A macro-siting method for wind power projects based on spatial data overlay analysis

Abstract

The application discloses a kind of wind power project macroscopic site selection method based on spatial data superposition analysis, it is related to geographic spatial analysis technical field, specifically includes: collection history and real-time spatial data, establish spatial elasticity network model, based on the hierarchical structure and influence intensity in two-dimensional coordinate system, dynamically optimize the mutual influence between different spatial regions, construct environmental migration diagram, analyze the influence of environmental change path on wind power project macroscopic site selection, construct three-dimensional virtual environment space, select the virtual optimal site of wind power project by distance, angle and non-dominated sorting iterative optimization, realize the preferred geographical site selection of wind power project by the reverse mapping and similarity comparison of virtual environment space and geographical area, combined with investment net present value;The application constructs virtual environment space by double influence superposition, selects optimal virtual site, and derives the best site of real area by reverse mapping, realizes accurate and efficient wind power project site selection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geographic spatial analysis, and in particular to a wind power project macro-siting method based on spatial data overlay analysis. BACKGROUND

[0002] Currently, wind power project siting mainly relies on traditional geographic information systems and environmental factor analysis. With the increasing complexity of environmental changes, the traditional siting method faces challenges in dynamically reflecting environmental fluctuations in different time periods and regions and responding to the changing needs of multi-factor and dynamic environment in wind farm siting decisions.

[0003] The prior art has the following disadvantages:

[0004] (1) The existing traditional wind power siting method cannot dynamically consider the changes of environment and resources in real time, resulting in low prediction accuracy;

[0005] (2) It mainly relies on a single data source and static model, and cannot comprehensively consider the interactive influence of multi-level spatial features, limiting accurate modeling of interactions between different spatial regions;

[0006] (3) When processing large-scale spatial data, the traditional method cannot efficiently select the optimal site through optimization algorithms, lacks sufficient flexibility and scalability, and is difficult to make quick and accurate decisions in complex environments;

[0007] The above shortcomings make the siting work of wind power projects time-consuming and difficult to adapt to the multi-dimensional challenges of future environmental changes.

[0008] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0009] The purpose of the present application is to provide a wind power project macro-siting method based on spatial data overlay analysis to solve the problems in the background art.

[0010] In order to achieve the above-mentioned purpose, the present application provides the following technical solution: a wind power project macro-siting method based on spatial data overlay analysis, specifically comprising:

[0011] Collecting historical and real-time spatial data and preprocessing;

[0012] Establishing a spatial elasticity network model, dynamically optimizing the mutual influence between different spatial regions based on the hierarchical structure and influence strength in the two-dimensional coordinate system, and realizing the smooth connection and update of multi-level spatial data;

[0013] Constructing environmental migration map, mapping environmental state change to wind power potential value adjustment and spatial layer influence, analyzing the influence of environmental change path on wind power project macro-siting;

[0014] Constructing three-dimensional virtual environment space, discretizing and expressing the double influence in grade combination, selecting virtual optimal siting of wind power project through distance, angle and non-dominated sorting iterative optimization;

[0015] Realizing optimal geographical siting of wind power project through reverse mapping and similarity comparison of virtual environment space and geographical area, combining with investment net present value;

[0016] Dynamic visualizing virtual environment space, real-time displaying future environmental and resource potential change.

[0017] Deriving optimal siting area in real spatial area through reverse mapping;

[0018] Preferably, as a preferred scheme of the wind power project macro-siting method based on spatial data superposition analysis, wherein:

[0019] The historical and real-time spatial data corresponding to the key factors of wind power project siting specifically include wind resource spatial data, terrain spatial data, landform spatial data, model spatial data, power grid spatial data, sensitive factor spatial data and financial estimation data;

[0020] The model spatial data is wind energy to electric energy conversion capability data, the sensitive factor data is area data where wind power project cannot be constructed, and the financial estimation data includes construction auxiliary engineering, equipment and installation engineering, building engineering, basic reserve, engineering static investment, construction period interest and engineering dynamic total investment;

[0021] The preprocessing specifically includes,

[0022] Cleaning and normalizing the spatial data;

[0023] Realizing spatial registration based on geographic information system;

[0024] Filling discontinuous spatial data and adjusting the spatial data to uniform spatial resolution;

[0025] Object-level fusion of the spatial data in the unit of ground object;

[0026] Establishing spatial information database to support fast access and query of spatial data.

