warning module includes: a significant bar construction unit, configured to determine the quality of each gas parameter in each gridded unit based on the new gas information, and draw a quality significant bar based on the corresponding gridded unit, wherein the quality significant bar includes a quality color block for each gas parameter and a comprehensive warning block; The early warning map construction unit is used to construct a gas quality early warning map based on the quality significant strips of all gridded units and output it.

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