Urban green land ecological service value prediction method and system

Abstract

The invention is suitable for the field of urban ecological evaluation, and provides an urban green land ecological service value prediction method and system, and the method comprises the steps: collecting vegetation physiological parameters, green land spatial layout, and social behavior data multi-source information, and building a multi-dimensional database; based on the multi-dimensional database, respectively constructing targeted quantitative calculation models based on different data and models; coupling quantitative calculation results of the three benefits to generate a comprehensive prediction system; and generating a comprehensive service value prediction result based on a comprehensive prediction system, and the method and system have the beneficial effects that the method and system accurately quantify the invisible benefits of the urban green land in the aspects of carbon neutralization support, emergency storage, community cohesion and the like through multi-source data fusion and an interdisciplinary model. A multi-dimensional database and an exclusive calculation model are constructed and coupled to form a comprehensive prediction system, so that the blind area of invisible value evaluation of a traditional method is broken through, a scientific basis is provided for urban planning, and optimization of greenbelt resource allocation is assisted.

Description

Technical Field

[0001] The present invention belongs to the field of urban ecological assessment, and in particular relates to a method and system for predicting the ecological service value of urban green space. Background Art

[0002] As a core component of urban ecosystems, the ecological service value of urban green spaces has become a key consideration in urban planning and sustainable development. Traditional assessment methods, through remote sensing monitoring, GIS spatial analysis, and ecological modeling, have developed relatively mature quantification systems for explicit ecological services such as air purification and stormwater interception provided by green spaces. However, existing technologies primarily focus on directly observable functions or those with significant short-term benefits, while generally overlooking the implicit value of urban green spaces.

[0003] With the acceleration of urbanization, the evaluation system that relies solely on explicit value has led to a series of planning imbalances. To address the above problems, it is urgent to invent a prediction method and system that can integrate multi-source data and fuse interdisciplinary models to achieve scientific prediction of the value of urban green space service benefits. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for predicting the ecological service value of urban green space, aiming to solve the problems raised in the above background technology.

[0005] The present invention is implemented as follows: on the one hand, a method for predicting the ecological service value of urban green space, the method comprising:

[0006] Collect multi-source information on vegetation physiological parameters, green space layout, and social behavior data to establish a multidimensional database;

[0007] Based on the multidimensional database, targeted quantitative calculation models based on different data and models are constructed;

[0008] Couple the quantitative calculation results of the three types of benefits to generate a comprehensive prediction system;

[0009] Generate comprehensive service value prediction results based on the comprehensive prediction system;

[0010] Wherein, the targeted quantitative calculation model includes:

[0011] A carbon neutrality support benefit quantification model that integrates vegetation carbon sequestration data and soil carbon pool dynamics data;

[0012] An emergency reserve benefit assessment model that couples hydrological dynamics data with climate response data;

[0013] A community cohesion benefit measurement model based on social network analysis data.

[0014] As a further solution of the present invention, the quantitative calculation models based on different data and models are constructed based on the multidimensional database for carbon neutrality support benefits, emergency reserve benefits, and community cohesion benefits, specifically including:

[0015] Based on vegetation carbon sequestration data, the vegetation type and leaf area index were inverted using hyperspectral remote sensing, combined with the measured biomass data of targeted urban green spaces, to determine the first Annual biomass increment per unit area of ​​vegetation type ;

[0016] The IPCC carbon conversion coefficient standard based on soil carbon pool dynamic data is differentiated by evergreen, deciduous, and herbaceous vegetation types. , combined with GIS precise mapping of vegetation area and forecast period , calculate the vegetation carbon sink value ;

[0017] The carbon sequestration value of vegetation The calculation process is:

[0018] ;

[0019] Where, For the Green space vegetation area, is the total number of categories of green vegetation areas;

[0020] Calculating soil organic carbon storage , combined with the real-time price of regional carbon trading market , calculate the soil carbon pool service value ;

[0021] The soil carbon pool service value The calculation process is:

[0022] ;

[0023] Where, For the Green land area, is the total number of green land soil areas;

[0024] Based on the carbon sequestration value of vegetation and soil carbon pool service value , calculate the carbon neutrality support benefit value ;

[0025] The carbon neutrality support benefit value The calculation process is:

[0026] .

