Intelligent propagation method and system for text travel promotion data

Abstract

The invention is suitable for the technical field of multi-dimensional data analysis, and provides an intelligent propagation method and system for text travel promotion data, and the method comprises the steps: obtaining a quantized real-time load index, a quantized comprehensive value index and a quantized operation efficiency index; constructing an efficiency evaluation model according to the comprehensive value index, the real-time load index and the operation efficiency index, and outputting an operation efficiency index; constructing a budget allocation model in real time according to the current propaganda and promotion budget, the operation efficiency index and the real-time load index, and outputting a dynamic promotion budget; according to the method, a multi-dimensional quantification scheme applied to the scenic area is introduced, the state of the whole scenic area is evaluated in real time through the parameters which can be remotely acquired, the mapping relation between the promotion budget and the scenic area state is constructed, the promotion budget is dynamically adjusted in real time based on the mapping relation, the number of the parameters which can be acquired is large, the evaluation and adjustment efficiency is extremely high, and the timeliness is high.

Description

Technical Field

[0001] This invention belongs to the field of multidimensional data analysis technology, and in particular relates to a method and system for intelligent dissemination of cultural tourism promotion data. Background Technology

[0002] With the continuous development of the national economy and the upgrading of consumption, the cultural tourism industry has become an important engine for regional economic growth. Local cultural tourism authorities and scenic area operators are increasingly emphasizing the use of digital and intelligent methods for tourism promotion and operation management, aiming to enhance the attractiveness of scenic spots, visitor experience, and overall benefits. However, existing cultural tourism promotion and operation management systems still have some problems. Their operation management logic is too simplistic, mostly relying on single financial or traffic indicators such as visitor flow and ticket revenue. This is due to poor data quantification capabilities and the existence of data silos. How to provide an integrated data analysis solution to obtain a more accurate and comprehensive understanding of the overall operational health of scenic spots is the technical problem that this invention aims to solve. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for intelligent dissemination of cultural and tourism promotion data, aiming to solve the problem that in the existing cultural and tourism promotion and operation management, management decisions still largely rely on human experience, resulting in slow response speed and strong subjectivity.

[0004] This invention is implemented as follows: a method for intelligent dissemination of cultural tourism promotion data, the method comprising: A real-time load monitoring model is constructed based on the scenic area's visitor density, ticket booking trend index, and overall transportation utilization rate, and the real-time load index is output. A tourist analysis model is constructed based on the proportion of overnight tourists, the average spending index per person, and the tourist satisfaction index, and a comprehensive value index is output. An efficiency analysis model is constructed based on personnel service saturation, digital facility coverage, and operating cost index, and an operating efficiency index is output. Under the influence of the environmental comfort index, an efficiency evaluation model is constructed based on the comprehensive value index, real-time load index, and operational efficiency index, and the operational efficiency index is output. A budget allocation model is built based on the current advertising and promotion budget, operational efficiency index, and real-time load index, and a dynamic advertising budget is output.

[0005] Furthermore, the step of constructing a real-time load monitoring model based on scenic area visitor density, ticket booking trend index, and comprehensive transportation utilization rate, and outputting a real-time load index, includes: Read data on visitor density, ticket booking trend index, and overall transportation utilization rate in scenic areas; The read scenic area visitor density, ticket booking trend index, and comprehensive transportation utilization rate are normalized to obtain visitor density coefficient, ticket booking trend coefficient, and comprehensive transportation utilization rate coefficient; the normalization process is used to normalize the scenic area visitor density, ticket booking trend index, and comprehensive transportation utilization rate to the range of 0 to 1. The passenger flow density coefficient, ticket booking trend coefficient, and comprehensive transportation utilization rate coefficient are calculated based on preset weights and used as the load monitoring model. The real-time load index is determined based on the load monitoring model.

[0006] Furthermore, the step of constructing a tourist analysis model based on the proportion of overnight tourists, the average spending index per capita, and the tourist satisfaction index, and outputting a comprehensive value index, includes: Read the entry and exit data from the access control system to determine the proportion of overnight visitors; Based on preset permissions, query the revenue and total number of people, and calculate the average consumption amount per person; Read the tourist satisfaction survey results and determine the average tourist satisfaction. The average spending per person and the average tourist satisfaction rate are normalized to obtain the average spending per person coefficient and the tourist satisfaction rate coefficient. The proportion of overnight tourists, the average spending per person, and the tourist satisfaction rate are calculated based on preset weights to form a tourist analysis model. The comprehensive value index is determined based on a tourist analysis model.

