Optimization method for accounting of ecosystem cultural services of multi-source passenger flow integration

Abstract

The application provides a multi-source passenger flow fusion ecological system cultural service accounting optimization method, relates to the technical field of data processing, and comprises the following steps: collecting periodic accounting data of a target region, mapping multi-source passenger flow data through coordinate conversion to generate standardized tourist space-time distribution data sets; obtaining an arbitrary landscape area to obtain an arbitrary actual reception capacity; taking the normalized product of the arbitrary landscape area and the arbitrary actual reception capacity as the comprehensive weight factor of an arbitrary landscape node, distributing the total cost of tourist travel, adding and calculating an arbitrary leisure and recreation value to obtain the target value of the target region. The technical problems of low ecological system cultural service accounting precision and poor adaptability caused by single data source and equal multi-scene cost allocation in the prior art are solved. Through multi-source passenger flow data fusion analysis, the precise accounting of the ecological system cultural service value node is realized, and the technical effects of improving the scientificity and accuracy of the accounting result are achieved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an ecosystem cultural service accounting optimization method that integrates multiple source passenger flows. Background Technology

[0002] Ecosystem cultural service accounting is a crucial component of ecosystem service value assessment. Current accounting methods primarily employ the travel cost approach and the hedonic value approach, but these methods have significant limitations when applied to complex watersheds and lakeside areas. Traditional accounting relies solely on ticket sales data from scenic area gates, failing to cover a large number of implicit recreational activities such as free wetlands around lakes and self-driving tours in the countryside. This leads to a systematic underestimation of the cultural value of open spaces like lakeside and suburban areas. Furthermore, tourist travel costs are simply averaged across multiple attractions without considering differentiated factors such as the ecological area and actual visitor volume of each attraction. This results in severely distorted calculations of high-value scenic nodes, making it difficult to meet the operational needs of refined ecological product value accounting and differentiated ecological compensation.

[0003] In summary, existing technologies suffer from problems such as a single data source and equal cost sharing among multiple scenic spots, resulting in low accuracy and poor adaptability in the accounting of cultural services for the ecosystem. Summary of the Invention

[0004] The purpose of this application is to provide an optimization method for ecosystem cultural service accounting based on multi-source visitor flow integration, in order to solve the technical problems of low accuracy and poor adaptability of existing technologies due to single data sources and equal cost sharing among multiple attractions.

[0005] In view of the above problems, this application provides an optimization method for ecosystem cultural service accounting based on multi-source visitor flow integration. The optimization method includes: periodically collecting accounting data for a target area using a predetermined accounting unit as a benchmark to obtain multi-source visitor flow data; mapping the multi-source visitor flow data through coordinate transformation using a regular grid with a preset spatial resolution as a unified spatial coordinate system benchmark to generate a standardized visitor spatiotemporal distribution dataset covering the target area; obtaining a target landscape node set for the target area, obtaining the arbitrary landscape area of ​​any landscape node in the target landscape node set, and combining it with the standardized visitor spatiotemporal distribution dataset to obtain the arbitrary actual reception volume of the arbitrary landscape node; using the normalized product of the arbitrary landscape area and the arbitrary actual reception volume as the comprehensive weight factor of the arbitrary landscape node, and using the proportion of the comprehensive weight factor to the sum of the comprehensive weight factors of all nodes in the target landscape node set as the allocation basis to allocate the total cost of visitor travel, obtaining the arbitrary leisure and recreation value corresponding to the arbitrary landscape node; and summing the arbitrary leisure and recreation values ​​to obtain the target value of the target area.

[0006] Optionally, the multi-source passenger flow data includes at least passenger flow recorded by scenic area gates, trajectory reported by satellite positioning terminals, location data from mobile communication network signaling, and on-site stratified sampling of tourist behavior questionnaire data; wherein, the trajectory reported by satellite positioning terminals includes self-driving trajectories around the lake, suburban check-in location information, and outdoor sports route data obtained from social media platforms.

[0007] Optionally, a set of free open nodes for the target area is constructed; a partitioned weighted interpolation method is used to fill in missing passenger flow for any free node in the set of free open nodes to obtain an arbitrary filled passenger volume; the arbitrary filled passenger volume is added to the standardized tourist spatiotemporal distribution dataset.

[0008] Optionally, if any free node is an ecological protection red line area or a core area of ​​a nature reserve, and the missing rate of passenger flow data of any free node exceeds a preset threshold, then an independent filling strategy is enabled for any free node, wherein the independent filling strategy includes constraining the upper limit of the interpolation interval and extending the length of the time sliding window.

[0009] Optionally, the arbitrary landscape area includes the weighted composite value of at least one of the area indicators among the forest area, wetland area, and water area corresponding to the arbitrary landscape node.

[0010] Optionally, a target water body in the target area is obtained; the boundary of the target water body is used as a spatial reference baseline, and the target area is divided according to the straight-line distance from the water body boundary to obtain target landscape layers; a landscape aesthetic premium coefficient is independently configured for each landscape layer in the target landscape layers, and real estate transaction market data within each layer is obtained respectively; the total annual asset appreciation brought by the landscape aesthetic quality of each layer in the target landscape layers is calculated in layers based on the landscape aesthetic premium coefficient corresponding to each layer; the total annual asset appreciation is used as the landscape aesthetic value of the target area and added to the target value.

[0011] Optionally, the distance threshold for the target landscape layer is set differently based on whether the target area belongs to different ecological and geographical types such as lake type, mountain type, hill type or karst type.

[0012] Optionally, the annual total carbon sequestration data and annual total water purification data of the target area are obtained; the total carbon sequestration data obtained by analyzing the annual total carbon sequestration data and the total water purification data obtained by analyzing the annual total water purification data are multiplied by the corresponding pre-calibrated regulation contribution weight coefficients and then summed to form a comprehensive linkage correction factor; the target value is multiplied by the comprehensive linkage correction factor to obtain the total value of the target ecosystem cultural services of the target area after ecological regulation service correction.

