A method and system for predicting urban forest ecological indicators

Abstract

The present invention is applicable to the field of environmental assessment and provides a method and system for predicting urban forest ecological indicators. The method includes: dividing the area of ​​a targeted urban forest area into equal parts to generate several sub-areas, and obtaining greening ratio values ​​of the corresponding sub-areas; matching sub-areas with greening ratio values ​​within a deviation threshold to generate several targeted sub-areas; setting a signal sniffing point and several targeted monitoring points in each targeted sub-area; calculating the pedestrian flow value of each targeted sub-area based on the signal sniffing point; calculating the mean ambient temperature of each targeted sub-area based on several targeted monitoring points, and generating a corresponding prediction coefficient for the target sub-area indicator value. Predicting the development of the ecological indicators of the overall prediction area through the sub-areas helps to gain an in-depth understanding of the differences in heat island effects in different local areas. At the same time, it can comprehensively evaluate the social development indicators of each sub-area, so that urban construction is more in line with the actual needs of citizens.

Description

Technical Field

[0001] The present invention belongs to the field of environmental assessment, and in particular relates to a method and system for predicting urban forest ecological indicators. Background Art

[0002] With the acceleration of global urbanization, a large number of people are migrating from rural areas to cities. Limited urban land is being stretched beyond its original carrying capacity to accommodate this growing population, leading to increasing damage to the urban ecological environment. However, in recent years, urban greening has received increasing attention, and street cleaning and greening projects have become a regular practice. Urban forests, as a key component of urban greening, not only improve the urban environment but also demonstrate a city's confidence and vitality.

[0003] Urban forests have multiple ecological functions, including energy conservation, carbon sequestration and absorption, air quality improvement, stormwater retention, and aesthetic appeal. These functions are crucial for improving the urban environment and enhancing residents' quality of life. However, existing methods for assessing urban forest ecological indicators are relatively simplistic, resulting in inaccurate results and a failure to fully reflect the current status and development trends of urban forest ecological indicators.

[0004] Therefore, it is crucial to develop an accurate and comprehensive method for assessing the current status and predicting the development of urban forest ecological indicators. This method requires comprehensive consideration of multiple ecological indicators of urban forests. Through scientific data collection and analysis, it can accurately assess and predict urban forest ecological indicators, providing a scientific basis for urban greening planning and management. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for predicting urban forest ecological indicators, aiming to solve the problems raised in the above background technology.

[0006] The present invention is achieved by providing, on the one hand, a method for predicting urban forest ecological indicators, the method comprising:

[0007] The targeted urban forest area is divided equally into several sub-areas, and the greening ratio values ​​of the corresponding sub-areas are obtained;

[0008] Match sub-regions whose greening ratio values ​​are within the deviation threshold to generate several targeted sub-regions;

[0009] A signal sniffing point and several targeted monitoring points are set up in each targeted sub-area;

[0010] Based on the signal sniffing point, calculate the pedestrian flow value of each targeted sub-area;

[0011] Based on several targeted monitoring points, the mean ambient temperature of each targeted sub-area is calculated;

[0012] Based on the mean ambient temperature of each targeted sub-region, a corresponding prediction coefficient of the targeted sub-region index value is generated;

[0013] Based on the passenger flow value and indicator value prediction coefficient of the targeted sub-area, the indicator development prediction index of the targeted sub-area is calculated.

[0014] As a further solution of the present invention, the calculation of the pedestrian flow value of each targeted sub-area based on the signal sniffing point specifically includes:

[0015] Setting and matching the signal range of the targeted subregion;

[0016] Monitor Bluetooth signals within the signal range of the targeted sub-area;

[0017] The number of Bluetooth signal occurrences within the target time is recorded to generate the pedestrian flow value of the targeted sub-area.

[0018] As a further solution of the present invention, generating a corresponding target sub-region index value prediction coefficient based on the mean ambient temperature of each target sub-region specifically includes:

[0019] Identify the built-up areas within which the targeted urban forest areas belong;

[0020] Obtain the ambient temperature value of the built-up area and the average ambient temperature of the targeted sub-area;

[0021] Calculate the difference between the ambient temperature of the built-up area and the mean ambient temperature of the targeted sub-area to generate the intensity value of the heat island effect;

[0022] Based on the heat island effect intensity values, a heat island effect intensity value sequence is generated in order from weak to strong.