[0007] Furthermore, the normalization process employs a maximum value normalization process, specifically as follows: For any given data, query the maximum value of the same data structure in historical data, calculate the ratio of the data to the maximum value, and obtain the normalized value.

[0008] Furthermore, the step of constructing an efficiency analysis model based on personnel service saturation, digital facility coverage, and operating cost index, and outputting the operating efficiency index, includes: Read the actual number of service personnel and the standard number of service personnel, and calculate the ratio of the actual number of service personnel to the standard number of service personnel as the staff service saturation. Divide the personnel service saturation by the historical best service saturation to obtain the saturation coefficient; The operating cost coefficient is obtained by subtracting the historical lowest operating cost from the actual operating cost and then dividing the difference between the historical highest operating cost and the historical lowest operating cost. Read the digital infrastructure coverage rate, and construct an efficiency analysis model based on the digital infrastructure coverage rate, saturation coefficient, and operating cost coefficient; The operational efficiency index is determined based on the efficiency analysis model.

[0009] Furthermore, the step of constructing an efficiency evaluation model based on the comprehensive value index, real-time load index, and operational efficiency index under the influence of the environmental comfort index, and outputting the operational efficiency index, includes: Read the preset environmental comfort level; Read the comprehensive value index, real-time load index, and operational efficiency index, and construct an efficiency evaluation model based on environmental comfort, comprehensive value index, real-time load index, and operational efficiency index to determine the operational efficiency index; Among them, the performance evaluation model is an increasing function of the comprehensive value index, operational efficiency index, and environmental comfort index, and a decreasing function of the real-time load index.

[0010] Furthermore, the step of constructing a budget allocation model based on the current advertising and promotion budget, operational efficiency index, and real-time load index, and outputting a dynamic advertising budget, includes: Read data based on the current advertising and promotion budget, operational efficiency index, and real-time load index; A budget allocation model is constructed based on the current advertising and promotion budget, operational efficiency index, and real-time load index; The dynamic promotion budget is determined based on the preset allocation model; The budget allocation model is as follows: ; The comprehensive operational efficiency impact coefficient, This is the load influence factor. ,and , All greater than , For the current advertising and promotion budget, As an operational efficiency index, This is the real-time load index. For dynamic promotion budget.

[0011] The present invention also provides a cultural tourism promotion data intelligent dissemination system, the system being used to implement the aforementioned cultural tourism promotion data intelligent dissemination system, the system comprising: The real-time load monitoring module is used to build a real-time load monitoring model based on the scenic area's visitor density, ticket booking trend index, and overall transportation utilization rate, and output the real-time load index. The tourist analysis module is used to build a tourist analysis model based on the proportion of overnight tourists, the average spending index per person, and the tourist satisfaction index, and output a comprehensive value index. The resource allocation analysis module is used to build an efficiency analysis model based on personnel service saturation, digital facility coverage, and operating cost index, and output the operating efficiency index. The performance evaluation module is used to construct a performance evaluation model based on the comprehensive value index, real-time load index, and operational efficiency index under the influence of the environmental comfort index, and output the operational performance index. The dynamic allocation module is used to build a budget allocation model based on the current advertising and promotion budget, operational efficiency index, and real-time load index, and output a dynamic promotion budget.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention introduces a multi-dimensional quantitative scheme for scenic areas. It evaluates the status of the entire scenic area in real time through remotely obtainable parameters, constructs a mapping relationship between the promotion budget and the status of the scenic area, and dynamically adjusts the promotion budget in real time based on the mapping relationship. It can obtain a large number of parameters, and the evaluation and adjustment efficiency is extremely high with strong timeliness. Attached Figure Description

[0013] Figure 1 A flowchart of a data-driven intelligent dissemination method for cultural tourism promotion is shown.

