Real-time monitoring system and method for atmospheric pollution in industrial plant area

Abstract

The invention provides an industrial plant atmospheric pollution real-time monitoring system and method, and the method comprises the steps: clustering the emission inflection points of a pollutant concentration curve of each monitoring point in an industrial plant into a plurality of abnormal emission events, and screening out a pollution source monitoring point in the industrial plant; calculating an included angle between a pointing vector of the pollution source monitoring point pointing to other monitoring points in the industrial plant and a wind direction vector to obtain a vector included angle of the pollution source monitoring point, and further determining an environmental influence confidence coefficient of the pollution source monitoring point through the vector included angle and the maximum pollutant concentration in each abnormal emission event; and when the environmental influence confidence coefficient is greater than an influence threshold value in the industrial plant area, performing optimal configuration on the sampling frequency of the downwind monitoring point according to the distance between the pollution source monitoring point and the downwind monitoring point. On the basis of the scheme, risk confidence assessment of the pollution source in industrial factory atmospheric pollution monitoring can be realized, so that the capture precision of the abnormal emission event can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pollution monitoring, and more particularly to an industrial plant atmospheric pollution real-time monitoring system and method. BACKGROUND

[0002] Industrial plant atmospheric pollution is mainly caused by fuel combustion, chemical reactions and material dispersion in the production process, emitting pollutants such as sulfur dioxide, nitrogen oxides, volatile organic compounds and particulate matter. Industrial plant atmospheric pollutants not only form local high-concentration pollution in the plant, but also diffuse with air flow, affecting the air quality of the surrounding area, threatening the ecological system and public health. Industrial plant atmospheric pollution is characterized by concentrated pollution sources, high emission intensity, complex types of pollutants, and is jointly influenced by plant layout and meteorological conditions.

[0003] In the traditional fixed frequency monitoring mode of industrial plant atmospheric pollution monitoring, there is a significant response delay and path prediction blind area due to the use of uniform sampling strategy, which cannot perceive the dynamic characteristics of pollution diffusion. When abnormal emissions occur, the monitoring points outside the pollution path will continue to produce invalid data, and the monitoring points on the path may miss the concentration peak due to sampling interval. The static monitoring network is difficult to construct the real-time transmission trajectory of pollutants under the action of wind field, which leads to the system triggering an alarm only after the pollutants reach the downstream monitoring point, not only delaying the golden window period of source tracing, but also failing to provide early warning and prediction for the areas not yet affected, so that the monitoring system is always in a passive state. Therefore, how to realize the risk confidence evaluation of the pollution source in industrial plant atmospheric pollution monitoring, so as to improve the capture accuracy of abnormal emission events has become a difficult problem in the industry. SUMMARY

[0004] The present application provides an industrial plant atmospheric pollution real-time monitoring system and method, which can realize the risk confidence evaluation of the pollution source in industrial plant atmospheric pollution monitoring, thereby improving the capture accuracy of abnormal emission events.

[0005] In a first aspect, the present application provides an industrial plant atmospheric pollution real-time monitoring method, comprising: Collecting pollutant concentration data in the industrial plant at an initial frequency through sensors arranged at each monitoring point in the industrial plant; Extracting pollutant concentration curves of each monitoring point from the pollutant concentration data, clustering emission inflection points in each pollutant concentration curve into multiple abnormal emission events, and then screening pollutant source monitoring points in the industrial plant through meteorological information of the industrial plant in each abnormal emission event and concentration gradient of each abnormal emission event; calculating an angle between a pointing vector of the pollution source monitoring point pointing to other monitoring points in the industrial plant and a wind direction vector according to meteorological information and pollutant concentration data of the industrial plant at a current time, obtaining a vector angle of the pollution source monitoring point, and further determining an environmental impact confidence of the pollution source monitoring point through the vector angle and a maximum pollutant concentration in each abnormal emission event; when the environmental impact confidence is greater than an impact threshold in the industrial plant, optimizing a sampling frequency of a downwind monitoring point according to a distance between the pollution source monitoring point and the downwind monitoring point.

[0006] In some embodiments, extracting a pollutant concentration curve of each monitoring point from the pollutant concentration data specifically includes: aligning all data by interpolating the pollutant concentration data at a uniform time reference, obtaining a concentration sequence of each monitoring point at an equal time interval; connecting each concentration sequence in time sequence, and constructing a pollutant concentration curve of each monitoring point.

[0007] In some embodiments, clustering emission inflection points in each pollutant concentration curve into a plurality of abnormal emission events specifically includes: obtaining a starting inflection point and an ending inflection point from each pollutant concentration curve, and further obtaining an emission inflection point pair in each pollutant concentration curve; determining a plurality of candidate abnormal segments through a concentration time period between the starting inflection point and the ending inflection point in each emission inflection point pair; spatiotemporally clustering all candidate abnormal segments, and obtaining a plurality of abnormal emission events.

