Evaluation method and system for urban forest space fine particulate matter concentration

Abstract

According to the urban forest space fine particulate matter concentration assessment method and system disclosed by the invention, the mobile monitoring equipment is arranged in different areas of an urban forest, so that fine particulate matter concentration data of each area can be obtained in real time; meanwhile, a more comprehensive and detailed data set can be formed by combining data of a fixed-point monitoring station, so that the distribution condition of the fine particulate matter concentration in the urban forest space can be reflected more accurately; by setting the fine particulate matter concentration warning threshold value, the management end is reminded to take corresponding measures in time, and the ecological safety of urban forests and the health of citizens are effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric environmental quality assessment, and more specifically, to a method and system for assessing the concentration of fine particulate matter in urban forest space. Background Art

[0002] At present, as an important part of the urban ecosystem, urban forests play a significant role in improving urban environmental quality, alleviating the urban heat island effect, reducing air pollution, etc. However, in terms of assessing the concentration of fine particulate matter in urban forest space, existing methods and technologies still have certain limitations. Traditional assessment methods often rely on fixed-point monitoring, obtaining data on the concentration of fine particulate matter by setting up monitoring stations at specific locations. Although this method can obtain relatively accurate data, limited by the number and distribution of monitoring stations, it is difficult to comprehensively reflect the overall situation of the concentration of fine particulate matter in urban forest space. Summary of the Invention

[0003] In view of the above problems, the purpose of the present invention is to provide a method and system for assessing the concentration of fine particulate matter in urban forest space. Mobile monitoring devices are set in different areas of the urban forest to obtain real-time data on the concentration of fine particulate matter in each area. At the same time, combined with the data of fixed monitoring stations, a more comprehensive and detailed dataset can be formed, thus more accurately reflecting the distribution of the concentration of fine particulate matter in urban forest space. By setting a warning threshold for the concentration of fine particulate matter, the management side can be timely reminded to take corresponding measures, effectively ensuring the ecological safety of urban forests and the health of citizens.

[0004] The first aspect of the present invention provides a method for assessing the concentration of fine particulate matter in urban forest space, including:

[0005] Obtain the area to be detected and divide the area to be detected into multi-level detection sub-areas;

[0006] Set mobile detection devices and fixed detection devices in the detection sub-areas respectively, and obtain detection data through the mobile detection devices and the fixed detection devices;

[0007] Intelligently analyze and process the detection data to determine the distribution map of the concentration of fine particulate matter in the detection sub-areas;

[0008] According to the distribution map of the concentration of fine particulate matter in the detection sub-areas, determine the concentration value of fine particulate matter in the corresponding detection sub-areas and the area value of the corresponding concentration value of fine particulate matter;

[0009] When the concentration value of fine particulate matter in the detection sub-areas is greater than the preset concentration threshold set for the corresponding detection sub-areas, or the area value of the corresponding concentration value of fine particulate matter in the corresponding detection sub-areas is greater than the preset area threshold of the corresponding concentration of fine particulate matter, trigger a warning message for fine particulate matter;

[0010] Send the fine particulate matter warning information to a preset management terminal for prompting.

[0011] In this solution, the step of respectively setting mobile detection devices and fixed detection devices in the detection sub-areas includes:

[0012] Extract the area and boundary length values of the detection sub-areas;

[0013] According to the area and boundary length values of the detection sub-areas, obtain the calculated value of the number of mobile detection devices for the corresponding detection sub-areas. The formula is where M i―1 represents the calculated value of the number of mobile detection devices in the detection sub-area i, S i , L i respectively represent the area value and boundary length value of the corresponding detection sub-area i, N i represents the boundary complexity of the corresponding detection sub-area i, and a1 and a2 are constants;

[0014] Obtain the average value of PM(2.5) in the detection sub-area i in the previous historical time period, denoted as PM1;

[0015] Obtain the human interference index A in the previous historical time period;

[0016] Revise the calculated value of the number of mobile detection devices in the current time period according to the human interference index A and PM1 in the previous historical time period to obtain the revised calculated value of the number of mobile detection devices. The formula is where PM0 is the set standard threshold of PM(2.5);

[0017] Round up the revised calculated value of the number of mobile detection devices to obtain the number value of the mobile detection devices in the corresponding detection sub-area i.

