A multi-point monitoring based ambient air quality detection method and device

Abstract

The present application relates to air quality detection technical field, specifically to a kind of environmental air quality detection method and equipment based on multi-point monitoring.The method obtains the volatile organic compound and carbon dioxide concentration of each monitoring point;Based on the change of both in specified time period, extract the degree of non-combustion source, to trigger initial time, determine the cut-off time in combination with wind speed and point spacing, and extract initial peak monitoring point and termination peak monitoring point respectively;Obtain the spatial distribution width of peak monitoring point, construct collaborative difference degree measurement matrix in combination with wind speed;Solve observation matching cost, and calculate mode consistency degree in combination with the benchmark matching cost calculation mode of random permutation;Finally, based on mode consistency degree, obtain pollution source area.The present application effectively eliminates exhaust interference, realizes adaptive tracing strategy through physical consistency diagnosis, avoids positioning divergence and misdirection in complex scene.

Description

Technical Field

[0001] This invention relates to the field of air quality detection technology, specifically to an environmental air quality detection method and equipment based on multi-point monitoring. Background Technology

[0002] In the field of urban ambient air quality monitoring, there is an increasingly urgent need for refined management of industrial parks or traffic-intensive areas. These areas are typically characterized by high-intensity background emission interference, such as mixed pollutants from vehicle exhaust and fuel combustion, which poses a challenge to the monitoring of occasional industrial leaks or fugitive emissions.

[0003] Existing multi-point monitoring technologies primarily rely on sensor networks for simple concentration threshold alarms. However, in reality, exhaust gases from urban traffic or legal industrial combustion often contain volatile organic compounds (VOCs). When combustion activity is frequent, sensor readings experience strong background fluctuations, making existing threshold systems prone to misinterpreting them as industrial organic leaks and triggering numerous false alarms. Secondly, single concentration threshold monitoring lacks the ability to analyze and determine the form of pollution sources. For sporadic pollution events, the source forms are typically categorized as single fixed-point source leaks (such as tank ruptures) and complex source emissions (such as multi-source unorganized superposition, mobile sources, etc.). Existing source tracing algorithms (such as triangulation or Gaussian diffusion inversion) generally operate based on the assumption of steady-state diffusion from a single fixed-point source. If a general source tracing algorithm is applied directly to all high-value data without source form diagnosis, the calculation will not only fail to converge when faced with complex superposition events caused by complex sources, but will also produce divergent coordinates that completely deviate from the true source location. This not only fails to provide accurate geographical guidance for environmental law enforcement but also seriously misleads the direction of investigation, reducing the practical application value of the monitoring system.

[0004] Therefore, existing multi-point monitoring technologies cannot effectively extract pure industrial abnormal leakage signals under strong background interference from traffic exhaust, nor can they intelligently diagnose and assess the physical diffusion evolution of pollution events before performing source tracing and location. This leads to a rigid source tracing strategy, which is prone to false alarms and misleading divergences when facing complex pollution scenarios. Summary of the Invention

[0005] To address the technical problems of existing multi-point monitoring technologies failing to effectively extract pure leakage signals under strong background interference, and the potential for divergent and misleading source tracing in complex scenarios due to the lack of source morphology diagnostics, the present invention aims to provide an ambient air quality detection method and device based on multi-point monitoring. The specific technical solution adopted is as follows: In a first aspect, one embodiment of the present invention provides an ambient air quality detection method based on multi-point monitoring, the method comprising the following steps: Obtain the concentrations of volatile organic compounds and carbon dioxide at each monitoring point within the monitoring area at each time point; Based on the changes in volatile organic compound (VOC) and carbon dioxide (CO2) concentrations at each monitoring point within a specified time period at each moment, the degree of non-combustion source at each monitoring point at each moment is obtained. The initial moment is triggered based on the degree of non-combustion source intensity. The cutoff moment is obtained by combining the wind speed at the initial moment with the distance between monitoring points. The initial peak monitoring point is obtained based on the degree of non-combustion source intensity at the initial moment. The final peak monitoring point is obtained based on the degree of non-combustion source intensity at the cutoff moment. Obtain the spatial distribution width corresponding to each peak monitoring point. Based on the position and spatial distribution width of the initial peak monitoring point and the final peak monitoring point, as well as the wind speed at the initial moment, construct a collaborative difference metric matrix. Solve for the observation matching cost of the collaborative difference metric matrix, and combine it with the baseline matching cost after random permutation of the final peak monitoring point to calculate the pattern consistency degree. Based on the degree of pattern consistency, the pollution source area is identified.

[0006] Furthermore, the method for obtaining the degree of non-combustion source is as follows: For any monitoring point at any given time, the time interval between that time and its previous adjacent time is taken as the first time interval; The average concentration of volatile organic compounds at the monitoring point at that time and a preset number of adjacent times is obtained as the smoothed concentration of organic compounds at the monitoring point at that time. The difference between the smoothed concentration of organic compounds at the monitoring point at that time and the smoothed concentration of organic compounds at the previous adjacent time is used as the first value. The ratio of the first value to the first duration is taken as the rate of change of volatile organic compound concentration at that monitoring point at that moment; The average carbon dioxide concentration at the monitoring point at that moment and the number of adjacent moments before that moment are obtained as the smoothed carbon dioxide concentration at the monitoring point at that moment. The difference between the smoothed carbon dioxide concentration at the monitoring point at that moment and the smoothed carbon dioxide concentration at the previous adjacent moment is used as the second value. The ratio of the second value to the first time period is taken as the rate of change of carbon dioxide concentration at that monitoring point at that moment; The sum of the products of the rate of change of volatile organic compound concentration and the rate of change of carbon dioxide concentration at all times within a specified time period at the monitoring point is obtained as the first analytical value. The sum of the squares of the rate of change of carbon dioxide concentration at the monitoring point during the specified time period at that moment is obtained as the second analytical value. The ratio of the first analytical value to the sum of the second analytical value and the first preset positive number is used as the correlation coefficient of the monitoring point at that time. Multiply the correlation coefficient by the rate of change of carbon dioxide concentration at that monitoring point at that time to obtain the background combustion component at that monitoring point at that time. Subtracting the background combustion component from the rate of change of volatile organic compound concentration at the monitoring point at that moment yields the degree of non-combustion source at that monitoring point at that moment.

[0007] Furthermore, the method for obtaining the initial time is as follows: For any given moment, the non-combustion source level at each monitoring point at that moment is compared with a preset noise threshold. If at least one monitoring point has a non-combustion source level greater than the preset noise threshold at a given moment, then that moment is taken as the initial moment.

[0008] Furthermore, the method for obtaining the cutoff time is as follows: Calculate the distance between any two monitoring points, and use it as the first distance; take the average of all first distances as the average spacing. The maximum value between the initial wind speed and the preset minimum wind speed is taken as the target wind speed; the ratio of the average distance to the target wind speed is taken as the delay time. The time corresponding to the initial time plus the delay time is taken as the cutoff time.

[0009] Furthermore, the method for obtaining the initial peak monitoring point and the final peak monitoring point is as follows: For any given monitoring point, all other monitoring points within a preset search radius of that monitoring point will be considered as its neighboring monitoring points. If the non-combustion source intensity at the initial moment is greater than that at all its neighboring monitoring points at the initial moment, then the monitoring point shall be regarded as the initial peak monitoring point. If the non-combustion source intensity at the cutoff time is greater than that at all its neighboring monitoring points at the cutoff time, then the monitoring point is designated as the termination peak monitoring point.

