Urban forest ecological index prediction method and system

Abstract

The invention is suitable for the field of environmental evaluation, and provides an urban forest ecological index prediction method and system, and the method comprises the steps: equally dividing the area of a targeted urban forest region, generating a plurality of sub-regions, and obtaining the greening proportion values of the corresponding sub-regions; matching the sub-regions of which the greening proportion values are within the deviation threshold, and generating a plurality of targeted sub-regions; setting a signal sniffing point and a plurality of target monitoring points in each target sub-region; based on the signal sniffing points, calculating a human traffic value of each targeted sub-region; based on a plurality of target monitoring points, calculating an ambient temperature mean value of each target sub-region, generating a corresponding target sub-region index value prediction coefficient, and predicting the ecological index development condition of the whole prediction region through the sub-regions, so that the heat island effect difference of different local regions can be deeply understood, and the prediction accuracy is improved. And meanwhile, the social development index of each sub-region can be comprehensively evaluated, so that the urban construction better meets the actual requirements of citizens.

Description

Technical Field

[0001] The 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 rural people have migrated to cities. The limited urban land has been carrying more and more people, which has exceeded its original carrying capacity, leading to the increasing damage to the urban ecological environment. However, in recent years, urban greening has received more and more attention, and street cleaning and greening projects have become normal work. As the main framework of urban greening, urban forests can not only improve the urban environment, but also demonstrate the confidence and vitality of the city.

[0003] Urban forests have multiple ecological functions, including energy conservation, carbon fixation and carbon dioxide absorption, air quality improvement, rainwater interception, and aesthetics. These functions are crucial to improving the urban environment and the quality of life of residents. However, the existing urban forest ecological indicator assessment methods are relatively simplistic, and the assessment results are inaccurate, making it difficult to fully reflect the current status and development trend of urban forest ecological indicators.

[0004] Therefore, it is particularly important to develop an accurate and comprehensive method for evaluating the current status and predicting the development of urban forest ecological indicators. This method needs to comprehensively consider multiple ecological indicators of urban forests, and through scientific data collection and analysis, accurately evaluate and predict urban forest ecological indicators, and provide 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 implemented in this way. On the one hand, a method for predicting urban forest ecological indicators 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 in each targeted sub-area; Based on the signal sniffing point, calculate the traffic 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 value of the ambient temperature of each targeted sub-region, a corresponding prediction coefficient of the index value of the targeted sub-region is generated; 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.

[0007] As a further solution of the present invention, the calculation of the flow of people in each targeted sub-area based on the signal sniffing point specifically includes: Setting and matching the signal range of the targeted sub-region; Monitor Bluetooth signals within the signal range of the targeted sub-area; The number of occurrences of Bluetooth signals within the target time is recorded to generate the flow value of people in the targeted sub-area.

[0008] As a further solution of the present invention, generating the corresponding target sub-region index value prediction coefficient based on the mean value of the 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 value of the built-up area and the average ambient temperature of the targeted sub-area to generate the intensity value of the heat island effect; Based on the heat island effect intensity values, a heat island effect intensity value sequence is generated in order from weak to strong.

[0009] As a further solution of the present invention, the generating of the corresponding target sub-region index value prediction coefficient based on the mean value of the ambient temperature of each target sub-region specifically includes: Extracting plant species classification within a domain for several targeted sub-regions; Based on the sequence of heat island effect intensity values, representative targeted sub-areas are generated; Generate a sequence of plant planting amounts within the domain representing the targeted sub-region in order from most to least; Obtain the mainstream plant species in the plant planting amount sequence within the domain; Calculate the planting area ratio of mainstream plant species in several targeted sub-areas ; Based on the percentage of planted area , generate the prediction coefficient of the index value of the targeted sub-region.

[0010] As a further solution of the present invention, on the other hand, a system for predicting urban forest ecological indicators 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; A second generation module is used to generate a plurality of targeted sub-regions; A monitoring module, 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 human 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; The 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.

[0011] As a further solution of the present invention, the first calculation module specifically includes: Setting a matching unit, used for setting and matching a signal range of a targeted sub-region; A monitoring unit, used to monitor Bluetooth signals within a signal range of a targeted sub-area; A recording unit, used to record the number of occurrences of the Bluetooth signal within a target time; The first generating unit is used to generate a targeted sub-area pedestrian flow value.

[0012] As a further solution of the present invention, 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 targeted sub-area; The second generating unit is used to generate a heat island effect intensity value; The third generating unit is used to generate a heat island effect intensity value sequence in order from weak to strong based on the heat island effect intensity value.