[0023] As a further solution of the present invention, generating a corresponding target sub-region index value prediction coefficient based on the average ambient temperature of each target sub-region specifically further includes:

[0024] Extracting plant species classification within several targeted sub-regions;

[0025] Based on the heat island effect intensity value sequence, representative targeted sub-areas are generated;

[0026] Generate a plant planting amount sequence within the domain representing the targeted sub-region in order from most to least;

[0027] Obtain the dominant plant species in the plant planting amount series within the domain;

[0028] Calculate the proportion of planting area of ​​mainstream plant species in several targeted sub-regions ;

[0029] Based on the proportion of planted area , generate the prediction coefficient of the target sub-region index value.

[0030] As a further embodiment of the present invention, in another aspect, a system for predicting urban forest ecological indicators is provided, the system comprising:

[0031] The equal division module is used to divide the area of ​​the targeted urban forest area into equal parts;

[0032] A first generating module is used to generate a plurality of sub-regions;

[0033] An acquisition module is used to obtain the greening ratio value of the corresponding sub-area;

[0034] A matching module, for matching sub-regions whose greening ratio values ​​are within a deviation threshold;

[0035] The second generation module is used to generate several targeted sub-regions;

[0036] A monitoring module is used to set a signal sniffing point and several targeted monitoring points in each targeted sub-area;

[0037] A first calculation module is used to calculate the pedestrian flow value of each targeted sub-area based on the signal sniffing point;

[0038] A second calculation module is used to calculate the average ambient temperature of each targeted sub-area based on a number of targeted monitoring points;

[0039] The third generating module is used to generate a corresponding target sub-region index value prediction coefficient based on the average ambient temperature of each target sub-region.

[0040] As a further solution of the present invention, the first calculation module specifically includes:

[0041] Setting a matching unit, for setting and matching a signal range of a targeted sub-region;

[0042] A monitoring unit, used to monitor Bluetooth signals within the signal range of the targeted sub-area;

[0043] A recording unit, used to record the number of occurrences of Bluetooth signals within a target time;

[0044] The first generating unit is configured to generate a targeted sub-area pedestrian flow value.

[0045] As a further solution of the present invention, the third generation module specifically includes:

[0046] Identification unit, used to identify the built-up area to which the targeted urban forest area belongs;

[0047] An acquisition unit, used to acquire the ambient temperature value of the built-up area and the average ambient temperature value of the targeted sub-area;

[0048] The first calculation unit is used to calculate the difference between the ambient temperature value of the built-up area and the average ambient temperature of the target sub-area;

[0049] The second generating unit is used to generate a heat island effect intensity value;

[0050] The third generating unit is configured to generate a heat island effect intensity value sequence based on the heat island effect intensity values ​​in an ascending order.

[0051] As a further solution of the present invention, the third generation module specifically further includes:

[0052] An extraction unit, configured to extract plant species classifications within a plurality of targeted sub-regions;

[0053] A fourth generating unit is used to generate a representative targeted sub-region based on the heat island effect intensity value sequence;

[0054] A fifth generating unit is configured to generate a sequence of plant planting amounts within the domain representing the targeted sub-region in order of most to least;

[0055] An acquisition unit, used for acquiring mainstream plant species in a plant planting amount sequence within a domain;

[0056] The second calculation unit is used to calculate the planting area ratio of mainstream plant species in several targeted sub-areas ;

[0057] The sixth generation unit is used to generate the value based on the proportion of planting area , generate the prediction coefficient of the target sub-region index value.

[0058] The present invention provides a method and system for predicting urban forest ecological indicators. The method and system divide the prediction area into sub-areas, and predict the development of ecological indicators of the overall prediction area through sub-areas, which helps to gain a deep understanding of the differences in heat island effects in different local areas, provides detailed data support for the targeted formulation of heat island effect mitigation strategies, and can screen out the most effective plant species for mitigating the heat island effect, facilitating their key promotion and planting in urban greening planning, thereby optimizing the urban ecological environment layout, and helping urban planners to give priority to the transformation and optimization of areas with high social development potential when carrying out urban renewal and public space planning, so that urban construction can better meet the actual needs of citizens. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a main flow chart of a method for predicting urban forest ecological indicators.

[0060] Figure 2The present invention is a flowchart of a method for predicting urban forest ecological indicators, which calculates the pedestrian flow value of each targeted sub-area based on signal sniffing points.

