A comprehensive prediction method and device for resource and environmental carrying capacity

Abstract

The present invention discloses a comprehensive prediction method and device for resource and environmental carrying capacity, relating to the field of resource and environmental data processing technology. The method includes determining initial assessment indicators for the resource and environmental carrying capacity of each partial area; determining urban effect particles for each partial area; obtaining radiation particles for surrounding partial areas based on radiation areas and radiation properties; optimizing the initial assessment indicators for each category of partial areas; and constructing a resource and environmental carrying capacity prediction model based on the assessment indicators for each resource and environmental carrying capacity and the overall assessment indicators. The method accurately describes the impact of urban effects and the radiation effects of other areas through urban effect particles and radiation particles. The combination of the two particles optimizes the initial assessment indicators, accurately describes the resource and environmental carrying capacity, improves the adaptability and accuracy of the comprehensive assessment and prediction of resource and environmental carrying capacity, and ensures the reliability of urban development.

Description

Technical Field

[0001] The present invention relates to the technical field of resource and environmental data processing, and in particular to a comprehensive prediction method and device for resource and environmental carrying capacity. Background Art

[0002] With the accelerating pace of urbanization and the continued concentration of population in metropolitan areas, resource depletion and environmental degradation are becoming increasingly severe. Assessing resource and environmental carrying capacity has become an urgent task. This assessment of a region's natural resources and environment is an evaluation of its ability to accommodate population, economic, or other activities over a given period of time. The concept of resource and environmental carrying capacity originated in the 1990s and developed on the basis of ecological carrying capacity. Assessments of resource and environmental carrying capacity involve multiple factors, including natural conditions, population size, economic development level, and environmental conditions. While the scope of these assessments is broad, the results serve as a foundation for government policymaking and urban development planning.

[0003] In existing technologies, there is no unified and standardized definition of resource and environmental carrying capacity, and the assessment lacks consistent indicators. In addition, multiple factors (such as urban effects) are not comprehensively considered during the assessment process, which affects the scientific nature of the assessment results, making the comprehensive assessment and prediction of resource and environmental carrying capacity poorly adaptable and inaccurate.

[0004] Therefore, how to improve the adaptability and accuracy of comprehensive assessment and prediction of resource and environmental carrying capacity is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems of poor adaptability and low accuracy of comprehensive assessment and prediction of resource and environmental carrying capacity in the prior art, and to propose a comprehensive prediction method for resource and environmental carrying capacity, which includes:

[0006] Obtaining geographic location information of the area to be analyzed, dividing the area to be analyzed into several sub-areas based on the geographic location information, obtaining resource and environmental information within each sub-area, and determining initial assessment indicators of resource and environmental carrying capacity of each sub-area based on the resource and environmental information;

[0007] Analyze the urban effect based on the resource and environmental information of the area to be analyzed, and establish a matching relationship between the urban effect and the initial evaluation indicator category. Determine the corresponding relationship between the urban effect and each partial area through the matching relationship between the urban effect and the initial evaluation indicator of the resource and environmental carrying capacity of each partial area in historical data, thereby determining the urban effect particles of each partial area;

[0008] Acquire radiation information within each partial area, determine all radiation points within the partial area based on the radiation information, determine the radiation area and radiation properties within the partial area based on the radiation points, and obtain radiation particles in the surrounding partial area based on the radiation area and radiation properties;

[0009] The partial areas are classified according to the particle situation of each partial area, and the initial evaluation indicators of the partial areas of each category are optimized to obtain the evaluation indicators of resource and environmental carrying capacity. The weight of the evaluation indicators of each resource and environmental carrying capacity is set according to the overall change scenario of the analyzed area, so as to integrate the evaluation indicators of each resource and environmental carrying capacity to obtain the overall evaluation indicator;

[0010] A resource and environmental carrying capacity prediction model is constructed based on the evaluation indicators of each resource and environmental carrying capacity and the overall evaluation indicators to predict the resource and environmental carrying capacity of the analyzed area.

[0011] In some embodiments of the present application, the area to be analyzed is divided into several partial areas according to the geographical location information, including:

[0012] The area to be analyzed is initially divided according to functional areas to obtain multiple partial functional areas, and the complexity of the partial functional areas is determined by the functional categories and the number of areas of the multiple partial functional areas;

[0013] The geographical feature similarity threshold is determined according to the complexity of the partial functional area, and the geographical features in each partial functional area are extracted. The partial functional area is divided twice according to the geographical features and the geographical feature similarity threshold to obtain several partial areas.

[0014] In some embodiments of the present application, the initial assessment index of the resource and environmental carrying capacity of each partial area is determined based on the resource and environmental information, including:

[0015] Based on the resource and environmental information, a functional relationship of each initial assessment indicator of the resource and environmental carrying capacity of each partial area is constructed. The initial assessment indicators of the resource and environmental carrying capacity include the initial land resource carrying capacity, water resource carrying capacity, environmental carrying capacity and ecological carrying capacity.

[0016] In some embodiments of the present application, the urban effect is analyzed based on the resource and environmental information of the area to be analyzed, and a matching relationship between the urban effect and the initial evaluation indicator category is established, including:

[0017] Numerical simulations are performed using climate models or urban canopy models based on resource and environmental information to obtain an urban effect simulation model, which is then used to determine the resource and environmental parameter categories affected by urban effects.