[0013] As a further solution of the present invention, the third generation module specifically further includes: An extraction unit, used for extracting plant species classifications within a region of a plurality of targeted sub-regions; A fourth generating unit is used to generate a representative targeted sub-area based on the heat island effect intensity value sequence; A fifth generating unit, for generating a sequence of plant planting amounts within a region representing a targeted sub-region in an 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 planting area ratio , generate the prediction coefficient of the target sub-region index value.

[0014] 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 is helpful to deeply understand the differences in urban heat island effects in different local areas, and provides detailed data support for the targeted formulation of strategies to alleviate the urban heat island effect. The most effective plant species for alleviating the urban heat island effect can be screened out, which is convenient for 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

[0015] Figure 1 It is a main flow chart of a method for predicting ecological indicators of urban forests.

[0016] Figure 2 The present invention is a flowchart of calculating the human flow value of each targeted sub-area based on the signal sniffing point in the urban forest ecological index prediction method.

[0017] 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 value of the ambient temperature of each target sub-region.

[0018] 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 value of the ambient temperature of each target sub-region.

[0019] Figure 5 It is a main structure diagram of an urban forest ecological index prediction system.

[0020] Figure 6 The present invention is a structural block diagram of the first calculation module in an urban forest ecological index prediction system.

[0021] Figure 7 The present invention is a structural block diagram of a first embodiment of a third generation module in an urban forest ecological index prediction system.

[0022] 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

[0023] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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.

[0024] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0025] The invention provides an urban forest ecological index prediction method and system, which solves the technical problems in the background technology.

[0026] like Figure 1 FIG. 1 is a main flow chart of a method for predicting an urban forest ecological index provided by an embodiment of the present invention. The method for predicting an urban forest ecological index includes: Step S100: dividing the area of ​​the targeted urban forest area into equal parts, generating a number of sub-areas, and obtaining greening ratio values ​​of the corresponding sub-areas; Step S200: matching sub-regions whose greening ratio values ​​are within the deviation threshold to generate a plurality of targeted sub-regions; Step S300: setting a signal sniffing point and a plurality of targeted monitoring points in each targeted sub-area; Step S400: Calculate the traffic value of each targeted sub-area based on the signal sniffing point; Step S500: Calculate the mean ambient temperature of each targeted sub-area based on a number of targeted monitoring points; Step S600: generating a corresponding target sub-region index value prediction coefficient based on the mean value of the ambient temperature of each target sub-region; When this embodiment is applied, the urban ecological area to be predicted is first divided into equal parts to generate several sub-areas, each of which has the same area as the targeted sub-areas, 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 greening 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, that is, sub-areas with similar greening areas are set as targeted sub-areas, and a signal sniffing point and several targeted monitoring points are set in each targeted sub-area. Temperature monitoring equipment is set at several monitoring points to calculate the average ambient temperature of each targeted sub-area, that is, after collecting temperature information from multiple targeted monitoring points, the average is calculated, 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 function 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 larger 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 mean 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 classifications in the domain of several targeted sub-areas are extracted to generate the corresponding mainstream plant planting amount sequence in the domain. Then, based on the heat island effect intensity value sequence and the mainstream plant planting amount sequence in 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 is judged. Finally, based on the heat island effect intensity value sequence and the mainstream plant planting amount sequence in the domain, the corresponding targeted sub-area index value prediction coefficient is quantitatively generated.

[0027] like Figure 2 As shown, as a preferred embodiment of the present invention, the calculation of the flow value of people in each targeted sub-area based on the signal sniffing point specifically includes: Step S401: Setting and matching the signal range of the targeted sub-region; Step S402: monitoring Bluetooth signals within the signal range of the targeted sub-area; Step S403: Record the number of occurrences of the Bluetooth signal within the target time, and generate a flow value of people in the targeted sub-area; 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 function 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 appearance time of the device to generate the flow of people in the targeted sub-area.

[0028] like Figure 3 As shown, as a preferred embodiment of the present invention, the generation of the corresponding target sub-region index value prediction coefficient based on the mean value of the ambient temperature of each target sub-region specifically includes: Step S601: Identify the built-up area to which the targeted urban forest area belongs; Step S602: Obtain the ambient temperature value of the built-up area and the average ambient temperature of the targeted sub-area; 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; Step S604: generating a heat island effect intensity value sequence based on the heat island effect intensity values ​​in order from weak to strong; 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 the temperature, and the monitoring time can be set in July-August each year. This period is summer, and the urban heat island effect is more obvious, which is conducive to improving the prediction accuracy.