[0018] In this solution, the step of respectively setting mobile detection devices and fixed detection devices in the detection sub-areas further includes:

[0019] Divide the detection sub-areas into multiple grid areas according to a preset grid, and extract the vegetation coverage rate and building density coefficient in each grid area;

[0020] Evaluate the score of each grid area according to the vegetation coverage rate and building density coefficient in each grid to obtain the evaluation score F. The formula is F j =k j *(P j +0.5*C j ), where F j represents the evaluation score of the grid area j, P jIndicates the vegetation coverage rate of grid area j, C j Indicates the building density coefficient of grid area j, k j Indicates the meteorological weight of grid area j;

[0021] When the evaluation score of the grid area is greater than the preset evaluation score threshold, a fixed detection device is set in the corresponding grid area.

[0022] In this solution, the step of obtaining the human disturbance index A in the previous historical time period specifically includes:

[0023] Obtain the human disturbance factors and the corresponding initial values of the human disturbance factors in the detection sub-area i in the previous historical time period;

[0024] Preprocess the corresponding initial value of the human disturbance factor to obtain the human disturbance factor value;

[0025] According to the human disturbance factor value, determine the human disturbance index in the detection sub-area i in the previous historical time period, and its formula is where n belongs to N, and N represents the set of human disturbance factors, b n Represents the weight coefficient of the human disturbance factor n, X n Represents the human disturbance factor value of the human disturbance factor n.

[0026] In this solution, the step of performing intelligent analysis and processing on the detection data to determine the fine particulate matter concentration distribution map of the detection sub-area specifically includes:

[0027] Obtain the floor plan of the area to be detected, and mark each detection data on the corresponding floor plan to construct detection data nodes;

[0028] Extract any two adjacent detection data nodes. If the corresponding fine particulate matter concentration values of the two adjacent detection data nodes are the same, connect the corresponding two detection data nodes to obtain the fine particulate matter concentration distribution line of the corresponding detection sub-area;

[0029] If the corresponding fine particulate matter concentration values of the two adjacent detection data nodes are different, set one of the detection data nodes as the reference point, and extract all adjacent detection data nodes of the reference point;

[0030] Calculate the difference between the fine particulate matter concentration values of the reference point and all adjacent detection data nodes to obtain a set of fine particulate matter concentration differences;

[0031] Construct a virtual connection line with the reference point corresponding to the maximum value in the set of fine particulate matter concentration differences and the adjacent detection data nodes, and sequentially mark on the corresponding virtual connection line the fine particulate matter concentration values of other adjacent detection data nodes corresponding to the reference point, which are set as virtual nodes;

[0032] Connect other adjacent detection data nodes and the corresponding virtual nodes to obtain the fine particulate matter concentration distribution line of the corresponding detection sub-region;

[0033] Based on the fine particulate matter concentration distribution line of the detection sub-region, obtain the fine particulate matter concentration distribution map of the corresponding detection sub-region.

[0034] In this solution, it further includes: when there are intersections in the fine particulate matter concentration distribution lines of the detection sub-region, extract the corresponding intersection points, set the corresponding intersection points as a detection data node, and delete the corresponding two intersecting fine particulate matter concentration distribution lines; and detect the data of the intersection points through a mobile detection device to obtain the fine particulate matter concentration value of the corresponding intersection points.

[0035] In this solution, the step of determining the fine particulate matter concentration value and the area value of the corresponding fine particulate matter concentration value in the corresponding detection sub-region according to the fine particulate matter concentration distribution map of the detection sub-region specifically includes:

[0036] Extract the fine particulate matter concentration distribution line in the fine particulate matter concentration distribution map of the detection sub-region, and set the corresponding fine particulate matter concentration distribution line as the fine particulate matter concentration line;

[0037] Extract any two adjacent fine particulate matter concentration lines, form a closed area with the corresponding two adjacent fine particulate matter concentration lines and the boundary line of the corresponding detection sub-region, and extract the area value of the corresponding closed area;

[0038] Set the area value of the corresponding closed area as the area value of the smaller fine particulate matter concentration value among the corresponding two adjacent fine particulate matter concentration lines.

[0039] The second aspect of the present invention provides an evaluation system for the fine particulate matter concentration in the urban forest space, including a memory and a processor. An evaluation method program for the fine particulate matter concentration in the urban forest space is stored in the memory. When the evaluation method program for the fine particulate matter concentration in the urban forest space is executed by the processor, the following steps are implemented:

[0040] Obtain the area to be detected, and divide the area to be detected into multi-level detection sub-regions;

[0041] Set a mobile detection device and a fixed detection device in the detection sub-region respectively, and obtain detection data through the mobile detection device and the fixed detection device;

[0042] Intelligently analyze and process the detection data to determine the fine particulate matter concentration distribution map of the detection sub-region;

[0043] According to the fine particulate matter concentration distribution map of the detection sub-region, determine the fine particulate matter concentration value in the corresponding detection sub-region and the area value corresponding to the fine particulate matter concentration value;

[0044] When the fine particulate matter concentration value in the detection sub-region is greater than the preset concentration threshold set for the corresponding detection sub-region, or the area value corresponding to the fine particulate matter concentration value in the corresponding detection sub-region is greater than the preset area threshold for the fine particulate matter concentration, trigger a fine particulate matter warning message;

[0045] Send the fine particulate matter warning message to a preset management terminal for prompting.

