Gaseous pollutant space sampling point layout method, system, equipment and medium

Abstract

The invention belongs to the technical field of gaseous pollutant simulation, and provides a gaseous pollutant space sampling point layout method and system, electronic equipment and a storage medium. The method comprises the following steps: simulating a gas flow field of a target space through fluid dynamics simulation, and establishing a pollutant diffusion model according to a gas diffusion rule in the gas flow field; defining a fitness function of sampling point layout according to the pollutant diffusion model, and reflecting pollutant concentration and redundancy of a sampling point coverage area; and iteratively optimizing the fitness function by using a bat algorithm, improving the pollutant concentration and reducing the redundancy to obtain an optimal sampling point layout. The optimal sampling point layout is achieved, redundant sampling points are reduced, the monitoring coverage rate is maximized, and it is ensured that pollutant leakage can be detected and responded in time.

Description

Technical Field

[0001] The present invention belongs to the technical field of gaseous pollutant simulation, and in particular relates to a method, system, electronic equipment and storage medium for arranging spatial sampling points of gaseous pollutants. Background Art

[0002] In the semiconductor production process, the monitoring of gaseous pollutants is particularly important, especially in environments with high cleanliness requirements, such as wafer fabs. The reasonable layout of sampling points is directly related to the real-time and detection accuracy of pollutant leakage detection.

[0003] Traditional sampling point arrangements usually rely on experience, which may result in sampling blind areas or redundant sampling points, making it impossible to respond to pollutant leaks in a timely manner. Summary of the invention

[0004] To solve the above problems, the present invention provides a method, system, electronic device and storage medium for spatial sampling point layout of gaseous pollutants. The method can automatically optimize the number and distribution of sampling points based on the spatial distribution of pollutant concentrations, maximize monitoring effects and reduce response delays.

[0005] In order to solve the above technical problems, the first aspect of the present invention proposes a method for arranging spatial sampling points of gaseous pollutants, the method comprising:

[0006] The airflow field of the target space is simulated by fluid dynamics simulation, and a pollutant diffusion model is established according to the gas diffusion law in the airflow field;

[0007] A fitness function for the sampling point layout is defined according to the pollutant diffusion model to reflect the pollutant concentration and redundancy of the area covered by the sampling points;

[0008] The fitness function is iteratively optimized using the bat algorithm to increase the pollutant concentration and reduce the redundancy, thereby obtaining the optimal sampling point layout.

[0009] According to a preferred embodiment of the present invention, simulating the airflow field of the target space by fluid dynamics simulation and establishing a pollutant diffusion model according to the gas diffusion law in the airflow field include:

[0010] By means of the fluid dynamics simulation, a velocity field corresponding to the airflow field of the target space is obtained;

[0011] Establishing the pollutant diffusion model based on the velocity field data of the leakage point;

[0012] When there are multiple pollution sources in the environment, the total concentration of the pollutant can be superimposed by the pollution source concentrations of each of the pollutant diffusion models.

[0013] According to a preferred embodiment of the present invention, the iterative optimization of the fitness function using the bat algorithm includes:

[0014] Generating a random sampling point layout using a bat algorithm, each random sampling point layout consisting of coordinates of a number of sampling points;

[0015] Each bat searches in the target space according to its position and speed, and gradually adjusts the layout of the sampling points.

[0016] According to a preferred embodiment of the present invention, the step of increasing the pollutant concentration and reducing the redundancy to obtain an optimal sampling point layout includes:

[0017] The bats are located at the sampling points, and each time the bat positions are updated, a new sampling point layout is re-evaluated according to the pollutant diffusion model and the fitness function;

[0018] After several iterations, a better sampling point layout is obtained;

[0019] Take any of the sampling points, and if other sampling points fall within the coverage radius of the sampling point, delete the redundant sampling points until the coverage areas of all the sampling points have no overlap.

[0020] In order to solve the above technical problems, the second aspect of the present invention proposes a gaseous pollutant spatial sampling point layout system, the system comprising: a model building module, a function definition module and a layout determination module;

[0021] The model building module is used to simulate the airflow field of the target space through fluid dynamics simulation, and to establish a pollutant diffusion model according to the gas diffusion law in the airflow field;

[0022] The definition function module is used to define the fitness function of the sampling point layout according to the pollutant diffusion model, reflecting the pollutant concentration and redundancy of the area covered by the sampling points;

[0023] The layout determination module is used to iteratively optimize the fitness function using the bat algorithm, increase the pollutant concentration and reduce the redundancy, and obtain the optimal sampling point layout.