[0027] Preferably, as a preferred scheme of the wind power project macro-siting method based on spatial data superposition analysis, wherein:

[0028] The space elasticity network model further comprises,

[0029] The space layers corresponding to the key factors of the wind power project site selection are divided;

[0030] Each space layer constitutes an independent space network, and the nodes represent the current space layer, and the edges represent the correlation of the space data in the adjacent layers.

[0031] A two-dimensional coordinate system is constructed, the vertical axis is defined as the space layers of the space elasticity network, and the horizontal axis is defined as the mutual influence strength index between the space regions.

[0032] The cross-layer nodes are positioned on the vertical axis of the space layer to which they belong.

[0033] According to the cross-layer and inter-layer interaction relationship, the mutual influence strength value is mapped to the horizontal axis.

[0034] The interaction weight in the multi-layer space network is calculated, and a continuous space mutual influence smooth line is constructed along the horizontal axis.

[0035] The space mutual influence smooth line is fixed on the first space layer on the vertical axis, and the other space layers are sequentially overlapped and translated.

[0036] The space layers of n front and back relationships are defined, and the node end belonging to a space layer and the node front end belonging to the space layer immediately following it are sequentially defined as A, B, …, Z.

[0037] By translating the immediately following space layer, the end and the front end are connected, so that the smoothness of the space mutual influence smooth line after the sequential connection of A, B, …, Z is the highest.

[0038] The horizontal axis interval corresponding to the smoothness change is locked, and the smoothness change amount represents the mutual influence strength.

[0039] The two-dimensional coordinate system is dynamically updated according to the collection time.

[0040] Preferably, as a preferred scheme of the wind power project macro site selection method based on space data superposition analysis, wherein:

[0041] A plurality of different environment spaces with only one set of environmental parameter state and space characteristics are preset.

[0042] The migration probability between different environmental space states is calculated through historical space data, and an environmental migration graph is constructed to describe the change path of the environmental space state under different conditions at different times.

[0043] Each environment space is mapped to the space elasticity network, and the potential influence of different environment spaces on the wind power project site selection is defined.

[0044] assigning a basic wind power potential value to each environmental space state, and adjusting the potential value according to the current space state;

[0045] allocating the basic wind power potential value to the space layer node of the site selection key factor of the wind power project in each environmental space state;

[0046] constructing a wind power potential distribution map of the corresponding environmental space according to a physical airflow model;

[0047] observing the wind power potential change trend of the change path of different environmental space states, and describing the influence of the environmental change path on the site selection of the wind power project.

[0048] Preferably, as a preferred scheme of the wind power project macro site selection method based on spatial data superposition analysis, wherein:

[0049] the virtual environmental space is constructed based on the double influence superposition, specifically including,

[0050] discretizing the double influence into n limited grade intervals, and representing each virtual environmental space state by a vector, the dimension of which corresponds to different grade combinations of the double influence;

[0051] defining the site selection target as maximizing the wind power potential, minimizing the mutual influence of the space layer, and minimizing the environmental influence;

[0052] establishing a three-dimensional virtual environmental space based on the site selection target, and allocating each node with a grade combination of the three-dimensional virtual environmental space, the total number of nodes in the virtual environmental space combination being n2;

[0053] based on the passage of the collection time, the nodes move in the three-dimensional virtual environmental space, connecting all the nodes, and judging the performance of each node in the three-dimensional virtual environmental space based on the site selection target according to the distance and angle, specifically including,

[0054] when the distance is the same but the angle is larger, it is judged that the current node performs best in the three-dimensional virtual environmental space based on the site selection target;

[0055] when the angle is the same but the distance is farther, it is judged that the current node performs best in the three-dimensional virtual environmental space based on the site selection target;

[0056] when the angles of multiple nodes are the same and the distances are the same, a trade-off scheme between different node selections is identified according to the weight of the site selection target through non-dominated sorting, and the search process is iterated until the current node performs best in the three-dimensional virtual environmental space based on the site selection target;

[0057] judging the node as the optimal virtual site selection.