[0027] As a further solution of the present invention, the quantitative calculation models based on different data and models are constructed for the carbon neutrality support benefits, emergency reserve benefits, and community cohesion benefits based on the multidimensional database, and specifically further include:

[0028] Calculate the green space rainwater storage capacity based on the rainstorm frequency recurrence data based on hydrodynamic data, combined with the municipal drainage cost , calculate flood storage benefit value ;

[0029] The flood storage benefit value The calculation process is:

[0030] ;

[0031] Where, For the The rainfall of the rainstorm, is the total number of rainstorms, To target green space, For the The canopy interception of the rainstorm, , is the retention rate, For the Soil infiltration during a rainstorm , is the saturated hydraulic conductivity, is the infiltration duration, is the underlying surface runoff coefficient;

[0032] Determine the shade area of ​​green space under high temperature weather based on climate response data and cooling effect coefficient , combined with the unit cooling energy cost , calculate the energy saving value ;

[0033] Energy saving value The calculation process is:

[0034] ;

[0035] Where, Number the high temperature period. is the total number of high temperature periods, is the duration of the high temperature period, The shade area of ​​green space in hot weather. is the cooling effect coefficient, is the unit cooling energy cost;

[0036] Based on flood storage benefit value and energy saving value , calculate the emergency reserve benefit value ;

[0037] The emergency reserve benefit value The calculation process is:

[0038] .

[0039] As a further solution of the present invention, the quantitative calculation models based on different data and models for carbon neutrality support benefits, emergency reserve benefits, and community cohesion benefits are constructed based on a multidimensional database and specifically include:

[0040] Based on social network analysis data, smart sensors are used to collect the average monthly frequency of green space use. , Number of participants in a single activity and duration ;

[0041] Combined with community population , calculate the basic interaction index ;

[0042] The basic interaction index The calculation process is:

[0043] ;

[0044] Where, is the population density correction factor, is the unit social capital monetization coefficient.

[0045] As a further solution of the present invention, the quantitative calculation results of the coupling three types of benefits are used to generate a comprehensive prediction system:

[0046] Obtain carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index ;

[0047] Based on carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index , calculate the comprehensive value forecast ;

[0048] The comprehensive value forecast The calculation process is:

[0049] ;

[0050] Where, Supporting benefit value for carbon neutrality , emergency reserve benefit value and basic interaction index The corresponding weight coefficient, and .

[0051] As a further embodiment of the present invention, in another aspect, a system for predicting the ecological service value of urban green space is provided, the system comprising:

[0052] The acquisition module is used to collect multi-source information on vegetation physiological parameters, green space layout, and social behavior data to establish a multidimensional database;

[0053] Targeted quantitative calculation model module, used to build targeted quantitative calculation models based on different data and models based on a multidimensional database;

[0054] The first generation module is used to couple the quantitative calculation results of the three types of benefits to generate a comprehensive prediction system;

[0055] The second generation module is used to generate comprehensive service value prediction results based on the comprehensive prediction system.

[0056] As a further solution of the present invention, the targeted quantitative calculation model module specifically includes:

[0057] The determination unit is used to determine the first Annual biomass increment per unit area of ​​vegetation type ;

[0058] The first calculation unit is used to calculate the IPCC carbon conversion coefficient standard based on soil carbon pool dynamic data, with differentiated values ​​according to evergreen, deciduous, and herbaceous vegetation types. , combined with GIS precise mapping of vegetation area and forecast period , calculate the vegetation carbon sink value ;

[0059] The second calculation unit is used to calculate the carbon sequestration value of vegetation and soil carbon pool service value , calculate the carbon neutrality support benefit value .

[0060] As a further solution of the present invention, the targeted quantitative calculation model module specifically further includes:

[0061] The third calculation unit is used to calculate the green space rainwater storage capacity based on the hydrological dynamics data and the rainstorm frequency recurrence period data;

[0062] The fourth calculation unit is used to combine municipal drainage costs , calculate flood storage benefit value ;

[0063] The fifth calculation unit is used to determine the shade area of ​​green space under high temperature weather based on climate response data and cooling effect coefficient , combined with the unit cooling energy cost , calculate the energy saving value ;

[0064] The sixth calculation unit is used to calculate the flood storage benefit value based on the flood storage benefit value. and energy saving value , calculate the emergency reserve benefit value.

[0065] As a further solution of the present invention, the targeted quantitative calculation model module specifically further includes:

[0066] The collection unit is used to collect the average monthly frequency of green space use based on social network analysis data using smart sensors , Number of participants in a single activity and duration ;

[0067] The seventh calculation unit is used to combine community population , calculate the basic interaction index ;

[0068] As a further solution of the present invention, the first generating module specifically includes:

[0069] Acquisition unit, used to obtain carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index ;

[0070] The seventh calculation unit is used to support the benefit value based on carbon neutrality , emergency reserve benefit value and basic interaction index , calculate the comprehensive value forecast ;

[0071] Obtain unit to obtain carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index , based on the carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index , the seventh calculation unit calculates the comprehensive value prediction value .