[0013] Optionally, the target area is divided into at least three landscape concentric circles according to the straight-line distance from the water body boundary. The distance division threshold for each landscape concentric circle is determined as follows: obtain historical real estate transaction data of the target area, and use the inflection point of the decay curve of each real estate transaction price as a function of distance from the water body boundary as the division threshold. The area within the first inflection point is designated as the waterfront core concentric circle, the area between the first and second inflection points is designated as the buffer transition concentric circle, and the area outside the second inflection point is designated as the offshore concentric circle.

[0014] Optionally, the total carbon sequestration is multiplied by a first regulatory contribution weighting coefficient to obtain a first component; the total water purification is multiplied by a second regulatory contribution weighting coefficient to obtain a second component; the first component, the second component, and a unit benchmark value are summed to obtain a comprehensive linkage correction factor; wherein the first regulatory contribution weighting coefficient and the second regulatory contribution weighting coefficient are pre-calibrated based on historical ecological monitoring data of the target area through regression analysis.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages: The method provided in this application collects multi-source visitor flow data by periodically calculating data for a target area using a predetermined calculation unit as a benchmark. Using a regular grid with a preset spatial resolution as a unified spatial coordinate system benchmark, the multi-source visitor flow data is mapped through coordinate transformation to generate a standardized visitor spatiotemporal distribution dataset covering the target area. A target landscape node set for the target area is obtained, along with the arbitrary landscape area of ​​any landscape node within the target landscape node set. This arbitrary landscape area is then combined with the standardized visitor spatiotemporal distribution dataset to obtain the arbitrary actual reception volume of each landscape node. The normalized product of the arbitrary landscape area and the arbitrary actual reception volume is used as the comprehensive weight factor for each landscape node. The proportion of this comprehensive weight factor to the sum of the comprehensive weight factors of all nodes in the target landscape node set is used as the allocation basis to allocate the total cost of visitor travel, thus obtaining the arbitrary leisure and recreation value corresponding to each landscape node. Finally, the arbitrary leisure and recreation values ​​are summed to obtain the target value of the target area. This method achieves the technical effect of accurately calculating the cultural service value nodes of an ecosystem through the fusion analysis of multi-source visitor flow data, thereby improving the scientific rigor and accuracy of the calculation results.

[0016] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the optimization method for ecosystem cultural service accounting based on multi-source passenger flow integration provided in this application.

[0019] Figure 2 A flowchart illustrating the process of obtaining the target landscape layer in the multi-source visitor flow integration ecosystem cultural service accounting optimization method provided in this application. Detailed Implementation

[0020] This application provides an optimization method for ecosystem cultural service accounting based on multi-source visitor flow fusion. This method addresses the technical problems of existing technologies, such as low accuracy and poor adaptability in ecosystem cultural service accounting due to single data sources and equal cost allocation across multiple attractions. It achieves the technical effect of accurately calculating the value nodes of ecosystem cultural services through multi-source visitor flow data fusion analysis, thereby improving the scientific rigor and accuracy of the accounting results.

[0021] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0022] like Figure 1 , Figure 2 As shown, this application provides an optimization method for ecosystem cultural service accounting based on multi-source visitor flow integration. The optimization method for ecosystem cultural service accounting based on multi-source visitor flow integration includes: Based on predetermined accounting units, periodic accounting data collection is performed on the target area to obtain multi-source passenger flow data.

[0023] Furthermore, the multi-source passenger flow data includes at least passenger flow recorded by scenic area gates, trajectory reported by satellite positioning terminals, location data from mobile communication network signaling, and on-site stratified sampling of tourist behavior questionnaire data; wherein, the trajectory reported by satellite positioning terminals includes self-driving trajectories around the lake, suburban check-in location information, and outdoor sports route data obtained from social media platforms.

[0024] Specifically, the predetermined accounting unit refers to the accounting boundary based on the management objectives and data availability of ecosystem cultural services accounting, using the watershed catchment area or regional jurisdiction boundary as the accounting boundary. The watershed catchment area is defined by the natural watershed of major water bodies within the target area, such as lakes and rivers. This boundary is obtained using a digital elevation model (DEM) with a resolution of 30m x 30m, through hydrological analysis tools such as the ArcGIS hydrological analysis module for flow direction analysis and catchment area extraction. Regional jurisdiction boundaries are defined using regional vector data, such as county-level or township-level civil affairs boundary survey data. The accounting time period is set according to the temporal variation characteristics of ecosystem cultural services in the target area, typically using a natural year (e.g., January 1st to December 31st) or a hydrological year (e.g., the complete cycle from the wet season to the dry season in a lake watershed). Within the accounting period, multi-source passenger flow data is collected monthly or quarterly.

[0025] The multi-source visitor flow data includes at least visitor flow recorded by scenic area gates, trajectory reported by satellite positioning terminals, mobile communication network signaling location data, and on-site stratified sampling of visitor behavior questionnaire data, used to achieve panoramic coverage of the spatiotemporal distribution of visitors. Specifically, the visitor flow recorded by scenic area gates is extracted from the electronic ticketing systems of all paid natural scenic areas, cultural relics sites, and theme parks within the target area via API interfaces or direct database connections, extracting visitor entry timestamps, ticket types, de-identified hash values ​​of visitor IDs, and the geographical coordinates of the respective scenic area node. Trajectories reported by satellite positioning terminals refer to trajectory data obtained from third-party location service open platforms, such as social media platforms and outdoor sports recording applications, which are either actively reported by users or shared with user authorization after de-identification, including but not limited to lakeside driving trajectories, suburban check-in location information, and outdoor sports route data. Mobile communication network signaling location data refers to the unique identifier of the user equipment, timestamp, and time difference of arrival (TDOA) positioning information in the serving cell base station identifier, triggered by location area updates and routing area updates during the mobile terminal's attachment to the network. Stratified sampling of tourist behavior questionnaire data involves selecting characteristic months within the accounting period, such as peak season, off-season, and shoulder season, and then sampling for 1-2 weeks each. Within the target area, quota sampling is conducted at three levels: paid scenic spots, free-admission areas, and ecological protection zones. The survey is conducted offline or electronically. The questionnaire design needs to collect data on tourists' socioeconomic attributes, travel purpose, length of stay, number of companions, and consumption structure. Socioeconomic attributes include origin, age, and occupation; travel purpose includes leisure and vacation, nature sightseeing, outdoor sports, or cultural experiences; and consumption structure includes expenses for transportation, accommodation, food, entrance fees, and souvenirs.