[0029] like Figure 4 As shown, as a preferred embodiment of the present invention, the generation of the corresponding target sub-region index value prediction coefficient based on the mean value of the ambient temperature of each target sub-region specifically includes: Step S605: extracting plant species classifications within a plurality of targeted sub-regions; Step S606: generating representative targeted sub-regions based on the heat island effect intensity value sequence; Step S607: generating a sequence of plant planting amounts in the region representing the targeted sub-region in order of most to least; Step S608: Obtaining the mainstream plant species in the plant planting amount sequence in the domain; Step S609: Calculate the planting area ratio of mainstream plant species in several targeted sub-areas ; Step S610: Based on the planting area ratio , generate the prediction coefficient of the index value of the targeted sub-region; It should be understood that, first, the classification of plant species in the domain of several targeted sub-areas is extracted, and based on the sequence of heat island effect intensity values, a representative targeted sub-area is generated, and the representative targeted sub-area is the targeted sub-area with the weakest heat island effect intensity. In order from most to least, a sequence of plant planting amount in the domain representing the targeted sub-area is generated, and the mainstream plant species in the sequence of plant planting amount in the domain are obtained. The mainstream plant species is the plant species with the largest planting amount in the plant planting amount in the domain, and the planting area proportion of the mainstream plant species in several targeted sub-areas is calculated. , the proportion of planting area The calculation process is as follows: the planting area of ​​mainstream plant species in the targeted sub-region / the total greening area of ​​the targeted sub-region, and the planting area ratio It is the prediction coefficient of the index value of the targeted sub-region.

[0030] 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 horizontally compared to understand the differences in development of different local areas, so as to optimize the urban ecology and construction intensity layout.

[0031] 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 comprises: An equal division module 100 is used to divide the area of ​​a targeted urban forest area into equal parts; A first generating module 200 is used to generate a plurality of sub-regions; An acquisition module 300 is used to acquire a greening ratio value of a corresponding sub-area; A matching module 400, for matching sub-regions whose greening ratio values ​​are within a deviation threshold; A second generating module 500 is used to generate a plurality of targeted sub-regions; A monitoring module 600 is used to set a signal sniffing point and a plurality of targeted monitoring points in each targeted sub-area; A first calculation module 700 is used to calculate the human flow value of each targeted sub-area based on the signal sniffing point; The second calculation module 800 is used to calculate the average value of the ambient temperature of each targeted sub-area based on a plurality of targeted monitoring points; The third generating module 900 is used to generate a corresponding target sub-region index value prediction coefficient based on the average value of the ambient temperature of each target sub-region; 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 a plurality of 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 a plurality of targeted sub-areas, the monitoring module 600 sets a signal sniffing point and a plurality of targeted monitoring points in each targeted sub-area, based on the signal sniffing point, the first calculating module 700 calculates the flow of people in each targeted sub-area, based on the plurality of targeted monitoring points, the second calculating module 800 calculates the mean ambient temperature of each targeted sub-area, based on the mean ambient temperature of each targeted sub-area, the third generating module 900 generates the corresponding targeted sub-area index value prediction coefficient.

[0032] like Figure 6 As shown, as another preferred embodiment of the present invention, the first calculation module 700 specifically includes: Setting a matching unit 701, used to set and match the signal range of the targeted sub-area; A monitoring unit 702, used to monitor Bluetooth signals within the signal range of the targeted sub-area; A recording unit 703, used to record the number of occurrences of the Bluetooth signal within a target time; The first generating unit 704 is used to generate a targeted sub-area flow value; 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 flow value of people in the targeted sub-area.

[0033] like Figure 7 As shown, as another preferred embodiment of the present invention, the third generating module 900 specifically includes: An identification unit 901 is used to identify the built-up area to which the targeted urban forest area belongs; An acquisition unit 902 is 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 903 is used to calculate the difference between the ambient temperature value of the built-up area and the average ambient temperature of the targeted sub-area; The second generating unit 904 is used to generate a heat island effect intensity value; The third generating unit 905 is used to generate a heat island effect intensity value sequence in order from weak to strong based on the heat island effect intensity value; 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 acquires 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 the 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.

[0034] like Figure 8 As shown, as another preferred embodiment of the present invention, the third generating module 900 specifically further includes: An extraction unit 906 is used to extract plant species classifications within a plurality of targeted sub-regions; A fourth generating unit 907 is used to generate a representative targeted sub-region based on the heat island effect intensity value sequence; A fifth generating unit 908 is used to generate a sequence of plant planting amounts within the domain representing the targeted sub-region in an order of most to least; An acquisition unit 909 is used to acquire the mainstream plant species in the plant planting amount sequence in the domain; The second calculation unit 910 is used to calculate the planting area ratio of the mainstream plant species in several targeted sub-areas ; The sixth generating unit 911 is used to generate the value based on the planting area ratio , generate the prediction coefficient of the index value of the targeted sub-region; When this embodiment is applied, the extraction unit 906 extracts the classification of plant species in the domain of several targeted sub-areas, 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 in the domain representing the targeted sub-area, the acquisition unit 909 acquires the mainstream plant species in the sequence of plant planting amounts in the domain, and the second calculation unit 910 calculates the planting area ratio of the mainstream plant species in several targeted sub-areas. , based on the percentage of planted area , the sixth generating unit 911 generates a prediction coefficient of a targeted sub-region index value.