[0072] This invention provides a method and system for predicting the ecological service value of urban green spaces. By integrating multi-source data and integrating interdisciplinary models, this method and system accurately quantifies the hidden benefits of urban green spaces in areas such as carbon neutrality support, emergency reserves, and community cohesion. By combining a multidimensional database with a proprietary computational model to form a comprehensive prediction system, this system overcomes the blind spots in traditional methods for assessing hidden value, providing a scientific basis for urban planning and helping to optimize green space resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a main flow chart of a method for predicting the ecological service value of urban green space.

[0074] Figure 2 It is a flowchart of the first embodiment of a method for predicting the ecological service value of urban green space, in which Y constructs a quantitative calculation model based on different data and models for carbon neutrality support benefits, emergency reserve benefits, and community cohesion benefits based on a multidimensional database.

[0075] Figure 3 It is a flowchart of the second embodiment of a method for predicting the ecological service value of urban green space, in which Y constructs a quantitative calculation model based on different data and models for carbon neutrality support benefits, emergency reserve benefits, and community cohesion benefits based on a multidimensional database.

[0076] Figure 4 It is a flowchart of the third embodiment of a method for predicting the ecological service value of urban green space, in which Y constructs a quantitative calculation model based on different data and models for carbon neutrality support benefits, emergency reserve benefits, and community cohesion benefits based on a multidimensional database.

[0077] Figure 5 The present invention is a flow chart of a comprehensive prediction system generated by coupling the quantitative calculation results of three types of benefits in the urban green space ecological service value prediction method.

[0078] Figure 6 It is the main structure diagram of an urban green space ecological service value prediction system.

[0079] Figure 7 The present invention is a structural block diagram of the first embodiment of a targeted quantitative calculation model module in an urban green space ecological service value prediction system.

[0080] Figure 8 The present invention is a structural block diagram of the second embodiment of the targeted quantitative calculation model module in the urban green space ecological service value prediction system.

[0081] Figure 9 The present invention is a structural block diagram of the third embodiment of a targeted quantitative calculation model module in an urban green space ecological service value prediction system.

[0082] Figure 10 It is a structural block diagram of the first generation module in the urban green space ecological service value prediction system. DETAILED DESCRIPTION

[0083] 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.

[0084] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0085] The present invention provides a method and system for predicting the ecological service value of urban green space, which solves the technical problems in the background technology.

[0086] like Figure 1 FIG. 1 is a main flow chart of a method for predicting the ecological service value of an urban green space provided by an embodiment of the present invention. The method for predicting the ecological service value of an urban green space includes:

[0087] Step S100: Collect multi-source information on vegetation physiological parameters, green space layout, and social behavior data to establish a multidimensional database;

[0088] Step S200: Based on the multidimensional database, constructing targeted quantitative calculation models based on different data and models;

[0089] Step S300: coupling the quantitative calculation results of the three types of benefits to generate a comprehensive prediction system;

[0090] Step S400: generating a comprehensive service value prediction result based on the comprehensive prediction system;

[0091] Wherein, the targeted quantitative calculation model includes:

[0092] A carbon neutrality support benefit quantification model that integrates vegetation carbon sequestration data and soil carbon pool dynamics data;

[0093] An emergency reserve benefit assessment model that couples hydrological dynamics data with climate response data;

[0094] A community cohesion benefit measurement model based on social network analysis data.

[0095] When this embodiment is applied, it first widely collects multi-source information on vegetation physiological parameters, green space layout, and social behavior to build a comprehensive and dynamically updated multidimensional database. This database provides a solid data foundation for subsequent accurate predictions. Then, based on the multidimensional database, quantitative calculation models are constructed for different benefits. For the carbon neutrality support benefit, it is calculated based on the vegetation carbon sink dynamic model, combined with data such as vegetation type and leaf area index; the emergency reserve benefit is calculated with the help of the green space hydrological regulation and climate buffer model, with reference to data such as rainstorm frequency and green space permeability; the community cohesion benefit is based on social network analysis and spatial behavior models, combined with data such as green space use frequency and resident interaction intensity. Carbon neutrality support benefit assessment quantifies the carbon sequestration capacity and carbon cycle contribution of green space vegetation, providing a scientific basis for optimizing urban emission reduction pathways and decomposing carbon peak and carbon neutrality targets. It also supports the development of ecological compensation mechanisms and green finance innovation. Emergency reserve benefit assessment reveals the hidden service value of green space in emergency scenarios, such as flood control, high temperature buffering, and habitat protection. This supports urban resilience planning and reduces extreme weather risks and disaster losses. Community cohesion benefit assessment uses resident behavioral data to analyze the role of green space in promoting social interaction, mental health, and cultural identity. This helps guide humanistic green space design, enhance community belonging, and revitalize public spaces. The three synergistic assessments break the limitations of single-source ecological value accounting and promote the transformation of urban green space from "ecological islands" to "multidimensional service hubs." The quantitative calculation results of the three types of benefits are then coupled and weighted to form a comprehensive prediction system. This system can flexibly adjust the weight of each benefit based on different urban development stages and needs to adapt to diverse application scenarios. Finally, based on the comprehensive prediction system, a comprehensive value prediction of urban green space ecological services is generated.

