Scenic area tourism planning and reception integrated management system

Abstract

The invention discloses a scenic spot tourism planning and reception integrated management system, and the system comprises a perception access layer which is responsible for collecting tourist distribution, environment data and facility states in real time, and converting fragmented physical information into a standardized data flow; the data intelligent layer is responsible for processing a data stream input by the perception access layer, integrating dispersed fragmented data by using a space-time knowledge graph, constructing a panoramic three-dimensional thermodynamic diagram, performing dominant implicit information, and constructing a tourist digital portrait based on a multi-dimensional data fusion and machine learning algorithm; the service center layer is responsible for constructing an intelligent scheduling platform on the basis of the panoramic three-dimensional thermodynamic diagram to monitor thermodynamic abnormity in real time and trigger graded early warning, and performing scene customization by applying a personalized recommendation engine in combination with tourist digital portraits and real-time thermodynamic distribution; and the interaction touch layer is responsible for receiving and converting the instruction of the service center layer into ground projection navigation and air suspension prompt visual forms and transmitting the ground projection navigation and the air suspension prompt visual forms to tourists.

Description

Technical Field

[0001] The present invention relates to the technical field of scenic area tourism planning and reception, in particular to a scenic area tourism planning and reception integrated management system. Background Art

[0002] The emergence of a comprehensive management system for scenic area tourism planning and reception is an inevitable outcome of the digital transformation of the tourism industry and breakthroughs in intelligent technology. With the continued expansion of the global tourism market, visitors' demand for travel experiences has evolved from simple "sightseeing check-in" to "full-scene immersive experiences." According to statistics, global annual visitor numbers have increased by 37% in the past five years alone. With the significant increase in visitor numbers to scenic areas, the comprehensive management system for scenic area tourism planning and reception has demonstrated its strong resilience, enhancing the visitor experience and providing a paradigm-shifting solution for the upgrading of the smart tourism industry.

[0003] However, the traditional scenic area tourism planning and reception integrated management system has the problem of information asymmetry. For example, when tourists plan their routes through traditional guide maps, they cannot be informed that the viewing platform 500 meters ahead is temporarily closed due to congestion, resulting in repeated backtracking, which greatly wastes travel time and reduces the tourists' travel experience. Therefore, a scenic area tourism planning and reception integrated management system is proposed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a comprehensive management system for tourism planning and reception in scenic areas.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A comprehensive management system for tourism planning and reception in scenic areas, comprising:

[0007] Perception access layer: Responsible for collecting visitor distribution, environmental data, and facility status in real time. It uses edge computing nodes to perform data cleaning and preliminary analysis, converting fragmented physical information into standardized data streams and transmitting them to the data intelligence layer.

[0008] Data Intelligence Layer: This layer processes data streams input from the sensor access layer and verifies their authenticity through blockchain evidence storage technology. It utilizes spatiotemporal knowledge graphs to integrate data streams from the sensor access layer (such as facility status and weather warnings from different sources), constructs panoramic three-dimensional heat maps, makes implicit information explicit, integrates multi-source data, and constructs hierarchical digital profiles of tourists. These panoramic three-dimensional heat maps and tourist digital profiles are then transmitted to the service hub layer. An LSTM neural network prediction model is used to transform historical data into dynamic trend insights.

[0009] Service Center Layer: Responsible for building an intelligent scheduling platform based on panoramic three-dimensional heat maps to monitor thermal anomalies in real time and trigger graded warnings. By integrating digital portraits of tourists with real-time thermal distribution data of scenic spots, a hybrid recommendation algorithm is used to generate scenario-based customization adapted to the current scenario. A decision-making instruction generator is set up to convert warning levels and scenario-based customization into instructions and send them to the interactive touch layer.

[0010] Interactive access layer: responsible for converting the instructions received from the service center layer into visual forms of ground projection navigation and air-suspended prompts to convey them to tourists, and converting abstract data such as facility status and queue time into three-dimensional dynamic presentations.

