Cultural tourism economy operation data monitoring method

Abstract

The invention discloses a cultural tourism economic operation data monitoring method, which comprises the following steps of: constructing a multi-dimensional value radar index system: establishing a multi-dimensional evaluation framework, and quantifying bridge and tunnel values; deploying a multi-source data acquisition network: constructing an integrated data acquisition system, and performing real-time mapping between a physical space and a digital space; and adaptive fusion analysis: constructing a quantum state superposition classifier, representing bridge and tunnel characteristics by using quantum bits, constructing a real-time digital twin mirror image engine, carrying out dynamic evaluation algorithm loading through linkage of a data acquisition network, a mirror image body and an algorithm pool, and introducing an ecological correction coefficient and a hidden value correction coefficient to carry out negative adjustment on an economic contribution value. Dynamically distributing an index weight by combining an entropy weight method and a Delphi method with a particle swarm optimization algorithm, and outputting an economic equivalent value in combination with the index weight; building a four-dimensional visual decision platform; and displaying the interactive radar map based on WebGL three-dimensional rendering and data binding technology.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method for monitoring data on the operation of cultural tourism economy. Background Technology

[0002] Against the backdrop of accelerated globalization, the booming digital economy, and the in-depth implementation of the cultural confidence strategy, and with the planning explicitly listing the cultural tourism industry as a strategic pillar industry and a happiness industry, as well as the quantitative requirements for high-quality development of the industry put forward by policy documents, the necessity and urgency of monitoring the operation data of the cultural tourism economy are becoming increasingly prominent. This monitoring system is both a systemic need to adapt to the integrated, experiential, and seasonal characteristics of the cultural tourism industry (involving cross-validation of data from multiple fields such as cultural experience, scenic spot operation, accommodation and catering, and transportation), and a technological breakthrough to address the lag and one-sidedness of traditional statistical methods (relying on big data, cloud computing, the Internet of Things, and artificial intelligence technologies to achieve data on visitor flow, consumption, visitor profiles, and satisfaction). Real-time dynamic collection of core indicators such as degree of accuracy is crucial for serving the government's precise policy implementation (e.g., resource allocation, emergency management, policy effect evaluation), enterprises' optimized operations (e.g., product iteration, marketing strategy adjustment), and tourists' enhanced experience (e.g., personalized recommendations, service early warning). Simultaneously, with the cultural tourism industry transitioning from "scale expansion" to "quality improvement," data monitoring has become a core tool for measuring industry contribution (GDP share, employment growth), cultural heritage preservation (intangible cultural heritage revitalization, cultural relic protection), and market activity (cross-regional flow, emerging business development). Its background deeply integrates the complex logic of policy guidance, technological empowerment, industrial upgrading, and social governance, collectively constructing a data foundation supporting the high-quality development of the cultural tourism economy.

[0003] In existing technologies, data monitoring methods for cultural tourism economic operations employ traditional discrete classification models (such as categorizing bridges, tunnels, and machinery into physically impressive, culturally narrative, or functionally service-oriented types). Their core purpose is to achieve efficient assessment and management of large-scale infrastructure through standardized classification. However, these traditional discrete classification models crudely fragment the inherent and dynamically intertwined multidimensional value relationships of bridges and tunnels, leading to a systematic underestimation of their physical innovation value. When the value elements of bridges and tunnels dynamically evolve, the static classification system, lacking a real-time response mechanism, cannot capture the strengthening of these value coupling relationships. This results in a continuously widening structural deviation between the assessment results and the true value, ultimately leading to a chain of decision-making errors such as resource allocation imbalances, cultural narrative disruptions, and homogenization of tourism development. Therefore, this paper proposes a new data monitoring method for cultural tourism economic operations. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for monitoring data on the operation of the cultural tourism economy.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for monitoring data on the operation of the cultural tourism economy includes the following steps: Constructing a multi-dimensional value radar indicator system: Establishing a multi-dimensional evaluation framework to quantify the value of bridges and tunnels, including physical characteristics, cultural perception, experience quality, ecological compatibility, and implicit value dimensions. The physical characteristics dimension monitors the structural health of bridges and tunnels; the cultural perception dimension quantifies the correlation of historical events and the dissemination of intangible cultural heritage stories; the experience quality dimension transforms subjective experience into objective indicators; the ecological compatibility dimension assesses the connectivity of biological corridors and the carbon emission reduction effect; and the implicit value dimension captures implicit value. Deploy a multi-source data acquisition network: Build an integrated data acquisition system to perform real-time mapping between physical and digital spaces, including structural health monitoring, cultural perception data acquisition, experience quality data acquisition, ecological compatibility data acquisition, and implicit value data acquisition; Adaptive fusion analysis: Construct a quantum state superposition classifier, use qubits to represent bridge and tunnel features, construct a real-time digital twin mirror engine, and dynamically evaluate algorithm loading through the linkage of data acquisition network-mirror body-algorithm pool. The evaluation algorithm includes structural aesthetics algorithm, story propagation power model and convenience evaluation matrix. The model evolves through local training-encrypted aggregation-global update through federated learning, and introduces ecological correction coefficient and implicit value correction coefficient to negatively adjust the economic contribution value. The index weight is dynamically allocated by combining entropy weight method and Delphi method with particle swarm optimization algorithm, and the economic equivalent value is output by combining the index weight. Build a four-dimensional visualization decision-making platform: display interactive radar charts based on WebGL 3D rendering and data binding technology, and provide intelligent early warning by combining time-series prediction algorithms and rule engines.