[0014] Figure 2 A structural diagram of a data-driven intelligent dissemination system for cultural tourism promotion is shown. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0017] Figure 1 A flowchart illustrating an intelligent dissemination method for cultural tourism promotion data is provided as an embodiment of the present invention. The method includes: Step S100: Construct a real-time load monitoring model based on the scenic area's visitor density, ticket booking trend index, and overall transportation utilization rate, and output the real-time load index; Step S200: Construct a tourist analysis model based on the proportion of overnight tourists, the average spending index per person, and the tourist satisfaction index, and output a comprehensive value index; Step S300: Construct an efficiency analysis model based on personnel service saturation, digital facility coverage, and operating cost index, and output the operating efficiency index; Step S400: Under the influence of the environmental comfort index, construct an efficiency evaluation model based on the comprehensive value index, real-time load index, and operational efficiency index, and output the operational efficiency index; Step S500: Build a budget allocation model based on the current advertising and promotion budget, operational efficiency index, and real-time load index, and output a dynamic promotion budget.

[0018] Steps S100 to S500 provide a multi-dimensional data processing scheme to output dynamic budget adjustment results. In fact, the output includes two parts: an operational efficiency index to assess the current operational status, and a dynamic promotion budget to represent ways to improve the current operational status. Specifically, it uses passenger flow density sensors, ticketing system interfaces, and traffic monitoring data to obtain scenic area passenger flow density, ticket booking trend index, and comprehensive traffic utilization rate. This provides multi-dimensional data that comprehensively reflects the real-time carrying capacity of the scenic area. It also obtains the proportion of overnight tourists, average spending per person, and tourist satisfaction index through hotel occupancy data collection, consumption record analysis, and satisfaction surveys. During this process, the satisfaction... Satisfaction surveys are typically conducted electronically. In some cases, no response is received for an electronic survey. In such situations, a lower, default satisfaction score is used as the final satisfaction level. Finally, the satisfaction scores of all tourists are tallied, and the average is calculated as the final satisfaction score. Staffing saturation, digital facility coverage, and operating cost index are determined using data from the staffing scheduling system, the deployment status of smart devices, and the cost accounting system. This can be determined using pre-defined mapping functions. For example, the service area of ​​each staff member at any given time can be determined based on the staffing scheduling system, and the area of ​​the union of these service areas can be calculated as the staffing saturation. Similarly, the digital facility coverage and operating cost index can also be determined using existing mapping relationships, which will not be elaborated upon here.

[0019] Based on the real-time load index, comprehensive value index, and operational efficiency index, the operational efficiency of the entire scenic area can be assessed using these indices. It's important to note that this assessment requires a pre-parameter: environmental data acquired through environmental sensors, including temperature and humidity. This data is output as a pre-defined normalized function to calculate a parameter reflecting environmental comfort, known as the environmental comfort index. The operational efficiency of the same scenic area varies under different environmental conditions. In essence, it introduces environmental comfort as a regulating factor to dynamically weight the load, value, and efficiency indices, generating the comprehensive efficiency index.

[0020] Furthermore, based on the calculated operational efficiency index, a budget allocation model is constructed according to the current promotion budget, operational efficiency index, and real-time load index. Its function is to calculate dynamic adjustment coefficients based on the current budget amount, combined with the efficiency index and load index, to achieve real-time matching of promotion resources and operational status. This ensures that the entire scenic area's information promotion process is in a constantly dynamic, real-time updated architecture. The data it relies on is all the latest data, making it extremely timely.

[0021] Specifically, the above technical solution integrates passenger flow density, ticketing trends, and transportation utilization data through a real-time load monitoring module. After normalization, a composite load index is generated to accurately reflect the real-time carrying capacity of the scenic area. The visitor analysis module simultaneously processes data on the proportion of overnight visitors, average spending per person, and satisfaction, generating a comprehensive value index to quantify visitor quality. The resource allocation analysis module analyzes personnel saturation, facility coverage, and cost data, outputting an efficiency index to assess the rationality of resource use. This solution constructs a multi-dimensional data fusion model, enabling collaborative analysis of indicators such as passenger flow carrying capacity, visitor value, and resource allocation. It introduces environmental comfort as a dynamic adjustment factor, making the performance evaluation more aligned with actual operational scenarios. Based on real-time load and the comprehensive efficiency index, a dynamic allocation model is established, resulting in extremely high resource response speed. During peak passenger flow periods, it automatically reduces promotional intensity to avoid overloading, while increasing promotional investment during periods of high operational efficiency to boost revenue.