[0011] The above technical solution further includes:

[0012] Furthermore, the real-time collection of visitor distribution, environmental data, and facility status, and the use of edge computing nodes to complete data cleaning and preliminary analysis, and convert fragmented physical information into standardized data streams, include the following steps:

[0013] The perception access layer deploys 5G+ Beidou positioning base stations, an environmental sensor matrix, and facility monitoring equipment to collect real-time data on the spatiotemporal distribution of visitors, environmental parameters, and facility status. At the edge computing node, sliding window filtering, spatiotemporal alignment, and feature extraction algorithms are used to clean, reduce noise, and perform preliminary analysis on the raw data, converting fragmented physical signals into structured information streams. For example, a 10m-precision heat map of pedestrian flow is generated through Gaussian kernel density estimation, and the environmental comfort index is calculated by combining temperature, humidity, and air quality data. The processed results are finally encapsulated in the standard Protocol Buffers format.

[0014] Furthermore, the authenticity verification using blockchain evidence storage technology includes the following steps:

[0015] Generate data fingerprint:

[0016] Perform a hash operation on raw data (such as sensor readings and device logs) to generate a unique digital fingerprint: H(D) = SHA-256(D), where D is the raw data and H(D) is the 256-bit hash value. This process is irreversible, and any change in the data will cause the hash value to change significantly (collision resistance);

[0017] Constructing the evidence block:

[0018] Combine the hash fingerprint with metadata (timestamp T, device ID dev , geographic coordinates Coord) is encapsulated as a block: Block = {Index, H (D), T, ID dev ,Coord,H(Block prev )}, where H(Block prev) is the hash of the previous block, forming a chain structure;

[0019] Consensus verification mechanism:

[0020] Use the Practical Byzantine Fault Tolerance (PBFT) algorithm to achieve data consistency between edge nodes:

[0021] Pre-preparation phase: the master node broadcasts the block proposal;

[0022] Preparation phase: Nodes verify the validity of the proposal (such as the rationality of the timestamp);

[0023] Commitment phase: After more than 2 / 3 of the nodes confirm, the block is put on the chain;

[0024] Authenticity Verification:

[0025]

[0026] This ensures the authenticity of basic data and eliminates interference from false information.

[0027] Furthermore, the method of utilizing the spatiotemporal knowledge graph to integrate the data streams inputted by the perception access layer and constructing a panoramic three-dimensional heat map includes the following steps:

[0028] Multi-source data fusion: Define the scenic area domain ontology, including core entities (attractions, facilities, weather phenomena) and relationships (such as "influence" and "located"); unify scattered data sources (such as weather stations and equipment logs) into a standard spatial framework through geocoding (GCJ-02 coordinate system conversion); and use the Dynamic Time Warping (DTW) algorithm to align data of different frequencies (such as 5-minute environmental data and hourly ticketing data).

[0029] Knowledge graph construction: Identify entity relationships from unstructured data (such as tourist reviews), for example: "heavy rain → caused → facility closure"; map entities to a high-dimensional vector space, incorporating spatiotemporal features:

[0030] e i,t =e i +Δe i,t , where e i is the static entity embedding, Δe i,t is the time-dependent dynamic offset;

[0031] 3D heat map generation:

[0032] Divide the scenic area into a 10m×10m×10m three-dimensional voxel grid;

[0033] Comprehensive weighted summation of multi-source data:

[0034] where w kis the data source weight, f k is a characteristic function (such as crowd density, facility failure rate);

[0035] Use color coding (such as Jet color system) to map thermal values ​​to RGB color space, and superimpose contour lines to generate three-dimensional terrain;

[0036] Furthermore, the construction of tourist digital portraits based on multi-dimensional data fusion and machine learning algorithms includes the following steps:

[0037] Fusion of multi-source data:

[0038] Convert structured data (such as consumption amounts) and unstructured data (such as travel diary text) into a unified vector space; use the Dynamic Time Warping (DTW) algorithm to align data of different frequencies (such as hourly location trajectories and daily consumption records);

[0039] Building a digital portrait:

[0040] First, use the rule engine to query the tourists’ age, gender, and place of origin;

[0041] At the same time, tourist behavior patterns are extracted by calculating the depth of tourists’ visits:

[0042]

[0043] Where I is the depth of the tour, T is the tour time, and t is the opening time of the scenic spot, and the behavior patterns of tourists are classified accordingly;

[0044] Calculate the preference strength of tourists and build tourist interest preference model:

[0045]

[0046] Where D is the preference intensity, M is the total tour time, m is the length of stay at a certain type of attraction, and k is the consumption frequency weight, based on which the preference labels of tourists are marked;

[0047] Finally, the Wide & Deep structure is used to mine implicit associations and infer tourists' potential needs. This creates a digital portrait of tourists based on age, gender, origin, behavior patterns, preference tags, and potential needs.