[0018] If the resource and environmental parameter category affected by the urban effect exists in the functional relationship of the initial assessment indicators of resource and environmental carrying capacity, or if there is a correlation between the resource and environmental parameter category affected by the urban effect and the parameters in the functional relationship of the initial assessment indicators of resource and environmental carrying capacity, then the correspondence between the resource and environmental parameter category and the initial assessment indicator is considered a matching relationship.

[0019] In some embodiments of the present application, the correspondence between the urban effect and each partial region is determined by matching the urban effect in historical data with the initial assessment index of the resource and environmental carrying capacity of each partial region, thereby determining the urban effect particles of each partial region, including:

[0020] Collect historical data according to the corresponding relationship between resource and environmental parameter categories and initial assessment indicators, and obtain historical data of resource and environmental parameters and historical data of initial assessment indicators for each partial area;

[0021] The historical data of resource and environmental parameters and the historical data of initial evaluation indicators of each partial region are mapped to two state spaces respectively. The causal indicators between the historical data of resource and environmental parameters and the historical data of initial evaluation indicators of each partial region are constructed according to the distance between the two state spaces, thereby determining the causal relationship between the resource and environmental parameter categories and the initial evaluation indicators of each partial region, and using this causal relationship as the corresponding relationship between the urban effect and each partial region;

[0022] All causal indicators of each partial area are integrated, and the integrated causal indicators are converted into urban effect particles of each partial area.

[0023] In some embodiments of the present application, obtaining radiation information within each partial area and determining all radiation points within the partial area through the radiation information includes:

[0024] Each type of radiation information is normalized and all types of radiation information are integrated to obtain the radiation intensity. The radiation source is screened in each partial area according to the radiation intensity, and the screened radiation source is used as the radiation point.

[0025] In some embodiments of the present application, determining a radiation area and radiation properties in a partial area according to a radiation point, and obtaining radiation particles in a partial area of the surrounding area based on the radiation area and radiation properties includes:

[0026] Evaluate the radiation influence range of each radiation point in each partial area, determine the regional radiation intensity and regional radiation range of the partial area based on the radiation intensity and radiation range of all radiation points in the same partial area, and use the regional radiation intensity and regional radiation range as the radiation attributes of the partial area;

[0027] The partial area where the regional radiation intensity and regional radiation range both exceed the corresponding threshold value is regarded as the radiation area, and the affected partial area around it is determined according to the radiation properties of the radiation area, and the radiation particles in the affected partial area are determined based on the distance between the affected partial area and the radiation area and the radiation properties of the radiation area.

[0028] In some embodiments of the present application, the partial regions are classified according to the particle conditions of each partial region, and the initial evaluation index of the partial regions of each category is optimized to obtain the evaluation index of resource and environmental carrying capacity, including:

[0029] If only urban effect particles exist in a part of the area, then the part of the area will be classified as a part of the area with urban effect particles;

[0030] If both urban effect particles and radiation particles exist in some areas, then the area will be classified as a double particle area;

[0031] For some areas of urban effect particles, the initial evaluation indicators of some areas are optimized through the urban effect particles corresponding to the part of the urban effect particles;

[0032] For some areas with double particles, the initial evaluation indicators of some areas are optimized by using two types of particles: urban effect particles and radiation particles;

[0033] ;

[0034] in, After optimization evaluation indicators, For the Initial evaluation indicators, For urban effect particles, For radiation particles, For the The elimination coefficient of the evaluation index, is the first particle obtained by mapping urban effect particles and radiation particles. The elimination coefficient of the evaluation index, is a preset constant.

[0035] In some embodiments of the present application, the weight of each resource and environmental carrying capacity assessment indicator is set based on the overall change scenario of the region to be analyzed, thereby integrating the assessment indicators of each resource and environmental carrying capacity to obtain an overall assessment indicator, including:

[0036] Analyze the overall change scenarios of the area to be analyzed, and transform each overall change scenario so that all overall change scenarios can be compared on the same scale. Set the weight of the assessment indicator of each resource and environmental carrying capacity, and perform weighted summation of the assessment indicators of resource and environmental carrying capacity to obtain the overall assessment indicator.

[0037] Correspondingly, the present application also provides a comprehensive prediction device for resource and environmental carrying capacity, including:

[0038] The first module is used to obtain the geographical location information of the area to be analyzed, and divide the area to be analyzed into several partial areas according to the geographical location information, obtain the resource and environmental information in each partial area, and determine the initial assessment index of the resource and environmental carrying capacity of each partial area based on the resource and environmental information;

[0039] The second module is used to analyze the urban effect based on the resource and environmental information of the area to be analyzed, and to establish a matching relationship between the urban effect and the initial evaluation indicator category. The corresponding relationship between the urban effect and each partial area is determined by matching the historical data of the urban effect with the initial evaluation indicators of the resource and environmental carrying capacity of each partial area, thereby determining the urban effect particles of each partial area;

[0040] The third module is used to obtain radiation information in each partial area, determine all radiation points in the partial area through the radiation information, determine the radiation area and radiation properties in the partial area based on the radiation points, and obtain radiation particles in the surrounding partial area based on the radiation area and radiation properties;

[0041] The fourth module is used to classify the partial areas according to the particle situation of each partial area, optimize the initial evaluation indicators of the partial areas in each category, and obtain the evaluation indicators of resource and environmental carrying capacity. The weight of each resource and environmental carrying capacity evaluation indicator is set according to the overall change scenario of the analyzed area, and the evaluation indicators of each resource and environmental carrying capacity are integrated to obtain the overall evaluation indicator.