[0058] Preferably, as a preferred scheme of the wind power project macro-siting method based on spatial data superposition analysis, wherein:

[0059] Cumulative financial estimation data and calculate the net present value of the project investment comparison;

[0060] The three-dimensional virtual environment space node is reversely mapped to the geographic area in the spatial information database;

[0061] According to the optimal virtual siting matching geographic area, the closest geographic area of the spatial data is obtained;

[0062] Compare the similarity between the regions, and sort the most cost-effective siting according to the net present value of the project investment, and take it as the optimal siting area.

[0063] In another aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein the computer program is executed by the processor to realize the functional steps of the wind power project macro-siting method based on spatial data superposition analysis as described above.

[0064] In another aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by the processor to realize the functional steps of the wind power project macro-siting method based on spatial data superposition analysis as described above.

[0065] In the above technical solution, the present application provides technical effects and advantages:

[0066] By using the spatial elastic network model, the spatial hierarchical structure and the influence strength are dynamically reflected, the influence relationship between different regions is smoothly connected, the hierarchical expression of spatial hierarchy and influence mechanism is highlighted, the environmental migration graph is constructed, the change path of the environmental state is scientifically simulated, the influence of the dynamic change of the environment on the potential value is considered, the adaptability and predictability of the siting are enhanced, the three-dimensional virtual environment space is established, the distance, angle and non-dominant sorting method are used for multi-objective comprehensive optimization, the optimal virtual siting is obtained, the limitation of traditional single evaluation index is broken through, the multi-objective trade-off is fully reflected, the reverse mapping of the virtual environment space and the actual geographic area, the similarity comparison and the investment net present value sorting are realized, the virtual optimization result can effectively guide the actual siting, the landing of the scheme is improved, the double influence is discretized, the hierarchical combined virtual environment space is established, which is helpful to refine the influence factors and improve the modeling accuracy of the environmental change and the scientificity of the strategy making. BRIEF DESCRIPTION OF DRAWINGS

[0067] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0068] Figure 1 This is a flowchart of a macroscopic site selection method for wind power projects based on spatial data overlay analysis, according to the present invention.

[0069] Table 1 shows the experimental data of a macroscopic site selection method for wind power projects based on spatial data overlay analysis according to the present invention.

[0070] Table 2 shows the technical parameters of the wind power project macro-site selection method based on spatial data overlay analysis according to the present invention. Detailed Implementation

[0071] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0072] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a macroscopic site selection method for wind power projects based on spatial data overlay analysis, specifically including:

[0073] S1. Collect historical and real-time spatial data and perform preprocessing;

[0074] The historical and real-time spatial data of the multiple corresponding key factors for wind power project site selection specifically include wind resource spatial data, terrain spatial data, geomorphological spatial data, turbine model spatial data, power grid spatial data, sensitive factor spatial data, and financial estimation data;

[0075] The model spatial data represents the wind energy to electricity conversion capacity data, the sensitive factor data represents the area data where wind power projects cannot be constructed, and the financial estimation data includes construction auxiliary works, equipment and installation works, building works, basic contingency funds, static investment of the project, construction period interest, and total dynamic investment of the project.

[0076] The preprocessing specifically includes,

[0077] The spatial data is cleaned and normalized;

[0078] Spatial registration based on geographic information systems;

[0079] Fill in the gaps in the spatial data and adjust the spatial data to a uniform spatial resolution;

[0080] The spatial data is fused at the object level, using ground features as the unit.

[0081] Establish a spatial information database to support fast access and query of spatial data.

[0082] S2. Establish a spatial elastic network model, based on the hierarchical structure and influence intensity in the two-dimensional coordinate system, dynamically optimize the mutual influence between different spatial regions, and realize the smooth connection and update of multi-layer spatial data.

[0083] The spatial layers that divide the key factors for wind power project site selection specifically include:

[0084] Establish a spatial elastic network model and divide it into multiple spatial layers corresponding to key factors in wind power project site selection;

[0085] Each spatial layer constitutes an independent spatial network, where nodes represent the current spatial layer and edges represent the association of spatial data within adjacent layers.