[0026] After data collection is completed, time registration and deduplication preprocessing of multi-source passenger flow data are performed: the gate record is used as the priority true counting benchmark for the charging node; the timestamp of the first entry into the accounting unit in the signaling data is used as the tourist's arrival time; and the timestamp of leaving the accounting unit boundary in the satellite positioning trajectory is used as the departure time. After mutual verification of the three, the data record with the earliest timestamp is retained as the unique entry event for that tourist, avoiding the duplicate accumulation of the same passenger flow across different data sources. Through the data collection and processing flow, multi-source passenger flow data is formed with the predetermined accounting unit as the spatial boundary and the accounting period as the time range.

[0027] By employing at least four data collection methods, we can achieve comprehensive coverage of passenger flow, avoid spatial monitoring blind spots caused by traditional single data sources, and ensure the accuracy and effectiveness of ecosystem cultural service accounting results.

[0028] Using a regular grid with a preset spatial resolution as a reference for a unified spatial coordinate system, the multi-source passenger flow data is mapped through coordinate transformation to generate a standardized spatiotemporal distribution dataset of tourists covering the target area.

[0029] Specifically, a predefined spatial resolution regular grid is established. This regular grid refers to dividing the target area into identical rectangular grid units of the same shape and size in a Cartesian coordinate system. Each grid unit has a unique row and column index and a clearly defined geographic spatial extent. The predefined spatial resolution is comprehensively set based on the area scale of the target area, the spatial positioning accuracy of the data source, and the minimum management unit requirements for ecosystem cultural service accounting. For example, a uniform 30m×30m grid is used as the reference for the unified spatial coordinate system. The original spatial coordinates of all data sources from the multi-source passenger flow data are uniformly transformed to the CGCS2000 National Geodetic Coordinate System. The 3-degree zone plane coordinates of the Gauss-Kruger projection are used as the final projected coordinate system. The central meridian is selected based on the actual longitude of the target area to ensure that all passenger flow event points fall within the same spatial reference frame.

[0030] On a unified spatial coordinate system benchmark, multi-source passenger flow data is mapped through coordinate transformation. For passenger flow recorded at scenic area gates and check-in location information reported by satellite positioning terminals, the row and column numbers of the regular grid where the data point is located are calculated based on its planar coordinates (X,Y). The calculation formula is: Row=floor((YY) origin ) / Cell Size ), Col=floor((XX origin ) / Cell Size In this matrix, Row is the vertical index number (row index) of the regular grid, representing the north-south arrangement number of the grid cell containing the target passenger flow event point. Col is the horizontal index number (column index) of the regular grid, representing the east-west orientation of the grid cell containing the target passenger flow event point. Both are non-negative integers and together constitute the unique spatial address of the grid cell in the two-dimensional matrix. For example, in the 3rd row and 5th column, (X... origin ,Y origin The coordinates of the starting point of the target area grid can be taken as the coordinates of the southwest corner of the outer rectangle of the target area after translation and alignment. Size The grid size is the number of people passing through the grid at any given time. The accumulated count is the number of people passing through the grid at any given time. The floor is used to divide the actual coordinate difference by the grid size and round down the real number quotient to obtain the grid row and column number into which the event point falls. This enables a fast determination of the grid cell from continuous spatial coordinates to discrete regular grid cells.

[0031] For the driving routes and outdoor activity paths around the lake reported by satellite positioning terminals, the Douglas-Puk algorithm was used to thin each route, retaining characteristic inflection points, with a thinning threshold set to 5 meters. Numerical integration was then used to assign weights to the grid cells traversed by the route, calculating the length L of the route segment within each traversed grid cell. ij The unit is meters, and the proportion of this length to the total trajectory length L is used as the contribution weight w=L of this trajectory to the grid.ij / L, then multiplied by the number of tourists represented by that trajectory. Typically, each trajectory corresponds to one person by default. If it's a cycling / driving trajectory with accompanying persons, the number is adjusted based on the accompanying persons field in the trajectory's supplementary information to obtain the tourist flow value generated by linear recreational activities for that rule grid. For mobile communication network signaling location data, an area-weighted interpolation method is used. For a certain base station coverage polygon P, its signaling statistics tourist count is N. p Calculate P and each regular grid G ij If the interaction area is A, then the number of signaling passengers assigned to this base station by the regular grid is N. p ×A / A p , where A p This represents the total area of ​​the base station coverage area. When a grid is simultaneously covered by multiple base station coverage areas, its signaling passenger traffic is the sum of the passenger traffic allocated to this grid by each base station, thus mapping the deduplicated total signaling passenger traffic to the corresponding rule grid space.

[0032] After mapping, each rule grid obtains a comprehensive visitor count value, representing the total number of visitors to that grid within the accounting period. Combined with the location of the grid, a standardized spatiotemporal distribution dataset of visitors covering the target area is generated. This effectively solves the incommensurability problem of different data sources in terms of spatial benchmark, geometric type and positioning accuracy, and provides an accurate spatial attribution basis for the subsequent extraction of the actual reception volume of each landscape node.

[0033] Furthermore, after mapping the multi-source passenger flow data through coordinate transformation to generate a standardized tourist spatiotemporal distribution dataset covering the target area, the process further includes: constructing a set of free open nodes for the target area; using a partitioned weighted interpolation method to fill in passenger flow gaps on any free node in the set of free open nodes to obtain an arbitrary filled reception volume; and adding the arbitrary filled reception volume to the standardized tourist spatiotemporal distribution dataset.

[0034] Furthermore, if any free node is located in an ecological protection red line area or a core area of ​​a nature reserve, and the missing rate of passenger flow data of any free node exceeds a preset threshold, then an independent filling strategy is enabled for the free node. The independent filling strategy includes constraining the upper limit of the interpolation interval and extending the length of the time sliding window.

