Tourist area phosphorus metabolism dynamic simulation method based on tourist behavior big data

Abstract

The application discloses a tourism area phosphorus metabolism dynamic simulation method based on tourist behavior big data, relates to the field of data processing technology in the resource and management class, and first gathers multiple source data to construct an integrated knowledge base, then delimits a phosphorus metabolism boundary, sets an independent tourist consumption link and three types of statistical time periods, and builds a basic mechanism framework; then, a coupling algorithm is run to calculate phosphorus load and generate tourist emission details; then, a geographic information platform is combined to realize pollution space mapping and water quality early warning; finally, a benefit optimization algorithm is used to organize core indicators, filter the optimal management and control scheme, and package the whole-process data assets; the application can accurately distinguish tourist source and local source phosphorus pollution, break the traditional accounting mixed limitations, realize time and area fine simulation, lock pollution hotspots through a space heat map and water quality early warning, generate a scientific and adaptive management and control scheme by combining multiple index optimization, give consideration to ecological management and travel development, and form whole-process reusable data assets.

Description

Technical Field

[0001] This invention relates to the field of data processing technology for resources and management, specifically a method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior. Background Technology

[0002] The large-scale development of China's cultural tourism industry continues, with a steady increase in visitor numbers in various tourist clusters. The external phosphorus input from tourists' food, accommodation, entertainment, and daily consumption is gradually becoming a significant contributor to phosphorus pollution in the water and soil of tourist areas. Excessive phosphorus accumulation in these areas can easily lead to ecological problems such as eutrophication. Therefore, accurately analyzing the phosphorus metabolism patterns in tourist areas has become a core task for regional ecological environmental protection. Currently, big data collection technology, geographic information technology, and environmental metabolism simulation technology are becoming increasingly mature. Data sources from multiple dimensions, including scenic area ticketing, mobile communications, environmental monitoring, and socio-economic statistics, are routinely retained, providing data support for the quantitative study of environmental pollutants. Ecological and environmental protection management is shifting from extensive overall management to refined source management. Relevant regulatory units need to clarify the phosphorus emission contributions of different pollution sources to support the implementation of environmental protection policies. In this environment of industry development and technological iteration, technologies for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior have become a key research direction in the field of tourism ecological governance.

[0003] Traditional research on phosphorus metabolism in tourist areas generally relies on on-site sampling and monitoring, along with existing statistical data, to complete calculations. This results in limited data acquisition channels, making it difficult to integrate massive amounts of data related to dynamic tourist behavior and accurately extract the amount of phosphorus pollution from tourist activities from the total phosphorus emissions across the entire area. Current technologies define phosphorus metabolism boundaries in a coarse manner, failing to consider seasonal changes in tourist flow to define statistical periods, thus hindering refined time-segmented phosphorus load calculations. Traditional methods lack spatial allocation and visualization capabilities, making it impossible to accurately locate high-incidence areas of regional phosphorus pollution. Predictions of changes in water phosphorus content rely solely on fixed-point water quality monitoring data, failing to incorporate future tourist flow changes for early risk warnings. Conventional remediation plans rely on manual experience, making it difficult to simultaneously balance pollution control investment, emission reduction benefits, and losses to the cultural and tourism industry. Optimal plan selection lacks quantitative basis, and research data is stored in a fragmented manner, failing to form standardized data assets, making the reuse of historical research results difficult.

[0004] This invention can be widely applied to the ecological environment management of tourism areas of various business types, covering a wide range of tourism land scenarios such as lakeside resorts, mountain scenic spots, and rural leisure tourism clusters. It can provide quantitative management and control technical support for ecological environment authorities, cultural and tourism management units, and scenic area operators. In the early planning stage of a scenic area, this method can be used to assess the environmental carrying capacity of new tourism areas, predict the potential level of phosphorus pollution in the region based on the planned tourist flow, and assist in optimizing the planning of supporting pollution control facilities. In the normalized operation stage of a scenic area, it can periodically complete the calculation of phosphorus metabolism across the entire area, identify pollution hotspots, and provide graded early warnings, guiding the optimization of the operation and maintenance of existing environmental protection facilities. Based on the standardized data assets produced by the method, it can also provide quantitative reference data for the establishment of watershed ecological restoration projects, local water and soil environment management planning, and the adjustment of regional cultural and tourism industry layout, possessing broad application potential in the field of long-term tourism ecological supervision. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies by providing a dynamic simulation method for phosphorus metabolism in tourist areas based on big data of tourist behavior. This method constructs an integrated standard knowledge base, establishes a framework for the basic mechanism of phosphorus metabolism including independent tourist consumption stages, and divides the statistical periods into peak season, off-season, and non-peak season. The method employs a tourist phosphorus coupling accounting algorithm to accurately calculate the phosphorus emission load from tourist sources, combines this with a geographic information platform to generate a spatial heat map of phosphorus pollution, predicts phosphorus concentrations in water bodies, and issues tiered early warnings. Furthermore, through a management benefit optimization algorithm, it comprehensively considers emission reduction benefits, construction costs, and cultural and tourism losses to select the optimal management scheme and encapsulate the entire process data assets.

[0006] To solve the above-mentioned technical problems, this invention provides the following technical solution: a dynamic simulation method for phosphorus metabolism in tourist areas based on big data of tourist behavior, the specific steps of which are as follows:

[0007] S100, Comprehensive Data Database Construction: Collect four types of raw data in the tourism area: socio-economic data, tourist behavior, phosphorus conversion parameters, and environmental costs; complete data regularization and standardization processing; label traceability identifiers and quality levels; and build an integrated standard knowledge base.

[0008] S200, Basic Framework Construction: Calling on integrated standard knowledge base data, defining the boundaries of phosphorus metabolism throughout its entire life cycle, sorting out the related links in phosphorus circulation, setting up independent tourist consumption links, dividing the statistical periods into peak season, off-season, and low season, and building a basic mechanism framework for phosphorus metabolism.

[0009] S300, Phosphorus Load Calculation: Based on the aforementioned basic mechanism framework of phosphorus metabolism, the tourist phosphorus coupling calculation algorithm is used to calculate the total phosphorus discharge load in the region, and the phosphorus emissions from tourist sources and local sources are separated to generate detailed data on tourist phosphorus emissions by region and time period.

[0010] S400 Spatial Mapping and Early Warning: Based on the aforementioned detailed data on phosphorus emissions from tourists and the geographic information platform, the spatial allocation of phosphorus pollution load is completed, a spatial heat map of phosphorus pollution is generated, changes in phosphorus concentration in water bodies are predicted, and graded pollution early warning information is issued.

[0011] S500, Control Plan Generation: Based on the aforementioned spatial heat map and graded pollution early warning information, the operational control benefit optimization algorithm calculates the control benefits of each scenario, takes into account the three core indicators of emission reduction benefits, construction costs and cultural and tourism losses, selects the optimal control plan and encapsulates the full-process data assets.