[0048] Real-time updates:

[0049] Update the profile parameters through Online Learning:

[0050] Where η is the learning rate and L is the loss function;

[0051] Assign time decay weights to historical behaviors:

[0052] w history =e -λ Δt, where λ is the attenuation coefficient and Δt is the time interval;

[0053] Furthermore, the use of the LSTM neural network prediction model to convert historical data into dynamic trend insights includes the following steps:

[0054] Time series data preprocessing:

[0055] Convert historical data into supervised learning format:

[0056] X=[x t -w,x t -w+ 1 ,…,x t - 1 ],Y=x t , where w is the window width, x t is the observation value at time t;

[0057] Using Min-Max normalization:

[0058]

[0059] LSTM network architecture:

[0060] The cell gating mechanism is as follows:

[0061] Forget gate: f t =σ(W f ·[h t-1 ,x t ]+b f ),

[0062] Input gate: i t =σ(W i ·[h t-1 ,x t ]+b i ),

[0063] Output gate: o t =σ(W o ·[h t-1 ,x t ]+b o ),

[0064] Candidate memory: c t =tanh(W c ·[h t-1 ,x t ]+b c ),

[0065] Memory update: c t=f t ⊙c t-1 +i t ⊙c t ,

[0066] Hidden state: h t =o t ⊙tanh(c t ), where σ is the sigmoid function, tanh is the hyperbolic tangent function, and ⊙ is the element-wise product;

[0067] Predict dynamic trends:

[0068] Using recursive prediction:

[0069] x t+1 =LSTM(X t ),x t+2 =LSTM([…,x t+1 ]),

[0070] Generate prediction intervals via Monte Carlo Dropout sampling:

[0071] PI α =[y low ,y high ]stP(y∈PI α )=α,

[0072] Furthermore, the construction of an intelligent scheduling platform to monitor thermal anomalies in real time and trigger graded warnings includes the following specific steps:

[0073] Thermal anomaly detection:

[0074] Calculate the moving average (MA) and standard deviation (STD) based on historical data and set adaptive thresholds:

[0075] Threshold upper =MA H +k·STD H , where H is the history window length and k is the adjustment coefficient (usually 2-3);

[0076] When the real-time thermal value H t Satisfy H t >Threshold upper or H t <Threshold lower When the warning process is triggered;

[0077] Graded early warning mechanism:

[0078] The risk index is constructed by comprehensively considering the intensity, duration and impact range of thermal anomalies:

[0079] R=w1·ΔH+w2·ΔT+w3·A affected , where w i is the weight coefficient, ΔH is the thermal deviation, ΔT is the duration, A affected is the area of ​​the affected region;

[0080] Red alert: R ≥ 0.8, activate emergency plan, and dispatch all personnel;

[0081] Orange alert: 0.5≤R<0.8, additional mobile personnel will be dispatched and facility status will be adjusted;

[0082] Yellow alert: R<0.5, issue a warning message and strengthen monitoring;

[0083] Dynamic instruction generation: pre-defined standardized instruction templates (e.g., "Area X is overloaded, please direct visitors to route Y") are used to populate template parameters (e.g., overloaded area coordinates, recommended routes) using real-time data. Voice instructions (TTS synthesis), text notifications (multilingual versions), and AR navigation (with overlay of thermal images) are then generated simultaneously.

[0084] Furthermore, the method of generating scenario-based customization adapted to the current scenario by fusing digital portraits of tourists with real-time thermal distribution data of scenic spots using a hybrid recommendation algorithm includes the following steps:

[0085] Thermal distribution analysis:

[0086] Calculate real-time visitor density distribution:

[0087] Where K is the kernel function (Gaussian kernel is commonly used), h is the bandwidth parameter, and n is the sample size;

[0088] Set the density threshold θ hot , filter hot spots:

[0089]

[0090] Mixed recommendations:

[0091] Based on the user-attraction rating matrix, calculate the similarity between tourists:

[0092]

[0093] Combined with the scenic spot feature matrix M poi And the tourist interest vector u, calculate the matching degree:

[0094] score content =u T M poi P, where p is the scenic spot feature vector;

[0095] Adjust the recommended weights based on heat distribution:

[0096] Where α is the balance coefficient, (x p ,y p ) are the coordinates of the scenic spot;

[0097] Real-time feedback optimization:

[0098] The ε-greedy strategy is used to balance exploration and utilization, dynamically adjust the recommendation list, and use click-through rate (CTR) as the reward function to optimize the recommendation strategy:

[0099] Q(s,a)←Q(s,a)+η[rQ(s,a)], where Q((s,a)) is the state-action value function and η is the learning rate, thus enabling personalized scenario customization.