[0006] The above further includes: Furthermore, in deploying a multi-source data acquisition network, the data acquisition steps for each data source are as follows: Structural health monitoring: Deploy LiDAR scanners to generate point cloud data regularly, compare and analyze structural health through BIM models, and use radar to collect traffic flow data in real time. The traffic flow data is cleaned on-site through edge computing nodes. Cultural perception data collection: Using NLP to analyze social media text, extract keywords, construct a local history knowledge graph, link bridges and tunnels with historical events, calculate the correlation of historical events and the dissemination of intangible cultural heritage stories; Experience quality data collection: The cultural experience path of tourists is tracked through Bluetooth beacons and UWB positioning base stations, and the experience quality details are captured by distributed acoustic sensors and gyroscope arrays; Ecological compatibility data collection: Infrared cameras are deployed around bridges and tunnels to monitor wildlife activity, and the ecological function score of the corridor is quantified to assess its ecological compatibility value; Implicit value data collection: Microphone arrays are installed inside bridges and tunnels to record tourists' exclamations of surprise, and AR navigation systems are used to track users' stops in virtual tours to capture implicit value.

[0007] Furthermore, in the adaptive fusion analysis, the specific steps for constructing a quantum state superposition classifier and using qubits to characterize the bridge-tunnel features are as follows: Feature quantization encoding: Mapping the physical characteristics, cultural perception characteristics, and experience quality characteristics of bridges and tunnels to qubits and defining three orthogonal ground states to construct a quantum superposition ground state, represented as... ,in, Physical characteristics As a characteristic of cultural perception, To experience quality characteristics, α, β, γ are complex probability amplitudes that satisfy the normalization condition: , , , These represent the probabilities that bridges and tunnels belong to the physical, cultural, and experiential categories, respectively. Quantum annealing optimization coefficients: A quantum annealing algorithm is introduced, using real-time bridge and tunnel data as input. An objective function is defined, including feature matching degree and normalization constraint regularization term. The probability amplitude is iteratively optimized through quantum annealing. The objective function is expressed as follows: ,in, The weights of the i-th type of features, and the matching degree. Let represent the correlation strength between bridges / tunnels and the i-th type of feature. For the normalization constraint regularity coefficients, through quantum annealing iteration, we search for the energy... smallest Value, to complete the superposition state ratio optimization; Real-time data-driven adjustment: Natural language processing is used to analyze real-time information, which is then used as the input variable for quantum annealing to drive the probability amplitude to be updated in real time according to the current value hotspots.

[0008] Furthermore, in adaptive fusion analysis, a real-time digital twin mirror engine is constructed, and the specific steps for dynamically evaluating algorithm loading are performed through the linkage of data acquisition network, mirror body, and algorithm pool: Real-time synchronization of the mirror body: Through the deployment of a multi-source data acquisition network and Kalman filter data fusion technology, a two-way data stream is constructed between the physical entity and the digital twin, and a high-fidelity mirror body is synchronized in real time. Algorithm probe and dynamic algorithm loading: A lightweight detection model embedded in the mirror body identifies valuable hotspots and defines triggering rules. The corresponding evaluation algorithm is dynamically loaded according to the triggering rules, and the selection of evaluation algorithm is optimized using quantum genetic algorithm. Structural Aesthetics Algorithm: Quantifying the aesthetic value of architecture / structure, represented as ,in, Weighting of aesthetic features For feature scoring functions; Story-based virality model: assesses the social virality potential of content, represented as... ,,in, For user interaction volume, For emotional polarity, For topic relevance, The weight parameters are optimized using a quantum support vector machine. Convenience Assessment Matrix: This matrix quantifies the transportation convenience and service accessibility of an entity using the Analytic Hierarchy Process (AHP), and is represented as follows: ,in, For the score of the j-th indicator, The weights are determined using the Analytic Hierarchy Process (AHP).

[0009] Furthermore, in adaptive fusion analysis, the specific steps for dynamically evaluating algorithm loading through the linkage of data acquisition network, mirror body, and algorithm pool are as follows: Local training: Each bridge and tunnel management unit trains its own algorithm model based on local data to obtain local parameters. ; Encrypted aggregation: Local parameters are encrypted using homomorphic encryption and then uploaded to a central server, where they are aggregated into global parameters. , represented as ,in, Weights for each bridge and tunnel data volume; Global update: The aggregated global parameters are distributed to each local model to complete the model iteration.