[0035] Specifically, free-access nodes refer to natural and cultural recreational space units located within the target area that are open to the public unconditionally or only by reservation, including but not limited to: free-access sections of urban comprehensive parks, suburban parks, wetland parks, waterfront public recreation belts, public spaces in historical and cultural blocks, and ecological science education bases. The process of constructing a set of free-access nodes in the target area involves: based on land use survey data of the target area, special planning maps of green spaces and open spaces in the national land spatial planning, and the list of scenic spots published by the tourism authorities, excluding scenic spots that charge fees, spatial mapping and boundary vectorization are performed to form a spatial range vector layer of free-access nodes. The spatial range vector layer is in Polygon format, and each node is assigned a unique identifier and attribute fields, such as name, type, area, and the managing entity.

[0036] Then, a zone-weighted interpolation method is used to fill in the missing passenger flow for any free node in the set of free access nodes. The target area is divided into several interpolation zones according to land use type, ecological function zoning, or accessibility from major passenger gathering and distribution centers, such as three types of zones: free nodes in urban built-up areas, free nodes in suburban transition zones, and ecological free nodes in remote suburbs. The area of ​​the free node F to be filled is denoted as A. F Its filling capacity V F The calculation formula is: V F =Σ(W k ×D k ×A F ), where k represents the k-th reference sample node within the partition where the free node is located. Reference sample nodes are selected from similar nodes within the same partition that already have reliable visitor flow data, including similar open areas within paid scenic areas, or a few free nodes whose accurate visitor volume has been obtained through on-site sampling surveys. W k The weighting coefficient is calculated based on the reciprocal of the spatial distance between the reference sample node and the node to be filled, which is 1 / d. k Determined, meaning that reference samples that are spatially closer are assigned higher weights, D k The visitor density per unit area (persons / km²) for the k-th reference sample node is obtained by dividing the actual number of visitors to that reference sample node by its landscape area. When the number of reference samples within the partition where the free node is located is insufficient, such as less than 3, the search radius is expanded to adjacent partitions, and an attenuation coefficient λ=e is introduced. -β×r The weights of cross-regional reference samples are penalized to prevent unreasonable extrapolation to distant heterogeneous regions, where r is the cross-regional search distance and β is the spatial decay constant, with a value of 0.01~0.05 / km.

[0037] After calculating the fill capacity of each free node in the set of free open nodes, the fill capacity is added to the standardized visitor spatiotemporal distribution dataset. Based on the set of rasters covered by the vector layer of the free node's spatial extent (i.e., all regular rasters contained within the polygon boundary), the fill capacity is proportionally allocated according to the area ratio of each raster within the free node. That is, the fill passenger flow obtained by any raster g within the node is V. g =V F ×(A g / A F ), where A g Let A be the area of ​​the grid. F The total area of ​​the free nodes is used to add the allocated filling passenger flow to the original passenger flow count value of each grid, thereby updating and completing the standardized tourist spatiotemporal distribution dataset.

[0038] Specifically, if any free node is located within an ecological protection red line area or a core area of ​​a nature reserve, and the missing visitor data rate of that free node exceeds a preset threshold, the missing visitor data rate refers to the percentage of rasters within the free node that are not covered by any original visitor data out of the total number of rasters in that node. The preset threshold is set differently according to the node type: 60% for ecological protection red line areas and 70% for core areas of nature reserves. When the missing visitor data rate exceeds the corresponding preset threshold, it indicates that the reference samples required for regular interpolation of the free node are extremely few, and the ecological vulnerability of the area is extremely high. In this case, an independent filling strategy is activated for the free node. The independent filling strategy includes constraining the upper limit of the interpolation interval and extending the time sliding window length. Constraining the upper limit of the interpolation interval means constraining the maximum number of visitors that can be filled for the free node. Its upper limit is set as the environmental carrying capacity threshold of the free node. The environmental carrying capacity threshold is calculated by multiplying the maximum daily visitor carrying capacity approved in the ecological sensitivity assessment report of the area by the number of days in the calculation period, and then multiplying by a safety factor of 0.3 to 0.5, so that the filling result is always constrained within the ecological carrying capacity safety boundary. Extending the time sliding window length means extending the time window used for interpolation calculation of the free node from the usual monthly window to a quarterly or semi-annual window. This is to introduce passenger flow average information over a larger time scale, so that statistically stable estimates can still be obtained even under extremely sparse data conditions. Furthermore, by constraining the upper limit of the interpolation interval, it is ensured that the filled value will not exceed the ecological carrying capacity threshold.

[0039] For example, a certain plateau lake basin has 17 free-access nodes, including the urban wetland park on the south bank, the lakeside ecological greenway on the east bank, the bird observation and science popularization base on the west bank, and 14 small suburban recreational sites, with a total free-access area of ​​approximately 26.7 square kilometers. Selecting the urban wetland park node, its area A... F=2.3 square kilometers, belonging to the free node zone of the urban-rural transition zone. There are two reliable reference sample nodes within the same zone: the free-access area surrounding the paid scenic area, the Lakeside Botanical Garden, with D1 = 15,600 visitors / square kilometer and a spatial distance d1 = 2.1 kilometers; and the birdwatching boardwalk node with D2 = 8,200 visitors / square kilometer and a spatial distance d2 = 3.5 kilometers, for which accurate visitor flow data has been obtained through on-site questionnaires. The reciprocals of the distances are 1 / 2.1 and 1 / 3.5 respectively, and their normalized weights are W1 = 0.625 and W2 = 0.375 respectively. Therefore, the filling density D... F =0.625×15600+0.375×8200=12825 person-times / square kilometer, multiplied by area A F =2.3 square kilometers, resulting in a fill capacity VF≈29498 visits. If the regular grid is 100m×100m, its grid area is 0.01 square kilometers / grid. By calculating 2.3 square kilometers ÷ 0.01 square kilometers / grid, we get 230 grids. The fill capacity is then evenly distributed among the 230 grids it covers, increasing the number of visits per grid by approximately 128. This data is then updated to the standardized visitor spatiotemporal distribution dataset.