[0046] In this solution, the step of respectively setting mobile detection devices and fixed detection devices in the detection sub-region includes:

[0047] Extract the area and boundary length values of the detection sub-region;

[0048] According to the area and boundary length values of the detection sub-region, obtain the calculated value of the number of mobile detection devices in the corresponding detection sub-region, and its formula is Where M i―1 Represents the calculated value of the number of mobile detection devices in detection sub-region i, S i , L i Respectively represent the area value and boundary length value of the corresponding detection sub-region i, N i Represents the boundary complexity of the corresponding detection sub-region i, and a1 and a2 are constants;

[0049] Obtain the average value of PM(2.5) in detection sub-region i in the previous historical time period, denoted as PM1;

[0050] Obtain the human interference index A in the previous historical time period;

[0051] Revise the calculated value of the number of mobile detection devices in the current time period according to the human interference index A and PM1 in the previous historical time period to obtain the revised calculated value of the number of mobile detection devices, and its formula is Where PM0 is the set standard threshold of PM(2.5);

[0052] Round up the revised calculated value of the number of mobile detection devices to obtain the number value of mobile detection devices in the corresponding detection sub-region i.

[0053] In this solution, the step of respectively setting mobile detection devices and fixed detection devices in the detection sub-region further includes:

[0054] Divide the detection sub - region into multiple grid regions according to a preset grid, and extract the vegetation coverage rate and building density coefficient within each grid region;

[0055] Evaluate the score of the corresponding grid region according to the vegetation coverage rate and building density coefficient in each grid to obtain the evaluation score F, and its formula is F j = k j *(P j + 0.5 * C j ), where F j represents the evaluation score of grid region j, P j represents the vegetation coverage rate of grid region j, C j represents the building density coefficient of grid region j, and k j represents the meteorological weight of grid region j;

[0056] When the evaluation score of the grid region is greater than the preset evaluation score threshold, set a fixed detection device within the corresponding grid region.

[0057] An evaluation method and system for fine particulate matter concentration in urban forest space disclosed by the present invention can obtain the fine particulate matter concentration data of each region in real - time by setting mobile monitoring devices in different regions of the urban forest; at the same time, combined with the data of fixed monitoring stations, a more comprehensive and detailed data set can be formed, so as to more accurately reflect the distribution of fine particulate matter concentration in urban forest space; by setting a fine particulate matter concentration warning threshold, the management side can be timely reminded to take corresponding measures, effectively ensuring the ecological safety of the urban forest and the health of citizens. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Shows a flowchart of an evaluation method for fine particulate matter concentration in urban forest space of the present invention;

[0059] Figure 2 Shows a block diagram of an evaluation system for fine particulate matter concentration in urban forest space of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0060] In order to more clearly understand the above - mentioned objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0061] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0062] Figure 1 The flowchart of an evaluation method for the concentration of fine particulate matter in the urban forest space of the present invention is shown.

[0063] As Figure 1 shown, the present invention discloses an evaluation method for the concentration of fine particulate matter in the urban forest space, including:

[0064] S101, Obtain the area to be detected and divide the area to be detected into multi-level detection sub-areas;

[0065] S102, Set mobile detection devices and fixed detection devices in the detection sub-areas respectively, and obtain detection data through the mobile detection devices and the fixed detection devices;

[0066] S103, Perform intelligent analysis and processing on the detection data to determine the distribution map of the concentration of fine particulate matter in the detection sub-areas;

[0067] S104, According to the distribution map of the concentration of fine particulate matter in the detection sub-areas, determine the concentration value of the fine particulate matter in the corresponding detection sub-areas and the area value corresponding to the concentration value of the fine particulate matter;

[0068] S105, When the concentration value of the fine particulate matter in the detection sub-areas is greater than the preset concentration threshold set for the corresponding detection sub-areas, or the area value corresponding to the concentration value of the fine particulate matter in the corresponding detection sub-areas is greater than the preset area threshold corresponding to the concentration of the fine particulate matter, trigger a warning message for the fine particulate matter;

[0069] S106, Send the warning message for the fine particulate matter to a preset management terminal for prompting.