[0022] Regarding step S100, the step of constructing a real-time load monitoring model based on scenic area visitor density, ticket booking trend index, and comprehensive transportation utilization rate, and outputting the real-time load index, includes: Read data on visitor density, ticket booking trend index, and overall transportation utilization rate in scenic areas; The read scenic area visitor density, ticket booking trend index, and comprehensive transportation utilization rate are normalized to obtain visitor density coefficient, ticket booking trend coefficient, and comprehensive transportation utilization rate coefficient; the normalization process is used to normalize the scenic area visitor density, ticket booking trend index, and comprehensive transportation utilization rate to the range of 0 to 1. The passenger flow density coefficient, ticket booking trend coefficient, and comprehensive transportation utilization rate coefficient are calculated based on preset weights and used as the load monitoring model. The real-time load index is determined based on the load monitoring model.

[0023] The above content explains the process of determining the scenic area's load. The scenic area's visitor density, ticket booking trend index, and overall transportation utilization rate are substituted into the maximum value normalization formula for normalization, and visitor density coefficient, ticket booking trend coefficient, and overall transportation utilization rate coefficient are generated sequentially. The real-time load monitoring model is as follows: ; in Weighted by passenger flow density, Weighting of ticket booking trends As a weight for comprehensive traffic utilization rate, ,and , as well as All greater than ; This is the passenger flow density coefficient. This is the ticket booking trend coefficient. The comprehensive traffic utilization rate coefficient, This is a real-time load index, and , , as well as The value range is between 0 and 1.

[0024] In this embodiment, the maximum value normalization formula refers to converting raw data with different dimensions into dimensionless coefficients within the range of 0-1 by calculating the ratio of the actual value to the preset maximum allowable value or historical maximum value. This eliminates the dimensional differences between passenger flow density, ticket booking trend, and traffic utilization rate, ensuring data comparability. The passenger flow density coefficient is the normalized value of the real-time number of tourists per unit area of ​​the scenic area. Specifically, it can be calculated by collecting the number of tourists through real-time monitoring equipment and combining it with the total area of ​​the scenic area, reflecting the instantaneous congestion level of the scenic area. The ticket booking trend coefficient is the normalized value of the ticket sales growth rate within the future booking period. Specifically, it can be calculated by statistically analyzing ticketing system data and comparing it with historical peaks, used to predict future passenger flow pressure. The comprehensive traffic utilization rate coefficient is the normalized value of the ratio of the carrying capacity of the surrounding traffic network to the real-time traffic flow. Specifically, it can be calculated by comparing traffic monitoring data with the preset network capacity, used to assess the traffic system's ability to support passenger flow. Weight setting refers to assigning weight values ​​to passenger flow density, ticketing trend, and traffic utilization rate that sum to 1 and are all greater than 0. Specifically, the weight can be determined using the analytic hierarchy process or the entropy weight method. This is used to dynamically adjust the impact of each indicator on the real-time load according to the scenic area's operation strategy.

[0025] Specifically, the maximum value normalization formula converts passenger flow density, ticket booking trend index, and comprehensive traffic utilization rate into coefficients within the range of 0-1, eliminating the non-additivity of the original data due to different units. For example, when the maximum carrying capacity of a scenic area is 5,000 people per square kilometer, the real-time monitored density of 3,000 people will be converted into a passenger flow density coefficient of 0.6. The ticket booking trend coefficient can be generated based on the ratio of the booking volume for the next 7 days to the highest value of the same period in history. If the current booking volume is 80% of the historical peak, the ticket booking trend coefficient is 0.8. The comprehensive traffic utilization rate coefficient can be calculated as a percentage of the real-time traffic flow to the road network's design capacity. If the current flow is 70% of the design capacity, the traffic utilization rate coefficient is 0.7. Subsequently, the three coefficients are substituted into a linear weighted model and fused according to preset weights. For example, if the weights are set to 0.5, 0.3, and 0.2 respectively, the real-time load index is 0.5×0.6+0.3×0.8+0.2×0.7=0.68. The weighting constraints ensure that the contribution ratio of each indicator is controllable, avoid the excessive influence of a single indicator on the evaluation results, and allow for adjustments to the weights to adapt to different scenic area characteristics or management needs.

[0026] Regarding step S200, the step of constructing a tourist analysis model based on the proportion of overnight tourists, the average spending index per person, and the tourist satisfaction index, and outputting a comprehensive value index, includes: Read the entry and exit data from the access control system to determine the proportion of overnight visitors; Based on preset permissions, query the revenue and total number of people, and calculate the average consumption amount per person; Read the tourist satisfaction survey results and determine the average tourist satisfaction. The average spending per person and the average tourist satisfaction rate are normalized to obtain the average spending per person coefficient and the tourist satisfaction rate coefficient. The proportion of overnight tourists, the average spending per person, and the tourist satisfaction rate are calculated based on preset weights to form a tourist analysis model. The comprehensive value index is determined based on a tourist analysis model.