[0010] Furthermore, the method for obtaining the collaborative difference metric matrix is ​​as follows: For any initial peak monitoring point and any final peak monitoring point, the product of the wind speed vector corresponding to the wind speed at the initial moment and the delay time is taken as the displacement vector corresponding to that initial peak monitoring point. Adding the displacement vector to the position of the initial peak monitoring point yields the theoretical evolution position; The distance between the theoretical evolution position and the position of the terminal peak monitoring point is taken as the advection transport deviation; The difference in width is obtained by subtracting the spatial distribution width of the initial peak monitoring point from the spatial distribution width of the final peak monitoring point. The maximum value between the width difference and 0 is taken as the diffusion constraint deviation; The weighted sum of the advection transport deviation and the diffusion constraint deviation is used as the metric element value corresponding to the initial peak monitoring point and the final peak monitoring point. The initial peak monitoring point is used as the row index, and the terminating peak monitoring point is used as the column index. All initial peak monitoring points and all terminating peak monitoring points are traversed, and a collaborative difference measurement matrix is ​​constructed based on all measurement element values.

[0011] Furthermore, the method for obtaining the degree of pattern consistency is as follows: If the number of initial peak monitoring points or the number of final peak monitoring points is 0, then the mode consistency level is directly assigned to 0. If the number of initial peak monitoring points and the number of final peak monitoring points are both greater than 0 and not equal, then add virtual rows or virtual columns to the collaborative difference measurement matrix to make the number of rows and columns of the matrix equal, and set the measurement element value corresponding to the added position to the preset penalty value to obtain the target measurement matrix. If the number of initial peak monitoring points and the number of final peak monitoring points are both greater than 0 and equal, then the collaborative difference measurement matrix will be used as the target measurement matrix. The minimum matching cost of the target metric matrix is ​​obtained by using a linear allocation algorithm, which is then used as the observation matching cost. Keeping all initial peak monitoring points unchanged, randomly permuting the positions of all termination peak monitoring points, and obtaining the minimum matching cost under the permutation according to the same calculation method as the observation matching cost; Repeat the above random permutation and minimum matching cost calculation operations a preset number of times, calculate the average of the minimum matching costs under all random permutations, and use it as the benchmark matching cost; The degree of pattern consistency is determined by dividing the baseline matching cost by the sum of the observation matching cost and the second preset positive number.

[0012] Furthermore, the method for obtaining the pollution source area is as follows: When the pattern consistency is greater than the preset pattern threshold, the matching combination of the initial peak monitoring point and the termination peak monitoring point is obtained from the matching scheme corresponding to the calculated observation matching cost, and used as the target matching pair; If there is only one target matching pair, then the position of the initial peak monitoring point in the target matching pair is extended by a preset distance in the opposite direction of the wind speed vector corresponding to the wind speed at the initial moment, and the position is taken as the target position. If the number of target matching pairs is greater than one, then based on the positions of the initial peak monitoring points and the termination peak monitoring points in all target matching pairs, calculate the direction vector from the initial peak monitoring point to the termination peak monitoring point in each target matching pair, and solve the target position by performing the inverse intersection method using the least squares method. The area within the preset range containing the target location is designated as the pollution source area; When the pattern consistency is less than or equal to the preset pattern threshold, for any grid point within the monitoring area, the position of the grid point and the positions of each monitoring point are substituted into the spatial kernel function for calculation to obtain the kernel function value corresponding to each monitoring point. The probability numerator value corresponding to each grid point is obtained by multiplying the degree of non-combustion source at the initial time of each monitoring point by the corresponding kernel function value and summing the results. The non-combustion source levels of all monitoring points at the initial time are summed, and a third preset positive number is added to obtain the probability denominator value; Divide the probability numerator by the probability denominator to obtain the risk probability density of that grid point. The area formed by grid points with a risk probability density greater than a preset density threshold is designated as the pollution source area.

[0013] Furthermore, the method for obtaining the spatial distribution width is as follows: For any peak monitoring point, its corresponding initial time or cutoff time is taken as the target time; The product of the non-combustion source level at the peak monitoring point at the target time and the preset attenuation ratio is used as the target attenuation threshold; Among the monitoring points outside the peak monitoring point in the monitoring area, those monitoring points whose non-combustion source degree is less than or equal to the target attenuation threshold at the target time are selected as candidate attenuation monitoring points. If candidate attenuation monitoring points exist, calculate the distance between the peak monitoring point and each candidate attenuation monitoring point, and take the minimum distance as the spatial distribution width of the peak monitoring point. If no candidate attenuation monitoring point exists, the average spacing will be used as the spatial distribution width of the peak monitoring point.

[0014] Secondly, another embodiment of the present invention provides an ambient air quality detection device based on multi-point monitoring. The device includes: a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above methods.

[0015] The present invention has the following beneficial effects: This invention first obtains the degree of non-combustion source at each monitoring point at each moment based on the changes in volatile organic compound (VOC) and carbon dioxide (CO2) concentrations within a specified time period at each moment. This accurately reflects the intensity of abnormal VOC emissions independent of the exhaust gas combustion process, which is beneficial for accurately extracting real industrial leak signals in environments with strong background interference, thus eliminating a large number of false alarms caused by conventional concentration threshold warnings. To accurately pinpoint the occurrence cycle of abnormal events in massive dynamic data and establish a physically aligned spatiotemporal window, an initial moment is triggered based on the magnitude of the non-combustion source degree, combined with the initial moment... The system calculates wind speed and distance between monitoring points to determine cutoff times, ensuring a strong causal relationship between the observed characteristic data at two different times in terms of fluid dynamics transport. To abstract continuous diffusion field data into traceable discrete spatial objects, the system obtains initial peak monitoring points based on the degree of non-combustion source at the initial time, thus locating the initial source-sink core of the pollution outbreak. Similarly, it obtains termination peak monitoring points based on the degree of non-combustion source at the cutoff time, identifying new aggregation cores after the pollution plume drifts and evolves with the wind field. Furthermore, the system quantifies the gap between theoretical physical paths and actual observation paths and transforms first principles of atmospheric dynamics into mathematical constraints. This process involves obtaining the spatial distribution width corresponding to each peak monitoring point, constructing a collaborative difference metric matrix based on the location and spatial distribution width of the initial and final peak monitoring points, as well as the initial wind speed, and establishing a global cost dictionary to evaluate the physical legitimacy of various possible movement trajectories. To establish a dimensionless evaluation scale that excludes the interference of absolute numerical values ​​of wind speed and spatial scale, the observation matching cost of the collaborative difference metric matrix is ​​solved. Combined with the benchmark matching cost after random permutation of the final peak monitoring point, the pattern consistency is calculated to accurately reflect the overall evolution law of the current pollution event and the assumption of linear diffusion from a single gas source. The degree of internal consistency facilitates intelligent diagnosis of the morphological type of pollution sources before performing source tracing and location, strictly separating ordered single-source pollution from chaotic multi-source / passive pollution; then, based on the degree of pattern consistency, the pollution source area is obtained, and the source tracing strategy is dynamically segmented based on the degree of pattern consistency (if steady-state diffusion is met, reverse geometric precise tracing is initiated; if it is in a nonlinear chaotic state, it degenerates into a global risk heat map mapping), effectively avoiding the problem of computational divergence or misleading environmental law enforcement caused by forcibly applying a single source tracing algorithm in complex scenarios, and ensuring that the final output geographical location conforms to the first physical causal laws of pollution formation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart illustrating an environmental air quality detection method based on multi-point monitoring, provided as an embodiment of the present invention. Figure 2 This is a structural diagram of an ambient air quality detection system based on multi-point monitoring, provided in one embodiment of the present invention. Figure 3 This is a schematic diagram of a computer device provided according to an embodiment of the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an ambient air quality detection method and device based on multi-point monitoring proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following description, in conjunction with the accompanying drawings, details a specific scheme for an ambient air quality detection method and equipment based on multi-point monitoring provided by the present invention.