[0070] According to the embodiments of the present invention, the area to be detected can be divided step by step in the order from the outside to the inside into a boundary layer detection sub-area, a transition layer detection sub-area, and a core layer detection sub-area. The fine particulate matter concentration detection devices are divided into mobile detection devices and fixed detection devices according to whether they can move, and fixed detection devices are arranged at preset intervals above the boundary lines of the detection sub-areas.

[0071] According to the embodiments of the present invention, the step of setting mobile detection devices and fixed detection devices in the detection sub-areas respectively includes:

[0072] Extract the area and boundary length values of the detection sub-areas;

[0073] According to the area and boundary length values of the detection sub-areas, obtain the calculated value of the number of mobile detection devices in the corresponding detection sub-areas, and its formula is where M i―1 represents the calculated value of the number of mobile detection devices in the detection sub-area i, S i 、L irespectively represent the area value and the boundary length value corresponding to the detection sub-region i, N i represents the boundary complexity corresponding to the detection sub-region i, and a1 and a2 are constants;

[0074] Obtain the average value of PM(2.5) in the detection sub-region i in the previous historical time period, denoted as PM1;

[0075] Obtain the human interference index A in the previous historical time period;

[0076] Revise the calculated value of the number of mobile detection devices in the current time period according to the human interference index A and PM1 in the previous historical time period, and obtain the revised calculated value of the number of mobile detection devices. The formula is where PM0 is the set standard threshold of PM(2.5);

[0077] Round up the revised calculated value of the number of mobile detection devices to obtain the number value of the mobile detection devices corresponding to the detection sub-region i.

[0078] It should be noted that the fine particulate matter concentration value in the urban forest space is evaluated at intervals of a preset time period. For example, if the preset time period is 3 hours, the fine particulate matter concentration value in the urban forest space is evaluated every 3 hours. The number of mobile detection devices is composed of the area and boundary length values of the corresponding detection sub-region, and it is revised according to the fine particulate matter concentration value and the human interference index A in the previous time period, which improves the optimization of the number of mobile detection devices, thereby saving the detection cost while ensuring the accuracy of the detection data.

[0079] According to the embodiment of the present invention, the step of respectively setting mobile detection devices and fixed detection devices in the detection sub-region further includes:

[0080] Divide the detection sub-region into multiple grid regions according to a preset grid, and extract the vegetation coverage rate and building density coefficient in each grid region;

[0081] Evaluate the score of the corresponding grid region according to the vegetation coverage rate and building density coefficient in each grid to obtain the evaluation score F. The formula is F j =k j *(P j +0.5*C j ), where F j represents the evaluation score of the grid region j, P j represents the vegetation coverage rate of the grid region j, C j represents the building density coefficient of the grid region j, and k j represents the meteorological weight of the grid region j;

[0082] When the evaluation score of the grid area is greater than the preset evaluation score threshold, a fixed detection device is set within the corresponding grid area.

[0083] It should be noted that the building area in the building density coefficient includes the solidified ground area, which is obtained by dividing the total building area within the grid by the area of the corresponding grid area; when a fixed detection device is set within the grid area, it is preferably set on the boundary line of the detection sub-area to which the grid area belongs.

[0084] According to the embodiment of the present invention, the step of obtaining the human interference index A in the previous historical time period specifically includes:

[0085] Obtain the human interference factors and the corresponding initial values of the human interference factors in the detection sub-area i in the previous historical time period;

[0086] Preprocess the corresponding initial values of the human interference factors to obtain the human interference factor values;

[0087] According to the human interference factor values, determine the human interference index in the detection sub-area i in the previous historical time period, and its formula is where n belongs to N, and N represents the set of human interference factors, b n represents the weight coefficient of the human interference factor n, and X n represents the human interference factor value of the human interference factor n.

[0088] It should be noted that the human interference factors at least include traffic flow, construction activity intensity, dynamic population density, and industrial emission equivalent; the preprocessing of the initial values of the human interference factors is normalization processing. For example, the initial traffic flow value corresponding to the traffic flow is the total number of vehicles passing through the corresponding detection sub-area in the previous historical time period. When the initial traffic flow value is greater than or equal to the preset maximum carrying capacity of vehicles, the interference factor value corresponding to the traffic flow is set to 1. When the initial traffic flow value is less than the preset maximum carrying capacity of vehicles, the interference factor value corresponding to the traffic flow is equal to the initial traffic flow value divided by the preset maximum carrying capacity of vehicles.