[0010] Furthermore, in the adaptive fusion analysis, the specific steps for negatively adjusting the economic contribution value using ecological correction coefficients and implicit value correction coefficients are as follows: Physical shock type: The formula for calculating the economic benefits of the physical shock type is expressed as follows: ,in: For the number of physical visitors, Based on the baseline number of tourists, ,in Scoring based on physical benchmarks, This refers to the unit price of the ticket. As a per capita consumption of the surrounding landmark economy, This is a correction factor; Cultural narrative type: The formula for calculating the economic benefits of the cultural narrative type is expressed as follows: ,in: For the number of cultural tourists, ,in Scoring based on cultural benchmarks, , The unit price for tickets and cultural experiences, The average amount spent on cultural activities in the surrounding area. This is a correction factor; Functional service type: The formula for calculating the economic benefits of functional service type is expressed as follows: ,in: For traffic flow, Based on the baseline traffic flow, ,in Assess the functional baseline score. This is the unit price of the toll. The average amount spent on cultural activities in the surrounding area. This is a correction factor; Calculating the ecological correction coefficient: The ecological correction coefficient is used to quantify the positive or negative impact of highway corridors on the ecosystem, adjusting the ecological rationality of economic contribution values. The calculation formula is expressed as follows: Among them, EPI is the investment in ecological protection of the corridor, EDL is the loss of ecological damage in the corridor, and BEV is the average annual benchmark monetary value of the ecosystem service function of the corridor area. Calculating the Implicit Value Correction Coefficient: The implicit value correction coefficient is used to quantify the indirect economic value of cultural tourism along highway corridors. The calculation formula is expressed as follows: Among them, CTV represents cultural heritage value, SIV represents social recognition value, BV represents brand premium, and BEC represents the average annual benchmark economic contribution value of the corridor region. Economic Equivalent Value: The adjusted comprehensive economic contribution value is obtained by multiplying the basic economic contribution value (ECV) by the ecological correction coefficient (ECC) and the implicit value correction coefficient (IVC). The calculation formula is expressed as follows: ,in , , , The weights are the indicator weights.

[0011] Furthermore, in adaptive fusion analysis, the specific steps for dynamically allocating index weights using the entropy weight method, Delphi method, and particle swarm optimization algorithm are as follows: Initial weight calculation: Given m evaluation indicators and n time points or samples, the original data matrix is ​​as follows. ,in This represents the j-th index value of the i-th sample. If the index units are different, they are standardized first. Calculate the weight of the indicators: For each indicator j, calculate the weight of the i-th sample. , represented as ; Calculate the entropy value: Entropy value The formula reflects the degree of information confusion in the indicator. ,in, ; Calculate the coefficient of variation: coefficient of variation Reflects the information utility of indicators, The larger the value, the higher the weight. The calculation formula is: ; Calculating the entropy weight method weights: The entropy weight method weights are obtained by normalizing the difference coefficients. ; Expert scoring: Invite l experts to score the importance of m indicators, resulting in a scoring matrix. ,in The score given by the k-th expert to the j-th indicator; Normalization yields subjective weights: For each indicator j, calculate the average score, normalize the average score, and then obtain the Delphi weights. ; Combined weight model: The index weights are obtained by linearly combining the weights of the entropy weight method and the Delphi method, expressed as follows: , where α is the dynamic coefficient to be optimized, controlling the proportion of the weights of the entropy weight method and the Delphi method; PSO optimization dynamic coefficients: Assuming the task is prediction, the actual value is... The predicted value is Then the objective function is Randomly generate N particles and initialize their velocities. and location For each particle, calculate the combined weights, then calculate the prediction error, and record the individual optimal value for each particle. and global optimal Perform speed and position updates: ; ; in, For inertial weights, , As a learning factor, , A random number in the range [0,1]. When the maximum number of iterations or the error E converges, the optimal dynamic coefficient is output. Dynamic adjustment mechanism: The entropy weight method and Delphi method weights are recalculated every k time intervals, and the dynamic coefficients are re-optimized.

[0012] Furthermore, in building a four-dimensional visualization decision-making platform, the specific steps for intelligent early warning by combining time-series prediction algorithms and rule engines are as follows: Stationary sequences are obtained by differencing historical data; Determine p and q using the ACF / PACF diagram; The model is represented as ,in, Let B be the time series observations, and let B be the lag operator. These are the autoregressive coefficients. The moving average coefficient is... The noise is white, c is a constant term, d is the difference order, and the fitting model parameters are... , c, Minimize AIC / BIC criterion; Predicting index values ​​for the next h periods ; Define early warning rules and trigger early warnings by combining predicted values ​​with real-time data.

[0013] Furthermore, early warning rules are selected based on business needs, including threshold early warning and rate of change early warning; Threshold warning: If the predicted value If the preset threshold T is exceeded, an alert is triggered. Rate of change warning: If the predicted value If the rate of change relative to the previous period or the same period exceeds the threshold, an early warning is triggered.