[0021] Example 1: This invention proposes an ambient air quality detection method based on multi-point monitoring. Please refer to [link / reference]. Figure 1 The diagram illustrates a schematic flowchart of an ambient air quality detection method based on multi-point monitoring according to an embodiment of the present invention. The method includes the following steps: Step S1: Obtain the concentrations of volatile organic compounds and carbon dioxide at each monitoring point within the monitoring area at each time point.

[0022] Specifically, this embodiment uses a designated industrial park monitoring area as an example for analysis, and all subsequent references to the monitoring area refer to this monitoring area. Considering the spatial continuity of pollutant gas diffusion and the obstruction of airflow by urban buildings, multiple monitoring points are set within the monitoring area to ensure comprehensive coverage of potential fugitive emission sources and effectively capture the spatial evolution characteristics of pollution plumes. In this embodiment, 50 monitoring points are set and arranged according to a grid pattern. Implementers can set the number and location of monitoring points according to the actual terrain area, key monitoring targets, and equipment budget, and there are no limitations here. To capture the instantaneous abrupt changes in pollutant gases in real time and extract reference components to distinguish them from background exhaust gases, this embodiment uses photoionization detectors or metal oxide semiconductor sensors and non-dispersive infrared sensors deployed at various monitoring points to acquire the concentrations of volatile organic compounds (VOCs) and carbon dioxide at each monitoring point at each moment within the monitoring area, forming a continuous multidimensional spatiotemporal observation data stream. In this embodiment, the concentrations of VOCs and carbon dioxide are collected synchronously, and the collection period is set to 3 seconds (i.e., the collection frequency is approximately 0.33Hz) to ensure that the data meets the accuracy requirements for subsequent time change rate calculations and the ability to capture high-frequency transient fluctuations in the time dimension. Implementers can set the data collection frequency according to the physical response time of the sensor hardware and the system network communication bandwidth, which is not limited here.

[0023] Step S2: Based on the changes in volatile organic compound concentration and carbon dioxide concentration at each monitoring point within a specified time period at each moment, obtain the degree of non-combustion source at each monitoring point at each moment.

[0024] Specifically, considering the dense background combustion sources such as vehicle exhaust in urban or complex industrial environments, these combustion processes simultaneously emit volatile organic compounds (VOCs) and carbon dioxide, which severely interfere with the readings of a single sensor and easily mask the true industrial leak signal. To eliminate background noise, we extract pure industrial leak and fugitive emission signals. Then, based on the changes in VOC and carbon dioxide concentrations at each monitoring point within a specified time period at each moment, we obtain the degree of non-combustion source at each monitoring point at each moment. This accurately reflects the true VOC emission intensity independent of the combustion process, which is beneficial for accurately locating abnormal pollution sources under strong background interference. The greater the degree of non-combustion source, the more severe the pollution caused by non-combustion anomalies (such as chemical tank leaks or solvent evaporation).

[0025] Preferably, in one feasible embodiment of this method, the method for obtaining the degree of non-combustion source is as follows: For any monitoring point at any time, the interval between that time and the previous adjacent time is taken as the first duration, i.e., the data sampling period of the system; in order to capture the instantaneous change trend of concentration data and effectively eliminate the influence of slow baseline drift caused by long-term operation of the sensor, and at the same time suppress the high-frequency random white noise generated by the sensor hardware itself being amplified by differential operation, the average value of the volatile organic compound concentration at that time and the preset number of adjacent times before it is obtained as the smoothed organic compound concentration at that time, which is equivalent to performing a low-pass filtering process in the time domain; in this embodiment, the preset number of adjacent times is set to 4 to ensure that the smoothing window (combined with a 3-second sampling period, the total duration is about 15 seconds) effectively suppresses the averaging of short-term atmospheric turbulence fluctuations without smoothing out the real industrial leakage pulse signal. The implementer can adjust the sensor according to the inherent information of the sensor. The noise ratio characteristics and the typical scale of local wind field turbulence are set by a preset number of neighboring units, which are not limited here. Then, the difference between the smoothed organic matter concentration at the monitoring point at the current moment and the smoothed organic matter concentration at the previous adjacent moment is taken as the first value. The ratio of the first value to the first duration is taken as the rate of change of volatile organic compound concentration at the monitoring point at the current moment, which accurately represents the instantaneous rise or fall rate of volatile organic compound concentration. Similarly, in order to simultaneously capture the transient indicator characteristics of background combustion activity, the average value of carbon dioxide concentration at the monitoring point at the current moment and the preset number of neighboring moments is obtained as the smoothed carbon dioxide concentration at the monitoring point at the current moment. Then, the difference between the smoothed carbon dioxide concentration at the monitoring point at the current moment and the smoothed carbon dioxide concentration at the previous adjacent moment is taken as the second value. The ratio of the second value to the first duration is taken as the rate of change of carbon dioxide concentration at the monitoring point at the current moment, which accurately represents the degree of drastic change of carbon dioxide as a combustion tracer gas. To quantify the covariance characteristics of volatile organic compounds (VOCs) and carbon dioxide over a period of time and capture the linear correlation between them in the background exhaust emissions, the sum of the products of the VOC concentration change rate and the carbon dioxide concentration change rate at all times within a specified time period at the monitoring point is obtained as the first analytical value, accurately reflecting the covariance trend of the concentration changes of the two gases within the specified time period. In this embodiment, the specified time period is set as a historical sliding time window of 10 minutes prior to the current time. This duration can effectively cover typical traffic flow fluctuation cycles. The implementer can adjust the length of the specified time period according to the actual background noise frequency, which is not limited here. Furthermore, the sum of the squares of the carbon dioxide concentration change rate at all times within the specified time period at the monitoring point is obtained as the second analytical value, accurately reflecting the total variance of the carbon dioxide fluctuation itself (i.e., background combustion fluctuation energy). To calculate the linear regression projection ratio of the change rates of the two gases, and to completely eliminate the program's division-by-zero crash vulnerability caused by a constant carbon dioxide concentration (second analytical value being zero) within a specified time period, the ratio of the first analytical value to the sum of the second analytical value and a first preset positive number is used as the correlation coefficient for that monitoring point at that moment. This accurately reflects the expected background volatile organic compound change ratio accompanying a unit change in carbon dioxide within a specified time period at that monitoring point. In this embodiment, the first preset positive number is set as... To avoid a denominator of 0, the implementer can set the size of the first preset positive number based on the lower limit of the machine precision supported by the computer system when processing floating-point operations; this is not limited here. To reconstruct the expected fluctuation of volatile organic compounds (VOCs) directly caused by combustion sources such as traffic exhaust at that moment, the correlation coefficient is multiplied by the rate of change of carbon dioxide concentration at that monitoring point at that moment to obtain the background combustion component at that monitoring point at that moment, quantifying the increase in VOCs caused solely by combustion interference. Considering that the core purpose of this embodiment is to monitor abnormal leaks caused by non-combustion, the rate of change of VOCs concentration at that monitoring point at that moment is subtracted from the background combustion component to obtain the degree of non-combustion source at that monitoring point at that moment. To ensure the physical meaning of the degree of non-combustion source as an emission intensity indicator and to avoid the risk of a negative or zero denominator in subsequent risk probability mapping calculations, if the difference between the calculated rate of change of VOCs concentration and the background combustion component is less than or equal to 0, the degree of non-combustion source at that monitoring point at that moment is uniformly reassigned to the fifth preset positive number. In this embodiment, the fifth preset positive number is set to... The implementer can set a fifth preset positive number according to the actual situation, without limitation here; this operation ensures that the feature values ​​of all nodes in the monitoring network are always positive, providing a stable numerical basis for the subsequent normalization of global risk distribution.