[0089] According to the embodiment of the present invention, the step of performing intelligent analysis and processing on the detection data to determine the fine particulate matter concentration distribution map of the detection sub-area specifically includes:

[0090] Obtain the plan view of the area to be detected, and mark each detection data on the corresponding plan view to construct detection data nodes;

[0091] Extract any two adjacent detection data nodes. If the corresponding fine particulate matter concentration values of the two adjacent detection data nodes are the same, then connect the corresponding two detection data nodes to obtain the fine particulate matter concentration distribution line of the corresponding detection sub-area;

[0092] If the PM2.5 concentration values corresponding to two adjacent detection data nodes are different, one of the detection data nodes is set as the reference point, and all adjacent detection data nodes of the reference point are extracted;

[0093] The difference calculation is performed on the PM2.5 concentration values of the reference point and all adjacent detection data nodes to obtain a set of PM2.5 concentration differences;

[0094] A virtual connection line is constructed with the reference point corresponding to the maximum value in the set of PM2.5 concentration differences and the adjacent detection data nodes, and the PM2.5 concentration values of the other adjacent detection data nodes corresponding to the reference point are sequentially marked on the corresponding virtual connection line, which are set as virtual nodes;

[0095] The other adjacent detection data nodes and the corresponding virtual nodes are connected to obtain the PM2.5 concentration distribution line of the corresponding detection sub-region;

[0096] Based on the PM2.5 concentration distribution line of the detection sub-region, the PM2.5 concentration distribution map of the corresponding detection sub-region is obtained.

[0097] It should be noted that when the mobile detection device detects the PM2.5 concentration at each location, the corresponding detection location is recorded, and the corresponding detection location and the corresponding PM2.5 concentration value are associated and stored. Each detection data node displayed on the plane map of the detection area reflects the PM2.5 concentration value at the corresponding location in reality. For example, if the PM2.5 concentration values corresponding to adjacent detection data nodes are 70, 80, and 75 respectively, with the unit of micrograms per cubic meter, the corresponding PM2.5 concentration differences are 10 and 5 respectively. Then, a virtual connection line is constructed with the detection data nodes corresponding to the PM2.5 concentration values of 70 and 80. According to the linear relationship, the middle position of the corresponding virtual connection line is set as the PM2.5 concentration value of 75, and a virtual node is set at the middle position of the corresponding virtual connection line.

[0098] According to the embodiment of the present invention, it further includes: when the PM2.5 concentration distribution lines of the detection sub-region intersect, the corresponding intersection points are extracted, the corresponding intersection points are set as a detection data node, and the corresponding two intersecting PM2.5 concentration distribution lines are deleted; and the detection data of the intersection points are obtained through the mobile detection device to obtain the PM2.5 concentration values of the corresponding intersection points.

[0099] According to the embodiment of the present invention, the step of determining the PM2.5 concentration value and the area value of the corresponding PM2.5 concentration value in the corresponding detection sub-region according to the PM2.5 concentration distribution map of the detection sub-region specifically includes:

[0100] Extract the fine particulate matter concentration distribution line in the fine particulate matter concentration distribution map of the detection sub-region, and set the corresponding fine particulate matter concentration distribution line as the fine particulate matter concentration line;

[0101] Extract any two adjacent fine particulate matter concentration lines, form a closed region with the corresponding two adjacent fine particulate matter concentration lines and the boundary line of the corresponding detection sub-region, and extract the area value of the corresponding closed region;

[0102] Set the area value of the corresponding closed region as the area value of the smaller fine particulate matter concentration value among the two adjacent fine particulate matter concentration lines.

[0103] It should be noted that, for example, the fine particulate matter concentration values corresponding to two adjacent fine particulate matter concentration lines are 70 and 75 respectively, with the unit of micrograms per cubic meter. Then the area of the closed region formed by the corresponding two adjacent fine particulate matter concentration lines and the boundary line of the corresponding detection sub-region is the area value of the fine particulate matter concentration value of 70.

[0104] Furthermore, accumulate the area values of each detection sub-region that are the same as or higher than the corresponding fine particulate matter concentration value to obtain the total area value of the corresponding fine particulate matter concentration value within the entire area to be detected. When the total area value of the fine particulate matter concentration value within the entire area to be detected is greater than the preset second area threshold, trigger a fine particulate matter warning message; for example, the area value of the fine particulate matter concentration value of 75 is 80 square meters, the area value of the fine particulate matter concentration value of 80 is 90 square meters, and the area value of the fine particulate matter concentration value of 70 is 100 square meters. Then the total area value of the fine particulate matter concentration value of 75 is 80 + 90 = 170 square meters; the total area value of the fine particulate matter concentration value of 70 is 100 + 80 + 90 = 270 square meters.