[0042] The fifth module is used to construct a resource and environmental carrying capacity prediction model based on the evaluation indicators of each resource and environmental carrying capacity and the overall evaluation indicators, and to predict the resource and environmental carrying capacity of the analyzed area.

[0043] By applying the above technical solution, the geographical location information of the area to be analyzed is obtained, and the area to be analyzed is divided into several partial areas according to the geographical location information, the resource and environmental information in each partial area is obtained, and the initial evaluation index of the resource and environmental carrying capacity of each partial area is determined according to the resource and environmental information; the urban effect is analyzed according to the resource and environmental information of the area to be analyzed, and a matching relationship between the urban effect and the category of the initial evaluation index is established, and the corresponding relationship between the urban effect and each partial area is determined through the matching relationship between the urban effect and the initial evaluation index of the resource and environmental carrying capacity of each partial area in historical data, thereby determining the urban effect particles of each partial area; the radiation information in each partial area is obtained, and the radiation information is used to determine the urban effect particles of each partial area. The information is used to determine all radiation points in the partial area, and the radiation area and radiation attributes in the partial area are determined according to the radiation points, and the radiation particles of the surrounding partial areas are obtained based on the radiation area and radiation attributes; the partial areas are classified according to the particle situation of each partial area, and the initial evaluation indicators of the partial areas of each category are optimized to obtain the evaluation indicators of resource and environmental carrying capacity, and the weight of the evaluation indicators of each resource and environmental carrying capacity is set according to the overall change scenario of the area to be analyzed, so as to integrate the evaluation indicators of each resource and environmental carrying capacity and obtain the overall evaluation indicators; a resource and environmental carrying capacity prediction model is constructed according to the evaluation indicators of each resource and environmental carrying capacity and the overall evaluation indicators to predict the resource and environmental carrying capacity of the area to be analyzed. This application determines the urban effect particles of each partial area by analyzing the urban effect, obtains the radiation particles of the surrounding partial areas based on the radiation area and radiation attributes, accurately describes the impact of the urban effect and the radiation impact of other areas through urban effect particles and radiation particles, combines the two particles to optimize the initial evaluation indicators, accurately describes the resource and environmental carrying capacity, improves the adaptability and accuracy of the comprehensive evaluation and prediction of the resource and environmental carrying capacity, and ensures the reliability of urban development. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of a comprehensive prediction method for resource and environmental carrying capacity proposed by the present invention;

[0045] Figure 2 This is a structural schematic diagram of a comprehensive prediction device for resource and environmental carrying capacity proposed by the present invention. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0047] Reference Figure 1 , a comprehensive prediction method for resource and environmental carrying capacity, including:

[0048] Step S101: obtain the geographic location information of the area to be analyzed, divide the area to be analyzed into several partial areas according to the geographic location information, obtain the resource and environmental information in each partial area, and determine the initial evaluation index of the resource and environmental carrying capacity of each partial area according to the resource and environmental information.

[0049] In this embodiment, the area to be analyzed primarily includes urbanized areas, but may also include surrounding non-urbanized areas. The area to be analyzed is divided into several sub-areas based on similarities in functional areas and geographical characteristics. Resource and environmental information includes data related to land resources, water resources, environmental quality (including air and water), and ecological conditions.

[0050] In some embodiments of the present application, the area to be analyzed is divided into several partial areas according to the geographical location information, including:

[0051] The area to be analyzed is initially divided according to functional areas to obtain multiple partial functional areas, and the complexity of the partial functional areas is determined by the functional categories and the number of areas of the multiple partial functional areas;

[0052] The geographical feature similarity threshold is determined according to the complexity of the partial functional area, and the geographical features in each partial functional area are extracted. The partial functional area is divided twice according to the geographical features and the geographical feature similarity threshold to obtain several partial areas.

[0053] In this embodiment, the area to be analyzed is initially divided according to functional areas, resulting in multiple partial functional areas, such as residential areas, commercial areas, industrial areas, and nature reserves. The complexity of the partial functional areas is determined by the functional categories and the number of areas in the partial functional areas. The number and the number of functional categories are combined to define a complexity. Different complexities correspond to different geographical feature similarity thresholds. If the complexity is high, a higher similarity threshold is required to ensure that only truly similar areas are classified as similar.

[0054] In some embodiments of the present application, the initial assessment index of the resource and environmental carrying capacity of each partial area is determined based on the resource and environmental information, including:

[0055] Based on the resource and environmental information, a functional relationship of each initial assessment indicator of the resource and environmental carrying capacity of each partial area is constructed. The initial assessment indicators of the resource and environmental carrying capacity include the initial land resource carrying capacity, water resource carrying capacity, environmental carrying capacity and ecological carrying capacity.

[0056] In this embodiment, land resource carrying capacity assessment: Land resource carrying capacity is primarily characterized by the land resource pressure index. The land resource pressure index = land use development intensity - development intensity threshold / development intensity threshold. Land use development intensity = existing construction land area / usable land area, where usable land area = total land area - land area with a slope greater than 25° - cultivated land area - earthquake fault zone.