[0014] The present invention has the following beneficial effects: In this invention, by introducing quantum state vector representation, a single bridge or tunnel can simultaneously carry multiple value attributes. Furthermore, by using the quantum annealing algorithm to dynamically optimize the superposition coefficient, the composite value characteristics of hybrid bridges and tunnels can be accurately captured. This continuous probability amplitude expression not only eliminates the fragmentation of multidimensional value correlation by traditional discrete classification, but also reduces the evaluation error rate by responding to dynamic changes in value in real time through the evolution of the Schrödinger equation. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the steps of a method for monitoring cultural tourism economic operation data proposed in this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 As shown, this invention is a method for monitoring data on the operation of cultural tourism economy, comprising the following steps: Constructing a multi-dimensional value radar indicator system: Establishing a multi-dimensional evaluation framework to quantify the value of bridges and tunnels, including physical characteristic dimensions, cultural perception dimensions, experience quality dimensions, ecological compatibility dimensions, and implicit value dimensions. The physical characteristic dimension (such as pier settlement early warning and cable fatigue assessment) monitors the structural health of bridges and tunnels. The cultural perception dimension quantifies the correlation of historical events (such as the frequency of co-occurrence in texts) and the dissemination of intangible cultural heritage stories (such as the quantity of cultural content spontaneously created by users). The experience quality dimension transforms subjective experience into objective indicators (such as soundscape comfort scores and projection interaction rates). The ecological compatibility dimension assesses the connectivity of biological corridors (such as species diversity index) and carbon emission reduction effects (such as the ratio of photovoltaic power generation to vehicle energy consumption). The implicit value dimension captures implicit value (such as soundscape emotional coefficient and cultural symbol exposure rate). Deploy a multi-source data acquisition network: Build an integrated data acquisition system to perform real-time mapping between physical and digital spaces, including structural health monitoring, cultural perception data acquisition, experience quality data acquisition, ecological compatibility data acquisition, and implicit value data acquisition; Adaptive Fusion Analysis: A quantum state superposition classifier is constructed, using qubits to represent bridge and tunnel features. A real-time digital twin mirror engine is built, and dynamic evaluation algorithms are loaded through the linkage of a data acquisition network, mirror body, and algorithm pool. The evaluation algorithms quantify the performance of core value dimensions and include structural aesthetics algorithms, story propagation power models, and convenience evaluation matrices. The model evolves through federated learning, involving local training, encrypted aggregation, and global updates. Ecological correction coefficients and implicit value correction coefficients are introduced to negatively adjust the economic contribution value. The index weights are dynamically allocated using the entropy weight method and Delphi method combined with particle swarm optimization algorithm. A bridge and tunnel classification knowledge graph is constructed using the Neo4j graph database and natural language processing technology, enabling the evaluation model to have intelligent evolution capabilities. The economic equivalent value is output based on the index weights. Build a four-dimensional visualization decision-making platform: display interactive radar charts based on WebGL 3D rendering and data binding technology, and provide intelligent early warning by combining time-series prediction algorithms and rule engines.

[0018] In one embodiment, the data acquisition steps for each data source in a multi-source data acquisition network deployment are as follows: Structural health monitoring: Deploy LiDAR scanners to generate point cloud data regularly, compare and analyze structural health through BIM models, and use radar to collect traffic flow data (vehicle volume, vehicle speed, and vehicle type distribution) in real time. Traffic flow data is cleaned on-site through edge computing nodes (supporting traffic flow simulation and prediction (such as holiday traffic peak warnings) and optimizing traffic restriction and control strategies). Cultural perception data collection: Through NLP analysis of social media text, keywords are extracted, a local history knowledge graph is constructed, bridges and tunnels are associated with historical events, and the correlation of historical events (such as the frequency of co-occurrence in the text) and the dissemination of intangible cultural heritage stories (such as the amount of cultural content spontaneously created by users) are calculated. Experience quality data collection: The cultural experience path of tourists is tracked through low-power Bluetooth beacons and UWB positioning base stations, and experience quality details are captured by distributed acoustic sensors and gyroscope arrays. Eco-compatible data collection: Infrared cameras are deployed around bridges and tunnels to monitor wildlife activity. Satellite remote sensing data is used to calculate vegetation cover (NDVI) and carbon sink, quantify the function score of biological corridors (such as animal migration route connectivity) and carbon sink increment (such as carbon sequestration capacity per unit area of ​​vegetation), and assess the eco-compatible value. Implicit value data collection: Microphone arrays are installed inside bridges and tunnels to record tourists' exclamations (such as decibel levels and durations). The AR navigation system tracks the user's stops in the virtual tour to capture implicit value (such as soundscape emotional coefficients and exposure rates of cultural symbols).

[0019] In one embodiment, the specific steps for constructing a quantum state superposition classifier and characterizing bridge-tunnel features using qubits in adaptive fusion analysis are as follows: Feature quantization encoding: Mapping the physical characteristics, cultural perception characteristics, and experience quality characteristics of bridges and tunnels to qubits and defining three orthogonal ground states (|physical>, |cultural>, |functional>), constructing a quantum superposition ground state, represented as... ,in, Physical characteristics As a characteristic of cultural perception, To experience quality characteristics, α, β, γ are complex probability amplitudes that satisfy the normalization condition: , , , These represent the probabilities of bridges and tunnels belonging to the physical, cultural, and experiential categories, respectively. Functionally, they allow a single bridge or tunnel to exist simultaneously in multiple feature spaces (e.g., the Borbosonte Bridge can be characterized as both physically impressive and culturally narrative). Quantum annealing optimization coefficients: A quantum annealing algorithm is introduced, using real-time bridge and tunnel data as input. An objective function is defined, including feature matching degree and normalization constraint regularization term. The probability amplitude is iteratively optimized through quantum annealing. The objective function is expressed as follows: ,in, The weights of the i-th type of features, and the matching degree. Let represent the correlation strength between bridges / tunnels and the i-th type of feature. The regularization coefficients are the normalization constraints. To optimize the probability amplitude of the objective function, quantum annealing iteration is used to find the energy... smallest Value, to complete the superposition state ratio optimization; Real-time data-driven adjustments: Natural language processing (NLP) is used to analyze real-time information such as visitor reviews and sensor monitoring of structural status, which are used as input variables for quantum annealing to drive the probability amplitude to be updated in real time according to current value hotspots (such as visitors' dual focus on physical awe and cultural narrative).