[0012] Furthermore, the socio-economic data includes local historical national economic statistics, development records of primary, secondary, and tertiary industries, crop planting scale, livestock and poultry breeding inventory data, food processing plant production and operation records, and urban and rural sewage treatment plant operation and maintenance records; tourist behavior data includes scenic area access control and ticketing data, mobile phone signaling data from telecommunications operators, online tourism platform consumption orders, hotel check-in registration records, and on-site tourist behavior questionnaire data; phosphorus conversion parameter data includes the primary phosphorus content of various food ingredients, phosphorus residue coefficient of detergents and daily chemical products, phosphorus leaching ratio of domestic waste and domestic sewage, and phosphorus retention ratio of sludge treatment; environmental cost data includes land use surveying data of the entire region, annual and monthly meteorological monitoring records, watershed soil physicochemical testing data, and construction cost and daily operation and maintenance cost information of various pollution control projects.

[0013] Furthermore, the comprehensive data database construction process involves refined processing of the four types of raw data collected, eliminating duplicate, distorted, and invalid data lacking key information, and unifying the statistical caliber, units of measurement, and spatiotemporal statistical scale of all data. Each selected valid data point is labeled with three traceability identifiers: collection source, sampling time, and data quality level. A domain knowledge graph is used to complete semantic matching and integration of various scattered data. This domain knowledge graph is a dedicated data association architecture adapted to the phosphorus metabolism scenario in tourist areas, using tourist behavior, phosphorus flow, environmental parameters, and governance indicators as core entities. It establishes the logical connections and correspondences between these entities, completing semantic matching and integration of various scattered data. All data is classified, organized, and archived, ultimately building a unified, complete, and fully traceable integrated standard knowledge base, providing a precise basic data source for subsequent framework construction and data accounting.

[0014] Furthermore, the phosphorus metabolism life cycle flow boundary is defined in a closed loop, with the entire administrative space of the tourism area as the physical boundary and the complete phosphorus flow process as the logical boundary. It fully covers the entire process of phosphorus input from outside the region, processing and transformation within the region, main consumption and utilization, waste collection and disposal, water and soil discharge, and phosphorus resource recycling and reuse. This ensures that the boundary fully covers the entire process of phosphorus from input to output, without including irrelevant processes outside the region. All phosphorus flow-related links within the boundary are sorted out, clarifying the inherent logical relationship of phosphorus migration, transformation, and retention in each link. The three core phosphorus input sources are identified: local agricultural production, urban and rural residents' lives, and consumption by tourists. The three core phosphorus output pathways are identified: sewage treatment, solid waste disposal, and surface runoff, forming a complete closed-loop phosphorus flow chain.

[0015] Furthermore, the aforementioned basic framework is built upon tourist consumption and phosphorus emission data within an integrated standard knowledge base. It separates and establishes independent tourist consumption links from the overall phosphorus metabolism chain, aggregating all phosphorus consumption and waste generation and emission paths resulting from tourist accommodation, sightseeing, and consumption. This achieves physical separation between tourist-sourced phosphorus generation paths and local production and living phosphorus generation paths. Based on historical passenger flow patterns, it divides the data into three statistical periods: peak season, off-season, and low season. It separately retains baseline parameters for each period, statistically analyzing the total number of visitors, average length of stay, and historical values ​​of various consumption frequencies. Combining the aforementioned defined metabolic boundaries and the interdependence of circulation links, it constructs a basic phosphorus metabolism mechanism framework, clarifies the intrinsic relationship between phosphorus input and output at each stage, and defines parameter input interfaces for each stage to adapt to subsequent phosphorus load calculations.

[0016] Furthermore, the mathematical expression of the tourist phosphorus coupling accounting algorithm is as follows: In the formula The total phosphorus efflux load in the region is calculated based on the independent induced total phosphorus emissions from tourists within and outside the accounting period, expressed in kg. The baseline coefficient for phosphorus leaching from the daily living conditions of overnight tourists is calculated in kg / (person·day). It is localized based on the measured phosphorus content of homestay toiletries and kitchen ingredients collected from the database of the entire region. The total number of overnight visitors and the number of days spent at the scenic area during the statistical period are calculated by multiplying the number of overnight visitors by the average number of days spent at the scenic area and summing the results. The baseline coefficient for phosphorus emissions per day's short-distance tourist consumption is expressed in kg / person-time and is determined by the phosphorus consumption test results of fast food and outdoor snacks in the scenic area. The total number of short-distance visitors entering the park on a single day within the statistical period is derived from operator signaling and scenic area access control visitor flow data; A specific correction factor for phosphorus leaching in surface runoff of tourist areas is assigned, based on measured values ​​of scenic area slope, average annual rainfall frequency, and soil cover properties of green areas collected from a comprehensive database. = 1· 2. 3, 1 represents the terrain slope correction factor, which is based on the measured slope value of the scenic area. 2 is the annual average rainfall leaching correction factor, which is determined from multi-year regional rainfall monitoring data; 3 is the correction coefficient for the physical and chemical interception of the soil covering the park, which is assigned a value based on the measured adsorption capacity of the green soil layer; The correction coefficient for fluctuations in tourist traffic during peak and off-peak seasons is generated by fitting statistical data on tourist traffic fluctuations during holidays and peak and off-peak seasons from a database built across the entire region over many years.

[0017] Furthermore, the detailed phosphorus emission data of tourists is generated based on the total phosphorus load of the whole region output by the tourist phosphorus coupling accounting algorithm. According to the basic framework, the spatial zoning range and the three statistical periods of peak and off-peak seasons are constructed. The total phosphorus load value obtained from the whole region accounting is split. First, the spatial dimension is divided according to the proportion of tourist flow in the scenic spots under the jurisdiction of the zoning. Then, the temporal split is completed by combining the proportion of tourist flow in different time periods of each zoning. After splitting, the detailed data is archived according to the two dimensions of zoning code and statistical period. The data records three sub-indicators: phosphorus production of overnight tourists, phosphorus production of short-distance tourists, and phosphorus production of runoff correction. Each group of data is bound to the corresponding zoning code and the time period identifier. After integration and standardization, the detailed data is formed.

[0018] Furthermore, the geographic information platform imports spatial layers of the study area's administrative boundaries, scenic spots, land use, and water systems. Combining this with the long-term spatial distribution characteristics of tourists, it matches detailed phosphorus emission data for different regions and time periods to corresponding geographic grid units. Based on regional land use attributes and tourist density, it completes the spatial allocation of phosphorus pollution load. The phosphorus load values ​​for each grid unit are assigned gradient values, matched with corresponding visualization color thresholds. A five-level gradient assignment range and corresponding visualization color thresholds are set according to the phosphorus load values ​​of each grid unit, with 0 < phosphorus load ≤ 0.05 kg / m² as the threshold. Low load is indicated by blue, 0.05 < phosphorus load ≤ 0.15 kg / m² by green, 0.15 < phosphorus load ≤ 0.30 kg / m² by yellow, 0.30 < phosphorus load ≤ 0.50 kg / m² by orange, and phosphorus load > 0.50 kg / m² by red. By overlaying the geographic information of the entire region and rendering and compositing, the differences in the intensity of phosphorus pollution load in different regions are presented intuitively, and finally a spatial heat map of phosphorus pollution across the entire region is generated, which can intuitively identify pollution hotspots and load distribution patterns.