[0086] A two-dimensional coordinate system is constructed, with the vertical axis defined as the various spatial layers of the spatial elastic network to reflect the hierarchical structure of influencing factors, and the horizontal axis defined as the mutual influence intensity index between spatial regions to measure the mutual influence intensity and range between different spatial layers or within the same layer.

[0087] Position the cross-layer node on the vertical axis of its spatial layer;

[0088] Based on the cross-layer and intra-layer interaction relationships, the numerical values ​​of mutual influence intensity are mapped to the horizontal axis;

[0089] The interaction weights are calculated in a multi-layer spatial network, and a continuous smooth line of spatial interaction is constructed along the horizontal axis.

[0090] The spatial interaction smoothing line is on the vertical axis, fixing the first spatial layer, and then sequentially overlapping and translating the other spatial layers;

[0091] Define n spatial layers with preceding and following relationships, and define the end of the node to which a spatial layer belongs and the front of the node to which the next spatial layer belongs, respectively, as A, B, ..., Z;

[0092] By translating the immediately following spatial layer and connecting the end and the beginning, the smoothness of the smooth line of mutual influence between the spatial elements after connecting each pair of points A, B, ..., Z is maximized.

[0093] Lock the horizontal axis interval corresponding to the smoothness change, and represent the mutual influence intensity by the amount of smoothness change;

[0094] The two-dimensional coordinate system is dynamically updated based on the acquisition time.

[0095] S3. Construct an environmental migration map to map changes in environmental status to wind power potential value adjustments and spatial layer effects, and analyze the impact of environmental change paths on the macro-site selection of wind power projects.

[0096] Pre-set multiple different environmental spaces with only one set of environmental parameter states and spatial characteristics;

[0097] The migration probability between different environmental spatial states is calculated using historical spatial data, and an environmental migration map is constructed to describe the change path of environmental spatial states under different time conditions.

[0098] Each environmental space is mapped to a spatial elastic network to define the potential impact of different environmental spaces on the site selection of wind power projects.

[0099] Assign a base wind power potential value to each environmental spatial state, and adjust the potential value according to the current spatial state;

[0100] In each environmental spatial state, the basic wind power potential value is allocated to the spatial layer nodes of the key factors for site selection of the corresponding wind power project;

[0101] A wind power potential distribution map for the corresponding environmental space is constructed based on a physical airflow model;

[0102] Observe the wind power potential change trend of different environmental spatial states and describe the impact of environmental change paths on wind power project site selection.

[0103] It should be further noted that the migration probability is the probability of transitioning from one environmental space state to another.

[0104] It should also be noted that the environment migration graph is used to show the transition relationship between different environmental spatial states, where each node represents an environmental spatial state and the edges between nodes represent the migration probability.

[0105] S4. Construct a three-dimensional virtual environment space, discretize the dual influences and represent them in a hierarchical combination, and select the virtual optimal site for wind power projects through distance, angle and non-dominated sorting iterative optimization.

[0106] The virtual environment space constructed based on the superposition of dual influences specifically includes,

[0107] The dual impact is discretized and divided into n finite level intervals. Each virtual environment space state is represented by a vector, and its dimension corresponds to different combinations of dual impact levels.

[0108] The site selection objectives are defined as maximizing wind power potential, minimizing spatial layer interactions, and minimizing environmental impact.

[0109] A three-dimensional virtual environment space is established based on the site selection target, and each node is assigned a combination of three-dimensional virtual environment spaces with a certain level combination. The total number of nodes in the combination of virtual environment spaces is n².

[0110] As the data acquisition time progresses, nodes move within the 3D virtual environment. Connecting all nodes, the performance of each node within the 3D virtual environment is determined based on distance and angle, according to the selected target. Specifically, this includes...

[0111] When nodes are at the same distance but have a larger angle, the current node is judged to be the best performing node in the three-dimensional virtual environment space based on the location target.

[0112] When nodes have the same angle but are farther apart, the current node is judged to be the best performing node in the three-dimensional virtual environment space based on the location target.

[0113] When multiple nodes have the same angle and the same distance, non-dominated sorting is used to identify the trade-off between different node selections based on the weight of the location selection target, and the search process is iterated until the current node performs optimally in the three-dimensional virtual environment space based on the location selection target.