[0027] The above content explains the process of determining the comprehensive value index. The average spending per tourist within the scenic area is normalized using the maximum normalization formula to obtain the average spending coefficient. Similarly, tourist satisfaction survey scores are normalized using the maximum normalization formula to obtain the tourist satisfaction coefficient. The tourist analysis model is as follows: ; in Weighting for the proportion of overnight visitors, As per capita consumption weight, As a weight for tourist satisfaction, ,and , as well as All greater than ; The ratio of overnight tourists. Dimensionless This represents the per capita consumption coefficient. The tourist satisfaction rating. It is a comprehensive value index, and , , as well as The value range is between 0 and 1.

[0028] Among them, maximum value normalization processing refers to dividing the original data by the preset maximum allowable value or the historical maximum value. Specifically, the historical peak value or the industry standard threshold can be used as the denominator to eliminate the interference of different units on the comparability of data.

[0029] The overnight visitor ratio refers to the ratio of overnight visitors to the total number of visitors. This can be statistically analyzed through data linkage between the ticketing and accommodation systems, reflecting the potential contribution of visitor length of stay to the scenic area's revenue. The per capita spending weight refers to the proportion of visitor spending within the scenic area to the overall value index. This can be determined using the analytic hierarchy process (AHP) or expert rating methods, balancing the relative importance of spending levels with other indicators. The visitor satisfaction weight refers to the degree of influence of visitor survey feedback on the overall value index. This can be dynamically adjusted through correlation analysis of questionnaire scores and behavioral data to ensure the objectivity of the satisfaction indicator.

[0030] The above process essentially converts average spending per person and tourist satisfaction scores into coefficients within the 0-1 range through maximum value normalization, making the data from high-end and low-end scenic spots comparable. The overnight visitor ratio coefficient directly reflects tourist stickiness and, together with the spending coefficient and satisfaction coefficient, constitutes a multi-dimensional evaluation system. The weight coefficients sum to 1, and each weight is greater than 0. The purpose of the weights is to prevent a single indicator from excessively influencing the evaluation results. For example, when a scenic spot relies too heavily on ticket revenue, the model can still ensure the analytical value of spending structure and satisfaction data through mandatory weight allocation. The resulting comprehensive value index quantifies tourist stay duration, spending power, and experience feedback, providing data support for scenic spots to optimize service content and promotional strategies.

[0031] In summary, the above scheme standardizes and quantifies tourist spending levels and satisfaction, making the evaluation results of different scenic spots and different time periods comparable. By analyzing the three parameters of overnight tourist ratio, spending power and satisfaction, it avoids evaluation distortion caused by abnormal data of a single indicator.

[0032] Furthermore, both steps S100 and S200 involve a normalization process, which employs a maximum value normalization process, specifically as follows: For any given data, query the maximum value of the same data structure in historical data, calculate the ratio of the data to the maximum value, and obtain the normalized value, which is presented in the following numerical form: ; in, This is the actual value. This refers to the maximum allowed value or the historical maximum value. The output value is the normalized value. The value of is between 0 and 1.

[0033] In this embodiment, the actual value refers to the raw indicator value obtained from the data acquisition device or business system. Specifically, it can be implemented using real-time monitoring data of scenic area visitor density, ticket booking volume, or traffic flow from sensors. This serves as the raw input reflecting the current operational status. The maximum allowable value refers to the upper limit of the threshold set in the business rules or safety specifications. Specifically, it can be implemented using the maximum carrying capacity of the scenic area, the design capacity of the ticketing system, or the traffic network capacity limit. This ensures that the normalization result meets safety management requirements. The historical maximum value refers to the peak value of the same indicator recorded in the database within a historical period. Specifically, it can be implemented using the highest daily visitor flow, the highest quarterly ticket booking volume, or the highest annual traffic utilization rate of the scenic area over the past three years. This adapts to dynamically changing operational scenarios.