[0040] After interpolation and padding, the completeness of the standardized tourist spatiotemporal distribution dataset was further improved. It not only covered paid scenic spots, but also supplemented the spatiotemporal distribution information of tourist flow in free-access nodes and ecologically sensitive areas, thereby improving the accuracy and reliability of ecosystem cultural service accounting optimization.

[0041] Obtain the target landscape node set of the target area, obtain the arbitrary landscape area of ​​any landscape node in the target landscape node set, and combine it with the standardized tourist spatiotemporal distribution dataset to obtain the arbitrary actual reception volume of any landscape node.

[0042] The normalized product of the arbitrary landscape area and the arbitrary actual reception volume is used as the comprehensive weight factor of the arbitrary landscape node. The proportion of the comprehensive weight factor to the sum of the comprehensive weight factors of all nodes in the target landscape node set is used as the allocation basis to allocate the total cost of tourist travel, thereby obtaining the arbitrary leisure and recreation value corresponding to the arbitrary landscape node.

[0043] The target value of the target area is obtained by summing up the values ​​of any of the aforementioned leisure and recreational activities.

[0044] Furthermore, the arbitrary landscape area includes the weighted composite value of at least one of the area indicators among the forest area, wetland area, and water area corresponding to the arbitrary landscape node.

[0045] Specifically, the process involves acquiring a target landscape node set for the target area. This target landscape node set refers to a collection of spatial units within the target area that provide ecosystem cultural services and attract tourists for leisure and recreational activities. Rasteres with positive visitor counts in the standardized visitor spatiotemporal distribution dataset are clustered and merged according to spatial connectivity (i.e., adjacent rasteres sharing edges or corners) to form independent visitor activity patches. The clustering results are then superimposed onto the pre-built free and open node set. Overlapping areas are corrected based on node boundaries to form a target landscape node set covering all visitor activity spaces within the target area.

[0046] Obtain the arbitrary landscape area of ​​any landscape node in the target landscape node set. This arbitrary landscape area does not refer to the total physical area of ​​the landscape node, but rather a weighted composite value including at least one area indicator among forest area, wetland area, and water area corresponding to the arbitrary landscape node. Based on high-resolution remote sensing image classification data, such as Sentinel-2 satellite imagery with a spatial resolution of 10 meters, extract land use types and statistically analyze the landscape nodes. i The area of ​​forest patches, wetland patches, and water patches within the boundary, in square kilometers, is denoted as Awood. i Awetland i Awater i If only one factor is selected, such as forest area, then the landscape area is directly equal to that area. If multiple factors are selected, a weighted average is calculated. The formula for calculating the weighted average value is: Aeco i =α×Awood i +β×Awetland i +γ×Awater i Wherein, α, β, and γ are the ecological weight coefficients for the corresponding landscape types, calibrated according to the dominant ecosystem service function type of the target area. For example, for areas where water resources are the dominant service, such as lake basins, α, β, and γ are taken as 0.3, 0.5, and 0.2, respectively; for areas where biodiversity conservation is the dominant service, they are taken as 0.4, 0.4, and 0.2; and for areas where carbon sequestration is the dominant function, they are taken as 0.6, 0.2, and 0.2. The sum of all weight coefficients is always equal to 1 to maintain dimensional consistency.

[0047] At the same time, for any landscape node i Based on the overlay relationship between its spatial boundary vector and the regular grids in the standardized tourist spatiotemporal distribution dataset, the set of grids covered by the landscape node is determined, that is, all regular grids contained within the node polygon. The visitor count values ​​of each grid in the grid set are summed to obtain the arbitrary landscape node. i Any actual number of visitors Vvisit i. Any landscape area Aeco i and any actual number of visitors Vvisit i The normalized product is used as the comprehensive weighting factor C for any landscape node. i C i =(Aeco i / Aeco total )×(Vvisit i / Vvisit total ), of which Aeco total Vvisit represents the total ecological landscape area of ​​all landscape nodes. total This represents the total actual reception volume of all nodes. The comprehensive weighting factor C is... i The total tourist travel cost (TC) is allocated based on the proportion of the sum of the comprehensive weight factors of all M nodes in the target landscape node set, thus obtaining the leisure and recreation value (RV) corresponding to any landscape node. i Its expression is: The total cost of tourist travel (TC) refers to all economic costs paid by all tourists for visiting the target area within the accounting period, including transportation costs, time costs, accommodation and catering costs, and ticket expenditures. It is obtained through questionnaire sampling data and is expressed in yuan per year.

[0048] After allocating the recreational value of all M landscape nodes within the target landscape node set, the recreational value RV of each landscape node is... i The target value of the target area is obtained by summing up the values, which represents the total economic value realized by the target area in providing ecological, cultural and recreational services within the accounting period.

[0049] By accurately breaking down the recreational value of each scenic node within the region, and combining the scenic area with the actual number of visitors to calculate the comprehensive weight, the total cost of visitor travel is allocated according to the weight, and finally the overall recreational value of the target area is obtained by summing them up. This provides node-level details and verifiable total results for the accounting of regional ecological product value, thereby improving the scientificity and operability of regional cultural and tourism resource pricing, planning and development, and ecological compensation decisions.

[0050] Furthermore, the method also includes: acquiring the target water body in the target area; using the boundary of the target water body as a spatial reference baseline, dividing the target area according to the straight-line distance from the water body boundary to obtain target landscape layers; independently configuring a landscape aesthetic premium coefficient for each landscape layer in the target landscape layers, and acquiring real estate transaction market data within each layer, and calculating the total annual asset appreciation brought by the landscape aesthetic quality of each layer in the target landscape layers in layers based on the landscape aesthetic premium coefficient corresponding to each layer; using the total annual asset appreciation as the landscape aesthetic value of the target area, and adding it to the target value.

[0051] Furthermore, the distance threshold for defining the target landscape layer is set differently based on whether the target area belongs to different ecological and geographical types such as lake type, mountain type, hill type or karst type.