[0100] The present invention has the following beneficial effects:

[0101] 1. In the present invention, by establishing a panoramic three-dimensional heat map, multi-dimensional data such as crowd density, facility status, and weather impact are superimposed on the electronic map in real time, and an intelligent scheduling platform is built to monitor thermal anomalies in real time and trigger graded warnings. At the same time, an LSTM neural network prediction model is used to convert historical data into dynamic trend insights, and a more reasonable and scientific analysis of future dynamic trends of tourists is conducted. This effectively solves the information asymmetry problem of the integrated management system, provides tourists with timely and effective information and suggestions in real time, greatly saves tourists' travel time, and effectively improves their travel experience.

[0102] 2. In the present invention, a method of constructing digital portraits of tourists is adopted, and a personalized recommendation engine and real-time heat distribution are combined to enable scenario-based customization of real-time messages and intelligent route recommendations, allowing different tourists to enjoy their own personalized information services. While providing tourists with reasonable and effective suggestions, it effectively reduces the problems encountered by tourists during the travel process, such as difficulty in making choices and problems affecting the itinerary due to sudden bad weather, thus providing effective guarantees for tourists' high-quality travel. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 This is a system block diagram of a scenic area tourism planning and reception integrated management system proposed by the present invention. DETAILED DESCRIPTION

[0104] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0105] See also Figure 1 As shown, the present invention is a comprehensive management system for tourism planning and reception in scenic areas, comprising:

[0106] Perception access layer: Responsible for collecting visitor distribution, environmental data, and facility status in real time. It uses edge computing nodes to perform data cleaning and preliminary analysis, converting fragmented physical information into standardized data streams and transmitting them to the data intelligence layer.

[0107] Data Intelligence Layer: This layer processes data streams input from the sensor access layer, ensuring the authenticity of underlying data through blockchain evidence storage technology and eliminating interference from false information. It utilizes spatiotemporal knowledge graphs to integrate data streams from the sensor access layer (such as facility status and weather warnings from different sources), constructs panoramic three-dimensional heat maps, makes implicit information explicit, integrates multi-source data, and constructs hierarchical digital profiles of tourists. These panoramic three-dimensional heat maps and tourist digital profiles are then transmitted to the service hub layer. An LSTM neural network prediction model is used to transform historical data into dynamic trend insights.

[0108] Service Center Layer: This layer is responsible for building an intelligent scheduling platform based on a panoramic three-dimensional heat map to monitor thermal anomalies in real time and trigger graded warnings. By integrating digital portraits of tourists with real-time thermal distribution data of scenic spots, a hybrid recommendation algorithm is used to generate scenario-based customization adapted to the current scenario. A decision-making instruction generator is set up to convert warning levels and scenario-based customization into instructions and send them to the interactive touch layer.

[0109] Interactive access layer: responsible for converting the instructions received from the service center layer into visual forms of ground projection navigation and air-suspended prompts to convey them to tourists, and converting abstract data such as facility status and queue time into three-dimensional dynamic presentations.

[0110] In one embodiment, the real-time collection of visitor distribution, environmental data, and facility status, and the use of edge computing nodes to perform data cleaning and preliminary analysis, thereby converting fragmented physical information into standardized data streams, include the following steps:

[0111] The perception access layer deploys 5G+ Beidou positioning base stations, an environmental sensor matrix, and facility monitoring equipment to collect real-time data on the spatiotemporal distribution of visitors, environmental parameters, and facility status. At the edge computing node, sliding window filtering, spatiotemporal alignment, and feature extraction algorithms are used to clean, reduce noise, and perform preliminary analysis on the raw data, converting fragmented physical signals into structured information streams. For example, a 10m-precision heat map of pedestrian flow is generated through Gaussian kernel density estimation, and the environmental comfort index is calculated by combining temperature, humidity, and air quality data. The processed results are finally encapsulated in the standard Protocol Buffers format.