[0019] Furthermore, the specific method for predicting changes in water phosphorus concentration and issuing graded pollution early warning information is as follows: Based on the generated spatial heat map of phosphorus pollution across the entire region and detailed data on phosphorus emissions from tourists, combined with real-time hydrological monitoring data of river and lake sections in the study area, meteorological forecast data, and pre-sale visitor flow data of scenic spots, a time-series prediction model is used to dynamically extrapolate the future trend of phosphorus concentration fluctuations in river and lake water. This time-series prediction model is a statistical or mechanistic model constructed based on historical water quality, visitor flow, and meteorological data. It can capture the dynamic coupling law between tourist phosphorus emissions, hydrological and meteorological fluctuations, and water phosphorus concentration, thus achieving short-cycle water phosphorus concentration prediction. Continuous simulation and analysis are conducted. Based on the critical threshold of phosphorus content in water bodies, three levels of pollution warnings are defined: mild, moderate, and severe. The warning thresholds are: mild (0.02 mg / L ≤ total phosphorus concentration < 0.1 mg / L), moderate (0.1 mg / L ≤ total phosphorus concentration < 0.2 mg / L), and severe (≥ 0.2 mg / L). The system automatically matches the corresponding warning level based on the real-time simulated phosphorus concentration values, generating standardized warning content that includes the polluted area, pollution level, and predicted duration. This enables the release of graded pollution warning information and provides risk support for optimizing control plans.

[0020] Furthermore, the mathematical expression of the optimization algorithm for control benefits is: In the formula This represents the overall net benefit of a single phosphorus control scheme after its implementation, expressed in ten thousand yuan. A higher value indicates a higher priority for the corresponding control scheme, and this value is used to select the optimal scheme. The conversion factor for the waste resource utilization benefits corresponding to a unit of phosphorus reduction is expressed in RMB 10,000 / kg and is taken from the statistical data of solid waste phosphorus recycling projects and product revenue from the whole-domain database. The total phosphorus load of tourists in the entire region is calculated and output as the phosphorus load. The value range is [0,1], which is determined based on the technical characteristics of different control measures and the risk level of pollution hotspot zones marked on the spatial load chart. The total one-time infrastructure investment for the entire control project is expressed in RMB 10,000, and is derived from the construction cost ledger of various pollution control facilities in the whole-domain data database. The correction coefficient for the operation and maintenance losses of environmental protection facilities throughout their entire life cycle is customized based on the load variation patterns of facilities in tourist areas during peak and off-peak seasons. The benchmark value for the potential economic losses to the cultural and tourism industry caused by visitor flow control restrictions is in ten thousand yuan and is determined based on the local cultural and tourism industry revenue statistics in the whole-domain data database. To fine-tune the economic losses caused by passenger flow control, the correction factor is determined based on the revenue fluctuation data of the local catering and accommodation industry, which is built on the basis of the whole-domain data database.

[0021] Furthermore, when selecting the optimal control scheme, the control combination with the highest overall net benefit is prioritized as the basic scheme. After the basic scheme is determined, it is adapted and adjusted according to the current status of regional cultural tourism development and environmental protection implementation conditions. The adaptation and adjustment include adjusting the implementation intensity of control measures according to the pollution risk level of different geographical zones, adjusting the implementation time of control measures according to the fluctuation pattern of tourist flow in different time periods, and adjusting control parameters according to the operation pattern of scenic area cultural tourism and pollution control implementation conditions. Simultaneously, the basic database, algorithm calculation results, spatial thermal results, pollution early warning documents and optimized control schemes generated throughout the entire process of this method are collected, uniformly organized, classified and archived in a standardized manner, and the entire set of standardized data assets is packaged to ensure that the results are complete, traceable and reusable.

[0022] Compared with existing technologies, this method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior has the following advantages:

[0023] I. This invention collects multi-category, cross-domain raw data and builds a standardized, traceable knowledge base. Relying on a domain-specific knowledge graph, it completes semantic matching and association integration of heterogeneous and fragmented data. It independently separates the tourist consumption flow from the overall phosphorus metabolism chain, delineates the closed-loop phosphorus metabolism lifecycle boundary, and divides differentiated statistical periods based on tourist flow patterns. This overcomes the technical bottleneck of traditional phosphorus measurement, which struggles to distinguish between tourist-sourced and local-source pollution, achieving physical separation of the two phosphorus production pathways. Standardized preprocessing eliminates calculation errors caused by invalid data, unifying statistical standards for various data types. It optimizes the simulation foundation from the underlying data architecture and metabolic mechanism framework, avoiding the problems of mixed pollution sources and coarse spatiotemporal division in past phosphorus pollution accounting. This improves the accuracy of time- and zone-based calculation of tourist-induced phosphorus emissions in tourist areas, providing stable and reliable data support for subsequent spatial pollution analysis and early warning assessment.

[0024] II. This invention achieves gridded spatial allocation of phosphorus pollution load by integrating geospatial layer data, generates a comprehensive pollution heat map based on gradient classification rules, and infers changes in water phosphorus concentration by linking hydrological, meteorological, and passenger flow pre-sale data, outputting graded early warnings. Furthermore, it uses comprehensive benefit accounting logic to coordinate multiple core indicators and select the optimal control measures. This approach overcomes the limitations of relying on manual experience to formulate pollution control plans. While accurately identifying pollution hotspots and pollution levels, it flexibly adjusts the details of control implementation based on regional environment and cultural tourism operation conditions, balancing ecological governance effectiveness with the stable development of the regional cultural tourism industry. The entire methodology forms a complete business chain from data modeling, pollution simulation, risk early warning to plan implementation, simultaneously archiving all kinds of results throughout the process to complete data asset encapsulation. This enhances the foresight of pollution early warnings, ensures that control plans are both scientific and adaptable to implementation, and enables long-term retention and reuse of the entire set of technical data.

[0025] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0027] Figure 1 A flowchart of a method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior;

[0028] Figure 2 This is a data transmission diagram illustrating a dynamic simulation method for phosphorus metabolism in tourist areas based on big data of tourist behavior.