[0096] like Figure 2 As shown, as a preferred embodiment of the present invention, the quantitative calculation models based on different data and models are constructed based on a multidimensional database for carbon neutrality support benefits, emergency reserve benefits, and community cohesion benefits, specifically including:

[0097] Step S201: Based on vegetation carbon sink data, use hyperspectral remote sensing to invert vegetation type and leaf area index, combined with measured biomass data of targeted urban green spaces, to determine the first Annual biomass increment per unit area of ​​vegetation type ;

[0098] Step S202: Based on the IPCC carbon conversion coefficient standard of soil carbon pool dynamic data, differentiated values ​​are taken according to evergreen, deciduous and herbaceous vegetation types. , combined with GIS precise mapping of vegetation area and forecast period , calculate the vegetation carbon sink value ;

[0099] The carbon sequestration value of vegetation The calculation process is:

[0100] ;

[0101] Where, For the Green space vegetation area, is the total number of categories of green vegetation areas;

[0102] Step S203: Calculate soil organic carbon storage , combined with the real-time price of regional carbon trading market , calculate the soil carbon pool service value ;

[0103] The soil carbon pool service value The calculation process is:

[0104] ;

[0105] Where, For the Green land area, is the total number of green land soil areas;

[0106] Step S204: Based on the vegetation carbon sink value and soil carbon pool service value , calculate the carbon neutrality support benefit value ;

[0107] The carbon neutrality support benefit value The calculation process is:

[0108] ;

[0109] When this embodiment is applied, first, the vegetation type and leaf area index are inverted using hyperspectral remote sensing technology, and then the measured biomass data of the targeted urban green space are combined to accurately determine the first The annual biomass increment per unit area of ​​vegetation is dynamically adjusted as the vegetation grows to ensure that the data fits the actual growth conditions. Secondly, based on the IPCC carbon conversion coefficient standard, differentiated values ​​are taken for different vegetation types such as evergreen, deciduous, and herbaceous. Combined with the vegetation area and prediction period accurately mapped by GIS, a quantitative assessment of the vegetation carbon sink value is achieved and the vegetation carbon sink value is calculated. Finally, by calculating the soil organic carbon reserves and introducing the real-time price of the regional carbon trading market, the soil carbon pool service value is calculated and the carbon neutrality support benefit value is calculated. , and then, the comprehensive vegetation carbon sequestration value and carbon neutrality support benefit value , calculate the carbon neutrality support benefit value .

[0110] like Figure 3 As shown, as a preferred embodiment of the present invention, the quantitative calculation models based on different data and models are constructed based on the multidimensional database for the carbon neutrality support benefit, emergency reserve benefit, and community cohesion benefit, and specifically also include:

[0111] Step S211: Calculate the green space rainwater storage capacity based on the rainstorm frequency recurrence period data based on the hydrodynamic data, combined with the municipal drainage cost , calculate flood storage benefit value ;

[0112] The flood storage benefit value The calculation process is:

[0113] ;

[0114] Where, For the The rainfall of the rainstorm, is the total number of rainstorms, To target green space, For the The canopy interception of the rainstorm, , is the retention rate, For the Soil infiltration during a rainstorm , is the saturated hydraulic conductivity, is the infiltration duration, is the underlying surface runoff coefficient;

[0115] Step S212: Determine the shaded area of ​​green space under high temperature weather based on climate response data and cooling effect coefficient , combined with the unit cooling energy cost , calculate the energy saving value ;

[0116] Energy saving value The calculation process is:

[0117] ;

[0118] Where, Number the high temperature period. is the total number of high temperature periods, is the duration of the high temperature period, The shade area of ​​green space in hot weather. is the cooling effect coefficient, is the unit cooling energy cost;

[0119] Step S213: Based on the flood storage benefit value and energy saving value , calculate the emergency reserve benefit value ;