[0020] In this embodiment, a certain bridge was originally a hybrid bridge-tunnel (physical attribute: long-span cable-stayed bridge; cultural attribute: historical significance of connecting two places; functional attribute: regional commuting core).

[0021] Step 1 Encoding: The initial quantum states are set as an equal-probability superposition: ,(Right now ).

[0022] Step 2 Optimization: Analyze real-time data using the quantum annealing algorithm (e.g., "spectacular across the sea" appears 50 times in tourist reviews, "historical significance of both places" appears 30 times, and "convenient commuting" appears 20 times) to calculate feature matching degree: physical attributes have the highest matching degree (50 times), followed by cultural attributes (30 times), and functional attributes have the lowest (20 times). After optimizing the objective function, the probability amplitude is adjusted to α=0.6, β=0.5, γ=0.3 (after normalization). =0.36, =0.25, =0.09, and the remaining probability is supplemented to 1 by orthogonality.

[0023] Step 3 Adjustment: If the frequency of "historical stories" increases in subsequent visitor reviews, then further increase β to make the superposition state more focused on cultural attributes and achieve dynamic classification.

[0024] Through the above steps, the quantum state superposition classifier transforms the evaluation of a bridge from a traditional single classification based on "function" to a superposition classification based on "physical + cultural" values, more accurately reflecting its mixed value and providing a foundation for subsequent dedicated algorithm allocation (such as simultaneously loading structural aesthetics and story communication models).

[0025] In one embodiment, the specific steps for constructing a real-time digital twin mirror engine in adaptive fusion analysis, and dynamically evaluating algorithm loading through the linkage of data acquisition network, mirror body, and algorithm pool, are as follows: Real-time synchronization of the mirror body: Through the deployment of a multi-source data acquisition network and Kalman filter data fusion technology, a two-way data stream is constructed between the physical entity and the digital twin, and a high-fidelity mirror body is synchronized in real time. Algorithm probe and dynamic algorithm loading: A lightweight detection model (such as YOLOv8 object detection) embedded in the mirror body identifies valuable hotspots (graffiti, cracks, crowd gathering, etc.), defines trigger rules, dynamically loads the corresponding evaluation algorithm according to the trigger rules, and uses quantum genetic algorithm to optimize the selection of evaluation algorithm; Structural Aesthetics Algorithm: Quantifying the aesthetic value of architecture / structure, represented as ,in, Weighting of aesthetic features (such as symmetry, color contrast, and density of cultural symbols). The feature scoring function includes span (L), height (H), material innovation (M), and visual impact (V). Story-based virality model: assesses the social virality potential of content (such as graffiti), represented as... ,,in, For user interaction volume, For emotional polarity, For topic relevance, The weight parameters are optimized using a quantum support vector machine (QSVM). Convenience Assessment Matrix: This matrix quantifies the transportation convenience and service accessibility of an entity using the Analytic Hierarchy Process (AHP), and is represented as follows: ,in, For the score of the j-th indicator, The weights are determined using the Analytic Hierarchy Process (AHP).

[0026] In one embodiment, the specific steps for dynamically evaluating algorithm loading through the linkage of data acquisition network, mirror body, and algorithm pool in adaptive fusion analysis are as follows: Local training: Each bridge and tunnel management unit trains its own algorithm model based on local data (such as structural monitoring data and visitor behavior data) to obtain local parameters. ; Encrypted aggregation: Local parameters are encrypted using homomorphic encryption and then uploaded to a central server, where they are aggregated into global parameters. , represented as ,in, Weights for each bridge and tunnel data volume; Global update: The aggregated global parameters are distributed to each local model to complete the model iteration.