[0061] Figure 3 The present invention is a flow chart of a first embodiment of a method for predicting an urban forest ecological index, which generates a corresponding target sub-region index value prediction coefficient based on the mean ambient temperature of each target sub-region.

[0062] Figure 4 The present invention is a flow chart of a second embodiment of a method for predicting an urban forest ecological index, which generates a corresponding target sub-region index value prediction coefficient based on the mean ambient temperature of each target sub-region.

[0063] Figure 5 It is a main structure diagram of an urban forest ecological index prediction system.

[0064] Figure 6 The present invention is a structural block diagram of the first calculation module in an urban forest ecological index prediction system.

[0065] Figure 7 The present invention is a structural block diagram of a first embodiment of the third generation module in the urban forest ecological index prediction system.

[0066] Figure 8 The present invention is a structural block diagram of a second embodiment of the third generation module in the urban forest ecological index prediction system. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0068] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0069] The present invention provides an urban forest ecological index prediction method and system, which solves the technical problems in the background technology.

[0070] like Figure 1 FIG. 1 is a main flow chart of a method for predicting urban forest ecological indicators according to an embodiment of the present invention. The method for predicting urban forest ecological indicators includes:

[0071] Step S100: dividing the targeted urban forest area into equal parts to generate a number of sub-areas, and obtaining greening ratio values ​​of the corresponding sub-areas;

[0072] Step S200: matching sub-regions whose greening ratio values ​​are within a deviation threshold to generate a plurality of targeted sub-regions;

[0073] Step S300: setting a signal sniffing point and several targeted monitoring points in each targeted sub-area;

[0074] Step S400: Calculating the pedestrian flow value of each targeted sub-area based on the signal sniffing point;

[0075] Step S500: Calculating the mean ambient temperature of each targeted sub-area based on a number of targeted monitoring points;

[0076] Step S600: generating a corresponding target sub-region index value prediction coefficient based on the average ambient temperature of each target sub-region;

[0077] When this embodiment is applied, the urban ecological area to be predicted is first divided into equal parts to generate several sub-areas. The area of ​​each targeted sub-area is the same, and the greening ratio value of the corresponding sub-area is obtained. In each sub-area, several types of plants are planted, thereby forming an urban forest ecology. A deviation threshold is preset. The greening ratio value is the ratio of the green area of ​​the sub-area to the total area of ​​the area. The sub-areas with greening ratio values ​​within the deviation threshold are matched to generate several targeted sub-areas, that is, sub-areas with similar green areas are set as targeted sub-areas. A signal sniffing point and several targeted monitoring points are set in each targeted sub-area, and temperature monitoring equipment is set at several monitoring points. The average ambient temperature of each targeted sub-area is calculated, that is, after collecting temperature information of multiple targeted monitoring points, the average calculation is performed, and the average can be more representative. Then, the built-up area to which the predicted urban ecological area belongs is identified, and the real-time ambient temperature value of the built-up area is obtained. Then, the heat island effect intensity value is calculated and generated. A Bluetooth detection device is set at the signal sniffing point. By deploying it in the area A Bluetooth detector is used to scan the surrounding mobile devices with Bluetooth enabled. When the device enters the signal range of the detector, it will be detected and recorded. The flow of people can be counted according to the strength of the Bluetooth signal and the time when the device appears. The greater the flow of people, the higher the economic indicators of the targeted sub-area and the degree of benefit to the masses. Then, based on the average ambient temperature of each targeted sub-area, the corresponding targeted sub-area index value prediction coefficient is generated. By obtaining the ambient temperature value of the targeted built-up area, the heat island effect intensity value is calculated and a heat island effect intensity value sequence is generated. Then, the plant classification within several targeted sub-areas is extracted to generate the corresponding mainstream plant planting amount sequence within the domain. Based on the heat island effect intensity value sequence and the mainstream plant planting amount sequence within the domain, the corresponding targeted sub-area index value prediction coefficient is generated. That is, the specific impact of the plant species with the largest planting amount in the targeted sub-area on the urban heat island effect in the region is judged. Finally, based on the heat island effect intensity value sequence and the mainstream plant planting amount sequence within the domain, the corresponding targeted sub-area index value prediction coefficient is quantitatively generated.