[0029] Figure 3 This is a flowchart outlining the steps involved in building a basic framework for a dynamic simulation method of phosphorus metabolism in tourist areas based on big data of tourist behavior. Detailed Implementation

[0030] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0031] Example 1:

[0032] This embodiment selects a 5A-level mountain scenic area in China, developed based on native mountain forests. The entire scenic area consists of a mountain forest tourism area, a valley homestay cluster, and a cluster of farmhouses and restaurants at the foot of the mountain. The administrative jurisdiction of the scenic area includes surrounding villages and towns, within which small-scale crop planting plots and scattered livestock and poultry farming industries are scattered. The entire scenic area is adjacent to a natural mountain tributary that runs through the entire area. The tributary's water body is ecologically fragile and highly susceptible to eutrophication caused by exogenous phosphorus input. Based on years of on-site visitor flow statistics, the summer and statutory holidays are designated as the peak season, the regular spring and autumn non-holiday tourism period as the shoulder season, and the deep winter rainy and snowy low temperature period as the off-season. Throughout the entire cycle, the dynamic simulation method of phosphorus metabolism in tourist areas based on tourist behavior big data is strictly implemented according to the five implementation steps from S100 to S500. Figure 1 As shown, the entire simulation and calculation work was carried out.

[0033] S100, Comprehensive Data Database Construction: The comprehensive data database construction phase officially commenced. Project staff collected four categories of raw data according to technical requirements: socio-economic data, tourist behavior data, phosphorus conversion parameters, and environmental cost data. Socio-economic data comprehensively included historical national economic statistics for townships within the jurisdiction, development records for primary, secondary, and tertiary industries, crop planting scale data, livestock and poultry breeding data, production and operation records of local small food processing plants, and operation and maintenance records of village and town-supporting urban and rural sewage treatment plants. Tourist behavior data collected item by item from scenic area access control and ticketing statistics, mobile signaling data from partner telecommunications operators, online booking and dining orders from mainstream tourism platforms, guesthouse occupancy registration records, and quarterly on-site tourist behavior questionnaires. Phosphorus conversion parameter data uniformly included measured data on the primary phosphorus content of various food ingredients, phosphorus residue coefficients in detergents and daily chemical products, phosphorus leaching ratios from domestic waste and sewage, and phosphorus retention ratios from regional sludge treatment. Environmental cost data summarized comprehensive mountain land use surveying data, monthly meteorological monitoring records, watershed soil physicochemical testing data, and construction costs and daily operation and maintenance costs of various pollution control projects. After completing the full data collection, a refined process was carried out on the massive heterogeneous raw data. Duplicate data, distorted and abnormal data, and invalid data lacking key information were screened out. The statistical caliber, units of measurement, and spatiotemporal statistical scale of all data were unified, so that data from different sources and collection periods have a unified comparison benchmark. The effective data that was screened and retained were labeled with three types of traceability identifiers: collection source, sampling time, and data quality level. Based on a dedicated domain knowledge graph adapted to the phosphorus metabolism scenario of tourist areas, data association and integration were carried out. This knowledge graph uses tourist behavior, phosphorus flow, environmental parameters, and governance indicators as four core entities, and establishes fixed association logic and correspondence between each entity. Based on the graph architecture, semantic matching of various scattered data was completed, so that fragmented information can be logically linked. Subsequently, the integrated data was classified, sorted, and archived, and finally a unified standard knowledge base with unified structure, complete content, and full traceability was built. Once the entire database construction process is implemented, all data sources within the knowledge base are traceable and the indicator definitions are consistent. This provides a stable and accurate basic data source for the subsequent construction of the basic mechanism framework of phosphorus metabolism and the quantitative accounting of phosphorus load, thus avoiding the measurement deviation problem caused by messy raw data from the source.

[0034] S200, Basic Framework Construction: Entering the basic framework construction implementation phase, the project team retrieved all data stored in the integrated standard knowledge base to conduct boundary delineation and link analysis. Using the entire administrative jurisdiction of the mountain scenic area as the physical boundary and the complete phosphorus circulation process as the logical boundary, a closed-loop delineation was completed. The delineated boundary fully covers the entire process of phosphorus source input from outside the area, processing and conversion within the area, main consumption and utilization, waste collection and disposal, water and soil discharge, and phosphorus resource recycling and reuse. The boundary delineation process eliminated irrelevant phosphorus metabolism processes outside the scenic area's administrative scope, ensuring a complete closed loop for the entire phosphorus life cycle circulation. Staff meticulously analyzed all phosphorus circulation links within the boundary, clarifying the inherent logical relationships of phosphorus migration, transformation, and retention in each link. This identified three core phosphorus input sources: local agricultural production, urban and rural residents' lives, and external tourist consumption. Simultaneously, three core phosphorus output pathways were determined: sewage treatment, solid waste disposal, and surface runoff. Based on the source and output links, a complete closed-loop phosphorus circulation link was constructed. Based on the overall phosphorus metabolism pathway, a separate tourist consumption segment is established, aggregating all phosphorus consumption and waste generation and discharge paths resulting from tourists' accommodation, meals, forest tours, and offline immediate consumption. This pathway separation physically isolates phosphorus generation paths originating from tourists from local production and daily life, enabling independent management of these two pollution sources. Utilizing historical tourist flow patterns stored in a knowledge base, three statistical periods—peak, off-peak, and shoulder seasons—are defined. Baseline parameters for each period are stored separately, and historical values ​​for total tourist arrivals, average length of stay, and frequency of various consumption activities are statistically analyzed for each period. A basic phosphorus metabolism mechanism framework is constructed based on the defined metabolic boundaries and interrelationships of the circulation links. Within this framework, the intrinsic relationships between phosphorus input and output at each stage are clearly defined, and parameter input interfaces for each stage are simultaneously defined. This completed mechanism framework forms a standardized data access channel, accurately handling the import of various parameters required for the next stage of phosphorus load accounting, ensuring data access adaptability for subsequent algorithm operation from an architectural perspective.

[0035] S300, Phosphorus Load Calculation: Based on the established framework of phosphorus metabolism mechanisms, the phosphorus load calculation work was officially launched. Project operators used the tourist phosphorus coupling calculation algorithm to calculate the total regional phosphorus efflux load independently induced by external tourists within the calculation period. The mathematical expression of the tourist phosphorus coupling calculation algorithm is: In the formula The calculation period is for the total phosphorus discharge load in the region independently induced by inbound and outbound tourists; The baseline coefficient for phosphorus leaching from the daily living expenses of overnight tourists; This refers to the total number of overnight visitors and days at the scenic area within the statistical period. This serves as the baseline coefficient for phosphorus emissions from a single consumption by tourists on a short-distance day trip. This refers to the total number of short-distance visitors entering the park on a single day within the statistical period; A specific correction factor for phosphorus leaching in surface runoff of tourist areas; To correct for fluctuations in tourist traffic during peak and off-peak seasons, the algorithm outputs precise separation of total phosphorus emissions across the region into two categories: tourist-origin and local-origin emissions. This isolates interference from local industrial pollution and quantifies the scale of phosphorus emissions caused by tourist activities. After calculation, based on the spatial zoning defined in the initial framework construction phase and the three statistical periods (peak, shoulder, and off-peak), the total phosphorus load data is stratified and segmented. The segmentation prioritizes spatial segmentation based on the proportion of tourist traffic to each zone's scenic areas, followed by temporal segmentation based on the proportion of tourist traffic at different times within each zone. This achieves a refined breakdown of phosphorus load data in both spatial and temporal dimensions. The detailed tourist phosphorus emission data, after segmentation, is archived according to both zone codes and statistical periods. The archived content records three sub-indicators: phosphorus production from overnight tourists, phosphorus production from short-distance tourists, and phosphorus production from runoff correction. Each set of statistical data is simultaneously bound to its corresponding zone code and time period identifier. All information is integrated and standardized to generate detailed tourist phosphorus emission data by zone and time period. This set of standardized detailed data accurately implements spatial and temporal quantitative classification, and can seamlessly connect with the data entry specifications of the geographic information platform, providing standardized underlying data support for subsequent spatial allocation of phosphorus pollution load and heat map drawing.