[0120] The emergency reserve benefit value The calculation process is:

[0121] ;

[0122] When this embodiment is applied, in the hydrological regulation module, the rainstorm frequency recurrence period data is used as the basis. This data reflects the probability of occurrence of rainstorms of different intensities and provides support for simulating extreme rainfall scenarios. Through the canopy interception model, the amount of rainwater retained by the vegetation canopy is calculated based on the interception rate; with the help of the soil infiltration model, the saturated hydraulic conductivity parameter (characterizing the soil's permeability) is combined with the time variable to quantify the dynamic process of soil absorption of rainwater. The combination of the two can accurately calculate the amount of rainwater storage in green spaces, and then associate it with the municipal drainage cost to evaluate the flood storage benefit value of green spaces in response to rainstorms. The climate buffer module uses a combination of surface temperature inversion technology (and three-dimensional microclimate simulation to determine the shade area of ​​green space and the cooling effect coefficient under high temperature weather conditions) The former clarifies the spatial scope of green space cooling, while the latter quantifies the cooling efficiency of green space per unit area. Combined with the unit cooling energy consumption cost , calculate the energy saving value , intuitively showing the role of green space in alleviating the urban heat island effect. Comprehensive flood storage benefit value and energy saving value , calculate the emergency reserve benefit value , from optimizing existing green spaces to laying out new green spaces, and based on the assessment results, enhancing the city’s resilience to extreme weather such as heavy rain and high temperatures.

[0123] like Figure 4 As shown, as a preferred embodiment of the present invention, the quantitative calculation models based on different data and models are constructed based on the multidimensional database for the carbon neutrality support benefit, emergency reserve benefit, and community cohesion benefit, and specifically also include:

[0124] Step S221: Based on social network analysis data, use smart sensors to collect the average monthly frequency of green space use , Number of participants in a single activity and duration ;

[0125] Step S222: Combine community population , calculate the basic interaction index ;

[0126] The basic interaction index The calculation process is:

[0127] ;

[0128] Where, is the population density correction factor, is the unit social capital monetization coefficient;

[0129] It should be understood that relying on social network analysis data, with the help of smart sensors, the average monthly frequency of green space use can be accurately collected. , Number of participants in a single activity and duration These data directly show the actual utilization of green space in community life, with the average monthly usage frequency Reflects the residents’ reliance on green space and the frequency of use, as well as the number of participants in a single activity and duration It represents the scale of each activity and the depth of residents’ participation. On this basis, combined with the population size of the community , considering the impact of population factors on social interaction. By introducing the population density correction coefficient , adjust the impact caused by differences in population density to ensure that the assessment is more in line with the real community environment; unit social capital monetization coefficient Converting social interaction into quantifiable monetary value makes the evaluation results more intuitive and comparable, by calculating the basic interaction index , effectively measure the role of green space in promoting resident interaction and enhancing community cohesion, and provide important quantitative basis for urban green space planning and optimization.

[0130] like Figure 5 As shown in FIG. 1 , as a preferred embodiment of the present invention, the quantitative calculation results of the three types of benefits are coupled to generate a comprehensive prediction system:

[0131] Step S301: Obtaining carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index ;

[0132] Step S302: Based on the carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index , calculate the comprehensive value forecast ;

[0133] The comprehensive value forecast The calculation process is:

[0134] ;

[0135] Where, Supporting benefit value for carbon neutrality , emergency reserve benefit value and basic interaction index The corresponding weight coefficient, and ;

[0136] In this embodiment, when applied, the weight coefficient is introduced scientifically. , couple the three types of benefits and calculate the comprehensive value forecast The coupling calculation method has significant advantages: first, it breaks the limitations of single-dimensional evaluation, organically integrates the three major benefits of ecology, emergency response, and society, more truly reflects the multiple values ​​of green space, and avoids decision-making bias caused by one-sided evaluation; second, synergy, the three types of benefits do not exist in isolation, and coupling calculation reveals the interaction and synergy between them.

[0137] like Figure 6 As shown, as another preferred embodiment of the present invention, on the other hand, a system for predicting the ecological service value of urban green space comprises:

[0138] The acquisition module 100 is used to collect multi-source information on vegetation physiological parameters, green space layout, and social behavior data to establish a multi-dimensional database;

[0139] The targeted quantitative calculation model module 200 is used to construct targeted quantitative calculation models based on different data and models based on a multidimensional database;

[0140] The first generation module 300 is used to couple the quantitative calculation results of the three types of benefits to generate a comprehensive prediction system;

[0141] The second generation module 400 is used to generate a comprehensive service value prediction result based on the comprehensive prediction system.