[0026] Step S3: Trigger the initial moment based on the degree of non-combustion source, and obtain the cutoff moment by combining the wind speed at the initial moment and the distance between monitoring points; obtain the initial peak monitoring point based on the degree of non-combustion source at the initial moment; obtain the final peak monitoring point based on the degree of non-combustion source at the cutoff moment.

[0027] Specifically, to accurately pinpoint the starting point of an abnormal pollution event within a massive, continuous data stream and avoid meaningless continuous calculations, this embodiment first triggers the initial moment based on the degree of non-combustion source, i.e., determining the instant of the first abrupt change in abnormally high concentration gas. This helps to strictly lock the subsequent analysis window within the actual leakage event cycle. To establish a reasonable spatiotemporal aligned evolution window based on physical causality, and then combine the wind speed at the initial moment with the distance between monitoring points, the cutoff moment is obtained. This determines the estimated arrival time of the pollution plume drifting with the wind field to the next logical observation area, ensuring that the phenomena observed at the initial and cutoff moments have a strong causal relationship in fluid dynamics. Since the entire network feature data distribution is presented in the form of discrete grid points, to abstract the continuously diffusing pollution field data into discrete feature objects that can be tracked and path planned, the initial peak monitoring point is obtained based on the degree of non-combustion source at the initial moment, locating the local extreme high point coordinate core at the beginning of the pollution event. Simultaneously, the termination peak monitoring point is obtained based on the degree of non-combustion source at the cutoff moment, identifying the new concentration accumulation core after the pollution plume has evolved and drifted for a period of time.

[0028] Preferably, in one feasible embodiment of this invention, the method for obtaining the initial time is as follows: Considering that normal monitoring data always contains minor fluctuations and system noise that are not completely filtered out, in order to accurately determine the occurrence of a substantial leakage event and eliminate low-frequency interference, for any given time, the non-combustion source level of each monitoring point at that time is compared with a preset noise threshold to analyze and determine whether any abnormal abrupt changes have occurred; if at least one monitoring point has a non-combustion source level greater than the preset noise threshold at that time, it indicates that a strong release of organic gases independent of combustion has occurred in the area (such as a tank rupture leak), and that time is taken as the initial time. In this embodiment, the preset noise threshold is set to three times the standard deviation of the non-combustion source level under historical healthy operating conditions, ensuring that 99.7% of accidental statistical fluctuations can be filtered out while maintaining high sensitivity. Implementers can set the preset noise threshold according to the distribution of local long-term monitoring baseline data, which is not limited here.

[0029] Preferably, in one feasible embodiment, the method for obtaining the cutoff time is as follows: Considering the need to establish a characteristic scale benchmark for the monitoring network to measure the typical distance that pollutant clouds move between discrete detectors, the distance between any two monitoring points is calculated as the first distance; then, the average of all first distances is used as the average spacing, thereby parameterizing the macroscopic topological structure of the monitoring area and providing a spatial scale reference for subsequent judgment on whether the airflow is sufficient to transport the pollutant cloud from one node to another; to prevent the divisor from reaching zero and to ensure the continuity of the evolution time calculation, the maximum value between the initial wind speed and the preset minimum wind speed is further used as the target wind speed. This lower limit protection operation fundamentally... This avoids the division-by-zero crash vulnerability caused by wind speeds of zero or close to zero in calm or extremely weak wind conditions. This embodiment sets a preset minimum wind speed of 0.5 meters per second to ensure that the system can still provide a fallback time slice calculation basis based on air Brownian diffusion in windless conditions. Implementers can set the preset minimum wind speed according to the local annual light wind statistical characteristics, which is not limited here. In order to calculate the theoretical physical time required for the polluted air mass to move the network characteristic distance under wind, the ratio of the average spacing to the target wind speed is used as the delay time, i.e., the time lag constant. In order to accurately align the evolution analysis window to the end of the physical drift cycle, the time corresponding to the initial time plus the delay time is used as the cutoff time.

[0030] Preferably, in one feasible method of this embodiment, the method for obtaining the initial peak monitoring point and the final peak monitoring point is as follows: for any monitoring point, other monitoring points that are less than a preset search radius from the monitoring point are all regarded as neighboring monitoring points of the monitoring point; in this embodiment, the preset search radius is set to 1.5 times the average spacing to ensure that the central extreme point is screened within a local topological envelope. The implementer can set the preset search radius according to the density of monitoring point deployment, which is not limited here; if the degree of non-combustion source of the monitoring point at the initial moment is greater than the degree of non-combustion source of all its neighboring monitoring points at the initial moment, it indicates that the coordinate is the core source and sink center of the local pollution gas cloud outbreak, and then the monitoring point is regarded as the initial peak monitoring point, realizing the dimensionality reduction extraction from the continuous field data of the entire network to the tracking feature point; If the degree of non-combustion source at the cutoff time of the monitoring point is greater than that of all its neighboring monitoring points at the cutoff time, it indicates that the coordinate is a new accumulation core formed after the pollution gas mass evolves and moves under the action of the wind field. In this case, the monitoring point is regarded as the termination peak monitoring point and as a key anchor point for subsequent verification of the physical transport evolution path.

[0031] Step S4: Obtain the spatial distribution width corresponding to each peak monitoring point. Based on the position and spatial distribution width of the initial peak monitoring point and the final peak monitoring point, as well as the wind speed at the initial moment, construct a collaborative difference metric matrix. Solve for the observation matching cost of the collaborative difference metric matrix, and combine it with the baseline matching cost after random permutation of the final peak monitoring point to calculate the pattern consistency degree.