[0105] Figure 2 The block diagram of an evaluation system for the fine particulate matter concentration in an urban forest space according to the present invention is shown.

[0106] As Figure 2 shown, the second aspect of the present invention provides an evaluation system 2 for the fine particulate matter concentration in an urban forest space, including a memory 21 and a processor 22. An evaluation method program for the fine particulate matter concentration in an urban forest space is stored in the memory. When the evaluation method program for the fine particulate matter concentration in an urban forest space is executed by the processor, the following steps are implemented:

[0107] Obtain the area to be detected, and divide the area to be detected into multi-level detection sub-regions;

[0108] Set mobile detection devices and fixed detection devices in the detection sub-regions respectively, and obtain detection data through the mobile detection devices and the fixed detection devices;

[0109] Intelligently analyze and process the detection data to determine the fine particulate matter concentration distribution map of the detection sub-region;

[0110] According to the fine particulate matter concentration distribution map of the detection sub-region, determine the fine particulate matter concentration value in the corresponding detection sub-region and the area value corresponding to the fine particulate matter concentration value;

[0111] When the fine particulate matter concentration value in the detection sub-region is greater than the preset concentration threshold set for the corresponding detection sub-region, or the area value corresponding to the fine particulate matter concentration value in the corresponding detection sub-region is greater than the preset area threshold for the fine particulate matter concentration, trigger a fine particulate matter warning message;

[0112] Send the fine particulate matter warning message to a preset management terminal for prompt.

[0113] In this solution, the step of respectively setting mobile detection devices and fixed detection devices in the detection sub-region includes:

[0114] Extract the area and boundary length values of the detection sub-region;

[0115] According to the area and boundary length values of the detection sub-region, obtain the calculated value of the number of mobile detection devices in the corresponding detection sub-region, and its formula is Where M i―1 represents the calculated value of the number of mobile detection devices in detection sub-region i, S i , L i respectively represent the area value and boundary length value of the corresponding detection sub-region i, N i represents the boundary complexity of the corresponding detection sub-region i, and a1 and a2 are constants;

[0116] Obtain the average value of PM(2.5) in detection sub-region i in the previous historical time period, denoted as PM1;

[0117] Obtain the human interference index A in the previous historical time period;

[0118] Revise the calculated value of the number of mobile detection devices in the current time period according to the human interference index A and PM1 in the previous historical time period to obtain the revised calculated value of the number of mobile detection devices, and its formula is Where PM0 is the set standard threshold of PM(2.5);

[0119] Round up the revised calculated value of the number of mobile detection devices to obtain the number value of mobile detection devices in the corresponding detection sub-region i.

[0120] In this solution, the step of respectively setting mobile detection devices and fixed detection devices in the detection sub-region further includes:

[0121] Divide the detection sub-region into multiple grid regions according to a preset grid, and extract the vegetation coverage rate and building density coefficient within each grid region;

[0122] Perform score evaluation on the grid regions according to the vegetation coverage rate and building density coefficient within each grid to obtain an evaluation score F, and its formula is F j = k j *(P j + 0.5*C j ), where F j represents the evaluation score of grid region j, P j represents the vegetation coverage rate of grid region j, C j represents the building density coefficient of grid region j, and k j represents the meteorological weight of grid region j;

[0123] When the evaluation score of the grid region is greater than the preset evaluation score threshold, set a fixed detection device within the corresponding grid region.

[0124] An evaluation method and system for the concentration of fine particulate matter in the urban forest space disclosed by the present invention can obtain the concentration data of fine particulate matter in each region in real time by setting mobile monitoring devices in different regions of the urban forest; at the same time, combined with the data of fixed monitoring stations, a more comprehensive and detailed data set can be formed, so as to more accurately reflect the distribution of the concentration of fine particulate matter in the urban forest space; by setting a warning threshold for the concentration of fine particulate matter, the management side can be timely reminded to take corresponding measures, effectively ensuring the ecological safety of the urban forest and the health of citizens.

[0125] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical, or other forms.

[0126] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, in each embodiment of the present invention, each functional unit can be entirely integrated into one processing unit, or each unit can be separately regarded as one unit, or two or more units can be integrated into one unit; the above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0128] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks and other various media that can store program codes.

[0129] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or in terms of the contribution to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks, or optical disks and other various media that can store program codes.