[0008] In some embodiments, screening a pollution source monitoring point in the industrial plant through meteorological information of the industrial plant in each abnormal emission event and a concentration gradient of each abnormal emission event specifically includes: for each abnormal emission event, determining a concentration gravity center of the abnormal emission event through the concentration gradient of the abnormal emission event; extracting a dominant upwind direction of the abnormal emission event from the meteorological information of the abnormal emission event; screening a pollution source point of the abnormal emission event in a dominant upwind direction sector of the concentration gravity center, and further obtaining a pollution source point of each abnormal emission event; determining a pollution source monitoring point in the industrial plant through all pollution source points.

[0009] In some embodiments, calculating an angle between a pointing vector of the pollution source monitoring point pointing to other monitoring points in the industrial plant and a wind direction vector according to meteorological information and pollutant concentration data of the industrial plant at a current time, obtaining a vector angle of the pollution source monitoring point specifically includes: obtaining a pointing vector of the pollution source monitoring point pointing to other monitoring points in the industrial plant from pollution concentration data of the industrial plant at a current time; obtaining a wind direction vector from meteorological information of the industrial plant at the current time; calculating an included angle of each pointing vector and the wind direction vector, and determining a vector included angle of the pollution source monitoring point through all the included angles.

[0010] In some embodiments, determining an environmental impact confidence of the pollution source monitoring point through the vector included angle and a maximum pollution concentration in each abnormal emission event specifically comprises: determining an included angle impact factor of the pollution source monitoring point through the vector included angle; determining a concentration impact factor of the pollution source monitoring point through a maximum pollution concentration of the pollution source monitoring point in each abnormal emission event; determining the environmental impact confidence of the pollution source monitoring point according to the included angle impact factor and the concentration impact factor.

[0011] In some embodiments, optimizing a sampling frequency of downwind monitoring points according to a distance between the pollution source monitoring point and the downwind monitoring points specifically comprises: obtaining a plurality of downwind monitoring points of the pollution source monitoring point; establishing a frequency adjustment rule of the downwind monitoring points; adjusting the sampling frequency of the downwind monitoring points according to the distance between the pollution source monitoring point and each downwind monitoring point according to the frequency adjustment rule.

[0012] In a second aspect, the present application provides an industrial plant atmospheric pollution real-time monitoring system, comprising a frequency optimization unit, the frequency optimization unit comprising: a collection module, configured to collect pollution concentration data in the industrial plant at an initial frequency through sensors arranged at each monitoring point in the industrial plant; a processing module, configured to extract a pollution concentration curve of each monitoring point from the pollution concentration data, cluster emission inflection points in each pollution concentration curve into a plurality of abnormal emission events, and further screen a pollution source monitoring point in the industrial plant through meteorological information of the industrial plant in each abnormal emission event and a concentration gradient of each abnormal emission event; The processing module is further configured to calculate an angle between a pointing vector of the pollution source monitoring point pointing to other monitoring points in the industrial plant and a wind direction vector according to meteorological information and pollution concentration data of the industrial plant at a current time, to obtain a vector angle of the pollution source monitoring point, and to determine an environmental impact confidence of the pollution source monitoring point according to the vector angle and a maximum pollution concentration in each abnormal emission event. The execution module is configured to optimize a sampling frequency of a downwind monitoring point according to a distance between the pollution source monitoring point and the downwind monitoring point when the environmental impact confidence is greater than an impact threshold in the industrial plant.

[0013] In a third aspect, the present application provides a computer device, which comprises a memory and a processor, the memory is configured to store a computer program, and the processor is configured to call and run the computer program from the memory, so that the computer device executes the industrial plant atmospheric pollution real-time monitoring method described above.

[0014] In a fourth aspect, the present application provides a computer readable storage medium, which stores instructions or codes, when the instructions or codes are run on a computer, the computer executes the industrial plant atmospheric pollution real-time monitoring method described above.

[0015] The technical scheme provided by the embodiments of the present application has the following beneficial effects: In the industrial plant atmospheric pollution real-time monitoring system and method provided by the present application, sensors arranged at each monitoring point in the industrial plant are used to collect pollution concentration data in the industrial plant at an initial frequency; pollution concentration curves of each monitoring point are extracted from the pollution concentration data, emission inflection points in each pollution concentration curve are clustered into a plurality of abnormal emission events, and then a pollution source monitoring point in the industrial plant is screened out according to meteorological information of the industrial plant in each abnormal emission event and a concentration gradient of each abnormal emission event; an angle between a pointing vector of the pollution source monitoring point pointing to other monitoring points in the industrial plant and a wind direction vector is calculated according to meteorological information and pollution concentration data of the industrial plant at a current time, to obtain a vector angle of the pollution source monitoring point, and then an environmental impact confidence of the pollution source monitoring point is determined according to the vector angle and a maximum pollution concentration in each abnormal emission event; when the environmental impact confidence is greater than an impact threshold in the industrial plant, a sampling frequency of a downwind monitoring point is optimized according to a distance between the pollution source monitoring point and the downwind monitoring point.