[0142] When this embodiment is applied, the acquisition module 100 collects multi-source information on vegetation physiological parameters, green space layout, and social behavior data to establish a multidimensional database. Based on the multidimensional database, the targeted quantitative calculation model module 200 constructs targeted quantitative calculation models based on different data and models respectively. The first generation module 300 couples the quantitative calculation results of the three types of benefits to generate a comprehensive prediction system. Based on the comprehensive prediction system, the second generation module 400 generates a comprehensive service value prediction result.

[0143] like Figure 7As shown, as another preferred embodiment of the present invention, the targeted quantitative calculation model module 200 specifically includes:

[0144] The determination unit 201 is used to determine the first Annual biomass increment per unit area of ​​vegetation type ;

[0145] The first calculation unit 202 is used to differentiate values ​​based on the IPCC carbon conversion coefficient standard of soil carbon pool dynamic data according to evergreen, deciduous and herbaceous vegetation types. , combined with GIS precise mapping of vegetation area and forecast period , calculate the vegetation carbon sink value ;

[0146] The second calculation unit 203 is used to calculate the carbon sink value of vegetation and soil carbon pool service value , calculate the carbon neutrality support benefit value .

[0147] When this embodiment is applied, based on vegetation carbon sink data, using hyperspectral remote sensing to invert vegetation type and leaf area index, combined with measured biomass data of targeted urban green space, the determination unit 201 determines the first Annual biomass increment per unit area of ​​vegetation type Based on the IPCC carbon conversion coefficient standard of soil carbon pool dynamic data, differentiated values ​​are taken according to evergreen, deciduous and herbaceous vegetation types. , combined with GIS precise mapping of vegetation area and forecast period The first calculation unit 202 calculates the vegetation carbon sink value , based on the carbon sequestration value of vegetation and soil carbon pool service value The second calculation unit 203 calculates the carbon neutrality support benefit value .

[0148] like Figure 8 As shown, as another preferred embodiment of the present invention, the targeted quantitative calculation model module 200 specifically further includes:

[0149] The third calculation unit 211 is used to calculate the green space rainwater storage capacity based on the hydrological dynamics data and the rainstorm frequency recurrence period data;

[0150] The fourth calculation unit 212 is used to combine the municipal drainage cost , calculate flood storage benefit value ;

[0151] The fifth calculation unit 213 is used to determine the shaded area of ​​green space under high temperature weather based on the climate response data. and cooling effect coefficient , combined with the unit cooling energy cost , calculate the energy saving value ;

[0152] The sixth calculation unit 214 is used to calculate the flood storage benefit value based on the flood storage benefit value. and energy saving value , calculate the emergency reserve benefit value ;

[0153] When this embodiment is applied, based on the hydrodynamic data and the rainstorm frequency recurrence period data, the third calculation unit 211 calculates the green space rainwater storage capacity, combined with the municipal drainage cost The fourth calculation unit 212 calculates the flood storage benefit value , based on climate response data, determine the shade area of ​​green space under high temperature weather and cooling effect coefficient , combined with the unit cooling energy cost , the fifth calculation unit 213 calculates the energy saving value , based on flood storage benefit value and energy saving value The sixth calculation unit 214 calculates the emergency reserve benefit value .

[0154] like Figure 9 As shown, as another preferred embodiment of the present invention, the targeted quantitative calculation model module 200 specifically further includes:

[0155] The collection unit 221 is used to collect the average monthly usage frequency of green space using smart sensors based on social network analysis data. , Number of participants in a single activity and duration ;

[0156] The seventh calculation unit 222 is used to combine the community population , calculate the basic interaction index ;

[0157] In this embodiment, based on the social network analysis, the data collection unit 221 uses smart sensors to collect the average monthly frequency of green space use. , Number of participants in a single activity and duration , combined with community population The seventh calculation unit 222 calculates the basic interaction index .

[0158] like Figure 10 As shown, as another preferred embodiment of the present invention, the first generating module 300 specifically includes:

[0159] Acquisition unit 301, used to obtain carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index ;

[0160] The seventh calculation unit 302 is used to support the benefit value based on the carbon neutrality , emergency reserve benefit value and basic interaction index , calculate the comprehensive value forecast .

[0161] When this embodiment is applied, the acquisition unit 301 acquires the carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index , based on the carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index , the seventh calculation unit 302 calculates the comprehensive value prediction value .