[0078] like Figure 2As shown in FIG. 1 , as a preferred embodiment of the present invention, the calculation of the pedestrian flow value of each targeted sub-area based on the signal sniffing point specifically includes:

[0079] Step S401: setting and matching the signal range of the targeted sub-region;

[0080] Step S402: monitoring Bluetooth signals within the signal range of the targeted sub-area;

[0081] Step S403: Record the number of occurrences of the Bluetooth signal within the target time and generate a pedestrian flow value for the targeted sub-area;

[0082] When this embodiment is applied, the signal range of the targeted sub-area is set and matched, a Bluetooth detection device is set in the targeted sub-area to monitor the Bluetooth signal within the signal range of the targeted sub-area, and the Bluetooth detectors deployed in the area are used to scan the surrounding mobile devices with Bluetooth functions turned on. When the device enters the signal range of the detector, it will be detected and recorded. The pedestrian flow value can be counted based on the strength of the Bluetooth signal and the appearance time of the device to generate the pedestrian flow value of the targeted sub-area.

[0083] like Figure 3 As shown in FIG. 1 , as a preferred embodiment of the present invention, generating a corresponding target sub-region index value prediction coefficient based on the mean ambient temperature of each target sub-region specifically includes:

[0084] Step S601: Identify the built-up area to which the targeted urban forest area belongs;

[0085] Step S602: Obtain the ambient temperature value of the built-up area and the average ambient temperature value of the targeted sub-area;

[0086] Step S603: Calculate the difference between the ambient temperature value of the built-up area and the average ambient temperature of the targeted sub-area to generate a heat island effect intensity value;

[0087] Step S604: generating a heat island effect intensity value sequence based on the heat island effect intensity values ​​in order from weak to strong;

[0088] When this embodiment is applied, the built-up area to which the targeted urban forest area belongs is first identified, the ambient temperature value of the built-up area and the average ambient temperature of the targeted sub-area are obtained, the difference between the ambient temperature value of the built-up area and the average ambient temperature of the targeted sub-area is calculated, and a heat island effect intensity value is generated. Based on the heat island effect intensity value, a heat island effect intensity value sequence is generated in order from weak to strong. The urban heat island effect refers to the phenomenon that the temperature in the urban area is significantly higher than the temperature in the surrounding suburbs during the same period due to the urban environment. The calculated heat island effect intensity value can indicate the impact of the targeted urban forest area on temperature. The monitoring time can be set to July and August each year. This period is summer and the urban heat island effect is more obvious, which is conducive to improving prediction accuracy.

[0089] like Figure 4 As shown, as a preferred embodiment of the present invention, generating the corresponding target sub-region index value prediction coefficient based on the average ambient temperature of each target sub-region specifically further includes:

[0090] Step S605: extracting plant species classifications within a plurality of targeted sub-regions;

[0091] Step S606: generating a representative targeted sub-region based on the heat island effect intensity value sequence;

[0092] Step S607: generating a plant planting amount sequence representing the target sub-region in the order of most to least;

[0093] Step S608: Obtaining the mainstream plant species in the plant planting amount sequence within the domain;

[0094] Step S609: Calculate the planting area ratio of mainstream plant species in several targeted sub-regions ;

[0095] Step S610: Based on the planting area ratio , generate the prediction coefficient of the target sub-region index value;

[0096] It should be understood that, first, the plant classifications within several targeted sub-regions are extracted, and based on the heat island effect intensity value sequence, representative targeted sub-regions are generated, and the representative targeted sub-regions are the targeted sub-regions with the weakest heat island effect intensity. In order from most to least, a sequence of plant planting amounts within the domain representing the targeted sub-regions is generated, and the mainstream plant species in the sequence of plant planting amounts within the domain are obtained. The mainstream plant species are the plant species with the largest planting amount in the domain, and the proportion of the planting area of ​​the mainstream plant species in several targeted sub-regions is calculated. , the proportion of planting area The calculation process is: the planting area of ​​mainstream plant species in the targeted sub-region / the total green area of ​​the targeted sub-region, and the proportion of the planting area It is the prediction coefficient of the target sub-region index value.

[0097] When this embodiment is applied, the indicator development prediction index of the targeted sub-region is calculated, and the indicator development prediction index of the targeted sub-region is used to reflect the regional development of the targeted sub-region. At the same time, the indicator development prediction indexes of several targeted sub-regions are compared horizontally to understand the differences in development of different local areas, which is convenient for optimizing the urban ecology and construction intensity layout.