[0024] According to a preferred embodiment of the present invention, it comprises:

[0025] The model building module is further used to obtain the velocity field corresponding to the airflow field of the target space through the fluid dynamics simulation;

[0026] The model building module is also used to build the pollutant diffusion model based on the velocity field data of the leakage point;

[0027] The model building module is also used when there are multiple pollution sources in the environment. The total concentration of the pollutant can be superimposed by the pollution source concentrations of each pollutant diffusion model.

[0028] According to a preferred embodiment of the present invention, it comprises:

[0029] The layout determination module is further used to generate a random sampling point layout using a bat algorithm, each random sampling point layout being composed of coordinates of a number of sampling points;

[0030] The layout determination module is also used for each bat to search in the target space according to the position and speed, and gradually adjust the layout of the sampling points.

[0031] According to a preferred embodiment of the present invention, it comprises:

[0032] The layout determination module is also used for the location of the bats at the sampling points, and each time the location of the bats is updated, the new sampling point layout is re-evaluated according to the pollutant diffusion model and the fitness function;

[0033] The layout determination module is further used to obtain a better sampling point layout after several iterations;

[0034] The layout determination module is further configured to select any one of the sampling points, and if other sampling points fall within the coverage radius of the sampling point, delete the redundant sampling points until the coverage areas of all the sampling points have no overlap.

[0035] In order to solve the above technical problem, the third aspect of the present invention provides an electronic device, comprising:

[0036] processor;

[0037] and a memory storing computer executable instructions, wherein the computer executable instructions, when executed, cause the processor to perform the method described in any one of the above embodiments.

[0038] In order to solve the above technical problems, the fourth aspect of the present invention proposes a computer storage medium, wherein the computer storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method described in any one of the above embodiments is implemented.

[0039] Compared with the prior art, the present invention has the following advantages: the present invention uses the airflow velocity field obtained by fluid dynamics simulation, combined with the pollutant diffusion model, to generate a pollutant concentration distribution map, and optimizes the layout of sampling points through the bat algorithm. By defining a reasonable fitness function, the bat algorithm can continuously adjust the distribution of sampling points in global and local searches, and ultimately achieve the optimal sampling point layout, reduce redundant sampling points and maximize monitoring coverage, ensuring timely detection and response when pollutants leak.

[0040] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A first schematic diagram of a flow chart of a method for arranging spatial sampling points of gaseous pollutants according to an embodiment of the present invention is shown;

[0043] Figure 2 A second schematic flow chart of a method for arranging spatial sampling points of gaseous pollutants according to an embodiment of the present invention is shown;

[0044] Figure 3 A third flow chart of a method for arranging spatial sampling points of gaseous pollutants according to an embodiment of the present invention is shown;

[0045] Figure 4 A fourth flow chart of a method for arranging spatial sampling points of gaseous pollutants according to an embodiment of the present invention is shown;

[0046] Figure 5 A structural diagram of a gaseous pollutant spatial sampling point layout system according to an embodiment of the present invention is shown;

[0047] Figure 6 A schematic structural diagram of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The same reference numerals in the drawings represent the same or similar elements, components or parts, and thus the repeated description of the same or similar elements, components or parts may be omitted below. It should also be understood that although the first, second, third and other attributives representing the numbers may be used herein to describe various devices, elements, components or parts, these devices, elements, components or parts should not be limited by these attributives. In other words, these attributives are only used to distinguish one from another. For example, the first device may also be called the second device, but it does not deviate from the essential technical solution of the present invention. In addition, the terms "and / or" and "and / or" refer to all combinations including any one or more of the listed items.

[0050] like Figure 1 As shown, the method includes:

[0051] S11. Simulating the airflow field of the target space through fluid dynamics simulation, and establishing a pollutant diffusion model according to the gas diffusion law in the airflow field.

[0052] In this embodiment, before optimizing the sampling point distribution, it is first necessary to simulate the airflow field in the clean room or target space through CFD simulation, and establish a pollutant diffusion model according to the gas diffusion law.

[0053] S12. Define a fitness function for the sampling point layout according to the pollutant diffusion model to reflect the pollutant concentration and redundancy of the area covered by the sampling points.

[0054] In this embodiment, in order to optimize the sampling point distribution, a fitness function is first defined, which should be able to reflect the pollutant coverage effect and redundancy of the sampling point layout.