[0057] Water resource carrying capacity assessment: Water resource carrying capacity mainly reflects the relationship between regional population and water resources, and is represented by the water resource carrying index. Its evaluation model is expressed as:

[0058] WCCI=Pa / WCC

[0059] Rp=(Pa-WCC) / WCC×100%=(WCC-1)×100%

[0060] Rw=(WCC-Pa) / WCC×100%=(1-WCC)×100%

[0061] Where WCCI is the water resources carrying index; WCC is the water resources carrying capacity; Pa is the actual population; Rp is the water resources overload rate; and Rw is the water resources surplus rate. The total water resources here refer to the sum of surface water and groundwater.

[0062] WCC=W / WPC

[0063] Where WCC is the water resource carrying capacity based on the national comprehensive water use standard (person or person / km2); W is the available water resources (m3); and WPC is the national per capita comprehensive water use standard (500 m3 / person).

[0064] Environmental carrying capacity assessment: Environmental carrying capacity is characterized by both the air quality and the water environment of a region. By integrating the results of these two assessments, we can derive an environmental carrying capacity assessment for a metropolitan area.

[0065] Ecological carrying capacity assessment: Ecological carrying capacity is primarily characterized by the Ecological Environment Index (EI) and the Habitat Quality Index (HQ). The Ecological Environment Index (EI) can be obtained directly from the website of each city's Department of Ecology and Environment and is graded accordingly. However, given that the Ecological Environment Index (EI) may have incomplete temporal coverage, the Habitat Quality Index (HQ) can also be used to characterize ecological carrying capacity. According to the "Technical Specifications for Ecological Environment Status Assessment (HJ 192-2015)", the Habitat Quality Index (HQ) is calculated as follows:

[0066] Habitat quality index = Abio × (0.35 × forest land + 0.21 × grassland + 0.28 × water wetland + 0.11 × cultivated land + 0.04 × construction land + 0.01 × unused land) / regional area.

[0067] Where: Abio refers to the normalized coefficient of the habitat quality index, and the reference value is 511.2642131067.

[0068] In step S102, the urban effect is analyzed based on the resource and environmental information of the area to be analyzed, and a matching relationship between the urban effect and the initial evaluation indicator category is established. The corresponding relationship between the urban effect and each partial area is determined by the matching relationship between the urban effect and the initial evaluation indicator of the resource and environmental carrying capacity of each partial area in historical data, thereby determining the urban effect particles of each partial area.

[0069] In this embodiment, urban effects include heat island effects and rain island effects. These are two prominent climatic characteristics in urban environments, closely related to dense urban construction, population concentration, and industrialization. The matching relationship between urban effects and initial evaluation indicator categories refers to the influence relationship between the parameters affected by urban effects and the evaluation indicator categories. Urban effects (heat island and rain island effects) affect evaluation indicators such as land resource carrying capacity, water resource carrying capacity, environmental carrying capacity, and ecological carrying capacity through changes in various resource and environmental parameters. The causal relationship between these two factors is analyzed to determine the corresponding relationship between urban effects and each sub-region, thereby determining the urban effect particle for each sub-region, that is, the specific impact of the urban effect on a particular sub-region.

[0070] The heat island effect occurs when the temperature in one area is higher than that of surrounding areas, like a high-temperature "island" surrounded by a cooler "ocean." In cities, due to dense buildings, relatively few green spaces and bodies of water, and the large amount of heat released by human activities (such as industrial production, transportation, and daily life), urban temperatures are significantly higher than those in surrounding suburbs or rural areas. This temperature difference, driven by atmospheric circulation, causes the air above the city to heat up and rise, while cooler air from the suburbs flows into the city to replenish the heat, thus forming a thermal circulation between the city and suburbs.

[0071] The rain island effect refers to the phenomenon in which the frequency and intensity of rainfall (including rain and snow) in cities is higher than in surrounding areas. This phenomenon is closely related to the urban heat island effect. Due to the urban heat island effect, the air above the city heats up and rises, forming convection currents with the cooler surrounding air. This convection promotes the condensation of water vapor and the formation of raindrops, thereby increasing urban rainfall. Furthermore, the surface materials of buildings, roads, and other structures in cities also affect the collection and discharge of rainwater, potentially further exacerbating the rain island effect.

[0072] In some embodiments of the present application, the urban effect is analyzed based on the resource and environmental information of the area to be analyzed, and a matching relationship between the urban effect and the initial evaluation indicator category is established, including:

[0073] Numerical simulations are performed using climate models or urban canopy models based on resource and environmental information to obtain an urban effect simulation model, which is then used to determine the resource and environmental parameter categories affected by urban effects.

[0074] If the resource and environmental parameter category affected by the urban effect exists in the functional relationship of the initial assessment indicators of resource and environmental carrying capacity, or if there is a correlation between the resource and environmental parameter category affected by the urban effect and the parameters in the functional relationship of the initial assessment indicators of resource and environmental carrying capacity, then the correspondence between the resource and environmental parameter category and the initial assessment indicator is considered a matching relationship.

[0075] In this embodiment, there is a correlation between the resource and environmental parameter categories affected by the urban effect and the parameters in the functional relationship of the initial assessment indicators of resource and environmental carrying capacity. This correlation can be determined by the Pearson correlation coefficient, etc., and if it exceeds a certain threshold, there is a correlation.