[0052] Furthermore, using the boundary of the target water body as a spatial reference baseline, the target area is divided according to the straight-line distance from the water body boundary to obtain target landscape layers. This includes dividing the target area into at least three landscape layers according to the straight-line distance from the water body boundary. The distance division threshold for each landscape layer is determined as follows: historical real estate transaction data of the target area is obtained, and the inflection point of the decay curve of each real estate transaction price changing with the distance from the water body boundary is used as the division threshold. The area within the first inflection point is defined as the waterfront core layer, the area between the first and second inflection points is defined as the buffer transition layer, and the area outside the second inflection point is defined as the offshore layer.

[0053] Specifically, the target water body within the target area is identified. This target water body refers to a core water area within the target area that possesses significant landscape aesthetic effects. For lake-type areas, the target water body is the main body of the lake; for river-type areas, it is the surface of the main stream or major tributaries; and for coastal areas, it is the nearshore sea area. The spatial extent of the target water body is determined by combining high-resolution remote sensing imagery with on-site boundary survey data to extract its land-water boundary line, which serves as the spatial reference baseline for subsequent layer delineation. When acquiring the land-water boundary line, seasonally flooded areas and temporary waterlogged areas must be eliminated, retaining only the perennially stable water area to ensure the stability and repeatability of the delineation benchmark.

[0054] Then, the boundary of the target water body is used as a spatial reference baseline, and the target area is divided according to the straight-line distance from the water body boundary to obtain the target landscape concentric circles. The straight-line distance refers to the shortest Euclidean distance from any land location point in the target area to the water body boundary line, which is calculated through Euclidean distance raster analysis on the GIS platform. The distance division threshold of the target landscape concentric circles is set differently according to the different ecological and geographical types of the target area, such as lake type, mountain type, hill type, or karst type. The target landscape concentric circles are at least three, including the waterfront core circle, the buffer transition circle, and the offshore circle. The distance division threshold of each landscape concentric circle is determined as follows: all residential and commercial real estate transaction records of the target area in the past 3 to 5 years are obtained from the local real estate registration center or real estate transaction platform, including fields such as transaction price (yuan / square meter), transaction time, property location coordinates, and property type, such as villas, multi-story buildings, high-rise buildings, and commercial properties. All transaction records were grouped and statistically analyzed based on their straight-line distance from the water body boundary, with a group interval of 50 meters. Distance was plotted on the x-axis (in meters), and transaction price per unit area was plotted on the y-axis (in yuan / square meter). A scatter plot was created, and a decay curve was fitted to represent the price decrease with distance from the water body boundary. Piecewise linear regression was used to identify the inflection points on the curve, i.e., the critical distance values ​​where the rate of price decay changes significantly. The first inflection point represents the core area boundary where the water body landscape premium effect is strongest. Within this distance, water visibility and accessibility are highest, and housing prices are higher than the regional average, with prices decreasing rapidly with increasing distance. Beyond this first inflection point, the rate of price decline slows significantly. The second inflection point represents the effective radiation boundary of the water body landscape premium effect. Beyond this second inflection point, the impact of the water body on housing prices approaches zero, and prices return to the regional benchmark level. The area within the first inflection point is designated as the core waterfront zone, possessing the highest landscape aesthetic quality and corresponding asset premium. The area between the first and second inflection points is designated as the buffer transition zone, with a moderate level of landscape aesthetic impact. The area outside the second inflection point is designated as the distant land zone. For example, for lake-type areas, such as plateau lakes and plain lakes, the water body is open and the visual radiation range is wide. Through analysis, the preferred division thresholds are set as follows: core waterfront zone 0-200 meters, buffer transition zone 200-800 meters, and distant land zone 800-2000 meters. For mountain-type areas, such as canyon and reservoir types, the visual radiation range is limited due to topographical undulations, so the division thresholds are compressed to 0-100 meters, 100-400 meters, and 400-1000 meters. For hilly areas, the division thresholds are set in the middle at 0-150 meters, 150-600 meters, and 600-1500 meters. By setting differentiated thresholds, the ecological and geographical adaptability of the zonal divisions is ensured.

[0055] After obtaining the target landscape layers, an independent landscape aesthetic premium coefficient P is assigned to each landscape layer within the target landscape layers. kk=1, 2, 3, corresponding to the waterfront core layer, buffer transition layer, and offshore layer, respectively, and the landscape aesthetic premium coefficient P. k This refers to the premium ratio in real estate transaction prices within a certain circle that can be attributed to the aesthetic quality of the water feature landscape, ranging from 0 to 1. The calibration method is as follows: using the Hedonic Price Model (HPM), with the real estate transaction unit price as the dependent variable, and building area, building age, floor, property type, distance from the city center, and surrounding amenities as control variables, and the straight-line distance from the water feature boundary as the core variable, the marginal implicit price of the water feature landscape is extracted through multiple regression analysis, and the landscape aesthetic premium coefficient is calculated. Specifically, the premium coefficient P3 for the far-land circle is calibrated to a baseline value of 0, the buffer transition circle P2 takes values ​​of 0.05 to 0.15 based on the regression results, and the core waterfront circle P1 takes values ​​of 0.15 to 0.35. The premium coefficients for different circles are configured independently and do not affect each other.

[0056] Simultaneously, real estate transaction market data within each concentric circle is acquired. This data includes at least: the total stock area S of real estate within that concentric circle. k (Unit: square meters), Annual average transaction price U k (RMB / square meter), Total annual transaction area T k (square meters) and the total value of the existing real estate in this area B k B k =S k ×U k Based on the landscape aesthetic premium coefficient corresponding to each landscape layer, the annual asset appreciation AV of each layer in the target landscape layer due to landscape aesthetic quality is calculated in layers. k AV k =B k ×P k After calculating the annual asset appreciation AV of the target area for each of the three landscape concentric circles, total =AV1+AV2+AV3, the total annual asset appreciation AV total As the landscape aesthetic value of the target area, it is added to the target value to obtain the total value of the ecosystem cultural services of the target area after correction by the landscape aesthetic dimension.