[0055] Specifically,

[0056] The pollutant concentration distribution L(y, z) serves as the basis for subsequent sampling point optimization.

[0057] Specifically,

[0058] Among them, M h is the intensity of the pollution source, (y h , z h) is the location of the pollution source, and σ represents the diffusion coefficient, which determines the diffusion range.

[0059] Specifically,

[0060] Among them, F(x) is the fitness function, M is the number of sampling points, and D i is the coverage area of ​​the i-th sampling point, L(y, z) is the pollutant concentration distribution, N redundant (x) represents the number of redundant sampling points in the layout, and β is the penalty coefficient.

[0061] In this embodiment, the coverage area of ​​the sampling point is usually a circle with the sampling point as the center and a radius of R. The pollutant concentration distribution represents the concentration value at each position (y, z). The number of redundant sampling points in the layout, that is, the situation where the coverage of the sampling points has a large overlap, indicates that there may be too many sampling points in the layout overlapping in the same concentration area. The penalty coefficient controls the penalty intensity of redundant sampling points and is used to balance the impact of coverage and redundant points.

[0062] In this embodiment, It represents the sum of the pollutant concentrations in the coverage area of ​​all sampling points. This encourages the sampling point layout to cover areas with higher concentrations as much as possible. i , we are in its coverage area D i The concentration of pollutants covered by the sampling points can be calculated by integrating the sample points within the sample points. This integration can be approximated by numerical methods, such as summing the coverage area of ​​the sampling points in the discrete concentration field.

[0063] In this embodiment, β·N redundant (x) is the penalty term for redundant sampling points. This term is used to reduce the redundant coverage of sampling points in the layout, that is, if the coverage of two sampling points overlaps, this will reduce the fitness of the layout solution.

[0064] S13. Utilize the bat algorithm to iteratively optimize the fitness function, increase the pollutant concentration and reduce the redundancy, and obtain an optimal sampling point layout.

[0065] In this embodiment, the bat algorithm gradually optimizes the sampling point layout by means of population search, and each bat individual represents a sampling point distribution scheme.

[0066] In this embodiment, the optimal sampling point layout scheme is finally obtained through the bat algorithm optimization process. This scheme has the following characteristics: Optimal number of sampling points: By removing redundant sampling points, the number of sampling points is reduced, reducing system costs. Maximized coverage: The distribution of sampling points is optimized to ensure that areas with the highest pollutant concentration are covered first, thereby improving detection efficiency and real-time performance. Minimized response delay: By optimizing the layout of sampling points, the detection response time of pollutant leakage is significantly reduced, ensuring the real-time performance of environmental monitoring.

[0067] like Figure 2 As shown, the method includes:

[0068] S21. Obtaining a velocity field corresponding to the airflow field of the target space through the fluid dynamics simulation.

[0069] In this embodiment, the velocity field u in the clean room or target environment is obtained through CFD simulation. j (y, z), where j = 1, ..., N represents the potential gas leakage point. Since the blowing direction of the fan filter unit (FFU) in the clean room is vertically downward from the top, the airflow mainly considers the velocity component u in the vertical plane. y and u z .

[0070] S22. Based on the velocity field data of the leakage point, establish the pollutant diffusion model, and in the diffusion model, use the pollution source to represent the leakage point.

[0071] In this embodiment, a pollutant diffusion model is established based on the velocity field data of the leakage point. Assume that the pollutants are h , z h ) diffuses outward, and the concentration distribution needs to be expressed by a formula.

[0072] S23. When there are multiple pollution sources in the environment, the total concentration of the pollutant is superimposed by the pollution source concentrations of each pollutant diffusion model.

[0073] In this embodiment, when there are multiple pollution sources in the environment, the total concentration of the pollutant can be obtained by adding the concentrations of each pollution source.

[0074] like Figure 3 As shown, the method includes:

[0075] S31. Generate a random sampling point layout using a bat algorithm, each of which is composed of coordinates of several sampling points.

[0076] In this embodiment, the bat algorithm first generates a set of random sampling point layout schemes, each of which consists of the coordinates of multiple sampling points. The initial population size is n, and the positions of these sampling points can be random or initially arranged according to the high response area in the pollutant concentration map L (y, z).

[0077] In this embodiment, bats perform global and local searches by simulating their echolocation behavior.