[0112] In one embodiment, the authenticity verification using blockchain evidence storage technology includes the following steps:

[0113] Generate data fingerprint:

[0114] Hashing raw data (such as sensor readings and device logs) generates a unique digital fingerprint: H(D) = SHA-256(D), where D is the raw data and H(D) is the 256-bit hash value. This process is irreversible, and any change to the data will cause the hash value to change significantly (collision resistance).

[0115] Constructing the evidence block:

[0116] Combine the hash fingerprint with metadata (timestamp T, device ID dev , geographic coordinates Coord) is encapsulated as a block: Block = {Index, H (D), T, ID dev ,Coord,H(Block prev )}, where H(Block prev ) is the hash of the previous block, forming a chain structure;

[0117] Consensus verification mechanism:

[0118] Use the Practical Byzantine Fault Tolerance (PBFT) algorithm to achieve data consistency between edge nodes:

[0119] Pre-preparation phase: the master node broadcasts the block proposal;

[0120] Preparation phase: Nodes verify the validity of the proposal (such as the rationality of the timestamp);

[0121] Commitment phase: After more than 2 / 3 of the nodes confirm, the block is put on the chain;

[0122] Authenticity Verification:

[0123]

[0124] This ensures the authenticity of basic data and eliminates interference from false information.

[0125] In one embodiment, the method of integrating the data streams input from the perception access layer using the spatiotemporal knowledge graph to construct a panoramic three-dimensional heat map includes the following steps:

[0126] Multi-source data fusion: Define the scenic area domain ontology, including core entities (attractions, facilities, weather phenomena) and relationships (such as "influence" and "located"); unify scattered data sources (such as weather stations and equipment logs) into a standard spatial framework through geocoding (GCJ-02 coordinate system conversion); and use the Dynamic Time Warping (DTW) algorithm to align data of different frequencies (such as 5-minute environmental data and hourly ticketing data).

[0127] Knowledge graph construction: Identify entity relationships from unstructured data (such as tourist reviews), for example: "heavy rain → caused → facility closure"; map entities to a high-dimensional vector space, incorporating spatiotemporal features:

[0128] e i,t =e i +Δe i,t , where e i is the static entity embedding, Δe i,t is the time-dependent dynamic offset;

[0129] 3D heat map generation:

[0130] Divide the scenic area into a 10m×10m×10m three-dimensional voxel grid;

[0131] Comprehensive weighted summation of multi-source data:

[0132] where w k is the data source weight, f k is a characteristic function (such as crowd density, facility failure rate);

[0133] Use color coding (such as Jet color system) to map thermal values ​​to RGB color space, and superimpose contour lines to generate three-dimensional terrain;

[0134] In one embodiment, the method of fusing multi-source data and constructing a hierarchical digital portrait of a tourist includes the following steps:

[0135] Fusion of multi-source data:

[0136] Convert structured data (such as consumption amounts) and unstructured data (such as travel diary text) into a unified vector space; use the Dynamic Time Warping (DTW) algorithm to align data of different frequencies (such as hourly location trajectories and daily consumption records);

[0137] Building a digital portrait:

[0138] First, use the rule engine to query the tourists’ age, gender, and place of origin;

[0139] At the same time, tourist behavior patterns are extracted by calculating the depth of tourists’ visits:

[0140]

[0141] Where I: tour depth, T: tour time, t: scenic spot opening hours, and thus the behavior patterns of tourists are classified;

[0142] Calculate the preference strength of tourists and build tourist interest preference model:

[0143]

[0144] Where D is the preference intensity, M is the total tour time, m is the length of stay at a certain type of attraction, and k is the consumption frequency weight. Based on this, the preference labels of tourists are marked.

[0145] Finally, the Wide & Deep structure is used to mine implicit associations and infer tourists' potential needs. This creates a digital portrait of tourists based on age, gender, origin, behavior patterns, preference tags, and potential needs.

[0146] Real-time updates:

[0147] Update the profile parameters through Online Learning:

[0148] Where η is the learning rate and L is the loss function;

[0149] Assign time decay weights to historical behaviors:

[0150] w history =e -λ Δt, where λ is the attenuation coefficient and Δt is the time interval;

[0151] In one embodiment, the use of an LSTM neural network prediction model to convert historical data into dynamic trend insights includes the following steps:

[0152] Time series data preprocessing:

[0153] Convert historical data into supervised learning format:

[0154] X=[x t -w,x t -w+ 1 ,…,x t - 1 ],Y=x t , where w is the window width, x t is the observation value at time t;

[0155] Using Min-Max normalization:

[0156]

[0157] LSTM network architecture:

[0158] The cell gating mechanism is as follows:

[0159] Forget gate: f t =σ(W f ·[h t-1 ,x t ]+b f ),

[0160] Input gate: i t =σ(W i ·[h t-1 ,x t ]+b i ),

[0161] Output gate: o t =σ(W o ·[h t-1 ,x t ]+b o ),

[0162] Candidate memory: c t =tanh(W c ·[h t-1 ,x t ]+b c ),

[0163] Memory update: c t =f t ⊙c t-1 +i t ⊙c t ,

[0164] Hidden state: h t =o t ⊙tanh(c t ), where σ is the sigmoid function, tanh is the hyperbolic tangent function, and ⊙ is the element-wise product;

[0165] Predict dynamic trends:

[0166] Using recursive prediction:

[0167] x t+1 =LSTM(X t ),x t+2 =LSTM([…,x t+1 ]);

[0168] Generate prediction intervals via Monte Carlo Dropout sampling:

[0169] PI α =[y low ,y high ]stP(y∈PI α )=α;

[0170] In one embodiment, the construction of an intelligent scheduling platform to monitor thermal anomalies in real time and trigger graded warnings includes the following specific steps:

[0171] Thermal anomaly detection:

[0172] Calculate the moving average (MA) and standard deviation (STD) based on historical data and set adaptive thresholds:

[0173] Threshold upper =MA H +k·STD H , where H is the history window length and k is the adjustment coefficient (usually 2-3);

[0174] When the real-time thermal value H t Satisfy H t >Threshold upper or H t <Threshold lower When the warning process is triggered;

[0175] Graded early warning mechanism:

[0176] The risk index is constructed by comprehensively considering the intensity, duration and impact range of thermal anomalies:

[0177] R=w1·ΔH+w2·ΔT+w3·A affected , where w i is the weight coefficient, ΔH is the thermal deviation, ΔT is the duration, A affected For the affected area,

[0178] Red alert: R ≥ 0.8, activate emergency plan, and dispatch all personnel;

[0179] Orange alert: 0.5≤R<0.8, additional mobile personnel will be dispatched and facility status will be adjusted;

[0180] Yellow alert: R<0.5, issue a warning message and strengthen monitoring;

[0181] Dynamic instruction generation: pre-defined standardized instruction templates (e.g., "Area X is overloaded, please direct visitors to route Y") are used to populate template parameters (e.g., overloaded area coordinates, recommended routes) using real-time data. Voice instructions (TTS synthesis), text notifications (multilingual versions), and AR navigation (with overlay of thermal images) are then generated simultaneously.

[0182] In one embodiment, the method of generating scenario-based customization adapted to the current scenario by fusing digital portraits of tourists with real-time thermal distribution data of scenic spots using a hybrid recommendation algorithm includes the following steps:

[0183] Thermal distribution analysis:

[0184] Calculate real-time visitor density distribution:

[0185] Where K is the kernel function (Gaussian kernel is commonly used), h is the bandwidth parameter, and n is the sample size;

[0186] Set the density threshold θ hot , filter hot spots:

[0187]

[0188] Mixed recommendations:

[0189] Based on the user-attraction rating matrix, calculate the similarity between tourists:

[0190]

[0191] Combined with the scenic spot feature matrix M poi And the tourist interest vector u, calculate the matching degree:

[0192] score content =u T M poi P, where p is the scenic spot feature vector;

[0193] Adjust the recommended weights based on heat distribution:

[0194] Where α is the balance coefficient, (x p ,y p ) are the coordinates of the scenic spot;

[0195] Real-time feedback optimization:

[0196] The ε-greedy strategy is used to balance exploration and utilization, dynamically adjust the recommendation list, and use click-through rate (CTR) as the reward function to optimize the recommendation strategy:

[0197] Q(s,a)←Q(s,a)+η[rQ(s,a)], where Q((s,a)) is the state-action value function and η is the learning rate, thus enabling personalized scenario customization.