[0032] Specifically, considering that atmospheric pollution clouds inevitably diffuse along their boundaries as they move with the wind, this embodiment obtains the spatial distribution width corresponding to each peak monitoring point to establish a complete physical verification boundary in the fluid dynamics dimension. This quantifies the spatial attenuation span of the pollution cloud, which is beneficial for subsequently penalizing anomalies that violate the diffusion law. To quantify the difference between the theoretical physical path and the actual observed path, the first principles of atmospheric dynamics are transformed into mathematical constraints. Then, based on the positions and spatial distribution widths of the initial and final peak monitoring points, as well as the initial wind speed, a cooperative difference metric matrix is ​​constructed as a cost dictionary for evaluating the physical legitimacy of various possible trajectories. To obtain the optimal total path cost of observational data under physical laws—that is, to find a mapping scheme that minimizes the violation of the laws of advection and diffusion, while allowing for a certain degree of splitting or merging of the polluted air mass—we need to solve for the observation matching cost of the cooperative difference metric matrix and extract the most reasonable total motion loss of the polluted air mass between the initial and cutoff moments. Simultaneously, to establish a dimensionless evaluation scale that excludes the absolute numerical interference of wind speed and spatial scale, we need to combine the baseline matching cost after random permutation of the terminal peak monitoring point to calculate the pattern consistency degree. This accurately reflects the degree of fit between the overall evolution of the current pollution event and the assumption of linear diffusion from a single point source, which is beneficial for strictly separating ordered single-source pollution from chaotic multi-source / passive pollution. A higher pattern consistency degree indicates that the evolution of the pollution field highly conforms to the typical physical characteristics of wind-borne diffusion from a single point source, providing conditions for accurate inversion and source tracing.

[0033] Preferably, in one feasible method of this embodiment, the spatial distribution width is obtained as follows: for any peak monitoring point, its corresponding initial time or cutoff time is taken as the target time; considering the need to find a scale to characterize the boundary of the air mass in an irregular monitoring network, in order to define the edge characteristics of a significant decrease in pollution concentration, the product of the non-combustion source degree of the peak monitoring point at the target time and the preset attenuation ratio is taken as the target attenuation threshold, that is, the half-width boundary strength of the pollution mass diffusion is defined; in this embodiment, the preset attenuation ratio is set to 50% to ensure that the half-high and half-wide boundary of the Gaussian distribution can be robustly defined. The implementer can set the preset attenuation ratio according to the actual gradient steepness of the pollution mass, which is not limited here; To geographically locate this concentration decay boundary, monitoring points outside the peak monitoring point within the monitoring area whose non-combustion source level at the target time is less than or equal to the target decay threshold are selected as candidate decay monitoring points, thus locking down all monitoring points that have fallen outside the decay boundary. If candidate decay monitoring points exist, it indicates that the pollution plume formed by the peak monitoring point exhibits a clear island diffusion boundary under the current monitoring network layout. To obtain the most conservative and reliable expansion radius scale, the distance between the peak monitoring point and each candidate decay monitoring point is calculated, and the minimum distance is taken as the spatial distribution width of the peak monitoring point, accurately representing the spatial occupancy of the gas plume. If no candidate decay monitoring points exist, it indicates that the pollution plume is extremely large, obscuring the entire network, or is located at an extremely peripheral position where decay cannot be detected. To ensure that the subsequent penalty matrix calculation does not collapse due to missing parameters, the average spacing is taken as the spatial distribution width of the peak monitoring point.

[0034] Preferably, in one feasible embodiment of this method, the method for obtaining the collaborative difference measurement matrix is ​​as follows: In order to quantify the mechanical drift error of the air mass during advection transport, and to penalize movements that violate wind field laws, for any initial peak monitoring point and any final peak monitoring point, the product of the wind speed vector corresponding to the wind speed at the initial moment and the delay time is used as the displacement vector corresponding to the initial peak monitoring point, accurately reflecting the theoretical purely mechanical translation trajectory of the pollution mass within a specified time period under ideal conditions; In order to anchor the coordinates that the air mass is expected to reach, the position of the initial peak monitoring point is added to the displacement vector to obtain the theoretical evolution position, that is, the endpoint coordinates that the pollution mass should appear when no variation occurs; In order to quantify the degree of misalignment between actual observation and theory, the distance between the theoretical evolution position and the position of the final peak monitoring point is used as the advection transport deviation, accurately reflecting the magnitude of the error of the air mass deviating from the wind direction axis; wherein, the larger the advection transport deviation, the more the evolution path formed by the two points violates the physical laws such as wind direction or wind speed, and the lower the correlation probability; To introduce the first-principles constraint from thermodynamics that the gas diffusion area must monotonically increase (i.e., concentration broadening irreversibly shrinks), the spatial distribution width of the initial peak monitoring point is subtracted from the spatial distribution width of the final peak monitoring point to obtain the width difference, which accurately reflects the degree to which the volume of the gas cloud is compressed or expanded during its evolution. Considering that the spatial distribution width of a normally freely diffused pollution cloud should increase or remain unchanged (width difference less than or equal to 0), while a sharp decrease in width implies a "reverse aggregation" phenomenon that violates the laws of physics, a one-way penalty is imposed on this anomaly. The maximum value between the width difference and 0 is taken as the diffusion constraint deviation, ensuring that the normal diffusion expansion process is fully contained, while the volume contraction that violates the laws of nature is strictly included in the deviation cost. The larger the diffusion constraint deviation, the more severe the degree to which the evolution path "violates the thermodynamic laws of diffusion". To comprehensively weigh the compliance of the two major physical laws of mechanical advection and thermodynamic diffusion, the weighted sum of advection transport deviation and diffusion constraint deviation is used as the metric value corresponding to the initial peak monitoring point and the final peak monitoring point. This accurately reflects the total physical violation cost of binding the initial peak point and the final peak point to the same air mass trajectory; the lower the value, the more reasonable the path. The formula for calculating the metric value is as follows: In the formula, The values ​​of the measurement elements corresponding to the i-th initial peak monitoring point and the j-th termination peak monitoring point; The advection transport deviation corresponds to the i-th initial peak monitoring point and the j-th final peak monitoring point; The diffusion constraint deviation is the difference between the i-th initial peak monitoring point and the j-th termination peak monitoring point. As the first preset weight; This is the second preset weight; this embodiment sets... Set to 1.0 The value is set to 1.5 to ensure that the advection displacement deviation and diffusion shrinkage penalty are balanced on a metric order of magnitude, with a slight emphasis on the diffusion penalty. Implementers can set this value according to the local topographic complexity and turbulent dissipation characteristics. and No specific restrictions are imposed here; In order to construct a global cost dictionary structure for the matching algorithm, the initial peak monitoring point is used as the row index and the terminating peak monitoring point is used as the column index. All initial peak monitoring points and all terminating peak monitoring points are traversed, and a collaborative difference measurement matrix is ​​constructed based on all measurement element values.