[0036] S400 Spatial Mapping and Early Warning: Implement spatial mapping and hierarchical early warning. Operators import four types of spatial layers into the geographic information platform: administrative boundaries of the scenic area, scenic spots, land use of the entire area, and water systems of mountain tributaries and ponds. Combined with the long-term spatial distribution characteristics of tourists over many years, the detailed data of tourists' phosphorus emissions by region and time period are matched in batches to the corresponding geographic grid units of the platform. Combined with the land use attributes of the area and the density of tourist gathering, the spatial allocation of phosphorus pollution load is completed, so that the phosphorus load value is implemented to the refined geographic grid. For all grid unit phosphorus load values, gradient classification values ​​are assigned and matched with corresponding visualization color thresholds. The classification standard strictly follows the established range: 0 < phosphorus load ≤ 0.05 kg / m² is low load and matched with blue; 0.05 < phosphorus load ≤ 0.15 kg / m² is relatively low load and matched with green; 0.15 < phosphorus load ≤ 0.30 kg / m² is medium load and matched with yellow; 0.30 < phosphorus load ≤ 0.50 kg / m² is relatively high load and matched with orange; and phosphorus load > 0.50 kg / m² is high load and matched with red. The classification color information is superimposed and rendered with the whole-area geographic layer information to intuitively present the differences in phosphorus pollution load intensity in different areas and generate a whole-area phosphorus pollution spatial heat map. Based on the heat map, the phosphorus pollution hotspots in the whole area can be quickly located. During the prediction and early warning phase of water body phosphorus concentration, based on the spatial heat map of phosphorus pollution across the entire region, detailed data on phosphorus emissions from tourists, and overlaid with real-time hydrological monitoring data of river and lake sections in the study area, meteorological forecast data, and online pre-sale visitor flow data of scenic spots, a time-series prediction model is used to dynamically extrapolate the future cycle of phosphorus concentration fluctuations in river and lake water bodies. This time-series prediction model is built based on historical water quality, visitor flow, and meteorological data. It can use either the LSTM time-series prediction model or the SWAT hydrological mechanism model. The LSTM model is suitable for short-cycle (within 7 days) water quality prediction, while the SWAT model is suitable for long-cycle (more than 30 days) watershed-scale phosphorus concentration extrapolation. It can accurately capture the dynamic coupling law between tourist phosphorus emissions, hydrological and meteorological fluctuations, and water body phosphorus concentration, and complete the continuous simulation extrapolation of short-cycle water body phosphorus concentration. The system classifies warnings into three levels based on predetermined concentration thresholds: a mild warning threshold of 0.02 mg / L ≤ total phosphorus concentration in water < 0.1 mg / L, a moderate warning threshold of 0.1 mg / L ≤ total phosphorus concentration in water < 0.2 mg / L, and a severe warning threshold of total phosphorus concentration in water ≥ 0.2 mg / L. The system automatically matches the corresponding warning level based on the projected phosphorus concentration in the water, generates standardized warning content that indicates the polluted area, pollution level, and predicted duration, and releases it to the public. The warning content can intuitively reflect the potential risks to the watershed's water environment and provide accurate risk reference for optimizing the development of backend control plans.

[0037] S500, Control Plan Generation: The control plan development phase uses a comprehensive phosphorus pollution spatial heat map and tiered pollution early warning information as core references. Project technicians use a control benefit optimization algorithm to calculate the overall net benefit for various treatment scenarios. The mathematical expression for the control benefit optimization algorithm is: In the formula The overall net benefit of implementing a single phosphorus control plan across the entire region; The waste resource recovery benefit conversion factor corresponding to a unit of phosphorus reduction; The total phosphorus load of tourists in the entire region is calculated and output as the phosphorus load. This refers to the achievement rate of phosphorus emission reduction targets under corresponding control measures; This refers to the total amount of one-time infrastructure investment for the entire management and control project; This is a correction factor for the operation and maintenance losses of environmental protection facilities throughout their entire life cycle. A benchmark value for the potential economic losses to the cultural and tourism industry caused by restrictions on passenger flow control; To fine-tune the economic losses from visitor flow control, three core indicators were calculated: phosphorus emission reduction benefits, pollution control project construction costs, and cultural tourism industry losses. The optimal control combination was selected through multi-scenario quantitative comparison. The control scheme with the highest overall net benefit was prioritized as the basic implementation plan. After the basic plan was finalized, adjustments were made based on the current state of cultural tourism development in the mountain scenic area and the construction conditions for mountain environmental protection projects. The adjustment process included adjusting the intensity of control measures according to the pollution risk level of different geographical zones, adjusting the implementation time of control measures based on the fluctuation patterns of visitor flow during peak, off-peak, and low seasons, and optimizing the supporting control parameters based on the operational patterns of cultural tourism in the mountain scenic area and the on-site pollution control implementation conditions. After the control plan was finalized, staff collected the basic database data generated throughout the entire implementation process, the results of two types of algorithms, the overall spatial thermal data, the graded pollution early warning data, and the final optimized control plan. All data was uniformly organized, classified, and archived in a standardized manner, completing the packaging of the entire set of standardized data assets. The standardized and packaged data assets retain complete simulation and calculation information across the entire chain. When similar mountain scenic areas conduct phosphorus metabolism simulation work in the future, they can be directly retrieved and reused, reducing the data preparation and model building cycle in the early stages of new projects.