[0076] In some embodiments of the present application, the correspondence between the urban effect and each partial region is determined by matching the urban effect in historical data with the initial assessment index of the resource and environmental carrying capacity of each partial region, thereby determining the urban effect particles of each partial region, including:

[0077] Collect historical data according to the corresponding relationship between resource and environmental parameter categories and initial assessment indicators, and obtain historical data of resource and environmental parameters and historical data of initial assessment indicators for each partial area;

[0078] The historical data of resource and environmental parameters and the historical data of initial evaluation indicators of each partial region are mapped to two state spaces respectively. The causal indicators between the historical data of resource and environmental parameters and the historical data of initial evaluation indicators of each partial region are constructed according to the distance between the two state spaces, thereby determining the causal relationship between the resource and environmental parameter categories and the initial evaluation indicators of each partial region, and using this causal relationship as the corresponding relationship between the urban effect and each partial region;

[0079] All causal indicators of each partial area are integrated, and the integrated causal indicators are converted into urban effect particles of each partial area.

[0080] In this embodiment, the causal indicator is a nonlinear interdependence indicator. It is based on state space reconstruction and neighbor distance methods to determine the direction and magnitude of causal relationships. For two independent systems or factors X and Y, the state spaces of the two systems are established according to state space reconstruction theory.

[0081] For a sample point xn, xrn,1, ..., xrn,k in the state space X, denote the k nearest neighbor points of xn in the state space X, and calculate the average Euclidean distance between xn and the k nearest neighbor points;

[0082] ;

[0083] For the sample point yn,ysn,1,...,ysn,k in the state space Y, it represents the k nearest neighbor points of yn in the state space Y. Map it to the state space X and calculate the average Euclidean distance between xn and its k nearest neighbor points xsn,1,...,xsn,k.

[0084] ;

[0085] In order to simplify the calculation, the average distance between xn and all N sample points can be used;

[0086] ;

[0087] The nonlinear interdependence indicator is the state space method, which determines the causal relationship between systems based on the mapping relationship in the state space and is defined as:

[0088] ;

[0089] According to the definition, 0< ≤1, when When it approaches 0, systems X and Y are independent of each other. When it is significantly greater than 0, there is a causal relationship from system X to Y, and the closer it is to 1, the stronger the causality.

[0090] It should be noted that other indicators that can represent causal relationships are also applicable, and this application only provides a specific method.

[0091] In this embodiment, all causal indicators of each partial area are integrated and converted into urban effect particles of each partial area. Different causal indicators after integration correspond to urban effect particles of different sizes. This particle describes the degree of influence of the urban effect on the indicator.

[0092] Step S103, obtain radiation information in each partial area, determine all radiation points in the partial area through the radiation information, and determine the radiation area and radiation properties in the partial area according to the radiation points, and obtain radiation particles in the surrounding partial area based on the radiation area and radiation properties.

[0093] In this embodiment, the radiation points are key points such as factories, commercial centers, and transportation hubs, and the radiation information is related information about these radiation points, such as the scale of the factory, the prosperity of the commercial center, etc. These radiation points will affect the resource and environmental conditions of the surrounding areas, thereby affecting the evaluation indicators.

[0094] In some embodiments of the present application, obtaining radiation information within each partial area and determining all radiation points within the partial area through the radiation information includes:

[0095] Each type of radiation information is normalized and all types of radiation information are integrated to obtain the radiation intensity. The radiation source is screened in each partial area according to the radiation intensity, and the screened radiation source is used as the radiation point.

[0096] In this embodiment, radiation sources with stronger radiation intensity are retained, and radiation sources with weaker radiation intensity are eliminated.

[0097] In some embodiments of the present application, determining a radiation area and radiation properties in a partial area according to a radiation point, and obtaining radiation particles in a partial area of the surrounding area based on the radiation area and radiation properties includes:

[0098] Evaluate the radiation influence range of each radiation point in each partial area, determine the regional radiation intensity and regional radiation range of the partial area based on the radiation intensity and radiation range of all radiation points in the same partial area, and use the regional radiation intensity and regional radiation range as the radiation attributes of the partial area;

[0099] The partial area where the regional radiation intensity and regional radiation range both exceed the corresponding threshold value is regarded as the radiation area, and the affected partial area around it is determined according to the radiation properties of the radiation area, and the radiation particles in the affected partial area are determined based on the distance between the affected partial area and the radiation area and the radiation properties of the radiation area.

[0100] In this embodiment, the radiation particles in the affected area are determined based on the distance between the affected area and the radiation area and the radiation properties of the radiation area. The distance and the radiation properties of the radiation area are input into a preset radiation simulation model to output the radiation particles. The radiation particles describe the degree of impact of the radiation area on the evaluation indicators in the surrounding area.

[0101] In step S104, the partial areas are classified according to the particle conditions of each partial area, and the initial evaluation indicators of the partial areas of each category are optimized to obtain the evaluation indicators of resource and environmental carrying capacity. The weight of the evaluation indicator of each resource and environmental carrying capacity is set according to the overall change scenario of the area to be analyzed, so as to integrate the evaluation indicators of each resource and environmental carrying capacity to obtain the overall evaluation indicator.