[0035] The above embodiment of the present invention provides a method for predicting urban forest ecological indicators, and provides a system for predicting urban forest ecological indicators. First, the urban ecological area to be predicted is divided into equal areas to generate a number of sub-areas, each targeted sub-area has the same area, 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 greening 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 a number of targeted sub-areas, that is, sub-areas with similar greening areas are set as targeted sub-areas, and a signal sniffing point and a number of signal sniffing points are set in each targeted sub-area. 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 from multiple targeted monitoring points, the average is calculated, 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 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 larger the flow of people, the more likely it is that the targeted area is in a hot spot. The higher the economic development of the sub-region and the degree of benefit to the masses, the corresponding targeted sub-region index value prediction coefficient is generated based on the mean ambient temperature of each targeted sub-region. The heat island effect intensity value is calculated and a heat island effect intensity value sequence is generated by obtaining the ambient temperature value of the targeted built-up area. Then, the plant classifications in the domain of 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 targeted 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 targeted 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 deeply understand the differences in urban heat island effects in different local areas, and provides detailed data support for the targeted formulation of strategies to mitigate the urban heat island effect. It can screen out the most effective plant species for mitigating the urban heat island effect, and facilitate 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.

[0036] In order to load the above-mentioned method and system and enable it to run smoothly, the system, in addition to the various modules mentioned above, 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.

[0037] The processor may be a central processing unit, or 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. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the system, and various interfaces and lines are used to connect various parts.

[0038] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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.

[0039] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0040] 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 protection scope 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 in each targeted sub-area; Based on the signal sniffing point, calculate the traffic 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 targeted sub-region index value prediction coefficient is generated.

2. The urban forest ecological index prediction method according to claim 1, characterized in that: The calculation of the human flow value of each targeted sub-area based on the signal sniffing point specifically includes: Setting and matching the signal range of the targeted sub-region; Monitor Bluetooth signals within the signal range of the targeted sub-area; The number of occurrences of Bluetooth signals within the target time is recorded to generate the flow value of people in the targeted sub-area.

3. The urban forest ecological index prediction method according to claim 1 is characterized in that: The generating of the corresponding target sub-region index value prediction coefficient based on the mean value of the 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 value of the built-up area and the average ambient temperature of the targeted sub-area to generate the intensity value of the heat island effect; Based on the heat island effect intensity values, a heat island effect intensity value sequence is generated in order from weak to strong.

4. The urban forest ecological index prediction method according to claim 3 is characterized in that: The generating of the corresponding target sub-region index value prediction coefficient based on the mean value of the ambient temperature of each target sub-region specifically includes: Extracting plant species classification within a domain for several targeted sub-regions; Based on the sequence of heat island effect intensity values, representative targeted sub-areas are generated; Generate a sequence of plant planting amounts within the domain representing the targeted sub-region in order from most to least; Obtain the mainstream plant species in the plant planting amount sequence within the domain; Calculate the planting area ratio of mainstream plant species in several targeted sub-areas ; Based on the percentage of planted area , generate the prediction coefficient of the target sub-region index value.

5. An urban forest ecological index prediction system, characterized in that: The method for predicting urban forest ecological indicators according to any one of claims 1 to 4 is applied, wherein 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; A second generation module is used to generate a plurality of targeted sub-regions; A monitoring module, 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 human 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; The 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.

6. The urban forest ecological index prediction system according to claim 5, characterized in that: The first calculation module specifically includes: Setting a matching unit, used for setting and matching a signal range of a targeted sub-region; A monitoring unit, used to monitor Bluetooth signals within a signal range of a targeted sub-area; A recording unit, used to record the number of occurrences of the Bluetooth signal within a target time; The first generating unit is used to generate a targeted sub-area pedestrian flow value.

7. The urban forest ecological index prediction system according to claim 5, characterized in that: 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 targeted sub-area; The second generating unit is used to generate a heat island effect intensity value; The third generating unit is used to generate a heat island effect intensity value sequence in order from weak to strong based on the heat island effect intensity value.

8. The urban forest ecological index prediction system according to claim 7, characterized in that: The third generation module specifically includes: An extraction unit, used for extracting plant species classifications within a region of a plurality of targeted sub-regions; A fourth generating unit is used to generate a representative targeted sub-area based on the heat island effect intensity value sequence; A fifth generating unit, for generating a sequence of plant planting amounts within a region representing a targeted sub-region in an 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 planting area ratio , generate the prediction coefficient of the target sub-region index value.

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