[0016] Therefore, in the present application, when the environmental influence confidence is greater than the influence threshold in the industrial plant, the sampling frequency of the downwind monitoring point is optimized according to the distance between the pollution source monitoring point and the downwind monitoring point. First, the pollution source monitoring point is determined, that is, the potential emission source position with high correlation in the physical space and the event causal chain is obtained, so as to upgrade the discrete and apparent concentration alarm event to a complete pollution event chain with a clear traceability direction. Through the fusion of meteorological information and the spatial analysis of the concentration gradient, the relationship between the successive triggering event of multiple monitoring points caused by pollutant diffusion and the real emission source is effectively distinguished, thereby overcoming the one-sidedness of the traditional monitoring which only alarms according to the concentration exceeding of a single monitoring point, and avoiding the risk of misjudging the affected point in the pollution downstream as the source. By locking the most possible source monitoring point for each abnormal emission event, instead of responding to the alarm of each sensor in isolation, a complete diffusion picture can be reconstructed, and the accuracy of event tracing is improved. Then, the environmental influence confidence is determined, that is, a dynamic evaluation index quantitatively representing the potential environmental hazard degree of the pollution source monitoring point is obtained, so as to realize the risk grading and priority ordering of multiple pollution source monitoring points, and drive the monitoring resources to realize adaptive optimization configuration. By comprehensively considering the static emission intensity history of the pollution source and the dynamic meteorological diffusion condition, a multi-dimensional risk assessment problem is converted into a quantitative confidence scalar. It is difficult to effectively distinguish between high-intensity emission under unfavorable diffusion conditions and low-intensity emission under efficient diffusion conditions in the traditional monitoring only by the absolute value of the concentration or fixed rules. According to the confidence index, those pollution sources with high emission history and diffusing to the sensitive area downwind can be identified, and monitoring resources are preferentially allocated, so that the industrial plant air pollution monitoring process can be changed into a focused, efficient and predictable monitoring mode. In summary, based on the above scheme, the risk confidence evaluation of the pollution source in the industrial plant air pollution monitoring can be realized, thereby improving the capture accuracy of abnormal emission events. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0018] Figure 1 is an exemplary flowchart of the industrial plant air pollution real-time monitoring method according to some embodiments of the present application; Figure 2 is a flowchart of determining the environmental influence confidence according to some embodiments of the present application; Figure 3 is a structural schematic diagram of a frequency optimization unit according to some embodiments of the present application; Figure 4 is a structural schematic diagram of a computer device for implementing an industrial plant atmospheric pollution real-time monitoring method according to some embodiments of the present application. DETAILED DESCRIPTION

[0019] In order to better understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below in combination with the drawings of the specification and specific embodiments.

[0020] Reference Figure 1 The figure is an exemplary flowchart of an industrial plant atmospheric pollution real-time monitoring method according to some embodiments of the present application, which mainly includes the following steps: In step 101, the pollutant concentration data in the industrial plant is collected at an initial frequency by sensors arranged at each monitoring point in the industrial plant.

[0021] It should be noted that in the present application, the pollutant concentration data is monitoring information containing the type of pollutants and the quantitative value of the concentration; in specific implementation, a continuously operating atmospheric pollutant concentration sensor is installed and calibrated at each monitoring point in the industrial plant, and a uniform initial frequency is configured for all sensors, which serves as the baseline sampling rate. Each sensor periodically (default 5s) measures the pollutant concentration at each sampling time point, and each sensor at each monitoring point continuously generates pollutant concentration readings with time stamps according to the time sequence instructions, and the collection of all pollutant concentration readings in a specified time period (default one week) is taken as the pollutant concentration data in the industrial plant.

[0022] In step 102, the pollutant concentration curves of each monitoring point are extracted from the pollutant concentration data, the emission inflection points in each pollutant concentration curve are clustered into multiple abnormal emission events, and then the pollutant source monitoring points in the industrial plant are screened out through the meteorological information of the industrial plant in each abnormal emission event and the concentration gradient of each abnormal emission event.