[0102] In this example, some areas have only two categories: one for areas with only urban effect particles, and the other for areas with both urban effect particles and radiation particles. The first category indicates areas affected only by urban effects, while the second category indicates areas affected by both urban effects and radiation. The optimized evaluation indicators for these two types of particles are integrated to produce an overall evaluation index (combining four indicators: land resource carrying capacity, water resource carrying capacity, environmental carrying capacity, and ecological carrying capacity).

[0103] In some embodiments of the present application, the partial regions are classified according to the particle conditions of each partial region, and the initial evaluation index of the partial regions of each category is optimized to obtain the evaluation index of resource and environmental carrying capacity, including:

[0104] If only urban effect particles exist in a part of the area, then the part of the area will be classified as a part of the area with urban effect particles;

[0105] If both urban effect particles and radiation particles exist in some areas, then the area will be classified as a double particle area;

[0106] For some areas of urban effect particles, the initial evaluation indicators of some areas are optimized through the urban effect particles corresponding to the part of the urban effect particles;

[0107] For some areas with double particles, the initial evaluation indicators of some areas are optimized by using two types of particles: urban effect particles and radiation particles;

[0108] ;

[0109] in, After optimization evaluation indicators, For the Initial evaluation indicators, For urban effect particles, For radiation particles, For the The elimination coefficient of the evaluation index, is the first particle obtained by mapping urban effect particles and radiation particles. The elimination coefficient of the evaluation index, is a preset constant.

[0110] In this embodiment, the initial evaluation index of some areas is optimized by urban effect particles corresponding to some areas of urban effect particles. Different urban effect particles correspond to different correction coefficients, and correction is performed in the form of correction coefficient*initial evaluation index.

[0111] In this embodiment, for the dual-particle area, the standardized urban effect particles and radiation particles are used to simultaneously correct the initial evaluation indicators. Because some errors may exist when the two particles are corrected simultaneously, an elimination coefficient is determined based on the two particles. Different two particles correspond to different elimination coefficients, and the errors corrected simultaneously are eliminated by the elimination coefficient.

[0112] In some embodiments of the present application, the weight of each resource and environmental carrying capacity assessment indicator is set based on the overall change scenario of the region to be analyzed, thereby integrating the assessment indicators of each resource and environmental carrying capacity to obtain an overall assessment indicator, including:

[0113] Analyze the overall change scenarios of the area to be analyzed, and transform each overall change scenario so that all overall change scenarios can be compared on the same scale. Set the weight of the assessment indicator of each resource and environmental carrying capacity, and perform weighted summation of the assessment indicators of resource and environmental carrying capacity to obtain the overall assessment indicator.

[0114] In this embodiment, the overall change scenarios include urbanization process, major economic activity types, population migration, etc. These overall change scenarios reflect the city's tendency towards the four evaluation indicators, which are used to set the weight of each resource and environmental carrying capacity evaluation indicator.

[0115] Step S105 : constructing a resource and environmental carrying capacity prediction model based on the evaluation index of each resource and environmental carrying capacity and the overall evaluation index, and predicting the resource and environmental carrying capacity of the area to be analyzed.

[0116] In this embodiment, time series analysis such as ARIMA can be selected as a resource and environmental carrying capacity prediction model to perform prediction analysis on the resource and environmental carrying capacity of a part of the region or the entire region.

[0117] By applying the above technical solution, the geographical location information of the area to be analyzed is obtained, and the area to be analyzed is divided into several partial areas according to the geographical location information, the resource and environmental information in each partial area is obtained, and the initial evaluation index of the resource and environmental carrying capacity of each partial area is determined according to the resource and environmental information; the urban effect is analyzed according to the resource and environmental information of the area to be analyzed, and a matching relationship between the urban effect and the category of the initial evaluation index is established, and the corresponding relationship between the urban effect and each partial area is determined through the matching relationship between the urban effect and the initial evaluation index of the resource and environmental carrying capacity of each partial area in historical data, thereby determining the urban effect particles of each partial area; the radiation information in each partial area is obtained, and the radiation information is used to determine the urban effect particles of each partial area. The information is used to determine all radiation points in the partial area, and the radiation area and radiation attributes in the partial area are determined according to the radiation points, and the radiation particles of the surrounding partial areas are obtained based on the radiation area and radiation attributes; the partial areas are classified according to the particle situation of each partial area, and the initial evaluation indicators of the partial areas of each category are optimized to obtain the evaluation indicators of resource and environmental carrying capacity, and the weight of the evaluation indicators of each resource and environmental carrying capacity is set according to the overall change scenario of the area to be analyzed, so as to integrate the evaluation indicators of each resource and environmental carrying capacity and obtain the overall evaluation indicators; a resource and environmental carrying capacity prediction model is constructed according to the evaluation indicators of each resource and environmental carrying capacity and the overall evaluation indicators to predict the resource and environmental carrying capacity of the area to be analyzed. This application determines the urban effect particles of each partial area by analyzing the urban effect, obtains the radiation particles of the surrounding partial areas based on the radiation area and radiation attributes, accurately describes the impact of the urban effect and the radiation impact of other areas through urban effect particles and radiation particles, combines the two particles to optimize the initial evaluation indicators, accurately describes the resource and environmental carrying capacity, improves the adaptability and accuracy of the comprehensive evaluation and prediction of the resource and environmental carrying capacity, and ensures the reliability of urban development.