[0098] like Figure 5 As shown, as another preferred embodiment of the present invention, on the other hand, a system for predicting urban forest ecological indicators includes:

[0099] An equal division module 100 is used to divide the area of ​​the targeted urban forest area into equal parts;

[0100] A first generating module 200 is used to generate a plurality of sub-regions;

[0101] An acquisition module 300 is used to obtain a greening ratio value of a corresponding sub-area;

[0102] A matching module 400 is used to match sub-regions whose greening ratio values ​​are within a deviation threshold;

[0103] A second generating module 500 is used to generate a plurality of targeted sub-regions;

[0104] A monitoring module 600 is used to set a signal sniffing point and several targeted monitoring points in each targeted sub-area;

[0105] A first calculation module 700 is configured to calculate a pedestrian flow value of each targeted sub-area based on a signal sniffing point;

[0106] A second calculation module 800 is configured to calculate the average ambient temperature of each targeted sub-area based on a plurality of targeted monitoring points;

[0107] The third generating module 900 is used to generate a corresponding target sub-region index value prediction coefficient based on the average ambient temperature of each target sub-region;

[0108] When this embodiment is applied, the dividing module 100 divides the area of ​​the targeted urban forest area into equal parts, the first generating module 200 generates several sub-areas, the acquiring module 300 acquires the greening ratio value of the corresponding sub-area, the matching module 400 matches the sub-area whose greening ratio value is within the deviation threshold, the second generating module 500 generates several targeted sub-areas, the monitoring module 600 sets a signal sniffing point and several targeted monitoring points in each targeted sub-area, based on the signal sniffing point, the first calculating module 700 calculates the pedestrian flow value of each targeted sub-area, based on the several targeted monitoring points, the second calculating module 800 calculates the average ambient temperature of each targeted sub-area, based on the average ambient temperature of each targeted sub-area, the third generating module 900 generates the corresponding targeted sub-area index value prediction coefficient.

[0109] like Figure 6 As shown, as another preferred embodiment of the present invention, the first calculation module 700 specifically includes:

[0110] A matching unit 701 is provided to set and match the signal range of the targeted sub-region;

[0111] A monitoring unit 702 is configured to monitor Bluetooth signals within a signal range of a targeted sub-area;

[0112] Recording unit 703, used to record the number of occurrences of Bluetooth signals within a target time;

[0113] The first generating unit 704 is configured to generate a targeted sub-area pedestrian flow value;

[0114] When this embodiment is applied, the matching unit 701 is set to set and match the signal range of the targeted sub-area, the monitoring unit 702 monitors the Bluetooth signal within the signal range of the targeted sub-area, the recording unit 703 records the number of occurrences of the Bluetooth signal within the target time, and the first generating unit 704 generates the traffic value of the targeted sub-area.

[0115] like Figure 7 As shown, as another preferred embodiment of the present invention, the third generating module 900 specifically includes:

[0116] Identification unit 901, used to identify the built-up area to which the targeted urban forest area belongs;

[0117] An acquisition unit 902 is configured to acquire the ambient temperature value of the built-up area and the average ambient temperature value of the targeted sub-area;

[0118] The first calculation unit 903 is used to calculate the difference between the ambient temperature value of the built-up area and the average ambient temperature of the target sub-area;

[0119] The second generating unit 904 is used to generate a heat island effect intensity value;

[0120] The third generating unit 905 is configured to generate a heat island effect intensity value sequence based on the heat island effect intensity values ​​in order from weak to strong;

[0121] When this embodiment is applied, the identification unit 901 identifies the built-up area to which the targeted urban forest area belongs, the acquisition unit 902 obtains the ambient temperature value of the built-up area and the average ambient temperature of the targeted sub-area, the first calculation unit 903 calculates the difference between the ambient temperature value of the built-up area and the average ambient temperature of the targeted sub-area, the second generation unit 904 generates a heat island effect intensity value, and based on the heat island effect intensity value, the third generation unit 905 generates a heat island effect intensity value sequence in order from weak to strong.