[0027] In one embodiment, the specific steps for negatively adjusting the economic contribution value using ecological correction coefficients and implicit value correction coefficients in adaptive fusion analysis are as follows: Physical shock type: The formula for calculating the economic benefits of the physical shock type is expressed as follows: ,in: For the number of physical visitors, Based on the baseline number of tourists, ,in Scoring based on physical benchmarks, This refers to the unit price of the ticket. As a per capita consumption of the surrounding landmark economy, This is a correction factor; Cultural narrative type: The formula for calculating the economic benefits of the cultural narrative type is expressed as follows: ,in: For the number of cultural tourists, ,in Scoring based on cultural benchmarks, , The unit price for tickets and cultural experiences, The average amount spent on cultural activities in the surrounding area. This is a correction factor; Functional service type: The formula for calculating the economic benefits of functional service type is expressed as follows: ,in: For traffic flow, Based on the baseline traffic flow, ,in Assess the functional baseline score. This is the unit price of the toll. The average amount spent on cultural activities in the surrounding area. This is a correction factor; Calculating the Ecological Correction Coefficient (ECC): The Ecological Correction Coefficient (ECC) is used to quantify the positive (protective) or negative (damaging) impacts of highway corridors on ecosystems, adjusting the ecological rationality of economic contribution values. The calculation formula is expressed as follows: Among them, EPI represents the investment in corridor ecological protection (such as expenditures on ecological restoration, pollution control, and biodiversity conservation), EDL represents the losses due to ecological damage in the corridor (such as costs of soil and water conservation, vegetation restoration, and pollution control), and BEV represents the average annual benchmark monetary value of the corridor's ecosystem service functions (which can be referenced in local ecological assessment reports, such as...). (The monetization value of absorption, water conservation, air purification, etc.) Calculating the Implicit Value Correction Coefficient: The Implicit Value Correction Coefficient (IVC) is used to quantify the indirect economic value of cultural tourism along highway corridors (such as cultural heritage, social recognition, brand premium, etc.). The calculation formula is expressed as follows: Among them, CTV represents Cultural Heritage Value (quantified through a survey of tourists' willingness to pay extra for cultural experiences (WTP), formula: CTV = N × AWTP, where N is the number of tourists and AWTP is the amount tourists are willing to pay extra for cultural experiences), SIV represents Social Identity Value (quantified through a survey of community residents' satisfaction with tourism development, formula: SIV = P × S × AVC, where P is the community population, S is the satisfaction coefficient (0-1), and AVC is the average tourism-related income per capita in the community), and BV represents Brand Premium (quantified through the price premium or search popularity of products associated with the corridor brand, formula: Where BS represents sales revenue of branded products and NBS represents sales revenue of non-branded products), BEC represents the average annual benchmark economic contribution of the corridor region (which can be referenced from the average local tourism revenue or the tourism portion of GDP). Economic Equivalent Value: The adjusted comprehensive economic contribution value is obtained by multiplying the basic economic contribution value (ECV) by the ecological correction coefficient (ECC) and the implicit value correction coefficient (IVC). The calculation formula is expressed as follows: ,in , , , The weights are the indicator weights.

[0028] In one embodiment, the specific steps for dynamically allocating index weights using a combination of the entropy weight method, the Delphi method, and the particle swarm optimization algorithm in adaptive fusion analysis are as follows: Initial weight calculation: Given m evaluation indicators and n time points or samples, the original data matrix is ​​as follows. ,in This represents the j-th index value of the i-th sample. If the index units are different, they are first standardized (e.g., min-max standardization). Calculate the weight of the indicators: For each indicator j, calculate the weight of the i-th sample. , represented as ; Calculate the entropy value: Entropy value The formula reflects the degree of information confusion in the indicator. ,in, (Guarantee 0≤) ≤1); Calculate the coefficient of variation: coefficient of variation Reflects the information utility of indicators, The larger the value, the higher the weight. The calculation formula is: ; Calculating the entropy weight method weights: The entropy weight method weights are obtained by normalizing the difference coefficients. ; Expert scoring: Invite l experts to score the importance of m indicators, resulting in a scoring matrix. ,in The score given by the k-th expert to the j-th indicator; Normalization yields subjective weights: For each indicator j, calculate the average score, normalize the average score, and then obtain the Delphi weights. ; Combined weight model: The index weights are obtained by linearly combining the weights of the entropy weight method (objective) and the Delphi method (subjective), expressed as follows: Where α is the dynamic coefficient to be optimized, controlling the proportion of the weights of the entropy weight method (objective) and the Delphi method (subjective); PSO optimization dynamic coefficients: Assuming the task is prediction, the actual value is... The predicted value is Then the objective function (prediction error) is: Randomly generate N particles (each particle represents an α value, α∈[0,1]), and initialize the particle velocities. and location For each particle, calculate the combined weights, then calculate the prediction error, and record the individual optimal value for each particle. and global optimal Perform speed and position updates: ; ; in, For inertial weights, , As a learning factor, , A random number in the range [0,1]. When the maximum number of iterations or the error E converges, the optimal dynamic coefficient is output. Dynamic adjustment mechanism: The entropy weight method and Delphi method weights are recalculated every k time intervals, and the dynamic coefficients are re-optimized.

[0029] In one embodiment, the specific steps for intelligent early warning by combining time-series prediction algorithms and rule engines in building a four-dimensional visualization decision-making platform are as follows: By differencing historical data (order d), a stationary sequence can be obtained; Determine p (autoregressive order) and q (moving average order) using the ACF / PACF plot; The model is represented as ,in, Let B be the time series observations, and B be the lag operator ( ), These are the autoregressive coefficients. The moving average coefficient is... The noise is white, c is a constant term, d is the difference order (to eliminate trends), and the fitting model parameters are... , c, Minimize AIC / BIC criterion; Predicting index values ​​for the next h periods ; Define early warning rules and trigger early warnings by combining predicted values ​​with real-time data.

[0030] In one embodiment, an early warning rule is selected according to business needs, and the early warning rule includes threshold early warning and rate of change early warning; Threshold warning: If the predicted value If the value exceeds (or falls below) a preset threshold T, an alert is triggered. Rate of change warning: If the predicted value If the rate of change relative to the previous period (month-on-month) or the same period (year-on-year) exceeds the threshold, an early warning is triggered.