[0034] Specifically, by comparing the actual value with the maximum allowable value or the historical maximum value, the raw data is linearly mapped to a standardized range of 0-1. When the maximum allowable value is selected, the normalization result directly reflects the degree to which the current state is close to the preset safety threshold. For example, when the real-time visitor flow of the scenic area reaches 80% of the maximum capacity, the visitor flow density coefficient is calculated to be 0.8. When the historical maximum value is selected, the normalization result can accommodate situations where the preset threshold may be exceeded in actual operation. For example, if the visitor flow on a certain day exceeds the historical peak, the coefficient is automatically adjusted to 1 to maintain model stability. The two maximum value definition methods can be flexibly switched according to business needs, which avoids the incomparability of data due to differences in units and eliminates the interference of different indicator value ranges on weighted calculations. For example, when the actual value of the scenic area's comprehensive traffic utilization rate is 1200 vehicles per hour and the historical maximum is 1500 vehicles per hour, its normalization coefficient is 0.8, which is linearly combined with the ticket booking trend coefficient of 0.75 at the same order of magnitude, effectively avoiding the weight offset problem caused by differences in numerical ranges. Meanwhile, by dynamically selecting the source of the maximum value, both security management requirements are met and the dynamic changes in operational data are adapted, providing a reliable data foundation for subsequent models.

[0035] Regarding step S300, the step of constructing an efficiency analysis model based on personnel service saturation, digital facility coverage, and operating cost index, and outputting the operating efficiency index, includes: Read the actual number of service personnel and the standard number of service personnel, and calculate the ratio of the actual number of service personnel to the standard number of service personnel as the staff service saturation. Divide the personnel service saturation by the historical best service saturation to obtain the saturation coefficient; The operating cost coefficient is obtained by subtracting the historical lowest operating cost from the actual operating cost and then dividing the difference between the historical highest operating cost and the historical lowest operating cost. Read the digital infrastructure coverage rate, and construct an efficiency analysis model based on the digital infrastructure coverage rate, saturation coefficient, and operating cost coefficient; The operational efficiency index is determined based on the efficiency analysis model.

[0036] In the above embodiment, the personnel service saturation coefficient is obtained by dividing the personnel service saturation (the ratio of actual service personnel to standard service personnel) by the historical best standard service saturation; the operating cost coefficient is obtained by dividing the difference between the actual operating cost and the historical lowest operating cost by the difference between the historical highest operating cost and the historical lowest operating cost; the efficiency analysis model is as follows: ; in, Weighting of service saturation for personnel. Weighting of digital infrastructure Weighted by operating costs, ,and , as well as All greater than , Service saturation coefficient for personnel To improve digital infrastructure coverage, Dimensionless This is the operating cost coefficient. It is an operational efficiency index, and , , as well as The value range is between 0 and 1.

[0037] Further explanation: The personnel service saturation coefficient refers to the relative value of the ratio of the actual number of service personnel to the standard number of service personnel relative to the historical best service capacity. Specifically, it can be achieved by dividing the ratio of the number of service personnel collected in real time to the preset standard number of service personnel by the saturation value when the optimal service state was reached within a historical period. This dynamically reflects the gap between the current service capacity and the historical best level. The operating cost coefficient refers to the relative position of the current operating cost relative to the historical cost fluctuation range. Specifically, it can be achieved by dividing the difference between the current cost and the historical lowest cost by the range between the historical highest cost and the lowest cost. This eliminates the impact of absolute cost differences on the evaluation results. The efficiency analysis model adopts a power function product form. Specifically, it can be achieved by exponentially weighting the personnel service saturation coefficient, digital facility coverage, and operating cost coefficient. The operating cost coefficient participates in the calculation in a reverse adjustment form to characterize the gain effect of cost control on overall efficiency.

[0038] Specifically, by acquiring real-time personnel service data, the current number of people served is compared with the preset standard number of people served. For example, if the standard number of people served is 3,000 per day, and the actual number of people served is 2,500, then the basic saturation is 0.83. This is further compared with the historical best standard service saturation (e.g., 0.95 reached in a certain period) to obtain a personnel service saturation coefficient of 0.87. Regarding operating costs, if the current cost is 800,000 yuan, the historical lowest cost is 600,000 yuan, and the highest cost is 1,000,000 yuan, then the operating cost coefficient is calculated as (800,000 - 600,000) / (1,000,000 - 600,000) = 0.5. In the model calculation, assuming weights α = 0.4, β = 0.3, γ = 0.3, and the digital facility coverage rate is 0.9, then the efficiency index is calculated as 0.87^0.4 × 0.9^0.3 × (1 - 0.5)^0.3 ≈ 0.68. This model uses an index weighting mechanism to create a dynamic balance between the positive contribution of personnel service capacity and facility coverage and the negative impact of cost control. When the digital facility coverage of a scenic area increases to 0.95, even if the operating cost coefficient rises to 0.6, the efficiency index can still be maintained above 0.65, reflecting the synergistic optimization effect of resource allocation.