[0038] In summary, this embodiment implements a complete phosphorus metabolism simulation method in a mountainous 5A-level scenic area. It builds a traceable, integrated knowledge base based on four types of multi-source data, constructs a dedicated phosphorus metabolism mechanism framework through boundary loop delineation and independent decomposition of tourist links, quantifies phosphorus pollution from tourist sources using a tourist-coupled phosphorus accounting algorithm, generates pollution heat maps using GIS grid-based hierarchical rendering, achieves graded early warning of phosphorus pollution in water bodies using a time-series prediction model, and finally selects a governance solution that balances environmental protection and cultural tourism benefits through a management benefit optimization algorithm. The entire process achieves full-chain digital implementation of phosphorus pollution from tourists in scenic areas, from data collection, quantitative accounting, spatial visualization, risk early warning to solution optimization, effectively solving the practical problems of difficulty in tracing the source of phosphorus pollution from tourists in mountainous scenic areas and the reliance on experience for management decisions.

[0039] Example 2:

[0040] This embodiment selects a comprehensive leisure and cultural tourism resort area located on the shore of a large natural lake in the suburbs. The resort area consists of three main business formats: a theme park area, a cluster of lakeside business hotels, and a lakeside themed catering and commercial street. Large-scale field crop planting bases and aquaculture industries are located in the area surrounding the lake. In terms of visitor structure, short-distance daily visitors account for more than 60% of the total visitor flow, while overnight guests are concentrated on statutory holidays and the summer peak season. Based on the annual visitor flow fluctuations, the summer and statutory holidays are classified as the peak season, regular weekend operating hours as the shoulder season, and the months with more closures due to low temperatures in winter as the off-season. The area is adjacent to a lake with strict eutrophication control standards; external phosphorus input can easily cause the lake water quality to exceed standards. Figure 2 As shown, the entire process follows steps S100 to S500 to implement the simulation method of this invention.

[0041] S100, Full-Domain Data Collection: During the implementation phase of full-domain data collection, staff members complete the collection of raw data across the entire domain in four categories. Socioeconomic data collection includes historical national economic statistics of the county where the resort area is located, development records of primary, secondary and tertiary industries, data on the scale of crop planting around the lake, data on the stock of aquatic products and livestock and poultry around the lake, production and operation records of supporting food processing plants around the lake, and operation and maintenance records of urban and rural sewage treatment plants around the lake; tourist behavior data collection includes theme park access control and ticketing data, mobile signaling data of the entire lake area from cooperating operators, amusement park and catering consumption orders from major online tourism platforms, check-in registration records of chain hotels around the lake, and on-site questionnaire data on tourist behavior collected quarterly; phosphorus conversion parameters collection includes the primary phosphorus content of various catering ingredients, the phosphorus residue coefficient of washing and cleaning products around the lake, the proportion of phosphorus leaching from domestic waste and domestic sewage in the resort area, and the proportion of phosphorus retention in the resource-based treatment of sludge around the lake; environmental cost data collection includes land use surveying data of the entire lake area, monthly meteorological monitoring records of the lake area throughout the year, soil physicochemical testing data of the lake basin, and construction cost and daily operation and maintenance cost information of sewage interception and treatment projects around the lake. After the raw data was collected, it underwent refined processing to remove duplicate and redundant data, distorted and abnormal data, and invalid data with missing key fields. The statistical caliber, units of measurement, and spatiotemporal statistical scales of all data were standardized to eliminate data format barriers caused by different institutions and collection periods. Each qualified data point was labeled with three types of traceability identifiers: collection source, sampling time, and data quality level. Data association was conducted using a knowledge graph specific to the phosphorus metabolism domain of the tourist area. This knowledge graph uses tourist behavior, phosphorus flow, environmental parameters, and governance indicators as four core entities to establish logical relationships between entities. Semantic matching enabled the interconnection of scattered data. After all data was classified and archived, an integrated standard knowledge base was constructed. The standardized knowledge base has a complete data traceability chain and unified indicators, providing a reliable data foundation for the subsequent construction of a phosphorus metabolism mechanism framework and avoiding logical misalignment issues caused by disorganized multi-source data.

[0042] S200, Basic Framework Construction: The basic framework construction phase uses the entire administrative jurisdiction of the lakeside resort area as the physical boundary and the complete flow chain of phosphorus from external sources to end-of-life phosphorus resource recycling and reuse as the logical boundary to achieve closed-loop delineation. The delineated boundary fully covers the entire process of phosphorus source input from outside the region, processing and conversion within the region, main consumption and utilization, waste collection and disposal, water and soil discharge, and phosphorus resource recycling and reuse, eliminating irrelevant phosphorus metabolism processes outside the administrative scope of the resort area. Staff meticulously analyzed all phosphorus flow links within the boundary, clarified the inherent logic of phosphorus migration, transformation, and retention in each link, identified three core phosphorus input sources: lakeside agricultural production, daily life of urban and rural residents around the lake, and consumption by tourists visiting the park, and identified three core phosphorus output pathways: sewage treatment and disposal, solid waste disposal, and surface runoff around the lake. Based on the source-sink relationship, a closed-loop phosphorus flow chain was built. This paper separates the independent tourist consumption segment from the overall phosphorus metabolism chain, integrating the entire path of phosphorus consumption and waste generation and discharge generated during tourist activities such as amusement park visits, lakeside dining, and hotel stays. This achieves physical separation between the phosphorus production path from tourist sources and the phosphorus production path from local farming and residential life around the lake, thus independently dividing the two types of pollution source chains. Based on years of visitor flow statistics from a knowledge base, three statistical periods—peak, off-peak, and shoulder seasons—are defined. Baseline parameters for each period are stored separately. Historical data on total visitor numbers, average stay duration, and consumption frequency for each period are statistically analyzed based on these parameters. A basic phosphorus metabolism mechanism framework is constructed by combining established metabolic boundaries and the interdependence of circulation links. The phosphorus input and output relationships within the framework are clarified, and dedicated parameter input interfaces are defined for each link. The finalized mechanism framework forms a standardized data access port, seamlessly handling the parameter import work required for the next stage of the tourist phosphorus coupling accounting algorithm, ensuring smooth data integration in the accounting process. Figure 3 As shown.

[0043] S300, Phosphorus Load Calculation: The phosphorus load calculation phase relies on the established framework of phosphorus metabolism mechanisms. A tourist-coupled phosphorus calculation algorithm is used to calculate the regional total phosphorus emission load induced by inbound tourists within the calculation period. Based on the algorithm's calculation results, tourist-sourced phosphorus emissions are separated from local lake-source phosphorus emissions, eliminating interference from surrounding agricultural and rural pollution sources, and accurately quantifying the phosphorus emission volume solely caused by tourist activities. The total phosphorus load of the entire region is divided according to the previously defined spatial zoning and three statistical periods: peak, off-peak, and non-peak. During the division, spatial segmentation is first completed based on the proportion of visitor flow corresponding to recreational facilities in each zone, and then temporal segmentation is completed by combining the visitor flow proportions of different zones and time periods, achieving refined decomposition of phosphorus load data in both spatial and temporal dimensions. The generated detailed data uses zone codes and statistical time periods as dual archiving dimensions, recording three sub-indicators: phosphorus production by overnight tourists, phosphorus production by short-distance tourists, and phosphorus production corrected by lake-circumference runoff. Each data set is bound to a corresponding zone code and time period identifier, and after integration, standardized detailed data on tourist phosphorus emissions by zone and time period are generated. The standardized detailed data fully matches the data entry specifications of the geographic information platform and can be directly imported into the platform to carry out subsequent gridded phosphorus load allocation calculations, providing a standardized data source for spatial heat map drawing.