[0031] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A cultural tourism economic operation data monitoring method, characterized in that, Includes the following steps: Constructing a multi-dimensional value radar indicator system: Establishing a multi-dimensional evaluation framework to quantify the value of bridges and tunnels, including physical characteristics, cultural perception, experience quality, ecological compatibility, and implicit value dimensions. The physical characteristics dimension monitors the structural health of bridges and tunnels; the cultural perception dimension quantifies the correlation of historical events and the dissemination of intangible cultural heritage stories; the experience quality dimension transforms subjective experience into objective indicators; the ecological compatibility dimension assesses the connectivity of biological corridors and the carbon emission reduction effect; and the implicit value dimension captures implicit value. Deploy a multi-source data acquisition network: Build an integrated data acquisition system to perform real-time mapping between physical and digital spaces, including structural health monitoring, cultural perception data acquisition, experience quality data acquisition, ecological compatibility data acquisition, and implicit value data acquisition; Adaptive fusion analysis: Construct a quantum state superposition classifier, use qubits to represent bridge and tunnel features, construct a real-time digital twin mirror engine, and dynamically evaluate algorithm loading through the linkage of data acquisition network-mirror body-algorithm pool. The evaluation algorithm includes structural aesthetics algorithm, story propagation power model and convenience evaluation matrix. The model evolves through local training-encrypted aggregation-global update through federated learning, and introduces ecological correction coefficient and implicit value correction coefficient to negatively adjust the economic contribution value. The index weight is dynamically allocated by combining entropy weight method and Delphi method with particle swarm optimization algorithm, and the economic equivalent value is output by combining the index weight. Build a four-dimensional visualization decision-making platform: display interactive radar charts based on WebGL 3D rendering and data binding technology, and provide intelligent early warning by combining time-series prediction algorithms and rule engines.

2. The cultural tourism economic operation data monitoring method according to claim 1, characterized in that, In deploying a multi-source data acquisition network, the steps for acquiring each data source are as follows: Structural health monitoring: Deploy LiDAR scanners to generate point cloud data regularly, compare and analyze structural health through BIM models, and use radar to collect traffic flow data in real time. The traffic flow data is cleaned on-site through edge computing nodes. Cultural perception data collection: Using NLP to analyze social media text, extract keywords, construct a local history knowledge graph, link bridges and tunnels with historical events, calculate the correlation of historical events and the dissemination of intangible cultural heritage stories; Experience quality data collection: The cultural experience path of tourists is tracked through Bluetooth beacons and UWB positioning base stations, and the experience quality details are captured by distributed acoustic sensors and gyroscope arrays; Ecological compatibility data collection: Infrared cameras are deployed around bridges and tunnels to monitor wildlife activity, and the ecological function score of the corridor is quantified to assess its ecological compatibility value; Implicit value data collection: Microphone arrays are installed inside bridges and tunnels to record tourists' exclamations of surprise, and AR navigation systems are used to track users' stops in virtual tours to capture implicit value.

3. The cultural tourism economic operation data monitoring method according to claim 1, characterized in that, In adaptive fusion analysis, the specific steps for constructing a quantum state superposition classifier and using qubits to characterize bridge and tunnel features are as follows: Feature quantumization encoding: the physical features, cultural perception features, and quality of experience features of the bridge are mapped to qubits and three orthogonal ground states are defined, a quantum superposition ground state is constructed, denoted as wherein, is the physical feature, is the cultural perception feature, is the quality of experience feature, and α, β, γ are complex probability amplitudes satisfying the normalization condition: , , , respectively represent the probabilities that the bridge belongs to the physical, cultural, and experience categories. Quantum annealing optimization coefficient: a quantum annealing algorithm is introduced, taking the real-time data of the bridge and tunnel as the input, defining a target function containing the feature matching degree and the normalized constraint regular term, and optimizing the probability amplitude through quantum annealing iteration. The target function is represented as wherein, is the weight of the i-th feature, and the matching degree is the association strength of the bridge and tunnel with the i-th feature, is the regularization coefficient of the normalized constraint, and through quantum annealing iteration, the value of that enables the energy to be minimized is found, and the superposition state proportion optimization is completed; Real-time data-driven adjustment: Natural language processing is used to analyze real-time information, which is then used as the input variable for quantum annealing to drive the probability amplitude to be updated in real time according to the current value hotspots.

4. The cultural tourism economic operation data monitoring method according to claim 3, characterized in that, In adaptive fusion analysis, the specific steps for constructing a real-time digital twin mirror engine and dynamically evaluating algorithm loading through the linkage of data acquisition network, mirror body, and algorithm pool are as follows: Real-time synchronization of the mirror body: Through the deployment of a multi-source data acquisition network and Kalman filter data fusion technology, a two-way data stream is constructed between the physical entity and the digital twin, and a high-fidelity mirror body is synchronized in real time. Algorithm probe and dynamic algorithm loading: A lightweight detection model embedded in the mirror body identifies valuable hotspots and defines triggering rules. The corresponding evaluation algorithm is dynamically loaded according to the triggering rules, and the selection of evaluation algorithm is optimized using quantum genetic algorithm. Structural Aesthetics Algorithm: quantifying the aesthetic value of a building / structure, denoted as where, is the aesthetic feature weight, is the feature scoring function; Story propagation model: to evaluate the social propagation potential of the content, denoted as , where is the user interaction amount, is the sentiment polarity, is the topic relevance, is the weight parameter, optimized by quantum support vector machine; Convenience evaluation matrix: quantifying the traffic convenience and service accessibility of the entity by AHP method, expressed as wherein, is the score of the jth index, is the weight, determined by AHP method.