[0122] like Figure 8 As shown, as another preferred embodiment of the present invention, the third generating module 900 specifically further includes:

[0123] An extraction unit 906 is used to extract plant species classifications within a plurality of targeted sub-regions;

[0124] A fourth generating unit 907 is configured to generate a representative targeted sub-region based on the heat island effect intensity value sequence;

[0125] A fifth generating unit 908 is configured to generate a sequence of plant planting amounts within the target sub-region in an order of most to least;

[0126] An acquisition unit 909 is used to acquire the mainstream plant species in the plant planting amount sequence within the domain;

[0127] The second calculation unit 910 is used to calculate the planting area ratio of the mainstream plant species in several targeted sub-areas ;

[0128] The sixth generating unit 911 is used to generate the value of the planting area ratio based on the planting area ratio. , generate the prediction coefficient of the target sub-region index value;

[0129] When this embodiment is applied, the extraction unit 906 extracts the classification of plant species within the domain of several targeted sub-areas, and based on the heat island effect intensity value sequence, the fourth generation unit 907 generates a representative targeted sub-area, and in order from most to least, the fifth generation unit 908 generates a sequence of plant planting amounts within the domain representing the targeted sub-area, the acquisition unit 909 obtains the mainstream plant species in the sequence of plant planting amounts within the domain, and the second calculation unit 910 calculates the proportion of the planting area of ​​the mainstream plant species in the several targeted sub-areas. , based on the percentage of planted area , the sixth generating unit 911 generates a prediction coefficient of the target sub-region index value.

[0130] The above embodiment of the present invention provides an urban forest ecological indicator prediction method and an urban forest ecological indicator prediction system. First, the urban ecological area to be predicted is divided into equal areas to generate several sub-areas. The area of ​​each targeted sub-area is consistent, and the greening ratio value of the corresponding sub-area is obtained. In each sub-area, several kinds of plants are planted to form an urban forest ecology. A deviation threshold is preset. The greening ratio value is the ratio of the green area of ​​the sub-area to the total area. The sub-areas with greening ratio values ​​within the deviation threshold are matched to generate several targeted sub-areas. Sub-areas with similar green areas are set as targeted sub-areas. Signal sniffing points and several There are several targeted monitoring points, and temperature monitoring equipment is set up at several monitoring points to calculate the average ambient temperature of each targeted sub-area. That is, after collecting temperature information of multiple targeted monitoring points, the average calculation is performed, and the average value can be more representative. Then the built-up area to which the predicted urban ecological area belongs is identified, and the real-time ambient temperature value of the built-up area is obtained. Then, the heat island effect intensity value is calculated and generated. A Bluetooth detection device is set up at the signal sniffing point. The Bluetooth detectors deployed in the area are used to scan the surrounding mobile devices with Bluetooth turned on. When the device enters the signal range of the detector, it will be detected and recorded. The flow of people can be counted according to the strength of the Bluetooth signal and the time when the device appears. The greater the flow of people, the more likely it is that the targeted area is getting hot. The higher the economic development of the sub-region and the degree of benefit to the masses, the corresponding target sub-region index value prediction coefficient is generated based on the mean ambient temperature of each targeted sub-region. By obtaining the ambient temperature value of the targeted built-up area, the heat island effect intensity value is calculated and a heat island effect intensity value sequence is generated. Then, the plant classifications in several targeted sub-regions are extracted to generate the corresponding mainstream plant planting amount sequence in the domain. Based on the heat island effect intensity value sequence and the mainstream plant planting amount sequence in the domain, the corresponding target sub-region index value prediction coefficient is generated. That is, the specific impact of the urban heat island effect in the region is judged by the plant species with the largest planting amount in the targeted sub-region. Finally, based on the heat island effect intensity value sequence and the mainstream plant planting amount sequence in the domain, the corresponding target sub-region index value prediction coefficient is generated. The method and system divide the prediction area into sub-regions, and predict the development of ecological indicators in the overall prediction area through sub-regions, which helps to gain a deeper understanding of the differences in heat island effects in different local areas, and provides detailed data support for the targeted formulation of heat island effect mitigation strategies. It can screen out the most effective plant species for mitigating the heat island effect, and facilitate their key promotion and planting in urban greening planning, thereby optimizing the urban ecological environment layout. It helps urban planners give priority to the transformation and optimization of areas with high social development potential when carrying out urban renewal and public space planning, so that urban construction can better meet the actual needs of citizens.

[0131] In order to enable the above-mentioned method and system to be loaded and run smoothly, in addition to the various modules mentioned above, the system may also include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, processors and memories, etc.

[0132] The processor may be a central processing unit, other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the system, connecting various components using various interfaces and lines.