[0162] The above embodiment of the present invention provides a method for predicting the ecological service value of urban green space, and provides a system for predicting the ecological service value of urban green space. First, a wide range of multi-source information on vegetation physiological parameters, green space spatial layout and social behavior is collected to build a comprehensive and dynamically updated multidimensional database. This database provides a solid data foundation for subsequent accurate predictions. Then, based on the multidimensional database, quantitative calculation models are constructed for different benefits. For the carbon neutrality support benefit, it is calculated based on the vegetation carbon sink dynamic model, combined with vegetation type, leaf area index and other data; the emergency reserve benefit is calculated with the help of the green space hydrological regulation and climate buffer model, with reference to data such as rainstorm frequency and green space permeability; the community cohesion benefit is based on social network analysis and spatial behavior model, combined with data such as green space use frequency and resident interaction intensity. Carbon neutrality support benefit assessments quantify the carbon sequestration capacity and carbon cycle contribution of green space vegetation, providing a scientific basis for optimizing urban emission reduction pathways and decomposing carbon peak and carbon neutrality targets. This assessment also supports the development of ecological compensation mechanisms and green finance innovation. Emergency reserve benefit assessments reveal the hidden service value of green space in emergency scenarios, such as flood control, high-temperature buffering, and habitat protection. This supports urban resilience planning and reduces extreme weather risks and disaster losses. Community cohesion benefit assessments analyze the role of green space in promoting social interaction, mental health, and cultural identity through resident behavioral data, guiding humanistic green space design, enhancing community belonging and public space vitality. These three collaborative assessments transcend the limitations of single-source ecological value accounting and promote the transformation of urban green space from "ecological islands" to "multidimensional service hubs." The quantitative calculation results of the three types of benefits are then coupled and weighted to form a comprehensive prediction system. This system can flexibly adjust the weight of each benefit based on different urban development stages and needs, adapting to diverse application scenarios. Finally, based on the comprehensive prediction system, a comprehensive value forecast for urban green space ecological services is generated. This method and system, through multi-source data fusion and interdisciplinary modeling, accurately quantifies the hidden benefits of urban green space in areas such as carbon neutrality support, emergency reserves, and community cohesion. By building a multidimensional database and coupling it with a proprietary computational model to form a comprehensive prediction system, this system overcomes the blind spots in traditional methods for assessing hidden value, providing a scientific basis for urban planning and helping to optimize green space resource allocation.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 the ecological service value of urban green space, characterized in that: The method comprises: Collect multi-source information on vegetation physiological parameters, green space layout, and social behavior data to establish a multidimensional database; Based on the multidimensional database, targeted quantitative calculation models based on different data and models are constructed; Couple the quantitative calculation results of the three types of benefits to generate a comprehensive prediction system; Generate comprehensive service value prediction results based on the comprehensive prediction system; Wherein, the targeted quantitative calculation model includes: A carbon neutrality support benefit quantification model that integrates vegetation carbon sequestration data and soil carbon pool dynamics data; An emergency reserve benefit assessment model that couples hydrological dynamics data with climate response data; A community cohesion benefit measurement model based on social network analysis data.

2. The urban green space ecological service value prediction method according to claim 1 is characterized in that: Based on the multidimensional database, the quantitative calculation models based on different data and models are constructed for the carbon neutrality support benefits, emergency reserve benefits, and community cohesion benefits. Specifically, the following are the examples: Based on vegetation carbon sequestration data, the vegetation type and leaf area index were inverted using hyperspectral remote sensing, combined with the measured biomass data of targeted urban green spaces, to determine the first Annual biomass increment per unit area of ​​vegetation type ; The IPCC carbon conversion coefficient standard based on soil carbon pool dynamic data is differentiated by evergreen, deciduous, and herbaceous vegetation types. , combined with GIS precise mapping of vegetation area and forecast period , calculate the vegetation carbon sink value ; The carbon sequestration value of vegetation The calculation process is: ； Where, For the Green space vegetation area, is the total number of categories of green vegetation areas; Calculating soil organic carbon storage , combined with the real-time price of regional carbon trading market , calculate the soil carbon pool service value ; The soil carbon pool service value The calculation process is: ； Where, For the Green land area, is the total number of green land soil areas; Based on the carbon sequestration value of vegetation and soil carbon pool service value , calculate the carbon neutrality support benefit value ; The carbon neutrality support benefit value The calculation process is: 。 3. The urban green space ecological service value prediction method according to claim 1 is characterized in that: The aforementioned quantitative calculation models based on different data and models are constructed based on a multidimensional database for carbon neutrality support benefits, emergency reserve benefits, and community cohesion benefits, and specifically include: Calculate the green space rainwater storage capacity based on the rainstorm frequency recurrence data based on hydrodynamic data, combined with the municipal drainage cost , calculate flood storage benefit value ; The flood storage benefit value The calculation process is: ； Where, For the The rainfall of the rainstorm, is the total number of rainstorms, To target green space, For the The canopy interception of the rainstorm, , is the retention rate, For the Soil infiltration during a rainstorm , is the saturated hydraulic conductivity, is the infiltration duration, is the underlying surface runoff coefficient; Determine the shade area of ​​green space under high temperature weather based on climate response data and cooling effect coefficient , combined with the unit cooling energy cost , calculate the energy saving value ; Energy saving value The calculation process is: ； Where, Number the high temperature period. is the total number of high temperature periods, is the duration of the high temperature period, The shade area of ​​green space in hot weather. is the cooling effect coefficient, is the unit cooling energy cost; Based on flood storage benefit value and energy saving value , calculate the emergency reserve benefit value ; The emergency reserve benefit value The calculation process is: 。 4. The urban green space ecological service value prediction method according to claim 1 is characterized in that: The aforementioned quantitative calculation models based on different data and models are constructed based on a multidimensional database for carbon neutrality support benefits, emergency reserve benefits, and community cohesion benefits, and specifically include: Based on social network analysis data, smart sensors are used to collect the average monthly frequency of green space use. , Number of participants in a single activity and duration ; Combined with community population , calculate the basic interaction index ; The basic interaction index The calculation process is: ； Where, is the population density correction factor, is the unit social capital monetization coefficient.