[0198] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive management system for tourism planning and reception in scenic areas, characterized in that: include: Perception access layer: Responsible for collecting visitor distribution, environmental data, and facility status in real time. It uses edge computing nodes to perform data cleaning and preliminary analysis, converting fragmented physical information into standardized data streams and transmitting them to the data intelligence layer. Data Intelligence Layer: This layer processes the data streams input from the sensor access layer and verifies their authenticity through blockchain evidence storage technology. It also uses spatiotemporal knowledge graphs to integrate the data streams input from the sensor access layer, constructing panoramic three-dimensional heat maps and making implicit information explicit. Integrate multi-source data to construct hierarchical digital portraits of tourists, and transmit panoramic three-dimensional heat maps and tourist digital portraits to the service center layer; use LSTM neural network prediction models to transform historical data into dynamic trend insights; Service Center: Based on the panoramic 3D thermal map, it is responsible for building an intelligent scheduling platform to monitor thermal anomalies in real time and trigger graded warnings. By integrating digital portraits of tourists with real-time thermal distribution data of scenic spots, a hybrid recommendation algorithm is used to generate scenario-based customization adapted to the current scenario. Set up a decision instruction generator to convert warning levels and scenario customization into instructions and send them to the interactive touch layer; Interactive access layer: responsible for converting the instructions received from the service center layer into visual forms of ground projection navigation and air-suspended prompts to convey them to tourists, and converting abstract data such as facility status and queue time into three-dimensional dynamic presentations.

2. A scenic area tourism planning and reception integrated management system according to claim 1, characterized in that: The real-time collection of visitor distribution, environmental data, and facility status, and the use of edge computing nodes to perform data cleaning and preliminary analysis, converting fragmented physical information into standardized data streams, include the following steps: The perception access layer deploys 5G+Beidou positioning base stations, environmental sensor matrices and facility monitoring equipment to collect real-time data on the spatiotemporal distribution of tourists, environmental parameters and facility status. At the edge computing nodes, sliding window filtering, spatiotemporal alignment and feature extraction algorithms are used to clean, reduce noise and conduct preliminary analysis on the raw data, converting fragmented physical signals into structured information streams.

3. A scenic area tourism planning and reception integrated management system according to claim 1, characterized in that: The authenticity verification using blockchain evidence storage technology includes the following steps: Generate data fingerprint: Perform a hash operation on the original data to generate a unique digital fingerprint: H(D) = SHA-256(D); Where D is the original data and H(D) is the 256-bit hash value. This process is irreversible, and any data change will cause the hash value to change significantly. Constructing the evidence block: Encapsulate the hash fingerprint and metadata into a block: Block={Index,H(D),T,ID dev ,Coord,H(Block prev )}, where H(Block prev ) is the hash of the previous block, forming a chain structure; Consensus verification mechanism: Use practical Byzantine fault-tolerant algorithms to achieve data consistency between edge nodes; Pre-preparation phase: the master node broadcasts the block proposal; Preparation phase: Nodes verify the validity of the proposal; Commitment phase: After more than 2 / 3 of the nodes confirm, the block is put on the chain; Authenticity Verification: This ensures the authenticity of basic data and eliminates interference from false information.

4. A scenic area tourism planning and reception integrated management system according to claim 1, characterized in that: The method of using the spatiotemporal knowledge graph to integrate the data streams input from the perception access layer and constructing a panoramic three-dimensional heat map includes the following steps: Multi-source data fusion: Define the scenic area domain ontology, including core entities and relationships; unify scattered data sources into a standard spatial framework through geocoding; and use dynamic time warping algorithms to align data of different frequencies. Knowledge graph construction: Identify entity relationships from unstructured data, map entities into high-dimensional vector space, and incorporate spatiotemporal features: e i,t =e i +Δe i,t , where e i is the static entity embedding, Δe i,t is the time-dependent dynamic offset; 3D heat map generation: Divide the scenic area into a 10m×10m×10m three-dimensional voxel grid; Comprehensive weighted summation of multi-source data: where w k is the data source weight, f k is the characteristic function; Color scale encoding is used to map thermal values ​​into RGB color space, and contour lines are superimposed to generate three-dimensional terrain.