[0044] S400, Spatial Mapping and Early Warning: During the spatial mapping and early warning operation phase, operators import the administrative boundaries of the resort area, the amusement points in the park, the land use layer of the entire lake area, and the water system layer of the lake and its tributaries into the geographic information platform. Combined with the spatial distribution characteristics of tourists throughout the year, standardized phosphorus emission details are matched to each geographic grid unit in the platform. Based on the land use attributes of the area and the density of tourist gatherings, the spatial allocation of phosphorus pollution load is completed. The grid-based phosphorus load classification follows the established thresholds and color standards: 0 < phosphorus load ≤ 0.05 kg / m² is low load and matched with blue; 0.05 < phosphorus load ≤ 0.15 kg / m² is relatively low load and matched with green; 0.15 < phosphorus load ≤ 0.30 kg / m² is medium load and matched with yellow; 0.30 < phosphorus load ≤ 0.50 kg / m² is relatively high load and matched with orange; and phosphorus load > 0.50 kg / m² is high load and matched with red. After layer overlay rendering, a spatial heat map of phosphorus pollution in the entire lakeside resort area is generated, which allows for intuitive location of high phosphorus pollution hotspots along the lake. The work on predicting phosphorus concentration in water bodies combines a comprehensive heat map, detailed phosphorus emissions from tourists, real-time hydrological monitoring data of the river and lake sections around the lake, regional meteorological forecast data, and pre-sale passenger flow data of resort tickets. It relies on a time-series prediction model to dynamically predict the subsequent trend of phosphorus concentration in lake water bodies. This model is built based on historical water quality, passenger flow, and meteorological data. It can use the LSTM time-series prediction model or the SWAT hydrological mechanism model. The LSTM model is suitable for short-term (within 7 days) water quality prediction, while the SWAT model is suitable for long-term (more than 30 days) watershed-scale phosphorus concentration extrapolation. It can realize continuous simulation extrapolation of phosphorus concentration in water bodies in short-term cycles. The early warning classification strictly follows the water body phosphorus concentration thresholds: mild warning: 0.02 mg / L ≤ total phosphorus concentration < 0.1 mg / L; moderate warning: 0.1 mg / L ≤ total phosphorus concentration < 0.2 mg / L; severe warning: total phosphorus concentration ≥ 0.2 mg / L. The system automatically matches the warning level based on the simulated concentration, compiles and releases standardized early warning information that marks the location of the polluted lake area, the pollution level, and the risk prediction cycle. The warning content can accurately reflect the development trend of water pollution around the lake, providing a basis for water environment risk for the optimization and adjustment of control plans.

[0045] S500, Control Plan Generation: The control plan development phase relies on the spatial thermal distribution map of phosphorus pollution and tiered pollution early warning information. An optimal algorithm for operational control benefits is used to calculate the overall net benefit across multiple control scenarios. This includes calculating three core indicators: the resource recovery benefits of phosphorus emission reduction, the cost of pollution control infrastructure investment, and the economic losses to the cultural tourism sector due to visitor flow control. The control combination with the highest overall net benefit is selected as the basic plan. Adaptation and optimization are then carried out based on the layout of lakeside and surrounding aquaculture industries and the commercial development conditions of the resort area. The intensity of control measures is adjusted according to the pollution risk level of different areas around the lake. The timing of control implementation is adjusted based on the peak, off-peak, and peak season visitor flow cycles. Supporting control parameters are fine-tuned according to the daily operation rules of the theme park. After the plan is finalized, project personnel collect the integrated knowledge base files, two types of algorithm calculation reports, the overall phosphorus pollution thermal map, tiered early warning documents, and the optimal control plan after implementation optimization. These are then uniformly categorized, organized, and archived, completing the encapsulation and storage of the entire project data asset. The unified packaged data assets fully preserve all data from the entire phosphorus metabolism simulation of the Binhu Resort Area. Subsequent new Binhu cultural tourism projects can directly access and reference this data, shortening the model building and on-site calculation cycle for new projects.

[0046] In summary, this embodiment applies the technical system of this invention to lakeside leisure resort areas. It establishes a standardized knowledge base based on multi-dimensional raw data from the entire region, isolates pollution-generating links specific to tourists from the overall phosphorus metabolism pathway, uses a tourist phosphorus coupling accounting algorithm to separate phosphorus emissions from tourists and local sources in the lakeside area, generates a lakeside pollution heat map using geographic grid hierarchical color matching, predicts lake water quality changes using a time-series prediction model and issues tiered early warnings, and uses a management benefit optimization algorithm to balance pollution control investment, emission reduction benefits, and cultural tourism losses to determine the optimal management plan. The entire technology is adapted to the water environment management needs of lakeside scenic areas, helping to achieve coordinated development of cultural tourism and water ecological protection in the lakeside region.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior, characterized in that, The specific steps of this method are as follows: S100, Comprehensive Data Database Construction: Collect four types of raw data in the tourism area: socio-economic data, tourist behavior, phosphorus conversion parameters, and environmental costs; complete data regularization and standardization processing; label traceability identifiers and quality levels; and build an integrated standard knowledge base. S200, Basic Framework Construction: Calling on integrated standard knowledge base data, defining the boundaries of phosphorus metabolism throughout its entire life cycle, sorting out the related links in phosphorus circulation, setting up independent tourist consumption links, dividing the statistical periods into peak season, off-season, and low season, and building a basic mechanism framework for phosphorus metabolism. S300, Phosphorus Load Calculation: Based on the aforementioned basic mechanism framework of phosphorus metabolism, the tourist phosphorus coupling calculation algorithm is used to calculate the total phosphorus discharge load in the region, and the phosphorus emissions from tourist sources and local sources are separated to generate detailed data on tourist phosphorus emissions by region and time period. S400 Spatial Mapping and Early Warning: Based on the aforementioned detailed data on phosphorus emissions from tourists and the geographic information platform, the spatial allocation of phosphorus pollution load is completed, a spatial heat map of phosphorus pollution is generated, changes in phosphorus concentration in water bodies are predicted, and graded pollution early warning information is issued. S500, Control Plan Generation: Based on the aforementioned spatial heat map and graded pollution early warning information, the control benefit optimization algorithm is used to calculate the control benefits of each scenario, select the optimal control plan, and encapsulate the full-process data assets.