[0057] By introducing a landscape aesthetic premium accounting mechanism based on real estate transaction data, the asset appreciation value derived from the quality of water landscape is added to the original recreational value. This expands the cultural services of the ecosystem from tourist travel to the dimensions of landscape existence and aesthetic enjoyment. At the same time, the threshold of the circle is defined by the inflection point of the decay curve, which ensures the spatial accuracy and regional adaptability of the premium accounting. Ultimately, the target value more comprehensively reflects the comprehensive contribution of the cultural services provided by the ecosystem.

[0058] Furthermore, the method also includes: acquiring annual total carbon sequestration data and annual total water purification data for the target area; multiplying the total carbon sequestration data obtained from analyzing the annual total carbon sequestration data and the total water purification data obtained from analyzing the annual total water purification data by corresponding pre-calibrated regulation contribution weight coefficients and summing them to form a comprehensive linkage correction factor; multiplying the target value by the comprehensive linkage correction factor to obtain the total value of target ecosystem cultural services of the target area after ecological regulation service correction.

[0059] Furthermore, the comprehensive linkage correction factor is constructed as follows: the total carbon sequestration is multiplied by the first regulatory contribution weight coefficient to obtain the first component; the total water purification is multiplied by the second regulatory contribution weight coefficient to obtain the second component; the first component, the second component, and the unit benchmark value are summed to obtain the comprehensive linkage correction factor; wherein, the first regulatory contribution weight coefficient and the second regulatory contribution weight coefficient are pre-calibrated based on the historical ecological monitoring data of the target area through regression analysis.

[0060] Specifically, the area distribution of each ecosystem type is extracted based on high-resolution remote sensing image data of the target area. Combined with a national or regional net primary productivity (NPP) model, such as the CASA model, the annual carbon sequestration by vegetation is calculated to obtain the total annual carbon sequestration data for the target area. This total annual carbon sequestration data refers to the net carbon converted from atmospheric carbon dioxide by vegetation photosynthesis within the target area during the accounting period. For example, the carbon sequestration rate is taken as 850 tons of carbon per square kilometer per year for temperate coniferous and broad-leaved mixed forests, 420 tons of carbon per square kilometer per year for shrublands, 280 tons of carbon per square kilometer per year for grasslands, and 80 tons of carbon per square kilometer per year for water bodies. The area of ​​each ecosystem type is multiplied by its corresponding carbon sequestration rate, and then summed to obtain the total annual carbon sequestration of the target area. Multiplying this by 3.67 according to the carbon-to-carbon dioxide conversion relationship (i.e., a CO2 / C molecular weight ratio of 44 / 12) yields the CO2 equivalent. If the target area has 320 square kilometers of forest land (×850) = 272,000 tons of carbon per year, 180 square kilometers of shrubland (×420) = 75,600 tons of carbon per year, 220 square kilometers of grassland (×280) = 61,600 tons of carbon per year, and 80 square kilometers of water area (×80) = 6,400 tons of carbon per year, the total annual carbon sequestration is the sum of the above four items, i.e., 272,000 + 75,600 + 61,600 + 6,400 = 415,600 tons of carbon per year.

[0061] Based on monthly water quality sampling data from hydrological monitoring stations within the target area, the inflow-outflow flux difference subtraction method is used to calculate the total amount of land-based pollutants intercepted and purified by various ecosystems, thus obtaining the annual total water quality purification amount. This annual total water quality purification amount refers to the total amount of pollutants removed from water bodies by wetland, water body, and forest-grassland ecosystems within the target area during the accounting period through processes such as physical sedimentation, chemical adsorption, and biodegradation. The formula for the inflow-outflow flux difference subtraction method is: C water =L in -L out L in To calculate the total load of pollutants entering the target area's water bodies through rivers, ditches, etc., during the accounting period, L out This refers to the total pollutant load discharged from the target area's water body through the outlet section during the same period. For example, the annual total nitrogen (TN) load Lin input from the upstream of the target area is calculated as 486 tons based on the daily cumulative sum of measured flow rate and concentration, and the annual total nitrogen (TN) load L at the downstream outlet section is... out The annual TN (Total Nitrogen) purification capacity is 486 - 160 = 326 tons TN / year. Similarly, the annual total phosphorus (TP) input load is 58 tons, and the output is 17 tons, so the annual TP purification capacity is 58 - 17 = 41 tons TP / year. After weighting each component by 0.5, the total annual water purification capacity C is obtained. water =0.5×326+0.5×41=163+20.5=183.5 tons / year.

[0062] The first component is obtained by multiplying the total carbon sequestration by the first regulatory contribution weighting coefficient, and the second component is obtained by multiplying the total water purification by the second regulatory contribution weighting coefficient. The first regulatory contribution weighting coefficient represents the marginal contribution rate of a unit of carbon sequestration to the cultural service value, i.e., the synergistic increase in cultural service value due to improved ecological health for every additional ton of annual carbon sequestration. The second regulatory contribution weighting coefficient represents the marginal contribution rate of a unit of water purification to the cultural service value. Both the first and second regulatory contribution weighting coefficients are pre-calibrated using regression analysis based on historical ecological monitoring data of the target area. The specific calibration method is as follows: Annual time-series data of the target area over the past 5-10 years were collected, including total carbon sequestration, total water purification, and cultural service value for each year. The cultural service value is the recreational value for each year. The cultural service value is used as the dependent variable, and the total carbon sequestration and total water purification for each year are used as independent variables. A multiple linear regression model is constructed, and the historical data is fitted using the least squares method to solve for the regression coefficients. The calibration values ​​of the first and second regulatory contribution weight coefficients are obtained to truly reflect the coupling strength between the unique ecological regulation services and cultural services of the target area.

[0063] The first component, the second component, and the unit benchmark value are summed to obtain the comprehensive linkage correction factor, where the unit benchmark value is a constant of 1. The target value is then multiplied by the comprehensive linkage correction factor to obtain the total value of the target ecosystem cultural services of the target area after ecological regulation service correction.

[0064] By introducing a regulatory contribution weighting coefficient based on regression calibration of historical ecological monitoring data, a comprehensive linkage correction factor for the cultural service value of two key regulatory services, carbon sequestration and water purification, was constructed. This enabled the accounting of the value of ecosystem cultural services to move from isolated estimation to multi-service coupled correction, so that the final accounting results can reflect both the explicit market value of cultural services and the supporting or restrictive effects of the underlying ecological regulatory service capacity, thereby improving the ecological integrity and scientific rigor of the accounting results.