[0023] In some embodiments, the pollutant concentration curves of each monitoring point can be extracted from the pollutant concentration data by the following steps: Interpolate and align all data with a uniform time reference to obtain concentration sequences of each monitoring point at equal time intervals; Connect each concentration sequence in time sequence to construct the pollutant concentration curve of each monitoring point.

[0024] It should be noted that in the present application, the pollutant concentration curve is a graphical representation for intuitively representing the continuous change trend of the pollutant concentration of a single monitoring point at different time points; and the concentration sequence is an ordered set of pollutant concentration values arranged in chronological order.

[0025] In a specific implementation, first, a common unified time reference (by default, 5s once) is set, and the concentration values of the pollutant at each standard time point are calculated by using a linear interpolation algorithm with the reference time as the axis, so as to generate a concentration sequence with equal time intervals for each monitoring point in strict alignment in the time dimension, that is, the concentration sequence with equal time intervals for each monitoring point is obtained; subsequently, for each monitoring point, the data points in the concentration sequence of the monitoring point are connected in chronological order in the Cartesian coordinate system, so that the connection result is taken as the pollutant concentration curve of the monitoring point, and the pollutant concentration curve of each monitoring point is obtained through the above-mentioned manner.

[0026] In some embodiments, clustering the emission inflection points in each pollutant concentration curve into a plurality of abnormal emission events can be achieved by using the following steps: Obtaining the start inflection point and the end inflection point from each pollutant concentration curve, and then obtaining the emission inflection point pair in each pollutant concentration curve; Determining a plurality of candidate abnormal segments through the concentration time period between the start inflection point and the end inflection point in each emission inflection point pair; Performing spatio-temporal clustering on all candidate abnormal segments to obtain a plurality of abnormal emission events.

[0027] It should be noted that in the present application, the abnormal emission event is a pollutant diffusion process represented by the associated candidate abnormal segments in space-time; the emission inflection point pair is the start and end time of a potential abnormal emission; and the candidate abnormal segment refers to a potential abnormal emission process to be confirmed.

[0028] In a specific implementation, first, for each pollutant concentration curve, the slope change value of the pollutant concentration curve at each standard time point is calculated, a concentration change rate threshold is preset through historical experience, a positive number greater than the concentration change rate threshold is taken as a starting inflection point for identifying the start of abnormal concentration rise, an absolute value of the slope change value greater than a negative number in the concentration change rate threshold is taken as an ending inflection point for identifying the start of abnormal concentration drop after the concentration peak, and the corresponding starting inflection point and ending inflection point are combined into an emission inflection point pair, so that the emission inflection point pair in the pollutant concentration curve is obtained. Through the above method, the emission inflection point pair in each pollutant concentration curve can be obtained. Then, according to the positions of the starting inflection point and the ending inflection point in the time axis in each emission inflection point pair, the corresponding concentration data segment in the time interval is intercepted, so that each data segment is determined as a candidate abnormal segment, and a plurality of candidate abnormal segments are obtained. Finally, the starting time of all candidate abnormal segments is taken as a time coordinate, and the geographical position of the monitoring point to which the candidate abnormal segment belongs is taken as a space coordinate. The density-based clustering algorithm is used to merge a plurality of candidate abnormal segments that simultaneously gather in time and space into the same emission group. Each emission group obtained by clustering is taken as an independent abnormal emission event, and a plurality of abnormal emission events are obtained.

[0029] In some embodiments, the pollution source monitoring point in the industrial plant can be screened out by using the meteorological information in each abnormal emission event and the concentration gradient of each abnormal emission event, and the following steps can be used: For each abnormal emission event, the concentration gravity center of the abnormal emission event is determined by using the concentration gradient of the abnormal emission event. The dominant upwind direction of the abnormal emission event is extracted from the meteorological information in the abnormal emission event. The pollution source point of the abnormal emission event is screened out in the dominant upwind direction sector of the concentration gravity center, and the pollution source point of each abnormal emission event is obtained. The pollution source monitoring point in the industrial plant is determined by using all the pollution source points.

[0030] It should be noted that in the present application, the pollution source monitoring point is a proxy point of the pollution source in the monitoring network; the dominant upwind direction is the opposite direction of the pollution plume source in each abnormal emission event; and the pollution source point is the actual geographical position of the plant releasing the batch of pollutants in each abnormal emission event.