[0078] Specifically, the update formula for the bat's speed and position is:

[0079]

[0080] in, is the speed of the lth bat at time t, is its current position, x * is the current global optimal solution, and f is the frequency adjustment parameter.

[0081] S32: Each bat searches in the target space according to its current position and speed, and gradually adjusts the layout of sampling points.

[0082] In this embodiment, each bat searches in space according to its position and speed, and gradually adjusts the layout of its sampling points. In the early stage, a global search is performed with a larger step size. As the iteration proceeds, the step size gradually decreases, and more reliance is placed on local search to optimize the layout details. Through the adaptive adjustment of loudness A and pulse emission rate r, bats can flexibly switch between global search and local search.

[0083] In this embodiment, during the search process of the bat algorithm, loudness A and pulse emission rate r are two key parameters used to dynamically adjust the search strategy. The bat algorithm can perform global search in a larger range or perform local fine search when a better solution is found by simulating the echolocation behavior of bats. These search behaviors are achieved through the dynamic adjustment of A and r.

[0084] In this embodiment, regarding loudness A, initially, the loudness is relatively large, allowing the bat to conduct a global search over a larger range. When a better solution is found, the loudness will gradually decrease, narrowing the search range and shifting to local optimization. This process simulates the process of a creature gradually approaching a target when hunting.

[0085] Specifically,

[0086] Among them, δ is a control parameter, usually between [0,1], which determines the decay rate of loudness.

[0087] In this embodiment, during the global search, at the initial stage, the loudness is relatively large, and the bats search the solution space in a large range. i The larger the loudness A is, the larger the position update amplitude is, and the bat can quickly jump to different areas of the solution space for extensive search. During local search, as the loudness A decreases, the speed v i The bat's step size is shortened, and it gradually turns to local search. The bat repeatedly adjusts its position near the optimal solution, thereby achieving a more refined search.

[0088] In this embodiment, regarding the pulse emission rate r, the pulse emission rate controls the bat's search frequency in the solution space. In the early stage, the pulse emission rate is low, and the bat will perform a global search; as the number of iterations increases, the pulse emission rate increases, and more searches are concentrated near the current solution to optimize the accuracy of the solution.

[0089] Specifically, i t+1 =r i 0 (1-exp(-γt));

[0090] Among them, γ is an adjustment parameter that controls the growth curve of the pulse emission rate over time. As the number of iterations t increases, the value of γ gradually increases.

[0091] In this embodiment, during global search, at the beginning of the algorithm, the pulse emission rate γ is low, and the bat will not frequently emit pulse signals, but will move with larger steps to explore more areas of the solution space. At this time, the bat performs a global search in the solution space with a lower pulse emission rate. During local search, as the number of iterations increases, the pulse emission rate γ increases, the frequency of bat emission signals increases, and local searches are frequent. When the bat emits a pulse, it will explore the surrounding environment in more detail, thereby performing a fine-grained search near the current position. At this time, the step size of the speed update and position update is relatively small.

[0092] like Figure 4 As shown, the method includes:

[0093] S41, the bats are located at a plurality of sampling points, and each time the bat positions are updated, the adjusted sampling point layout is re-evaluated according to the pollutant diffusion model and the fitness function.

[0094] In this embodiment, in each iteration, the new sampling point layout will be evaluated according to the fitness function F(x). If the new layout scheme improves the pollutant coverage and reduces redundant points, the scheme will replace the original scheme and gradually approach the optimal solution.

[0095] In this embodiment, each time the bat position is updated, the new sampling point layout scheme is re-evaluated based on the pollutant concentration field L(y, z) and the objective function F(x). A scheme with a higher fitness indicates a higher pollutant concentration covered and a lower redundancy.

[0096] S42: After several iterations, a better sampling point layout is obtained.

[0097] In this embodiment, after multiple rounds of iterative optimization, a better sampling point layout solution is obtained. At this time, redundant sampling points are further eliminated.

[0098] S43, randomly select a sampling point where a bat is located, and if the sampling points where other bats are located fall within the coverage radius of the sampling point, delete the redundant sampling points until the coverage areas of the sampling points where all bats are located have no overlap.

[0099] In this embodiment, if any sampling point is selected and other sampling points fall within its coverage radius R, the redundant sampling points are deleted until the coverage areas of all sampling points have no overlap.