5. The urban green space ecological service value prediction method according to claim 1 is characterized in that: The quantitative calculation results of the three types of benefits are coupled to generate a comprehensive prediction system: Obtain carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index ; Based on carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index , calculate the comprehensive value forecast ; The comprehensive value forecast The calculation process is: ； Where, Supporting benefit value for carbon neutrality , emergency reserve benefit value and basic interaction index The corresponding weight coefficient, and .

6. An urban green space ecological service value prediction system, characterized by: The method for predicting the ecological service value of urban green space according to any one of claims 1 to 5 is applied, wherein the system comprises: The acquisition module is used to collect multi-source information on vegetation physiological parameters, green space layout, and social behavior data to establish a multidimensional database; Targeted quantitative calculation model module, used to build targeted quantitative calculation models based on different data and models based on a multidimensional database; The first generation module is used to couple the quantitative calculation results of the three types of benefits to generate a comprehensive prediction system; The second generation module is used to generate comprehensive service value prediction results based on the comprehensive prediction system.

7. The urban green space ecological service value prediction system according to claim 6 is characterized in that: The targeted quantitative calculation model module specifically includes: The determination unit is used to determine the first Annual biomass increment per unit area of ​​vegetation type ; The first calculation unit is used to calculate the IPCC carbon conversion coefficient standard based on soil carbon pool dynamic data, with differentiated values ​​according to evergreen, deciduous, and herbaceous vegetation types. , combined with GIS precise mapping of vegetation area and forecast period , calculate the vegetation carbon sink value ; The second calculation unit is used to calculate the carbon sink value based on vegetation and soil carbon pool service value , calculate the carbon neutrality support benefit value .

8. The urban green space ecological service value prediction system according to claim 6 is characterized in that: The targeted quantitative calculation model module specifically includes: The third calculation unit is used to calculate the green space rainwater storage capacity based on the hydrological dynamics data and the rainstorm frequency recurrence period data; The fourth calculation unit is used to combine municipal drainage costs , calculate flood storage benefit value ; The fifth calculation unit is used to determine the shade area of ​​green space under high temperature weather based on climate response data and cooling effect coefficient , combined with the unit cooling energy cost , calculate the energy saving value ; The sixth calculation unit is used to calculate the flood storage benefit value based on the flood storage benefit value. and energy saving value , calculate the emergency reserve benefit value .

9. The urban green space ecological service value prediction system according to claim 6 is characterized in that: The targeted quantitative calculation model module specifically includes: The collection unit is used to collect the average monthly frequency of green space use based on social network analysis data using smart sensors , Number of participants in a single activity and duration ; The seventh calculation unit is used to combine community population , calculate the basic interaction index .

10. The urban green space ecological service value prediction system according to claim 6, characterized in that: The first generation module specifically includes: Acquisition unit, used to obtain carbon neutrality support benefit value , emergency reserve benefit value and basic interaction index ; The seventh calculation unit is used to support the benefit value based on carbon neutrality , emergency reserve benefit value and basic interaction index , calculate the comprehensive value forecast .

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