[0031] In a specific implementation, first, for each abnormal emission event, the monitoring point with the highest concentration in the abnormal emission event is selected, and a weighted average is calculated by taking the geographic position coordinates of the monitoring point as a vector and taking the corresponding pollutant concentration value as a weight, so as to determine the spatial coordinate point calculated as the concentration center of gravity of the abnormal emission event; then, the historical wind direction data in the duration of the abnormal emission event is obtained from the meteorological information in the abnormal emission event, the average wind direction in the historical wind direction data in the duration of the abnormal emission event is calculated, and the opposite direction of the average wind direction is taken as the dominant upwind direction of the abnormal emission event; then, a preset angle (30° by default) sector search area is defined along the dominant upwind direction axis with the concentration center of gravity as the vertex, the monitoring point with the highest concentration peak or the fastest concentration rising rate in the event is selected in the sector area, and the actual geographic position of the monitoring point is taken as the pollution source point of the abnormal emission event, so as to obtain the pollution source point of each abnormal emission event through the above method; finally, the set of pollution source points after coordinate deduplication is taken as the pollution source monitoring point in the industrial plant. It should be noted that in the present application, if the pollution source monitoring point is one, the subsequent sampling frequency is adjusted for the single pollution source monitoring point; if the pollution source monitoring point is multiple, the same method is used to adjust the sampling frequency of each pollution source monitoring point.

[0032] In step 103, the angle between the pointing vector of the pollution source monitoring point pointing to other monitoring points in the industrial plant and the wind direction vector is calculated according to the meteorological information and pollutant concentration data of the industrial plant at the current time, and the vector angle of the pollution source monitoring point is obtained, and then the environmental impact confidence of the pollution source monitoring point is determined through the vector angle and the maximum pollutant concentration in each abnormal emission event.

[0033] In some embodiments, the angle between the pointing vector of the pollution source monitoring point pointing to other monitoring points in the industrial plant and the wind direction vector can be calculated according to the meteorological information and pollutant concentration data of the industrial plant at the current time, and the vector angle of the pollution source monitoring point is obtained, and the environmental impact confidence of the pollution source monitoring point is determined through the vector angle and the maximum pollutant concentration in each abnormal emission event. The pointing vector of the pollution source monitoring point pointing to other monitoring points in the industrial plant is obtained from the pollutant concentration data of the industrial plant at the current time. The wind direction vector is obtained from the meteorological information of the industrial plant at the current time. The angles between each pointing vector and the wind direction vector are calculated, and the vector angle of the pollution source monitoring point is determined through all the angles.

[0034] It should be noted that in the present application, the vector angle represents the potential influence direction distribution of the current pollution source on the positions of all monitoring points under the current wind field condition; the pointing vector represents the spatial direction from the pollution source to the target monitoring point; and the wind direction vector represents the direction of the wind at the current time.

[0035] In a specific implementation, first, a unified plane rectangular coordinate system is established, and the geographical position coordinates of the pollution source monitoring point and all other monitoring points in the industrial plant are placed in the coordinate system. For each other monitoring point, a vector from the pollution source monitoring point coordinate to the monitoring point coordinate is calculated, so as to obtain a set of pointing vectors. In this way, the pointing vector of the pollution source monitoring point to the other monitoring points in the industrial plant is obtained. Then, the wind direction data at the current time of the industrial plant is read from the meteorological information, and the wind direction (the direction of the wind) is converted into a unit vector pointing to the direction in which the wind blows in the unified coordinate system according to the meteorological convention, as the wind direction vector. Finally, for each pointing vector, the angle between the pointing vector and the wind direction vector is calculated as the angle between the pointing vector and the wind direction vector by using the vector angle formula. In this way, the angle between each pointing vector and the wind direction vector is obtained, and the set of all angles is taken as the vector angle of the pollution source monitoring point.

[0036] In some embodiments, the environmental impact confidence of the pollution source monitoring point is determined by the vector angle and the maximum pollutant concentration in each abnormal emission event, and the environmental impact confidence of the pollution source monitoring point is determined by the vector angle and the maximum pollutant concentration in each abnormal emission event. Figure 2 The figure is a flowchart for determining the environmental impact confidence in some embodiments of the present application. The environmental impact confidence in the present embodiment can be achieved by the following steps: In step 1031, the angle influence factor of the pollution source monitoring point is determined by the vector angle. In step 1032, the concentration influence factor of the pollution source monitoring point is determined by the maximum pollutant concentration of the pollution source monitoring point in each abnormal emission event. In step 1033, the environmental impact confidence of the pollution source monitoring point is determined according to the angle influence factor and the concentration influence factor.

[0037] It should be noted that in the present application, the environmental impact confidence is a quantitative evaluation index for finally judging the possibility of pollution risk of the pollution source monitoring point to the environment; the angle influence factor is a quantitative numerical index for characterizing the favorable degree of the pollution source monitoring point to the positions of all other monitoring points in the industrial plant under the current wind field condition; and the concentration influence factor is a quantitative numerical index for characterizing the pollutant release intensity level of the pollution source monitoring point in the historical abnormal emission event.