Claims

1. An evaluation method for the concentration of fine particulate matter in urban forest space, characterized in that, Including: Obtain the area to be detected and divide the area to be detected into multi-level detection sub-areas; Set mobile detection devices and fixed detection devices in the detection sub-areas respectively, and obtain detection data through the mobile detection devices and the fixed detection devices; Intelligently analyze and process the detection data to determine the fine particulate matter concentration distribution map of the detection sub-areas; According to the fine particulate matter concentration distribution map of the detection sub-areas, determine the fine particulate matter concentration value in the corresponding detection sub-areas and the area value corresponding to the fine particulate matter concentration value; When the fine particulate matter concentration value in the detection sub-areas is greater than the preset concentration threshold set for the corresponding detection sub-areas, or the area value corresponding to the fine particulate matter concentration value in the corresponding detection sub-areas is greater than the preset area threshold corresponding to the fine particulate matter concentration, trigger a fine particulate matter warning message; Send the fine particulate matter warning message to a preset management terminal for prompting.

2. The evaluation method for the concentration of fine particulate matter in the urban forest space according to claim 1, characterized in that The step of setting mobile detection devices and fixed detection devices in the detection sub-areas respectively includes: Extract the area and boundary length value of the detection sub-areas; According to the area and boundary length value of the detection sub-region, a calculated value of the number of mobile detection devices for the corresponding detection sub-region is obtained, and the formula is where M i―1 represents the calculated value of the number of mobile detection devices for detection sub-region i, S i , L i respectively represent the area value and boundary length value of the corresponding detection sub-region i, N i represents the boundary complexity of the corresponding detection sub-region i, and a1 and a2 are constants; Obtain the average value of PM(2.5) in the detection sub-area i in the previous historical time period, denoted as PM1; Obtain the human interference index A in the previous historical time period; Revise the calculated value of the number of mobile detection devices in the current time period according to the human interference index A and PM1 in the previous historical time period to obtain the revised calculated value of the number of mobile detection devices. The formula is where PM0 is the set standard threshold of PM(2.5); Round up the revised calculated value of the number of mobile detection devices and obtain the number value of the mobile detection devices corresponding to the detection sub-area i.

3. The evaluation method for the concentration of fine particulate matter in the urban forest space according to claim 2, characterized in that, The step of setting mobile detection devices and fixed detection devices in the detection sub-areas respectively further includes: Divide the detection sub-areas into multiple grid areas according to a preset grid, and extract the vegetation coverage rate and building density coefficient in each grid area; Evaluate the score of each grid area according to the vegetation coverage rate and building density coefficient in the grid to obtain the evaluation score F, and its formula is F j = k j *(P j + 0.5*C j ), where F j represents the evaluation score of grid area j, P j represents the vegetation coverage rate of grid area j, C j represents the building density coefficient of grid area j, and k j represents the meteorological weight of grid area j; When the evaluation score of the grid area is greater than the preset evaluation score threshold, set a fixed detection device in the corresponding grid area.

4. The evaluation method for the concentration of fine particulate matter in the urban forest space according to claim 2, wherein The step of obtaining the human interference index A in the previous historical time period specifically includes: Obtain the human interference factors and the corresponding initial values of the human interference factors in the detection sub-area i in the previous historical time period; Preprocess the corresponding initial values of the human interference factors to obtain the human interference factor values; Determine the human interference index in the corresponding detection sub-region i in the previous historical time period according to the human interference factor value. The formula is where n belongs to N, and N represents the set of human interference factors, b n represents the weight coefficient of the human interference factor n, and X n represents the human interference factor value of the human interference factor n.

5. The evaluation method for the concentration of fine particulate matter in the urban forest space according to claim 1, characterized in that, The step of intelligently analyzing and processing the detection data to determine the fine particulate matter concentration distribution map of the detection sub-areas specifically includes: Obtain the floor plan of the area to be detected, and mark each detection data on the corresponding floor plan to construct detection data nodes; Extract any two adjacent detection data nodes. If the fine particulate matter concentration values corresponding to the two adjacent detection data nodes are the same, connect the two corresponding detection data nodes to obtain the fine particulate matter concentration distribution line of the corresponding detection sub-area; If the fine particulate matter concentration values corresponding to the two adjacent detection data nodes are different, set one of the detection data nodes as the reference point and extract all adjacent detection data nodes of the reference point; Calculate the difference between the fine particulate matter concentration values of the reference point and all adjacent detection data nodes to obtain a set of fine particulate matter concentration differences; Construct a virtual connection line with the reference point corresponding to the maximum value in the set of fine particulate matter concentration differences and the adjacent detection data nodes, and sequentially mark the fine particulate matter concentration values of other adjacent detection data nodes corresponding to the reference point on the corresponding virtual connection line, which are set as virtual nodes; Connect other adjacent detection data nodes and the corresponding virtual nodes to obtain the fine particulate matter concentration distribution line of the corresponding detection sub-region; Based on the fine particulate matter concentration distribution line of the detection sub-region, obtain the fine particulate matter concentration distribution map of the corresponding detection sub-region.