[0065] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0066] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. An optimization method for accounting of cultural services in an ecosystem that integrates multiple passenger flows, characterized in that, include: Based on predetermined accounting units, periodic accounting data collection is performed on the target area to obtain multi-source passenger flow data; Using a regular grid with a preset spatial resolution as a unified spatial coordinate system reference, the multi-source passenger flow data is mapped through coordinate transformation to generate a standardized spatiotemporal distribution dataset of tourists covering the target area; Obtain the target landscape node set of the target area, obtain the arbitrary landscape area of ​​any landscape node in the target landscape node set, and combine it with the standardized tourist spatiotemporal distribution dataset to obtain the arbitrary actual reception volume of any landscape node. The normalized product of the arbitrary landscape area and the arbitrary actual reception volume is used as the comprehensive weight factor of the arbitrary landscape node. The proportion of the comprehensive weight factor to the sum of the comprehensive weight factors of all nodes in the target landscape node set is used as the allocation basis to allocate the total cost of tourist travel, thereby obtaining the arbitrary leisure and recreation value corresponding to the arbitrary landscape node. The target value of the target area is obtained by summing up the values ​​of any of the aforementioned leisure and recreational activities.

2. The method for optimizing the accounting of cultural services in an ecosystem based on the integration of multi-source passenger flow according to claim 1, characterized in that, The multi-source passenger flow data includes at least passenger flow recorded by scenic area gates, trajectory reported by satellite positioning terminals, location data from mobile communication network signaling, and on-site stratified sampling of tourist behavior questionnaire data. The satellite positioning terminal reports trajectories including self-driving routes around the lake, location information for rural check-ins, and outdoor sports routes obtained from social media platforms.

3. The method for optimizing the accounting of cultural services in an ecosystem based on the integration of multi-source passenger flow according to claim 1, characterized in that, The multi-source passenger flow data is mapped through coordinate transformation to generate a standardized spatiotemporal distribution dataset of tourists covering the target area, and then the process further includes: Establish a set of free and open nodes for the target area; The missing passenger flow is filled by interpolating any free node in the set of free open nodes using a partitioned weighted interpolation method to obtain the arbitrary filled reception volume; Add the arbitrary number of visitors to the standardized tourist spatiotemporal distribution dataset.

4. The method for optimizing the accounting of cultural services in an ecosystem based on the integration of multi-source passenger flow according to claim 3, characterized in that, If any free node is located in an ecological protection red line area or a core area of ​​a nature reserve, and the missing passenger flow data rate of any free node exceeds a preset threshold, then an independent filling strategy is enabled for the free node. The independent filling strategy includes constraining the upper limit of the interpolation interval and extending the length of the time sliding window.

5. The method for optimizing the accounting of cultural services in an ecosystem based on the integration of multiple passenger flows according to claim 1, characterized in that, The arbitrary landscape area includes the weighted composite value of at least one of the area indicators among the forest area, wetland area, and water area corresponding to the arbitrary landscape node.

6. The method for optimizing the accounting of cultural services in an ecosystem based on the integration of multi-source passenger flow according to claim 1, characterized in that, Also includes: Obtain the target water body in the target area; Using the boundary of the target water body as a spatial reference baseline, the target area is divided according to the straight-line distance from the water body boundary to obtain the target landscape layer; Each landscape circle in the target landscape circle is independently configured with a landscape aesthetic premium coefficient, and real estate transaction market data within each circle is obtained. The total annual asset appreciation brought about by the landscape aesthetic quality of each circle in the target landscape circle is calculated in layers based on the landscape aesthetic premium coefficient corresponding to each circle. The total annual asset appreciation is used as the landscape aesthetic value of the target area and added to the target value.

7. The method for optimizing the accounting of cultural services in an ecosystem based on the integration of multi-source passenger flow according to claim 6, characterized in that, The distance threshold for defining the target landscape layer is set differently based on whether the target area belongs to different ecological and geographical types such as lake type, mountain type, hill type or karst type.

8. The method for optimizing the accounting of cultural services in an ecosystem based on the integration of multiple passenger flows according to claim 6, characterized in that, Also includes: Obtain annual total carbon sequestration data and annual total water purification data for the target area; The total carbon sequestration obtained by analyzing the total annual carbon sequestration data and the total water purification obtained by analyzing the total annual water purification data are multiplied by the corresponding pre-calibrated adjustment contribution weight coefficients and then summed to form a comprehensive linkage correction factor. Multiplying the target value by the comprehensive linkage correction factor yields the total value of the target ecosystem cultural services of the target area after ecological regulation service correction.

9. The method for optimizing the accounting of cultural services in an ecosystem based on the integration of multiple passenger flows according to claim 6, characterized in that, Using the boundary of the target water body as a spatial reference baseline, the target area is divided according to the straight-line distance from the water body boundary to obtain the target landscape layer, including: The target area is divided into at least three landscape concentric circles based on the straight-line distance from the water body boundary, wherein the distance threshold for each landscape concentric circle is determined as follows: Historical real estate transaction data of the target area is obtained. The inflection point of the decay curve of each real estate transaction price as a function of distance from the water boundary is used as the dividing threshold. The area within the first inflection point is defined as the waterfront core layer, the area between the first and second inflection points is defined as the buffer transition layer, and the area outside the second inflection point is defined as the offshore layer.

10. The method for optimizing the accounting of cultural services in an ecosystem based on the integration of multi-source passenger flow according to claim 8, characterized in that, The comprehensive linkage correction factor is constructed in the following manner: The first component is obtained by multiplying the total carbon sequestration amount by the first adjustment contribution weighting coefficient; The second component is obtained by multiplying the total amount of water purification by the second regulation contribution weighting coefficient; The comprehensive linkage correction factor is obtained by summing the first component, the second component, and the unit reference value. The first adjustment contribution weight coefficient and the second adjustment contribution weight coefficient are pre-calibrated based on the historical ecological monitoring data of the target area through regression analysis.

Images