[0038] In a specific implementation, first, all vector angles of the pollution source monitoring point are read, and an arithmetic mean of cosine values of all vector angles is calculated as an angle influence factor of the pollution source monitoring point; then, pollution concentration data in each abnormal emission event associated with the pollution source monitoring point are retrieved, a maximum pollution concentration value of the pollution source monitoring point is selected from each pollution concentration data, and the maximum pollution concentration value is normalized using a preset background concentration value and a global maximum concentration value, so that a value obtained after the normalization is taken as a concentration influence factor of the pollution source monitoring point; finally, weight coefficients of the angle influence factor and the concentration influence factor are obtained from a preset weight configuration table respectively, the weighted angle influence factor and the weighted concentration influence factor are added together, and a weighted sum obtained by the calculation is taken as an environmental impact confidence of the pollution source monitoring point.

[0039] In step 104, when the environmental impact confidence is greater than an impact threshold in the industrial plant, a sampling frequency of the downwind monitoring point is optimized according to a distance between the pollution source monitoring point and the downwind monitoring point.

[0040] It should be noted that in the present application, when the environmental impact confidence is greater than the impact threshold in the industrial plant, the pollution source monitoring point has high diffusion risk of being located in a key downwind direction and high pollution potential of having high historical emission intensity, and the possibility of causing regional pollution events has reached a condition requiring intervention monitoring, so intervention measures must be taken to improve the monitoring frequency of the pollution source monitoring point and its downstream area to capture more detailed pollution diffusion dynamics, gain valuable time for accurate tracing and emergency response, and finally preferentially invest limited data resources in the highest risk monitoring target.

[0041] In some embodiments, the optimization of the sampling frequency of the downwind monitoring point according to the distance between the pollution source monitoring point and the downwind monitoring point can be implemented by the following steps: Obtaining a plurality of downwind monitoring points of the pollution source monitoring point; Establishing a frequency adjustment rule of the downwind monitoring point; Adjusting the sampling frequency of the downwind monitoring point according to the distance between the pollution source monitoring point and each downwind monitoring point according to the frequency adjustment rule.

[0042] It should be noted that in the present application, the downwind monitoring point is a monitoring point located on a pollution diffusion path; and the frequency adjustment rule is a logical rule that clearly specifies the mathematical relationship between the distance parameter and the sampling frequency.

[0043] In a specific implementation, first, according to current wind field data and spatial positions of monitoring points, an angle between a vector from a pollution source monitoring point to each of other monitoring points in a factory area and a wind direction vector is calculated, monitoring points with an angle less than or equal to a preset threshold (30° by default) are screened out as downwind monitoring points of the pollution source monitoring point, and multiple downwind monitoring points of the pollution source monitoring point are obtained; subsequently, a preset mathematical mapping model is called as a frequency adjustment rule, which clearly stipulates an inverse relationship between an adjustment amount of a sampling frequency and a distance between the pollution source monitoring point and the downwind monitoring point, that is, the closer the distance, the greater the frequency improvement; finally, for each downwind monitoring point, a distance value between the downwind monitoring point and the pollution source monitoring point is read, the distance value is substituted into the aforementioned frequency adjustment rule for calculation, and a new value obtained by the calculation is used as an adjusted sampling frequency of the downwind monitoring point, and the adjusted sampling frequency of each downwind monitoring point is obtained through the above-mentioned manner.

[0044] In addition, another aspect of the present application, in some embodiments, the present application provides an industrial plant atmospheric pollution real-time monitoring system, the industrial plant atmospheric pollution real-time monitoring system comprises a frequency optimization unit, reference Figure 3 The figure is a structural schematic diagram of a frequency optimization unit according to some embodiments of the present application, which comprises a collection module 201, a processing module 202 and an execution module 203, which are described as follows: The collection module 201 is mainly used to collect pollutant concentration data in the industrial plant at an initial frequency through sensors arranged at each monitoring point in the industrial plant. The processing module 202 is used to extract pollutant concentration curves of each monitoring point from the pollutant concentration data, cluster emission inflection points in each pollutant concentration curve into multiple abnormal emission events, and then screen out a pollution source monitoring point in the industrial plant through meteorological information of the industrial plant in each abnormal emission event and a concentration gradient of each abnormal emission event. It should be noted that the processing module 202 is also used to calculate an angle between a pointing vector from the pollution source monitoring point to other monitoring points in the industrial plant and a wind direction vector according to meteorological information and pollutant concentration data of the industrial plant at a current time, obtain a vector angle of the pollution source monitoring point, and then determine an environmental impact confidence of the pollution source monitoring point through the vector angle and a maximum pollutant concentration in each abnormal emission event. The execution module 203 is mainly used to optimize and configure a sampling frequency of a downwind monitoring point according to a distance between the pollution source monitoring point and the downwind monitoring point when the environmental impact confidence is greater than an impact threshold in the industrial plant.