[0133] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting urban forest ecological indicators, characterized in that: The method comprises: The targeted urban forest area is divided equally into several sub-areas, and the greening ratio values ​​of the corresponding sub-areas are obtained; Match sub-regions whose greening ratio values ​​are within the deviation threshold to generate several targeted sub-regions; A signal sniffing point and several targeted monitoring points are set up in each targeted sub-area; Based on the signal sniffing point, calculate the pedestrian flow value of each targeted sub-area; Based on several targeted monitoring points, the mean ambient temperature of each targeted sub-area is calculated; Based on the mean ambient temperature of each targeted sub-region, a corresponding prediction coefficient of the targeted sub-region index value is generated; Calculating the pedestrian flow value of each targeted sub-area based on the signal sniffing point specifically includes: Setting and matching the signal range of the targeted subregion; Monitor Bluetooth signals within the signal range of the targeted sub-area; Record the number of Bluetooth signal occurrences within the target time and generate the pedestrian flow value of the targeted sub-area; The generating of the corresponding target sub-region index value prediction coefficient based on the mean ambient temperature of each target sub-region specifically includes: Identify the built-up areas within which the targeted urban forest areas belong; Obtain the ambient temperature value of the built-up area and the average ambient temperature of the targeted sub-area; Calculate the difference between the ambient temperature of the built-up area and the mean ambient temperature of the targeted sub-area to generate the intensity value of the heat island effect; Based on the heat island effect intensity value, a heat island effect intensity value sequence is generated in order from weak to strong; The step of generating a corresponding target sub-region index value prediction coefficient based on the average ambient temperature of each target sub-region specifically includes: Extracting plant species classification within several targeted sub-regions; Based on the heat island effect intensity value sequence, representative targeted sub-areas are generated; Generate a plant planting amount sequence within the domain representing the targeted sub-region in order from most to least; Obtain the dominant plant species in the plant planting amount series within the domain; Calculate the proportion of planting area of ​​mainstream plant species in several targeted sub-regions ; Based on the proportion of planted area , generate the prediction coefficient of the target sub-region index value.

2. An urban forest ecological index prediction system, characterized in that: Applying the urban forest ecological indicator prediction method according to claim 1, the system comprises: The equal division module is used to divide the area of ​​the targeted urban forest area into equal parts; A first generating module is used to generate a plurality of sub-regions; An acquisition module is used to obtain the greening ratio value of the corresponding sub-area; A matching module, for matching sub-regions whose greening ratio values ​​are within a deviation threshold; The second generation module is used to generate several targeted sub-regions; A monitoring module is used to set a signal sniffing point and several targeted monitoring points in each targeted sub-area; A first calculation module is used to calculate the pedestrian flow value of each targeted sub-area based on the signal sniffing point; A second calculation module is used to calculate the average ambient temperature of each targeted sub-area based on a number of targeted monitoring points; A third generation module is used to generate a corresponding target sub-region index value prediction coefficient based on the average ambient temperature of each target sub-region; The first calculation module specifically includes: Setting a matching unit, for setting and matching a signal range of a targeted sub-region; A monitoring unit, used to monitor Bluetooth signals within the signal range of the targeted sub-area; A recording unit, used to record the number of occurrences of Bluetooth signals within a target time; A first generating unit is used to generate a targeted sub-area pedestrian flow value; The third generation module specifically includes: Identification unit, used to identify the built-up area to which the targeted urban forest area belongs; An acquisition unit, used to acquire the ambient temperature value of the built-up area and the average ambient temperature value of the targeted sub-area; The first calculation unit is used to calculate the difference between the ambient temperature value of the built-up area and the average ambient temperature of the target sub-area; The second generating unit is used to generate a heat island effect intensity value; A third generating unit is configured to generate a heat island effect intensity value sequence based on the heat island effect intensity value in order from weak to strong; The third generation module specifically further includes: An extraction unit, configured to extract plant species classifications within a plurality of targeted sub-regions; A fourth generating unit is used to generate a representative targeted sub-region based on the heat island effect intensity value sequence; A fifth generating unit is configured to generate a sequence of plant planting amounts within the domain representing the targeted sub-region in order of most to least; An acquisition unit, used for acquiring mainstream plant species in a plant planting amount sequence within a domain; The second calculation unit is used to calculate the planting area ratio of mainstream plant species in several targeted sub-areas ; The sixth generation unit is used to generate the value based on the proportion of planting area , generate the prediction coefficient of the target sub-region index value.

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