[0118] Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented via hardware or via software combined with a necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product. This software product can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or external hard drive) and includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various implementation scenarios of the present invention.

[0119] Correspondingly, this application also provides a comprehensive prediction device for resource and environmental carrying capacity, such as Figure 2 Shown, including:

[0120] The first module is used to obtain the geographical location information of the area to be analyzed, and divide the area to be analyzed into several partial areas according to the geographical location information, obtain the resource and environmental information in each partial area, and determine the initial assessment index of the resource and environmental carrying capacity of each partial area based on the resource and environmental information;

[0121] The second module is used to analyze the urban effect based on the resource and environmental information of the area to be analyzed, and to establish a matching relationship between the urban effect and the initial evaluation indicator category. The corresponding relationship between the urban effect and each partial area is determined by matching the historical data of the urban effect with the initial evaluation indicators of the resource and environmental carrying capacity of each partial area, thereby determining the urban effect particles of each partial area;

[0122] The third module is used to obtain radiation information in each partial area, determine all radiation points in the partial area through the radiation information, determine the radiation area and radiation properties in the partial area based on the radiation points, and obtain radiation particles in the surrounding partial area based on the radiation area and radiation properties;

[0123] The fourth module is used to classify the partial areas according to the particle situation of each partial area, optimize the initial evaluation indicators of the partial areas in each category, and obtain the evaluation indicators of resource and environmental carrying capacity. The weight of each resource and environmental carrying capacity evaluation indicator is set according to the overall change scenario of the analyzed area, and the evaluation indicators of each resource and environmental carrying capacity are integrated to obtain the overall evaluation indicator.

[0124] The fifth module is used to construct a resource and environmental carrying capacity prediction model based on the evaluation indicators of each resource and environmental carrying capacity and the overall evaluation indicators, and to predict the resource and environmental carrying capacity of the analyzed area.

[0125] Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the implementation scenario description, or can be modified accordingly and located in one or more devices different from the implementation scenario. The modules in the above implementation scenario can be combined into one module or further split into multiple submodules.

[0126] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A comprehensive prediction method for resource and environmental carrying capacity, characterized in that: include: Obtaining geographic location information of the area to be analyzed, dividing the area to be analyzed into several sub-areas based on the geographic location information, obtaining resource and environmental information within each sub-area, and determining initial assessment indicators of resource and environmental carrying capacity of each sub-area based on the resource and environmental information; Analyze the urban effect based on the resource and environmental information of the area to be analyzed, and establish a matching relationship between the urban effect and the initial evaluation indicator category. Determine the corresponding relationship between the urban effect and each partial area through the matching relationship between the urban effect and the initial evaluation indicator of the resource and environmental carrying capacity of each partial area in historical data, thereby determining the urban effect particles of each partial area; Acquire radiation information within each partial area, determine all radiation points within the partial area based on the radiation information, determine the radiation area and radiation properties within the partial area based on the radiation points, and obtain radiation particles in the surrounding partial area based on the radiation area and radiation properties; The partial areas are classified according to the particle situation of each partial area, and the initial evaluation indicators of the partial areas of each category are optimized to obtain the evaluation indicators of resource and environmental carrying capacity. The weight of the evaluation indicators of each resource and environmental carrying capacity is set according to the overall change scenario of the analyzed area, so as to integrate the evaluation indicators of each resource and environmental carrying capacity to obtain the overall evaluation indicator; A resource and environmental carrying capacity prediction model is constructed based on the evaluation indicators of each resource and environmental carrying capacity and the overall evaluation indicators to predict the resource and environmental carrying capacity of the analyzed area; in, Based on the resource and environmental information, the initial assessment indicators for the resource and environmental carrying capacity of each sub-region are determined, including: Constructing a functional relationship between each initial assessment indicator of resource and environmental carrying capacity for each partial region based on resource and environmental information. The initial assessment indicators of resource and environmental carrying capacity include initial land resource carrying capacity, water resource carrying capacity, environmental carrying capacity, and ecological carrying capacity. Analyze the urban effects based on the resource and environmental information of the area to be analyzed, and establish a matching relationship between the urban effects and the initial evaluation indicator categories, including: Numerical simulations are performed using climate models or urban canopy models based on resource and environmental information to obtain an urban effect simulation model, which is then used to determine the resource and environmental parameter categories affected by urban effects. If the resource and environmental parameter category affected by the urban effect exists in the functional relationship of the initial assessment index of resource and environmental carrying capacity, or if there is a correlation between the resource and environmental parameter category affected by the urban effect and the parameters in the functional relationship of the initial assessment index of resource and environmental carrying capacity, then the corresponding relationship between the resource and environmental parameter category and the initial assessment index is considered a matching relationship; The corresponding relationship between urban effects and each partial region is determined by matching the historical data of urban effects with the initial assessment indicators of resource and environmental carrying capacity of each partial region, thereby determining the urban effect particles of each partial region, including: Collect historical data according to the corresponding relationship between resource and environmental parameter categories and initial assessment indicators, and obtain historical data of resource and environmental parameters and historical data of initial assessment indicators for each partial area; The historical data of resource and environmental parameters and the historical data of initial evaluation indicators of each partial region are mapped to two state spaces respectively. The causal indicators between the historical data of resource and environmental parameters and the historical data of initial evaluation indicators of each partial region are constructed according to the distance between the two state spaces, thereby determining the causal relationship between the resource and environmental parameter categories and the initial evaluation indicators of each partial region, and using this causal relationship as the corresponding relationship between the urban effect and each partial region; Integrate all causal indicators of each partial area, and convert the integrated causal indicators into urban effect particles of each partial area; The radiation area and radiation properties in the partial area are determined according to the radiation point, and radiation particles in the partial area of the surrounding area are obtained based on the radiation area and radiation properties, including: Evaluate the radiation influence range of each radiation point in each partial area, determine the regional radiation intensity and regional radiation range of the partial area based on the radiation intensity and radiation range of all radiation points in the same partial area, and use the regional radiation intensity and regional radiation range as the radiation attributes of the partial area; The area where both the regional radiation intensity and the regional radiation range exceed the corresponding threshold value is regarded as the radiation area, and the affected area around it is determined according to the radiation properties of the radiation area. The radiation particles in the affected area are determined based on the distance between the affected area and the radiation area and the radiation properties of the radiation area. The partial areas are classified according to the particle conditions of each partial area, and the initial evaluation indicators of the partial areas in each category are optimized to obtain the evaluation indicators of resource and environmental carrying capacity, including: If only urban effect particles exist in a part of the area, then the part of the area will be classified as a part of the area with urban effect particles; If both urban effect particles and radiation particles exist in some areas, then the area will be classified as a double particle area; For some areas of urban effect particles, the initial evaluation indicators of some areas are optimized through the urban effect particles corresponding to the part of the urban effect particles; For some areas with double particles, the initial evaluation indicators of some areas are optimized by using two types of particles: urban effect particles and radiation particles; ； in, After optimization evaluation indicators, For the Initial evaluation indicators, For urban effect particles, For radiation particles, For the The elimination coefficient of the evaluation index, is the first particle obtained by mapping urban effect particles and radiation particles. The elimination coefficient of the evaluation index, is a preset constant.