[0114] The node determined is taken as the optimal virtual location.

[0115] It should also be noted that non-dominated sorting can identify the position where the optimal balance is achieved between wind power potential and environmental impact. In non-dominated sorting, the optimization algorithm matches the optimal solution set among multiple objectives, representing the best trade-off between maximizing wind power potential, minimizing spatial layer interactions, and minimizing environmental impact.

[0116] It provides a frontier solution that balances the trade-off between maximizing wind power potential and environmental impact as the optimal virtual site selection.

[0117] S5. By inversely mapping and comparing the similarity between the virtual environment space and the geographical region, and combining the net present value of investment, the optimal site selection for wind power projects can be achieved;

[0118] Accumulate the financial estimates and calculate the net present value of the project investment for comparison.

[0119] Reverse mapping of spatial nodes in a 3D virtual environment to geographical regions in a spatial information database;

[0120] The best virtual location is used to match the geographic region with the closest spatial data.

[0121] Compare the similarity between regions and rank the most cost-effective sites according to the net present value of project investment, and select the best site as the optimal site.

[0122] It should also be noted that the reverse mapping process involves matching the spatial data of the wind power potential of the nodes with the corresponding areas of environmental impact to find the most similar areas in the actual geographic space.

[0123] It should also be noted that the matching rules take into account multiple key factors for the site selection of corresponding wind power projects, and try to avoid selecting areas that have a significant negative impact on the ecosystem.

[0124] S6: Dynamically visualized virtual environment space, displaying real-time changes in future environment and resource potential;

[0125] Furthermore, by using GIS technology to combine virtual ecological environment data with real-world spatial data, multiple key factors for the site selection of corresponding wind power projects can be overlaid and displayed on a map view based on a time axis, and the environmental and resource potential changes of a certain area can be viewed in real time through a 3D view.

[0126] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0127] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of implementing a macroscopic site selection method for wind power projects based on spatial data overlay analysis as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0128] Example 2

[0129] The following is another embodiment of the present invention, which provides a macroscopic site selection method for wind power projects based on spatial data overlay analysis. In order to verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0130] In this experiment, the specific steps of the wind power project macro-site selection method based on spatial data overlay analysis of this invention were executed, and the experimental data specifically included:

[0131]

[0132] According to the experimental data, t1 and t4 are high wind environments with relatively high potential values ​​of 1500 and 1520 respectively, indicating that the wind energy potential is greatest under high wind conditions.

[0133] t2 and t5 are stroke environments with moderate potential values ​​of 1200 and 1220 respectively.

[0134] t3 is a low-wind environment with the lowest potential value of 900;

[0135] This indicates that the potential value is positively correlated with wind speed, and high winds bring higher wind energy potential.

[0136] The perturbations of t1 and t4 are "slight perturbations", and the potential values ​​are relatively high, indicating that the potential performance is better when the environmental perturbation is small;

[0137] t2 and t5 are "moderate disturbances", with potential values ​​slightly lower than those for high-wind conditions;

[0138] t3 represents "extreme disturbance" with the lowest potential value, indicating that the greater the environmental disturbance, the more the wind energy potential is suppressed.

[0139] High wind conditions: 0.8 (t1), 0.82 (t4), indicating a strong environmental impact, but the potential value is higher under high wind conditions;

[0140] Stroke status: 0.5 (t2), 0.52 (t5), with moderate spatial impact;

[0141] Low wind conditions: 0.3 (t3), with weak spatial impact;

[0142] The higher the intensity of spatial influence, the greater the potential value, reflecting the positive driving effect of spatial influence on wind energy potential.

[0143] t4 (1520 potential, slight disturbance) has the highest value of 5.2 million yuan, demonstrating good economic performance brought about by high potential and environmental conditions;

[0144] The net values ​​of t2 and t5 are quite similar, at 4.5 million and 4.6 million yuan respectively, indicating relatively low potential but still acceptable.

[0145] The lowest value was t3, at 3 million yuan, indicating a significant decrease in returns under low wind and extreme disturbance conditions.

[0146] High wind conditions (t1, t4) are both level 4, representing the optimal level;

[0147] Stroke (t2, t5) is classified as level 3;

[0148] Low wind (t3) is level 2.