6. The evaluation method for the concentration of fine particulate matter in the urban forest space according to claim 5, wherein It also includes: When there are intersections in the fine particulate matter concentration distribution line of the detection sub-region, extract the corresponding intersection points, set the corresponding intersection points as a detection data node, and delete the corresponding two intersecting fine particulate matter concentration distribution lines; And detect the data of the intersection point through the mobile detection device to obtain the fine particulate matter concentration value of the corresponding intersection point.

7. The evaluation method of fine particulate matter concentration in an urban forest space according to claim 1, wherein The step of determining the fine particulate matter concentration value and the area value of the corresponding fine particulate matter concentration value in the corresponding detection sub-region according to the fine particulate matter concentration distribution map of the detection sub-region specifically includes: Extract the fine particulate matter concentration distribution line in the fine particulate matter concentration distribution map of the detection sub-region, and set the corresponding fine particulate matter concentration distribution line as the fine particulate matter concentration line; Extract any two adjacent fine particulate matter concentration lines, form a closed area with the corresponding two adjacent fine particulate matter concentration lines and the boundary line of the corresponding detection sub-region, and extract the area value of the corresponding closed area; Set the area value of the corresponding closed area as the area value of the smaller fine particulate matter concentration value among the corresponding two adjacent fine particulate matter concentration lines.

8. An evaluation system for the concentration of fine particulate matter in urban forest space, characterized in that, It includes a memory and a processor. An evaluation method program for the fine particulate matter concentration in the urban forest space is stored in the memory. When the evaluation method program for the fine particulate matter concentration in the urban forest space is executed by the processor, the following steps are implemented: Obtain the area to be detected and divide the area to be detected into multi-level detection sub-regions; Set up mobile detection devices and fixed detection devices in the detection sub-regions respectively, and obtain detection data through the mobile detection devices and the fixed detection devices; Intelligently analyze and process the detection data to determine the fine particulate matter concentration distribution map of the detection sub-region; According to the fine particulate matter concentration distribution map of the detection sub-region, determine the fine particulate matter concentration value and the area value of the corresponding fine particulate matter concentration value in the corresponding detection sub-region; When the fine particulate matter concentration value in the detection sub-region is greater than the preset concentration threshold set for the corresponding detection sub-region, or the area value of the corresponding fine particulate matter concentration value in the corresponding detection sub-region is greater than the preset area threshold of the corresponding fine particulate matter concentration, trigger a fine particulate matter warning message; Send the fine particulate matter warning message to a preset management terminal for prompting.

9. The evaluation system for the concentration of fine particulate matter in the urban forest space according to claim 8, wherein The step of setting up mobile detection devices and fixed detection devices in the detection sub-regions respectively includes: Extract the area and boundary length values of the detection sub-region; Based on the area and boundary length values of the detection sub-region, a calculated value of the number of mobile detection devices for the corresponding detection sub-region is obtained, and the formula is where M i―1 represents the calculated value of the number of mobile detection devices for detection sub-region i, S i , L i respectively represent the area value and boundary length value of the corresponding detection sub-region i, N i represents the boundary complexity of the corresponding detection sub-region i, and a1 and a2 are constants; Obtain the average value of PM(2.5) in the detection sub-region i in the previous historical time period, which is set as PM1; Obtain the human interference index A in the previous historical time period; Revise the calculated value of the number of mobile detection devices in the current time period according to the human interference index A and PM1 in the previous historical time period to obtain the revised calculated value of the number of mobile detection devices. The formula is where PM0 is the set standard threshold of PM(2.5); Round up the revised calculated value of the number of mobile detection devices to obtain the number value of the mobile detection devices corresponding to the detection sub-region i.

10. The evaluation system for fine particulate matter concentration in an urban forest space according to claim 9, wherein, The step of respectively setting mobile detection devices and fixed detection devices in the detection sub-regions further includes: Dividing the detection sub-region into a plurality of grid regions according to a preset grid, and extracting the vegetation coverage rate and building density coefficient within each grid region; Evaluate the score of each grid area according to the vegetation coverage rate and building density coefficient in the grid to obtain the evaluation score F, and its formula is F j = k j *(P j + 0.5 * C j ), where F j represents the evaluation score of grid area j, P j represents the vegetation coverage rate of grid area j, C j represents the building density coefficient of grid area j, and k j represents the meteorological weight of grid area j; When the evaluation score of the grid region is greater than a preset evaluation score threshold, set a fixed detection device within the corresponding grid region.

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