[0039] Furthermore, the above-mentioned scheme avoids misjudgments caused by unreasonable static threshold settings for service capacity by historical benchmarking of the personnel service saturation coefficient; it enables horizontal efficiency comparisons of scenic spots with different operating scales by normalizing the range of the operating cost coefficient; and it accurately depicts the dynamic balance between service capacity improvement, facility optimization, and cost reduction using a nonlinear model structure, providing a quantitative basis for scenic spot managers to optimize resource allocation.

[0040] Regarding step S400, the step of constructing an efficiency evaluation model based on the comprehensive value index, real-time load index, and operational efficiency index under the influence of the environmental comfort index, and outputting the operational efficiency index, includes: Read the preset environmental comfort level; Read the comprehensive value index, real-time load index, and operational efficiency index, and construct an efficiency evaluation model based on environmental comfort, comprehensive value index, real-time load index, and operational efficiency index to determine the operational efficiency index; Among them, the performance evaluation model is an increasing function of the comprehensive value index, operational efficiency index, and environmental comfort index, and a decreasing function of the real-time load index.

[0041] The above content provides the specific application process of various parameters, and the performance evaluation model is as follows: ; in, For comprehensive value weighting, Weighted by operational efficiency, For real-time load weighting, Assigning weight to environmental comfort , , , , as well as All greater than , As a comprehensive value index, This is an operational efficiency index. This is the real-time load index. For environmental comfort index, As an operational efficiency index, The value of is between 0 and 1.

[0042] The overall value weight refers to the relative importance of tourist value contribution and operational efficiency in performance evaluation. It can be determined using the analytic hierarchy process (AHP) or expert scoring, and is used to balance the impact of tourist spending power and resource allocation efficiency on overall performance. The operational efficiency weight refers to the contribution of personnel services, facility coverage, and cost control to operational efficiency. It can be dynamically adjusted using the entropy weight method, and is used to reflect the priority of resource utilization efficiency at different operational stages. The real-time load weight refers to the degree to which the current tourist flow pressure inhibits performance evaluation. It can be set through historical data regression analysis, and is used to quantify the negative impact of tourist density on service quality. The environmental comfort weight refers to the adverse impact of environmental factors such as temperature and humidity on tourist experience. It can be obtained through real-time sensor data collection and normalization, and is used to dynamically correct the constraints of environmental factors on operational efficiency.

[0043] Furthermore, this model dynamically couples positive performance indicators with negative constraint indicators through a fractional structure. The comprehensive value index and operational efficiency index in the numerator are weighted and summed to reflect the potential for improving the scenic area's operational quality, while the real-time load index and environmental comfort index in the denominator are weighted and summed to reflect the objective constraints on operational efficiency. Weight normalization constraints ensure the balance within the positive and negative indicators; for example, when environmental comfort decreases, the weighted index in the denominator... An increase in the number of factors leads to a decrease in the overall efficiency index, thus accurately reflecting the inhibitory effect of environmental degradation on operations. The fractional ratio format visually demonstrates the dynamic balance of scenic area operations across multiple dimensions, including resource input, visitor value, and environmental carrying capacity.

[0044] Regarding step S500, the step of constructing a budget allocation model based on the current advertising and promotion budget, operational efficiency index, and real-time load index, and outputting a dynamic promotion budget includes: Read data based on the current advertising and promotion budget, operational efficiency index, and real-time load index; A budget allocation model is constructed based on the current advertising and promotion budget, operational efficiency index, and real-time load index; The dynamic promotion budget is determined based on the preset allocation model; The budget allocation model is as follows: ; The comprehensive operational efficiency impact coefficient, This is the load influence factor. ,and , All greater than , For the current advertising and promotion budget, As an operational efficiency index, This is the real-time load index. For dynamic promotion budget.