[0149] Region A: It exhibits high potential value and net investment value during high-risk conditions (t1, t4), verifying its suitability;

[0150] Region B: Stroke state (t2, t5) shows moderate potential and relatively high investment value;

[0151] Region C: Low wind conditions (t3) have the lowest potential and the worst return on investment.

[0152] It should also be noted that the model space data is only a definition of an attribute, not a spatial representation of the model. Specifically, it includes the following:

[0153]

[0154] Table 2 shows that, in terms of technology, the selected models are all technologically advanced and mature variable-speed constant-frequency wind turbine generator sets. These units allow for adjustment of the rotor blade pitch angle, and the generator can also be speed-adjusted, outputting constant-frequency, constant-power electrical energy. At wind speeds below the rated speed, the wind turbine generator operates at its optimal peak speed and outputs maximum power by changing the rotor speed and blade pitch angle; at wind speeds above the rated speed, the power output is stabilized at the rated power by changing the blade pitch angle. WTG2, WTG3, and WTG4 are doubly-fed induction generator wind turbine generator sets. The rotor of these wind turbine generator sets uses AC excitation, and the active and reactive power outputs of the generator are adjusted by regulating parameters such as the frequency and amplitude of the excitation current. Reactive power is generated or absorbed according to the needs of the power grid to improve the power quality of the local grid. Because pitch operation can be adjusted according to wind speed changes, the unit always operates close to the optimal condition, capturing the most wind energy and thus improving the power generation capacity of the wind turbine generator set. WTG1 adopts a direct-drive, permanent magnet synchronous generator grid-connected design, with the following advantages:

[0155] 1. The reduction in transmission system components improves the reliability and availability of wind turbine generator sets.

[0156] 2. The adoption of permanent magnet power generation technology and variable speed constant frequency technology has improved the efficiency of wind turbine generator sets.

[0157] 3. Improved reliability of wind turbine generator sets reduces the operation and maintenance costs of wind turbine generator sets.

[0158] By utilizing a spatial elastic network model, the spatial hierarchy and influence intensity are dynamically reflected, enabling smooth connections between different regions and highlighting the hierarchical expression of spatial levels and influence mechanisms. An environmental migration map is constructed to scientifically simulate the path of environmental state changes, considering the impact of dynamic environmental changes on potential values, thus enhancing the adaptability and predictability of site selection. A three-dimensional virtual environmental space is established, and multi-objective comprehensive optimization is performed based on distance, angle, and non-dominated ranking methods to obtain the optimal virtual site selection. This breaks through the limitations of traditional single evaluation indicators, fully reflecting multi-objective trade-offs, and achieving inverse mapping, similarity comparison, and net present value ranking between the virtual environmental space and actual geographical areas. This ensures that the virtual optimization results can effectively guide actual site selection, improve the feasibility of the scheme, and discretize the dual influences. Establishing a hierarchical combination of virtual environmental spaces helps to refine influencing factors, improve the modeling accuracy of environmental changes, and enhance the scientific nature of strategy formulation.

[0159] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A macroscopic site selection method for wind power projects based on spatial data overlay analysis, characterized in that, Specifically, it includes: Collect historical and real-time spatial data and perform preprocessing; A spatial elastic network model is established, and based on the hierarchical structure and influence intensity in a two-dimensional coordinate system, the mutual influence between different spatial regions is dynamically optimized to achieve smooth connection and updating of multi-layer spatial data. The establishment of the spatial elastic network model further includes, Divide the space into multiple spatial layers corresponding to key factors in the site selection of wind power projects; Each spatial layer constitutes an independent spatial network, where nodes represent the current spatial layer and edges represent the association of spatial data within adjacent layers. An environmental migration map is constructed to map changes in environmental status to adjustments in wind power potential and spatial layer effects, and the impact of environmental change paths on the macro-site selection of wind power projects is analyzed. The analysis of the impact of environmental change paths on the macro-site selection of wind power projects specifically includes... Pre-set multiple different environmental spaces with only one set of environmental parameter states and spatial characteristics; The migration probability between different environmental spatial states is calculated using historical spatial data, and an environmental migration map is constructed to describe the change path of environmental spatial states under different time conditions. Each environmental space is mapped to a spatial elastic network to define the potential impact of different environmental spaces on the site selection of wind power projects. A base wind power potential value is assigned to each environmental spatial state, and the potential value is adjusted according to the current spatial state. In each environmental spatial state, the basic wind power potential value is allocated to the spatial layer nodes of the key factors for site selection of the corresponding wind power project; A wind power potential distribution map for the corresponding environmental space is constructed based on a physical airflow model; Observe the wind power potential change trend along different environmental spatial states and describe the impact of environmental change paths on wind power project site selection. A three-dimensional virtual environment space is constructed, the dual influences are discretized and represented by a combination of levels, and the virtual optimal site for wind power projects is selected through iterative optimization of distance, angle and non-dominated sorting. By inversely mapping virtual environment space to geographical regions and comparing similarity, combined with net present value of investment, optimal site selection for wind power projects can be achieved; A dynamically visualized virtual environment space that displays real-time changes in future environmental and resource potential.