[0035] Preferably, in one feasible embodiment, the method for obtaining the pattern consistency degree is as follows: In order to satisfy the matrix condition constraint that classical linear allocation algorithms (such as the Jonker-Volgenant algorithm or the Hungarian algorithm) must operate under a strictly bipartite graph, and to solve the deadlock problem caused by the unequal number of peak points before and after the splitting or merging of pollution clusters, if the number of initial peak monitoring points and the number of final peak monitoring points are both greater than 0 and unequal, then virtual rows or virtual columns are added to the collaborative difference metric matrix to make the number of rows equal to the number of columns, and the metric element value corresponding to the added position is set to a preset penalty value to obtain the target metric matrix; it should be noted that the criterion for adding virtual rows or virtual columns is: compare the number of rows with the number of columns. If the number of rows is less than the number of columns, then add several virtual rows until they are equal to the number of columns; if the number of columns is less than the number of rows, then add several virtual columns until they are equal to the number of rows. In this embodiment, the preset penalty value is set to twice the historical average metric value, indicating a strong pessimism towards forced matching; if the number of initial peak monitoring points and the number of final peak monitoring points are both greater than 0 and equal, then the collaborative difference metric matrix is ​​used as the target metric matrix; among them, the Jonker-Volgenant algorithm and the Hungarian algorithm are well-known and will not be described in detail. To find the trajectory family scheme with the minimum global cost among all the complex and chaotic paths, which is the one that best conforms to fluid dynamics, a linear allocation algorithm is used to solve for the minimum matching cost of the target metric matrix, which is then used as the observation matching cost to extract the most likely physical evolution path loss under the observation data. To quantify the observation matching cost, a benchmark reference system based on the disordered state needs to be established to eliminate the natural influence of topography and distance. Thus, keeping all initial peak monitoring points unchanged, the positions of all terminal peak monitoring points are randomly permuted, and the minimum matching cost under the permutation is obtained according to the same calculation method as the observation matching cost. The above random permutation and minimum matching cost calculation operation is repeated a preset number of times, and the mean of the minimum matching cost under all random permutations is calculated as the benchmark matching cost. Monte Carlo statistical averaging is used to smooth out the accidental jump phenomenon of a single random shuffle. In this embodiment, the preset number of times is set to 50 to ensure that the benchmark mean converges and has statistical representativeness. The implementer can set the preset number of times according to the processor computing power allocation, which is not limited here. To calculate the superiority of the observed state compared to a completely disordered state, the baseline matching cost is divided by the sum of the observation matching cost and a second preset positive number, which is then used as the pattern consistency degree. In this embodiment, the second preset positive number is set to... This is used to defend against a division-by-zero crash vulnerability when the observation state perfectly conforms to the laws of physics, resulting in a matching cost of 0. The implementer can set a second preset positive number based on the underlying calculation accuracy tolerance; no specific limit is imposed here. If the number of initial peak monitoring points or the number of final peak monitoring points is 0, the mode consistency level is directly assigned a value of 0.

[0036] Step S5: Based on the degree of pattern consistency, obtain the pollution source area.

[0037] Specifically, it is known that different forms of pollution emission (single-point steady-state leakage and multi-source chaotic diffusion) will lead to drastically different spatiotemporal evolution characteristics. In order to avoid the divergence of location results or misleading environmental law enforcement due to the use of a single fixed source tracing algorithm, the pollution source area is obtained based on the degree of pattern consistency. The source tracing strategy is dynamically segmented based on this indicator as the gating basis to ensure that the final output geographical location conforms to the first physical causality of pollution formation.

[0038] Preferably, in one achievable manner in this embodiment, the method for obtaining the pollution source area is as follows: when the pattern consistency is greater than a preset pattern threshold, it indicates that the current pollution field evolution process is highly ordered and is very likely to originate from emissions from a single fixed point source transported by the laminar wind field. At this time, the prerequisite for reverse geometric tracing is met. In order to extract the most solid physical trajectory link, the matching combination of the initial peak monitoring point and the termination peak monitoring point is obtained from the matching scheme corresponding to the calculated observation matching cost as the target matching pair, and irrelevant noise points or floating background sporadic aggregation points are accurately filtered out. If there is only one target matching pair, it indicates that the pollution event is small in scale or in the initial stage, and only an isolated evolution trajectory has been captured. In order to prevent the calculation from crashing due to the forced running of the least squares intersection algorithm in this ill-conditioned equation scenario, the position of the initial peak monitoring point in the target matching pair is extended by a preset distance in the opposite direction of the wind speed vector corresponding to the wind speed at the initial moment, and the position corresponding to this distance is taken as the target position to determine the unique emission rough estimation point. In this embodiment, the preset distance is set to half of the wind speed multiplied by the delay time to ensure that the backtracking depth is exactly at the center node position of the two observation times at the initial moment and the cutoff moment. The implementer can set the preset distance according to the drag coefficient of the terrain affecting the backtracking efficiency, which is not limited here. If the number of target matching pairs is greater than one, it indicates that multiple fission branches or outward expansion trajectory groups in the evolution process of the pollution plume have been captured, and the conditions for geometric intersection calculation are met. In order to accurately find the unique source anchor point of these physical rays convergence, based on the positions of the initial peak monitoring points and the positions of the termination peak monitoring points in all target matching pairs, the direction vector from the initial peak monitoring point to the termination peak monitoring point in each target matching pair is calculated, and the target position is obtained by inverse intersection solution using the least squares method. The specific process is as follows: For any target matching pair, the difference vector between the position of its termination peak monitoring point and the position of its initial peak monitoring point is calculated as the observed transport direction vector of the target matching pair, and a spatial straight line equation is constructed that passes through its initial peak monitoring point and whose direction is collinear with the observed transport direction vector. Since the evolution trajectory captured by different monitoring points has local perturbation differences in the actual atmospheric environment, the observed transport direction vectors are not completely parallel in space, thus meeting the intersection conditions; an objective function is constructed that minimizes the sum of the squares of multiple straight lines from a certain unknown point: In the formula, The coordinates of the target location; The operation to find the unknown variable L that minimizes the subsequent objective function; M is the total number of target matching pairs; Let L be the position coordinates of the initial peak monitoring point in the m-th target matching pair; L is the unknown coordinates to be solved in space. The observed transport direction vector corresponding to the m-th target matching pair is obtained by subtracting the initial peak monitoring point position from the position of the termination peak monitoring point in the matching pair. The modulo symbol; The cross product operator for vectors; The fourth preset positive number is used to prevent division-by-zero crashes caused by the wind speed vector magnitude being zero in calm weather. In this embodiment, it is set to... The settings can be adjusted according to the actual situation; no specific limitations are imposed here. The coordinate vector is obtained by finding the partial derivative using linear algebra methods and solving for the zero solution. As the target location; It is known that actual leaks are always accompanied by slight turbulent deflections, and a single theoretical coordinate point is insufficient to guide the investigation scope. In order to help environmental law enforcement personnel delineate a reasonable encirclement and search blockade line, and thus designate a preset range area including the target location as the pollution source area, this embodiment sets the preset range area as a circular buffer zone with the target location as the center and a radius of 50 meters to ensure coverage of the area occupied by a typical factory workshop or a specific tank group. Implementers can set the preset range area according to the park plan and the density of investigation personnel, and there are no restrictions here. When the pattern consistency is less than or equal to the preset pattern threshold, it indicates that the evolution between the initial and cutoff times exhibits a highly nonlinear, abrupt, or chaotic state. This is highly likely due to multiple unorganized emissions from a mobile convoy or surface-source disturbances from a chemical plant. To avoid misleading reconnaissance by providing false converged single-point coordinates, a degradation strategy is adopted to depict the global state. For any grid point within the monitoring area, the position of that grid point and the positions of all monitoring points are substituted into the spatial kernel function for calculation to obtain the kernel function value corresponding to each monitoring point. This accurately reflects the weight ratio of the distance attenuation of the grid point by the monitoring points. The specific calculation process of the kernel function value is as follows: The Gaussian radial basis kernel function formula is: In the formula, The kernel function value corresponding to the nth monitoring point for this grid point; x is the coordinate of this grid point; Let these be the coordinates of the nth monitoring point; This is the Euclidean distance between the grid point and the nth monitoring point; is the smoothing parameter (usually set to the average spacing); exp is an exponential function with the natural constant as the base; the method for obtaining the Euclidean distance is a well-known technique and will not be elaborated further. To calculate the spatially weighted radiation impact of the non-combustion characteristic intensity of the entire network on the grid point, the non-combustion source intensity of each monitoring point at the initial time is multiplied by the corresponding kernel function value and then summed to obtain the probability numerator value corresponding to the grid point. This is then used to smoothly interpolate the estimated pollution intensity in areas where no sensors are deployed. To normalize the values ​​and obtain an absolute distribution surface in a statistical probability sense, the non-combustion source intensity of all monitoring points at the initial time is summed and a third preset positive number is added to obtain the probability denominator value. This prevents division-by-zero collapse when the non-combustion source intensity of the entire network is truncated to an extremely weak negative value. In this embodiment, the third preset positive number is set to... The implementer may set a third preset positive number according to the floating-point precision type of the computing device, which is not limited here; To characterize the relative probability that a grid point is identified as a pollution hazard source, the probability numerator is divided by the probability denominator to obtain the risk probability density of the grid point, directly forming a smooth risk heat map covering the entire monitoring area. To output the final set of warning area coordinates and remove low-risk background areas, the area formed by grid points with a risk probability density greater than a preset density threshold is taken as the pollution source area.