[0100] In this embodiment, during the sampling point optimization process, the effective coverage radius of the sampling point refers to the range in which a sampling point can effectively monitor pollutants. This is a physical concept, which indicates the spatial range that a sampling point can cover. We use this radius to determine whether there is overlap between the coverage of the sampling points, so as to remove redundant sampling points during the optimization process; the effective coverage radius of the sampling point is a parameter related to the change in pollutant concentration and the performance of the sampling equipment, and is usually determined based on the performance parameters of the equipment or environmental requirements in practical applications.

[0101] In this embodiment, input-output continuity: The pollutant concentration field L(y, z) output in the first step is the core data in the optimization process of the bat algorithm. The bat algorithm evaluates the advantages and disadvantages of each sampling point layout based on L(y, z), thereby gradually optimizing the sampling point location.

[0102] In this embodiment, fitness function continuity: the design of the objective function F(x) directly depends on the concentration value in L(y, z), evaluating whether the layout of each sampling point covers the area with higher pollutant concentration, thereby providing an optimization direction for the bat algorithm.

[0103] In this embodiment, iterative optimization is coupled with concentration field calculation: in each round of iteration, the bat algorithm continuously updates the position of the sampling point, and each position update requires re-evaluation of the interaction between the sampling point and the concentration field to ensure that the area covered by the sampling point is the area with the highest pollutant concentration.

[0104] like Figure 5 As shown, the system includes: a model building module, a function definition module and a layout determination module.

[0105] In this embodiment, the model building module is specifically used to simulate the airflow field of the target space through fluid dynamics simulation, and to build a pollutant diffusion model according to the gas diffusion law in the airflow field.

[0106] In this embodiment, the definition function module is specifically used to define the fitness function of the sampling point layout according to the pollutant diffusion model, reflecting the pollutant concentration and redundancy of the area covered by the sampling points.

[0107] In this embodiment, the layout determination module is specifically used to iteratively optimize the fitness function using the bat algorithm, increase the pollutant concentration and reduce the redundancy, and obtain the optimal sampling point layout.

[0108] In this embodiment, the model building module is specifically used to obtain the velocity field corresponding to the airflow field of the target space through the fluid dynamics simulation.

[0109] In this embodiment, the model building module is specifically used to build the pollutant diffusion model based on the velocity field data of the leakage point.

[0110] In this embodiment, the model building module is specifically used when there are multiple pollution sources in the environment, and the total concentration of the pollutant can be superimposed by the pollution source concentrations of each of the pollutant diffusion models.

[0111] In this embodiment, the layout determination module is specifically used to generate a random sampling point layout using a bat algorithm, and each random sampling point layout is composed of coordinates of a number of sampling points.

[0112] In this embodiment, the layout determination module is specifically configured to allow each bat to search in the target space according to its position and speed, and gradually adjust the layout of the sampling points.

[0113] In this embodiment, the layout determination module is specifically used for the location of the bats at the sampling points. After each update of the bat positions, the new sampling point layout is re-evaluated according to the pollutant diffusion model and the fitness function.

[0114] In this embodiment, the layout determination module is specifically configured to obtain a more optimal sampling point layout after several iterations.

[0115] In this embodiment, the layout determination module is specifically configured to select any one of the sampling points, and if other sampling points fall within the coverage radius of the sampling point, delete the redundant sampling points until the coverage areas of all the sampling points have no overlap.

[0116] like Figure 6As shown, an embodiment of the present invention provides an electronic device, including a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0117] Memory 1130, used for storing computer programs;

[0118] The processor 1110 is configured to implement any of the above determination methods when executing a program stored in the memory 1130 .

[0119] In the electronic device provided by the embodiment of the present invention, the processor 1110 obtains the fluid flow rate of each branch in different switching states by executing the program stored in the memory 1130, and determines the initial volume flow rate of each branch; corrects the initial volume flow rate according to the pipeline parameters and fluid parameters of each branch when it is in working condition and standard condition, and obtains the standard condition volume flow rate of each branch; obtains multiple total standard condition volume flow rates according to the standard condition volume flow rates of each branch in different switching states, and determines the optimal switching state of each branch through the switching state of each branch when the total standard condition volume flow rate reaches a preset target.

[0120] The communication bus 1140 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0121] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0122] The memory 1130 may include a random access memory 1130 (RAM), or may include a non-volatile memory 1130 (non-volatile memory), such as at least one disk memory 1130. Optionally, the memory 1130 may also be at least one storage device located away from the processor 1110.