2. The macroscopic site selection method for wind power projects based on spatial data overlay analysis according to claim 1, characterized in that: The two-dimensional coordinate system specifically includes A two-dimensional coordinate system is constructed, with the vertical axis defined as the various spatial layers of the spatial elastic network to reflect the hierarchical structure of influencing factors, and the horizontal axis defined as the mutual influence intensity index between spatial regions to measure the mutual influence intensity and range between different spatial layers or within the same layer. Position the cross-layer node on the vertical axis of its spatial layer; Based on the cross-layer and intra-layer interaction relationships, the numerical values ​​of mutual influence intensity are mapped to the horizontal axis; Interaction weights are calculated in a multi-layer spatial network, and a continuous smooth line of spatial interaction is constructed along the horizontal axis.

3. The macroscopic site selection method for wind power projects based on spatial data overlay analysis according to claim 2, characterized in that: The spatial interaction smoothing line is on the vertical axis, fixing the first spatial layer, and then sequentially overlapping and translating the other spatial layers; Define n spatial layers with preceding and following relationships, and define the end of the node to which a spatial layer belongs and the front of the node to which the next spatial layer belongs, respectively, as A, B, ..., Z; By translating the immediately following spatial layer and connecting the end and the beginning, the smoothness of the smooth line of mutual influence between the spatial elements after connecting each pair of points A, B, ..., Z is maximized. Lock the horizontal axis interval corresponding to the smoothness change, and represent the mutual influence intensity by the amount of smoothness change.

4. The macroscopic site selection method for wind power projects based on spatial data overlay analysis according to claim 1, characterized in that: The virtual environment space constructed based on the superposition of dual influences specifically includes, The dual impact is discretized and divided into n finite level intervals. Each virtual environment space state is represented by a vector, and its dimension corresponds to different combinations of dual impact levels. The site selection objectives are defined as maximizing wind power potential, minimizing spatial layer interactions, and minimizing environmental impact. A three-dimensional virtual environment space is established based on the site selection target, and each node is assigned a combination of three-dimensional virtual environment spaces with a certain level combination. The total number of nodes in the combination of virtual environment spaces is n². As the acquisition time progresses, nodes move within the three-dimensional virtual environment space, connecting all nodes. Based on distance and angle, the performance of each node in the three-dimensional virtual environment space is determined according to the selected target.

5. The macroscopic site selection method for wind power projects based on spatial data overlay analysis according to claim 4, characterized in that: The determination of each node's performance in the three-dimensional virtual environment space based on the selected target specifically includes: When nodes are at the same distance but have a larger angle, the current node is judged to be the best performing node in the three-dimensional virtual environment space based on the location target. When nodes have the same angle but are farther apart, the current node is judged to be the best performing node in the three-dimensional virtual environment space based on the location target. When multiple nodes have the same angle and distance, non-dominated sorting is used to identify the trade-off between different node selections based on the weight of the location selection objective, and the search process is iterated until the current node performs optimally in the three-dimensional virtual environment space based on the location selection objective.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the functional module of the macroscopic site selection method for wind power projects based on spatial data overlay analysis as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the functional module of the macroscopic site selection method for wind power projects based on spatial data overlay analysis as described in any one of claims 1 to 5.

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