2. The method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior according to claim 1, characterized in that, In step S100, the global data database construction process involves refining and standardizing the four types of raw data collected, eliminating duplicate data, distorted and abnormal data, and invalid data lacking key information, and unifying the statistical caliber, units of measurement, and spatiotemporal statistical scale of all data. Each of the selected valid data is labeled with three types of traceability identifiers: collection source, sampling time, and data quality level. Semantic matching and association integration of various scattered data are completed using a domain knowledge graph. All data is classified, organized, and archived, ultimately building an integrated standard knowledge base with a unified structure, complete content, and full traceability.

3. The method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior according to claim 1, characterized in that, In step S200, the phosphorus metabolism life cycle flow boundary is defined in a closed loop, with the entire administrative space of the tourist area as the physical boundary and the complete phosphorus flow process as the logical boundary. This fully covers the entire process of phosphorus source input from outside the region, processing and conversion within the region, main consumption and utilization, waste collection and disposal, water and soil discharge, and phosphorus resource recycling and reuse. All phosphorus flow-related links within the boundary are sorted out, clarifying the inherent logical relationship of phosphorus migration, conversion, and retention in each link. The three core phosphorus input sources are identified: local agricultural production, urban and rural residents' lives, and consumption by tourists. The three core phosphorus output pathways are identified: sewage treatment, solid waste disposal, and surface runoff, forming a complete closed-loop phosphorus flow chain.

4. The method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior according to claim 1, characterized in that, In step S200, the basic framework is built based on tourist consumption and phosphorus emission data in the integrated standard knowledge base. Independent tourist consumption links are set up from the overall phosphorus metabolism chain, and all phosphorus consumption and waste generation and discharge paths brought about by tourists' food, accommodation, sightseeing and consumption are collected. Based on the annual tourist flow change pattern, three statistical periods are divided into peak season, off-season and low season. The tourist flow benchmark parameters of each period are stored separately. The basic mechanism framework of phosphorus metabolism is built by combining the aforementioned defined metabolic boundary and the interdependence of circulation links.

5. The method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior according to claim 1, characterized in that, In step S300, the mathematical expression of the tourist phosphorus coupling calculation algorithm is: In the formula The calculation period is for the total phosphorus discharge load in the region independently induced by inbound and outbound tourists; The baseline coefficient for phosphorus leaching from the daily living expenses of overnight tourists; This refers to the total number of overnight visitors and days at the scenic area within the statistical period. This serves as the baseline coefficient for phosphorus emissions from a single consumption by tourists on a short-distance day trip. This refers to the total number of short-distance visitors entering the park on a single day within the statistical period; A specific correction factor for phosphorus leaching in surface runoff of tourist areas; This is a correction factor for fluctuations in tourist traffic during peak and off-peak seasons in the cultural and tourism industry.

6. The method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior according to claim 1, characterized in that, In step S300, the detailed data on phosphorus emissions from tourists is generated based on the total phosphorus load breakdown of the entire region output by the tourist phosphorus coupling accounting algorithm. According to the basic framework, the spatial zoning range and the three statistical periods of peak and off-peak seasons are constructed. The total phosphorus load value obtained from the total region accounting is broken down. First, the spatial dimension is divided according to the proportion of tourist flow in the scenic spots under the jurisdiction of the zoning, and then the temporal division is completed by combining the proportion of tourist flow in different time periods of each zoning. After the breakdown, the detailed data is archived according to the two dimensions of zoning code and statistical period. The data records three sub-indicators: phosphorus production of overnight tourists, phosphorus production of short-distance tourists, and phosphorus production of runoff correction. Each group of data is bound to the corresponding zoning code and the time period identifier. After integration and standardization, the detailed data is formed.

7. The method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior according to claim 1, characterized in that, In step S400, the geographic information platform imports the administrative boundaries, scenic spots, land use, and water system spatial layers of the study area. Combining the long-term spatial aggregation and distribution characteristics of tourists, it matches the detailed data of tourists' phosphorus emissions by region and time period to the corresponding geographic grid units. Based on the regional land use attributes and tourist aggregation density, it completes the spatial allocation of phosphorus pollution load. The phosphorus load values ​​of each grid unit are assigned gradient grades, matched with corresponding visualization color thresholds, and rendered and synthesized by overlaying the information of the whole-area geographic layer. This intuitively presents the differences in the intensity of phosphorus pollution load in different regions, and finally generates a whole-area phosphorus pollution spatial heat map that can intuitively identify pollution hotspots and load distribution patterns.

8. The method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior according to claim 1, characterized in that, In step S400, the specific method for predicting changes in phosphorus concentration in water and issuing graded pollution early warning information is as follows: Based on the generated spatial heat map of phosphorus pollution across the entire region and detailed data on phosphorus emissions from tourists, combined with real-time hydrological monitoring data of river and lake sections in the study area, meteorological forecast data, and pre-sale visitor flow data of scenic spots, a time-series prediction model is used to dynamically predict the trend of phosphorus concentration fluctuations in river and lake water bodies in the future; based on the critical threshold of phosphorus content in water bodies, three levels of pollution early warning are divided into light, moderate, and heavy levels; the corresponding early warning level is automatically matched according to the real-time predicted phosphorus concentration values ​​in water bodies, and standardized early warning content including the polluted area, pollution level, and predicted duration is generated to complete the issuance of graded pollution early warning information.

9. The method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior according to claim 1, characterized in that, In step S500, the mathematical expression of the control benefit optimization algorithm is: In the formula The overall net benefit of implementing a single phosphorus control plan across the entire region; The waste resource recovery benefit conversion factor corresponding to a unit of phosphorus reduction; The total phosphorus load of tourists in the entire region is calculated and output as the phosphorus load. This refers to the achievement rate of phosphorus emission reduction targets under corresponding control measures; This refers to the total amount of one-time infrastructure investment for the entire management and control project; This is a correction factor for the operation and maintenance losses of environmental protection facilities throughout their entire life cycle. A benchmark value for the potential economic losses to the cultural and tourism industry caused by restrictions on passenger flow control; Adjustment factor for economic losses due to passenger flow control.

10. The method for dynamic simulation of phosphorus metabolism in tourist areas based on big data of tourist behavior according to claim 1, characterized in that, In step S500, when selecting the optimal control scheme, the control combination with the highest comprehensive net benefit value is selected as the basic scheme. After the basic scheme is determined, it is adapted and adjusted according to the current status of regional cultural tourism development and environmental protection implementation conditions. The adaptation and adjustment include adjusting the implementation intensity of control measures according to the pollution risk level of different geographical zones, adjusting the implementation time of control measures according to the fluctuation pattern of passenger flow in different time periods, and adjusting control parameters according to the operation pattern of scenic area cultural tourism and pollution control implementation conditions. Simultaneously, the basic database, algorithm calculation results, spatial thermal results, pollution early warning documents and optimized control schemes generated by the entire process of this method are collected, uniformly organized, classified and archived, and the entire set of standardized data assets is packaged.

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