[0039] In summary, this embodiment obtains the concentrations of volatile organic compounds (VOCs) and carbon dioxide at each monitoring point; based on the changes in these concentrations over a specified time period, the degree of non-combustion source is extracted to trigger the initial time; the cutoff time is determined by combining wind speed and point spacing, and the initial peak monitoring point and the final peak monitoring point are extracted respectively; the spatial distribution width of the peak monitoring points is obtained, and a collaborative difference metric matrix is ​​constructed by combining wind speed; the observation matching cost is solved, and the pattern consistency degree is calculated by combining the benchmark matching cost of random permutation; finally, the pollution source area is obtained based on the pattern consistency degree. This invention effectively eliminates exhaust gas interference, achieves adaptive source tracing strategy through physical consistency diagnosis, and avoids location divergence and misleading in complex scenarios.

[0040] Example 2: This invention also proposes an ambient air quality detection system based on multi-point monitoring; please refer to [link / reference]. Figure 2 The diagram illustrates a multi-point monitoring-based ambient air quality detection system according to an embodiment of the present invention. The system includes: a data acquisition module 10, a non-combustion source degree acquisition module 20, a peak monitoring point acquisition module 30, a pattern consistency degree acquisition module 40, and a pollution source area acquisition module 50.

[0041] The data acquisition module 10 is used to acquire the concentrations of volatile organic compounds and carbon dioxide at each monitoring point within the monitoring area at each time.

[0042] The non-combustion source degree acquisition module 20 is used to acquire the non-combustion source degree of each monitoring point at each time based on the changes in the concentration of volatile organic compounds and carbon dioxide at each monitoring point within a specified time period at each time.

[0043] The peak monitoring point acquisition module 30 is used to trigger the initial moment based on the degree of non-combustion source, and obtain the cutoff moment by combining the wind speed at the initial moment and the distance between monitoring points; obtain the initial peak monitoring point based on the degree of non-combustion source at the initial moment; and obtain the final peak monitoring point based on the degree of non-combustion source at the cutoff moment.

[0044] The pattern consistency acquisition module 40 is used to acquire the spatial distribution width corresponding to each peak monitoring point. Based on the position and spatial distribution width of the initial peak monitoring point and the final peak monitoring point, as well as the wind speed at the initial moment, a cooperative difference measurement matrix is ​​constructed. The observation matching cost of the cooperative difference measurement matrix is ​​solved, and the pattern consistency is calculated by combining the benchmark matching cost after random permutation of the final peak monitoring point.

[0045] The pollution source area acquisition module 50 is used to acquire pollution source areas based on the degree of pattern consistency.

[0046] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the environmental air quality detection system based on multi-point monitoring and the environmental air quality detection method based on multi-point monitoring provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0047] Example 3: This invention also proposes an ambient air quality detection device based on multi-point monitoring. The device includes a memory and a processor. The memory stores executable program code, and the processor calls and executes the executable program code to perform an ambient air quality detection method based on multi-point monitoring provided in the embodiments of this application. Specifically, the device may be a chip, component, or module. The chip may include a connected processor and memory; the memory stores instructions, and when the processor calls and executes the instructions, the chip can perform the ambient air quality detection method based on multi-point monitoring provided in the above embodiments.

[0048] In addition, this embodiment also protects a computer device; please refer to [link to relevant documentation]. Figure 3 The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. When the processor 402 executes the computer program 403, the computer device can execute any of the aforementioned multi-point monitoring-based ambient air quality detection methods.

[0049] Example 4: The present invention also provides a computer-readable storage medium storing computer program code, which, when executed on a computer, causes the computer to perform the aforementioned method steps to implement the ambient air quality detection method based on multi-point monitoring provided in the above embodiments.

[0050] Example 5: The present invention also provides a computer program product, which, when run on a computer, causes the computer to perform the above-mentioned related steps to realize the ambient air quality detection method based on multi-point monitoring provided in the above embodiments.

[0051] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0052] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0053] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for detecting ambient air quality based on multi-point monitoring, characterized in that, The method includes the following steps: Obtain the concentrations of volatile organic compounds and carbon dioxide at each monitoring point within the monitoring area at each time point; Based on the changes in volatile organic compound (VOC) and carbon dioxide (CO2) concentrations at each monitoring point within a specified time period at each moment, the degree of non-combustion source at each monitoring point at each moment is obtained. The initial moment is triggered based on the degree of non-combustion source intensity. The cutoff moment is obtained by combining the wind speed at the initial moment with the distance between monitoring points. The initial peak monitoring point is obtained based on the degree of non-combustion source intensity at the initial moment. The final peak monitoring point is obtained based on the degree of non-combustion source intensity at the cutoff moment. Obtain the spatial distribution width corresponding to each peak monitoring point. Based on the position and spatial distribution width of the initial peak monitoring point and the final peak monitoring point, as well as the wind speed at the initial moment, construct a collaborative difference metric matrix. Solve for the observation matching cost of the collaborative difference metric matrix, and combine it with the baseline matching cost after random permutation of the final peak monitoring point to calculate the pattern consistency degree. Based on the degree of pattern consistency, the pollution source area is identified.

2. The ambient air quality detection method based on multi-point monitoring as described in claim 1, characterized in that, The method for obtaining the degree of non-combustion source is as follows: For any monitoring point at any given time, the time interval between that time and its previous adjacent time is taken as the first time interval; The average concentration of volatile organic compounds at the monitoring point at that time and a preset number of adjacent times is obtained as the smoothed concentration of organic compounds at the monitoring point at that time. The difference between the smoothed concentration of organic compounds at the monitoring point at that time and the smoothed concentration of organic compounds at the previous adjacent time is used as the first value. The ratio of the first value to the first duration is taken as the rate of change of volatile organic compound concentration at that monitoring point at that moment; The average carbon dioxide concentration at the monitoring point at that moment and the number of adjacent moments before that moment are obtained as the smoothed carbon dioxide concentration at the monitoring point at that moment. The difference between the smoothed carbon dioxide concentration at the monitoring point at that moment and the smoothed carbon dioxide concentration at the previous adjacent moment is used as the second value. The ratio of the second value to the first time period is taken as the rate of change of carbon dioxide concentration at that monitoring point at that moment; The sum of the products of the rate of change of volatile organic compound concentration and the rate of change of carbon dioxide concentration at all times within a specified time period at the monitoring point is obtained as the first analytical value. The sum of the squares of the rate of change of carbon dioxide concentration at the monitoring point during the specified time period at that moment is obtained as the second analytical value. The ratio of the first analytical value to the sum of the second analytical value and the first preset positive number is used as the correlation coefficient of the monitoring point at that time. Multiply the correlation coefficient by the rate of change of carbon dioxide concentration at that monitoring point at that time to obtain the background combustion component at that monitoring point at that time. Subtracting the background combustion component from the rate of change of volatile organic compound concentration at the monitoring point at that moment yields the degree of non-combustion source at that monitoring point at that moment.