2. The comprehensive prediction method for resource and environmental carrying capacity according to claim 1, characterized in that: The area to be analyzed is divided into several sub-areas based on the geographical location information, including: The area to be analyzed is initially divided according to functional areas to obtain multiple partial functional areas, and the complexity of the partial functional areas is determined by the functional categories and the number of areas of the multiple partial functional areas; The geographical feature similarity threshold is determined according to the complexity of the partial functional area, and the geographical features in each partial functional area are extracted. The partial functional area is divided twice according to the geographical features and the geographical feature similarity threshold to obtain several partial areas.

3. The comprehensive prediction method for resource and environmental carrying capacity according to claim 1, characterized in that: Obtain radiation information within each partial area, and use the radiation information to determine all radiation points within the partial area, including: Each type of radiation information is normalized and all types of radiation information are integrated to obtain the radiation intensity. The radiation source is screened in each partial area according to the radiation intensity, and the screened radiation source is used as the radiation point.

4. The comprehensive prediction method for resource and environmental carrying capacity according to claim 1, characterized in that: The weight of each resource and environmental carrying capacity assessment indicator is set based on the overall change scenario of the region to be analyzed, thereby integrating the assessment indicators of each resource and environmental carrying capacity to obtain the overall assessment indicators, including: Analyze the overall change scenarios of the area to be analyzed, and transform each overall change scenario so that all overall change scenarios can be compared on the same scale. Set the weight of the assessment indicator of each resource and environmental carrying capacity, and perform weighted summation of the assessment indicators of resource and environmental carrying capacity to obtain the overall assessment indicator.

5. A comprehensive prediction device for resource and environmental carrying capacity, characterized in that: The device is used to implement the comprehensive prediction method of resource and environmental carrying capacity according to any one of claims 1 to 4, comprising: The first module is used to obtain the geographical location information of the area to be analyzed, and divide the area to be analyzed into several partial areas according to the geographical location information, obtain the resource and environmental information in each partial area, and determine the initial assessment index of the resource and environmental carrying capacity of each partial area based on the resource and environmental information; The second module is used to analyze the urban effect based on the resource and environmental information of the area to be analyzed, and to establish a matching relationship between the urban effect and the initial evaluation indicator category. The corresponding relationship between the urban effect and each partial area is determined by matching the historical data of the urban effect with the initial evaluation indicators of the resource and environmental carrying capacity of each partial area, thereby determining the urban effect particles of each partial area; The third module is used to obtain radiation information in each partial area, determine all radiation points in the partial area through the radiation information, determine the radiation area and radiation properties in the partial area based on the radiation points, and obtain radiation particles in the surrounding partial area based on the radiation area and radiation properties; The fourth module is used to classify the partial areas according to the particle situation of each partial area, optimize the initial evaluation indicators of the partial areas in each category, and obtain the evaluation indicators of resource and environmental carrying capacity. The weight of each resource and environmental carrying capacity evaluation indicator is set according to the overall change scenario of the analyzed area, and the evaluation indicators of each resource and environmental carrying capacity are integrated to obtain the overall evaluation indicator. The fifth module is used to construct a resource and environmental carrying capacity prediction model based on the evaluation indicators of each resource and environmental carrying capacity and the overall evaluation indicators, and to predict the resource and environmental carrying capacity of the analyzed area.

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