[0045] The above describes examples of the industrial plant atmospheric pollution real-time monitoring system and method provided by the embodiments of the present application in detail. It can be understood that, in order to implement the above functions, the corresponding device comprises a hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is implemented in hardware or computer software driven hardware depends on the specific application of the technical solution and design constraints. The skilled person can use different methods to implement the described functions for each specified application, but such implementation should not be considered beyond the scope of the present application.

[0046] In some embodiments, the present application also provides a computer device, comprising a memory for storing a computer program and a processor for calling and running the computer program from the memory, so that the computer device executes the industrial plant atmospheric pollution real-time monitoring method described above.

[0047] In some embodiments, with reference to Figure 4 The dashed line in the figure indicates that the unit or the module is optional. The figure is a structural schematic diagram of a computer device for implementing the industrial plant atmospheric pollution real-time monitoring method according to the embodiments of the present application. The industrial plant atmospheric pollution real-time monitoring method described in the above embodiments can be implemented by the computer device shown in the figure, which comprises at least one processor 301, a memory 302 and at least one communication unit 305. The computer device can be a terminal device or a server or a chip. Figure 4 The processor 301 can be a general-purpose processor or a special-purpose processor. For example, the processor 301 can be a central processing unit (CPU), which can be used to control the computer device, execute software programs, and process data of the software programs. The computer device can also comprise a communication unit 305 to realize input (reception) and output (transmission) of signals.

[0048] For example, the computer device can be a chip, and the communication unit 305 can be an input and / or output circuit of the chip, or the communication unit 305 can be a communication interface of the chip. The chip can be used as a component of a terminal device or a network device or other device.

[0049] For example, the computer device can be a chip, and the communication unit 305 can be an input and / or output circuit of the chip, or the communication unit 305 can be a communication interface of the chip. The chip can be used as a component of a terminal device or a network device or other device.

[0050] For another example, the computer device can be a terminal device or a server, the communication unit 305 can be a transceiver of the terminal device or the server, or the communication unit 305 can be a transceiving circuit of the terminal device or the server.

[0051] The computer device can include one or more memories 302, on which a program 304 is stored, the program 304 can be run by the processor 301 to generate instructions 303, so that the processor 301 executes the method described in the above method embodiments according to the instructions 303. Alternatively, the memory 302 can also store data (such as a target audit model). Alternatively, the processor 301 can also read the data stored in the memory 302, and the data can be stored in the same storage address as the program 304, or the data can be stored in a different storage address from the program 304.

[0052] The processor 301 and the memory 302 can be separately arranged, or can be integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

[0053] It should be understood that each step of the above method embodiments can be completed by a logic circuit in the form of hardware or an instruction in the form of software in the processor 301, and the processor 301 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, for example, discrete gates, transistor logic devices, or discrete hardware components.

[0054] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0055] For example, in some embodiments, the present application also provides a computer readable storage medium, the computer readable storage medium stores instructions or codes, when the instructions or codes are run on a computer, the computer is caused to perform the above-mentioned industrial plant atmospheric pollution real-time monitoring method.

[0056] While the preferred embodiments of the application have been described, additional variations and modifications can be made to these embodiments by those skilled in the art once they have the benefit of the present disclosure without departing from the spirit and scope of the application. Accordingly, it is intended that such additions and modifications be included within the scope of the application. It is the following claims, including any amendments thereto, which define the scope of the application.

[0057] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for real-time monitoring of atmospheric pollution in an industrial plant, characterized in that, The method comprises the following steps: Collecting pollutant concentration data in the industrial plant at initial frequency through sensors arranged at each monitoring point in the industrial plant; Extracting pollutant concentration curves of each monitoring point from the pollutant concentration data, clustering emission inflection points in each pollutant concentration curve into multiple abnormal emission events, and further screening pollutant source monitoring points in the industrial plant through meteorological information of the industrial plant in each abnormal emission event and concentration gradient of each abnormal emission event; Calculating an angle between a pointing vector of the pollutant source monitoring point pointing to other monitoring points in the industrial plant and a wind direction vector according to meteorological information of the industrial plant at the current time and the pollutant concentration data, obtaining a vector angle of the pollutant source monitoring point, and further determining an environmental impact confidence of the pollutant source monitoring point through the vector angle and maximum pollutant concentration in each abnormal emission event; When the environmental impact confidence is greater than an impact threshold in the industrial plant, optimizing and configuring a sampling frequency of a downwind monitoring point according to a distance between the pollutant source monitoring point and the downwind monitoring point.