3. The ambient air quality detection method based on multi-point monitoring as described in claim 1, characterized in that, The method for obtaining the initial time is as follows: For any given moment, the non-combustion source level at each monitoring point at that moment is compared with a preset noise threshold. If at least one monitoring point has a non-combustion source level greater than the preset noise threshold at a given moment, then that moment is taken as the initial moment.

4. The ambient air quality detection method based on multi-point monitoring as described in claim 1, characterized in that, The method for obtaining the cutoff time is as follows: Calculate the distance between any two monitoring points, and use it as the first distance; take the average of all first distances as the average spacing. The maximum value between the initial wind speed and the preset minimum wind speed is taken as the target wind speed; The ratio of the average spacing to the target wind speed is used as the delay time. The time corresponding to the initial time plus the delay time is taken as the cutoff time.

5. The ambient air quality detection method based on multi-point monitoring as described in claim 1, characterized in that, The method for obtaining the initial peak monitoring point and the final peak monitoring point is as follows: For any given monitoring point, all other monitoring points within a preset search radius of that monitoring point will be considered as its neighboring monitoring points. If the non-combustion source intensity at the initial moment is greater than that at all its neighboring monitoring points at the initial moment, then the monitoring point shall be regarded as the initial peak monitoring point. If the non-combustion source intensity at the cutoff time is greater than that at all its neighboring monitoring points at the cutoff time, then the monitoring point is designated as the termination peak monitoring point.

6. The ambient air quality detection method based on multi-point monitoring as described in claim 4, characterized in that, The method for obtaining the collaborative difference measurement matrix is ​​as follows: For any initial peak monitoring point and any final peak monitoring point, the product of the wind speed vector corresponding to the wind speed at the initial moment and the delay time is taken as the displacement vector corresponding to that initial peak monitoring point. Adding the displacement vector to the position of the initial peak monitoring point yields the theoretical evolution position; The distance between the theoretical evolution position and the position of the terminal peak monitoring point is taken as the advection transport deviation; The difference in width is obtained by subtracting the spatial distribution width of the initial peak monitoring point from the spatial distribution width of the final peak monitoring point. The maximum value between the width difference and 0 is taken as the diffusion constraint deviation; The weighted sum of the advection transport deviation and the diffusion constraint deviation is used as the metric element value corresponding to the initial peak monitoring point and the final peak monitoring point. The initial peak monitoring point is used as the row index, and the terminating peak monitoring point is used as the column index. All initial peak monitoring points and all terminating peak monitoring points are traversed, and a collaborative difference measurement matrix is ​​constructed based on all measurement element values.

7. The ambient air quality detection method based on multi-point monitoring as described in claim 6, characterized in that, The method for obtaining the pattern consistency degree is as follows: If the number of initial peak monitoring points or the number of final peak monitoring points is 0, then the mode consistency level is directly assigned to 0. If the number of initial peak monitoring points and the number of final peak monitoring points are both greater than 0 and not equal, then add virtual rows or virtual columns to the collaborative difference measurement matrix to make the number of rows and columns of the matrix equal, and set the measurement element value corresponding to the added position to the preset penalty value to obtain the target measurement matrix. If the number of initial peak monitoring points and the number of final peak monitoring points are both greater than 0 and equal, then the collaborative difference measurement matrix will be used as the target measurement matrix. The minimum matching cost of the target metric matrix is ​​obtained by using a linear allocation algorithm, which is then used as the observation matching cost. Keeping all initial peak monitoring points unchanged, randomly permuting the positions of all termination peak monitoring points, and obtaining the minimum matching cost under the permutation according to the same calculation method as the observation matching cost; Repeat the above random permutation and minimum matching cost calculation operations a preset number of times, calculate the average of the minimum matching costs under all random permutations, and use it as the benchmark matching cost; The degree of pattern consistency is determined by dividing the baseline matching cost by the sum of the observation matching cost and the second preset positive number.

8. The ambient air quality detection method based on multi-point monitoring as described in claim 7, characterized in that, The method for obtaining the pollution source area is as follows: When the pattern consistency is greater than the preset pattern threshold, the matching combination of the initial peak monitoring point and the termination peak monitoring point is obtained from the matching scheme corresponding to the calculated observation matching cost, and used as the target matching pair; If there is only one target matching pair, then the position of the initial peak monitoring point in the target matching pair is extended by a preset distance in the opposite direction of the wind speed vector corresponding to the wind speed at the initial moment, and the position is taken as the target position. If the number of target matching pairs is greater than one, then based on the positions of the initial peak monitoring points and the termination peak monitoring points in all target matching pairs, calculate the direction vector from the initial peak monitoring point to the termination peak monitoring point in each target matching pair, and solve the target position by performing the inverse intersection method using the least squares method. The area within the preset range containing the target location is designated as the pollution source area; When the pattern consistency is less than or equal to the preset pattern threshold, for any grid point within the monitoring area, the position of the grid point and the positions of each monitoring point are substituted into the spatial kernel function for calculation to obtain the kernel function value corresponding to each monitoring point. The probability numerator value corresponding to each grid point is obtained by multiplying the degree of non-combustion source at the initial time of each monitoring point by the corresponding kernel function value and summing the results. The non-combustion source levels of all monitoring points at the initial time are summed, and a third preset positive number is added to obtain the probability denominator value; Divide the probability numerator by the probability denominator to obtain the risk probability density of that grid point. The area formed by grid points with a risk probability density greater than a preset density threshold is designated as the pollution source area.

9. The ambient air quality detection method based on multi-point monitoring as described in claim 4, characterized in that, The method for obtaining the spatial distribution width is as follows: For any peak monitoring point, its corresponding initial time or cutoff time is taken as the target time; The product of the non-combustion source level at the peak monitoring point at the target time and the preset attenuation ratio is used as the target attenuation threshold; Among the monitoring points outside the peak monitoring point in the monitoring area, those monitoring points whose non-combustion source degree is less than or equal to the target attenuation threshold at the target time are selected as candidate attenuation monitoring points. If candidate attenuation monitoring points exist, calculate the distance between the peak monitoring point and each candidate attenuation monitoring point, and take the minimum distance as the spatial distribution width of the peak monitoring point. If no candidate attenuation monitoring point exists, the average spacing will be used as the spatial distribution width of the peak monitoring point.

10. An ambient air quality detection device based on multi-point monitoring, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the ambient air quality detection method based on multi-point monitoring as described in any one of claims 1-9.

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