[0123] The above-mentioned processor 1110 can be a general-purpose processor 1110, including a central processing unit 1110 (CPU), a network processor 1110 (NP), etc.; it can also be a digital signal processor 1110 (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0124] An embodiment of the present invention provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors 1110 to implement the determination method of any of the above embodiments.

[0125] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidStateDisk (SSD)), etc.

[0126] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for arranging spatial sampling points of gaseous pollutants, characterized in that: The method comprises: The airflow field of the target space is simulated by fluid dynamics simulation, and a pollutant diffusion model is established according to the gas diffusion law in the airflow field; A fitness function for the sampling point layout is defined according to the pollutant diffusion model to reflect the pollutant concentration and redundancy of the area covered by the sampling points; The fitness function is iteratively optimized using the bat algorithm to increase the pollutant concentration and reduce the redundancy, thereby obtaining the optimal sampling point layout.

2. The layout method according to claim 1, characterized in that: The method of simulating the airflow field of the target space by fluid dynamics simulation and establishing a pollutant diffusion model according to the gas diffusion law in the airflow field includes: By means of the fluid dynamics simulation, a velocity field corresponding to the airflow field of the target space is obtained; Based on the velocity field data of the leakage point, the pollutant diffusion model is established, and in the diffusion model, the leakage point is represented by a pollution source; When there are multiple pollution sources in the environment, the total concentration of the pollutant is superimposed by the pollution source concentrations of each pollutant diffusion model.

3. The layout method according to claim 1, characterized in that: The iterative optimization of the fitness function using the bat algorithm includes: Generating a random sampling point layout using a bat algorithm, each of the random sampling point layouts consisting of coordinates of a number of sampling points; Each bat searches in the target space according to its current position and speed, and gradually adjusts the layout of sampling points.

4. The layout method according to claim 1 or 3, characterized in that: The step of increasing the pollutant concentration and reducing the redundancy to obtain an optimal sampling point layout includes: The bats are located at a plurality of sampling points, and each time the bat positions are updated, the adjusted sampling point layout is re-evaluated according to the pollutant diffusion model and the fitness function; After several iterations, a better sampling point layout is obtained; Take any sampling point where a bat is located. If the sampling points where other bats are located fall within the coverage radius of the sampling point, then delete the redundant sampling points until the coverage areas of the sampling points where all bats are located have no overlap.

5. A gaseous pollutant spatial sampling point layout system, characterized in that: The system comprises: a model building module, a function definition module and a layout determination module; The model building module is used to simulate the airflow field of the target space through fluid dynamics simulation, and to establish a pollutant diffusion model according to the gas diffusion law in the airflow field; The definition function module is used to define the fitness function of the sampling point layout according to the pollutant diffusion model, reflecting the pollutant concentration and redundancy of the area covered by the sampling points; The layout determination module is used to iteratively optimize the fitness function using the bat algorithm, increase the pollutant concentration and reduce the redundancy, and obtain the optimal sampling point layout.

6. The layout system according to claim 5, characterized in that: include: The model building module is further used to obtain the velocity field corresponding to the airflow field of the target space through the fluid dynamics simulation; The model building module is further used to build the pollutant diffusion model based on the velocity field data of the leakage point, and in the diffusion model, the leakage point is represented by a pollution source; The model building module is also used for when there are multiple pollution sources in the environment, the total concentration of pollutants can be superimposed by the pollution source concentrations of each pollutant diffusion model.

7. The layout system according to claim 5, characterized in that: include: The layout determination module is further used to generate a random sampling point layout using a bat algorithm, each of the random sampling point layouts being composed of coordinates of a number of sampling points; The layout determination module is also used for each bat to search in the target space according to the current position and speed, and gradually adjust the layout of the sampling points.

8. The layout system according to claim 5 or 7, characterized in that: include: The layout determination module is also used for the bat to be located at the position of the bat at a plurality of sampling points, and after each update of the bat's position, the adjusted sampling point layout is re-evaluated according to the pollutant diffusion model and the fitness function; The layout determination module is further used to obtain a better sampling point layout after several iterations; The layout determination module is further used to randomly select a sampling point where a bat is located, and if the sampling points where other bats are located fall within the coverage radius of the sampling point, then delete the redundant sampling points until the coverage areas of the sampling points where all bats are located have no overlap.

9. An electronic device, characterized in that: include: processor; and a memory storing computer executable instructions which, when executed, cause the processor to perform the method according to any one of claims 1 to 4.

10. A computer storage medium, characterized in that: in, The computer storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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