2. The method of claim 1, wherein, The step of extracting pollutant concentration curves of each monitoring point from the pollutant concentration data specifically comprises: Interpolating and aligning all data with a uniform time reference to obtain concentration sequences of each monitoring point at equal time intervals; Connecting each concentration sequence in time sequence to construct a pollutant concentration curve of each monitoring point.

3. The method of claim 1, wherein, The step of clustering emission inflection points in each pollutant concentration curve into multiple abnormal emission events specifically comprises: Obtaining a starting inflection point and an ending inflection point from each pollutant concentration curve, and further obtaining an emission inflection point pair in each pollutant concentration curve; Determining multiple candidate abnormal segments through a concentration time period between the starting inflection point and the ending inflection point in each emission inflection point pair; Performing time and space clustering on all candidate abnormal segments to obtain multiple abnormal emission events.

4. The method of claim 1, wherein, The step of screening pollutant source monitoring points in the industrial plant through meteorological information of the industrial plant in each abnormal emission event and concentration gradient of each abnormal emission event specifically comprises: For each abnormal emission event, determining a concentration gravity center of the abnormal emission event through the concentration gradient of the abnormal emission event; Extracting a dominant upwind direction of the abnormal emission event from the meteorological information of the abnormal emission event; Screening a pollutant source point of the abnormal emission event in a dominant upwind direction sector of the concentration gravity center, and further obtaining a pollutant source point of each abnormal emission event; Determining the pollutant source monitoring points in the industrial plant through all pollutant source points.

5. The method of claim 1, wherein, The step of calculating an angle between a pointing vector of the pollutant source monitoring point pointing to other monitoring points in the industrial plant and a wind direction vector according to meteorological information of the industrial plant at the current time and the pollutant concentration data, obtaining a vector angle of the pollutant source monitoring point specifically comprises: Obtaining the pointing vector of the pollutant source monitoring point pointing to other monitoring points in the industrial plant from the pollutant concentration data of the industrial plant at the current time; Obtaining the wind direction vector from the meteorological information of the industrial plant at the current time; Calculating an angle between each pointing vector and the wind direction vector, and further determining the vector angle of the pollutant source monitoring point through all angles.

6. The method of claim 1, wherein, The environmental impact confidence of the pollution source monitoring point is determined by the vector angle and the maximum pollutant concentration in each abnormal emission event, and specifically includes: The angle impact factor of the pollution source monitoring point is determined by the vector angle; The concentration impact factor of the pollution source monitoring point is determined by the maximum pollutant concentration of the pollution source monitoring point in each abnormal emission event; The environmental impact confidence of the pollution source monitoring point is determined according to the angle impact factor and the concentration impact factor.

7. The method of claim 1, wherein, The sampling frequency of the downwind monitoring point is optimized according to the distance between the pollution source monitoring point and the downwind monitoring point, and specifically includes: A plurality of downwind monitoring points of the pollution source monitoring point are obtained; A frequency adjustment rule of the downwind monitoring point is established; The sampling frequency of the downwind monitoring point is adjusted according to the distance between the pollution source monitoring point and each downwind monitoring point and the frequency adjustment rule.

8. An industrial plant atmospheric pollution real-time monitoring system comprising a frequency optimization unit, which carries out industrial plant atmospheric pollution real-time monitoring using the method according to any one of claims 1 to 7, characterized in that, The frequency optimization unit includes: The acquisition module is configured to collect pollutant concentration data in the industrial plant at an initial frequency through sensors arranged at each monitoring point in the industrial plant; The processing module is configured to extract a pollutant concentration curve of each monitoring point from the pollutant concentration data, cluster emission inflection points in each pollutant concentration curve into a plurality of abnormal emission events, and further screen a pollution source monitoring point in the industrial plant through meteorological information of the industrial plant in each abnormal emission event and a concentration gradient of each abnormal emission event; The processing module is further configured to calculate an angle between a pointing vector of the pollution source monitoring point pointing to other monitoring points in the industrial plant and a wind direction vector according to meteorological information of the industrial plant at a current time and the pollutant concentration data, obtain a vector angle of the pollution source monitoring point, and further determine an environmental impact confidence of the pollution source monitoring point through the vector angle and the maximum pollutant concentration in each abnormal emission event; The execution module is configured to optimize the sampling frequency of the downwind monitoring point according to the distance between the pollution source monitoring point and the downwind monitoring point when the environmental impact confidence is greater than an impact threshold in the industrial plant.

9. A computer device, comprising: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the industrial plant atmospheric pollution real-time monitoring method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions or codes, and when the instructions or codes are run on the computer, the computer executes the industrial plant atmospheric pollution real-time monitoring method in any one of claims 1 to 7.

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