5. A scenic area tourism planning and reception integrated management system according to claim 1, characterized in that: The method of fusing multi-source data and constructing a hierarchical digital portrait of a tourist includes the following steps: Fusion of multi-source data: Convert structured and unstructured data into a unified vector space; use dynamic time warping algorithm to align data of different frequencies; Building a digital portrait: First, use the rule engine to query the tourists’ age, gender, and place of origin; At the same time, tourist behavior patterns are extracted by calculating the depth of tourists’ visits: Where I is the depth of the tour, T is the tour time, and t is the opening time of the scenic spot, and the behavior patterns of tourists are classified accordingly; Calculate the preference strength of tourists and build tourist interest preference model: Where D is the preference intensity, M is the total tour time, m is the length of stay at a certain type of attraction, and k is the consumption frequency weight, based on which the preference labels of tourists are marked; Finally, the Wide & Deep structure is used to mine implicit associations and infer tourists' potential needs. This creates a digital portrait of tourists based on age, gender, origin, behavior patterns, preference tags, and potential needs. Real-time updates: Update profile parameters through Online Learning; Where η is the learning rate and L is the loss function; Assign time decay weights to historical behaviors: w history =e -λ·Δt , where λ is the attenuation coefficient and Δt is the time interval.

6. A scenic area tourism planning and reception integrated management system according to claim 1, characterized in that: The LSTM neural network prediction model is used to transform historical data into dynamic trend insights, including the following steps: Time series data preprocessing: Convert historical data into supervised learning format: X=[x t-w ,x t-w+1 ,…,x t-1 ],Y=x t , Where w is the window width, x t is the observation value at time t; Min-Max normalization LSTM network architecture: The cell gating mechanism is as follows: Forget gate: f t =σ(W f ·[h t-1 ,x t ]+b f ); Input gate: i t =σ(W i ·[h t-1 ,x t ]+b i ); Output gate: o t =σ(W o ·[h t-1 ,x t ]+b o ); Candidate memory: c t =tanh(W c ·[h t-1 ,x t ]+b c ); Memory update: c t =f t ⊙c t -1+i t ⊙c t ; Hidden state: h t =o t ⊙tanh(c t ); Where σ is the sigmoid function, tanh is the hyperbolic tangent function, and ⊙ is the element-by-element product; Predict dynamic trends: Use recursive prediction x t+1 =LSTM(X t ),x t+2 =LSTM([…,x t+1 ]); Generate prediction interval PI through Monte Carlo Dropout sampling α =[y low ,y high ]stP(y∈PI α )=α.

7. A scenic area tourism planning and reception integrated management system according to claim 1, characterized in that: The construction of the intelligent scheduling platform to monitor thermal anomalies in real time and trigger graded warnings includes the following specific steps: Thermal anomaly detection: Calculate the moving average and standard deviation based on historical data and set adaptive thresholds: Threshold upper =MA H +k·STD H , where H is the history window length and k is the adjustment coefficient; When the real-time thermal value H t Satisfy H t >Threshold upper or H t <Threshold lower When the warning process is triggered; Graded early warning mechanism: The risk index is constructed by comprehensively considering the intensity, duration and impact range of thermal anomalies: R=w1·ΔH+w2·ΔT+w3·A affected , where w i is the weight coefficient, ΔH is the thermal deviation, ΔT is the duration, A affected is the area of ​​the affected region; Red alert: R ≥ 0.8, activate emergency plan, and dispatch all personnel; Orange alert: 0.5≤R<0.8, additional mobile personnel will be dispatched and facility status will be adjusted; Yellow alert: R<0.5, issue a warning message and strengthen monitoring; Dynamic instruction generation: Predefine standardized command templates, fill in template parameters based on real-time data, and simultaneously generate voice commands, text notifications, and AR navigation.

8. A scenic area tourism planning and reception integrated management system according to claim 1, characterized in that: The method of generating scenario-based customization adapted to the current scenario by fusing digital portraits of tourists with real-time thermal distribution data of scenic spots using a hybrid recommendation algorithm includes the following steps: Thermal distribution analysis: Calculate real-time visitor density distribution: Where K is the kernel function, h is the bandwidth parameter, and n is the sample size; Set the density threshold θ to filter the hotspot area: Mixed recommendations: Based on the user-attraction rating matrix, calculate the similarity between tourists: Combined with the scenic spot feature matrix M poi And the tourist interest vector u, calculate the matching degree: score content =u T M po i P, where p is the scenic spot feature vector; Adjust the recommended weights based on heat distribution: Where α is the balance coefficient, (x p ,y p ) are the coordinates of the scenic spot; Real-time feedback optimization: The ε-greedy strategy is used to balance exploration and utilization, dynamically adjust the recommendation list, and use click-through rate as the reward function to optimize the recommendation strategy: Q(s,a)←Q(s,a)+η[rQ(s,a)], where Q(s,a) is the state-action value function and η is the learning rate, thereby performing personalized scenario customization.

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