5. The cultural tourism economic operation data monitoring method according to claim 4, characterized in that, In adaptive fusion analysis, the specific steps for dynamically evaluating algorithm loading through the linkage of data acquisition network, mirror body, and algorithm pool are as follows: Local training: Each bridge tunnel management party trains a dedicated algorithm model based on local data to obtain local parameters ; Encryption aggregation: encrypt local parameters through homomorphic encryption technology and upload to the central server to aggregate into global parameters , is expressed as , wherein, is the weight of each bridge data volume. Global update: The aggregated global parameters are distributed to each local model to complete the model iteration.

6. The cultural tourism economic operation data monitoring method according to claim 5, characterized in that, In adaptive fusion analysis, the specific steps for negatively adjusting the economic contribution value by introducing ecological correction coefficients and implicit value correction coefficients are as follows: Physical shock type: the economic benefit calculation formula of the physical shock type is expressed as wherein: is the number of physical tourists, is the reference tourist quantity, wherein is the physical reference score, is the single price of tickets, is the per capita consumption of surrounding landmarks, is a correction coefficient; Cultural narrative type: the economic benefit calculation formula of cultural narrative type is expressed as Wherein: is the number of cultural tourists, Wherein is the cultural benchmark score, , is the single price of ticket and cultural experience, is the per capita amount of peripheral cultural consumption, is the correction coefficient; Functional service type: the functional service type economic benefit calculation formula is expressed as Wherein: is the traffic flow, is the reference traffic flow, Wherein is the functional reference score, is the toll unit price, is the per capita amount of surrounding cultural consumption, is the correction coefficient; The ecological correction coefficient is used to quantify the positive or negative impact of the highway corridor on the ecosystem, adjust the ecological rationality of the economic contribution value, and the calculation formula is represented as wherein EPI is the corridor ecological protection input, EDL is the corridor ecological damage loss, and BEV is the annual average benchmark monetary value of the ecosystem service function of the corridor area. The implicit value correction coefficient is used to quantify the non-direct economic value of the cultural tourism of the highway corridor. The calculation formula is represented as wherein CTV is the cultural heritage value, SIV is the social identity value, BV is the brand premium, and BEC is the annual average benchmark economic contribution value of the corridor region. Economic equivalent value: multiply the basic economic contribution value ECV with the ecological correction coefficient ECC and the implicit value correction coefficient IVC to obtain the adjusted comprehensive economic contribution value, the calculation formula is represented by , , , , is the index weight.

7. The cultural tourism economic operation data monitoring method according to claim 6, characterized in that, In adaptive fusion analysis, the specific steps for dynamically allocating index weights using a combination of entropy weighting, Delphi method, and particle swarm optimization algorithm are as follows: Initial weight calculation: there are m evaluation indexes and n time points or samples, and the original data matrix is , where represents the jth index value of the ith sample, and if the index dimension is different, standardization is performed first; Compute indicator weight: For each indicator j, compute the weight of the ith sample , denoted as ; Calculate the entropy value: entropy value Reflect the information disorder degree of index, formula is Wherein, ; Calculate the difference coefficient: difference coefficient Reflect the information utility of the index, The greater, the higher the weight, the calculation formula is ; Computing entropy weight method weight: normalized difference coefficient gets entropy weight method weight ; Expert scoring: invite l experts to score the importance of m indexes, and get the scoring matrix wherein is the score of the kth expert on the jth index. Normalization yields subjective weights: For each indicator j, calculate the average score, normalize the average score, and then obtain the Delphi weights. ; Combined weight model: The index weights are obtained by linearly combining the weights of the entropy weight method and the Delphi method, expressed as follows: , where α is the dynamic coefficient to be optimized, controlling the proportion of the weights of the entropy weight method and the Delphi method; PSO optimization dynamic coefficients: Assuming the task is prediction, the actual value is... The predicted value is Then the objective function is Randomly generate N particles and initialize their velocities. and location For each particle, calculate the combined weights, then calculate the prediction error, and record the individual optimal value for each particle. and global optimal Perform speed and position updates: ； ； in, For inertial weights, , As a learning factor, , A random number in the range [0,1]. When the maximum number of iterations or the error E converges, the optimal dynamic coefficient is output. Dynamic adjustment mechanism: The entropy weight method and Delphi method weights are recalculated every k time intervals, and the dynamic coefficients are re-optimized.

8. The method for monitoring cultural tourism economic operation data according to claim 1, characterized in that, The specific steps for building a four-dimensional visualization decision-making platform, combining time-series prediction algorithms with rule engines for intelligent early warning, are as follows: Stationary sequences are obtained by differencing historical data; Determine p and q using the ACF / PACF diagram; The model is represented as ,in, Let B be the time series observations, and let B be the lag operator. These are the autoregressive coefficients. The moving average coefficient is... The noise is white, c is a constant term, d is the difference order, and the fitting model parameters are... , c, Minimize AIC / BIC criterion; Predicting index values ​​for the next h periods ; Define early warning rules and trigger early warnings by combining predicted values ​​with real-time data.

9. The method for monitoring cultural tourism economic operation data according to claim 8, characterized in that, Select early warning rules based on business needs; the early warning rules include threshold early warning and rate of change early warning. Threshold warning: If the predicted value If the preset threshold T is exceeded, an alert is triggered. Rate of change warning: If the predicted value If the rate of change relative to the previous period or the same period exceeds the threshold, an early warning is triggered.

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