[0045] The comprehensive operational efficiency impact coefficient refers to the contribution weight of the operational efficiency index to budget adjustments. This weight can be determined through preset proportions or historical data analysis. The value is set to 0.6 to reflect the dominant role of operational efficiency in long-term promotion strategies. The load impact coefficient refers to the inverse adjustment weight of the real-time load index on budget adjustments, which can be implemented through a real-time load threshold trigger mechanism; for example, when L exceeds 0.8, it automatically increases. The index is set at 0.5 to prioritize alleviating congestion in scenic areas. The real-time load index is a normalized indicator calculated using visitor density, ticket booking trends, and traffic utilization, ranging from 0 to 1, used to quantify the current carrying capacity of the scenic area. The operational efficiency index is an assessment result integrating visitor value, resource allocation efficiency, and environmental comfort, also ranging from 0 to 1, used to measure the overall operational quality of the scenic area.

[0046] Regarding the calculation process, the dynamic promotion budget is calculated based on the current promotion budget, and is dynamically adjusted by linearly superimposing the inverse terms of the operational efficiency index and the real-time load index. When the scenic area's operational efficiency is high, the operational efficiency index approaches 1, driving the budget towards enhancing long-term value; when the real-time load index increases... If the item value decreases, the promotion budget will be automatically reduced to avoid excessive traffic generation. Weighting coefficient and The normalization constraint ensures that both work together on budget allocation, for example when =0.7、 When the value is 0.3, the model prioritizes responding to changes in operational efficiency while also taking into account short-term adjustments to load pressure.

[0047] In practical applications, during periods of high tourist load, the pressure to attract new visitors can be reduced by lowering the promotion budget; during periods of low load, the budget can be increased to attract potential tourists. For example, when the real-time load index reaches 0.9, the dynamic promotion budget can be reduced to 40% of the original budget, while when the load index is 0.3, the budget can be increased to 150% of the original budget, thus achieving a balance between resource utilization and tourist experience.

[0048] Figure 2 The diagram illustrates the structural composition of a cultural tourism promotion data intelligent dissemination system. The present invention also provides a cultural tourism promotion data intelligent dissemination system, wherein system 10 includes: The real-time load monitoring module 11 is used to construct a real-time load monitoring model based on the scenic area's visitor density, ticket booking trend index, and comprehensive transportation utilization rate, and output the real-time load index. The tourist analysis module 12 is used to build a tourist analysis model based on the proportion of overnight tourists, the average spending index per person, and the tourist satisfaction index, and output a comprehensive value index. Resource allocation analysis module 13 is used to build an efficiency analysis model based on personnel service saturation, digital facility coverage and operating cost index, and output the operating efficiency index. The performance evaluation module 14 is used to construct a performance evaluation model based on the comprehensive value index, real-time load index and operational efficiency index under the influence of the environmental comfort index, and output the operational efficiency index. The dynamic allocation module 15 is used to build a budget allocation model based on the current advertising and promotion budget, operational efficiency index and real-time load index, and output a dynamic promotion budget.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent propagation of travel promotion data, characterized in that, The method includes: A real-time load monitoring model is constructed based on the scenic area's visitor density, ticket booking trend index, and overall transportation utilization rate, and the real-time load index is output. A tourist analysis model is constructed based on the proportion of overnight tourists, the average spending index per person, and the tourist satisfaction index, and a comprehensive value index is output. An efficiency analysis model is constructed based on personnel service saturation, digital facility coverage, and operating cost index, and an operating efficiency index is output. Under the influence of the environmental comfort index, an efficiency evaluation model is constructed based on the comprehensive value index, real-time load index, and operational efficiency index, and the operational efficiency index is output. A budget allocation model is built based on the current advertising and promotion budget, operational efficiency index, and real-time load index, and a dynamic advertising budget is output.

2. The intelligent communication method of tourism promotion data according to claim 1, characterized in that, The steps of constructing a real-time load monitoring model based on scenic area visitor density, ticket booking trend index, and comprehensive transportation utilization rate, and outputting a real-time load index, include: Read data on visitor density, ticket booking trend index, and overall transportation utilization rate in scenic areas; The read scenic area visitor density, ticket booking trend index, and comprehensive transportation utilization rate are normalized to obtain visitor density coefficient, ticket booking trend coefficient, and comprehensive transportation utilization rate coefficient; the normalization process is used to normalize the scenic area visitor density, ticket booking trend index, and comprehensive transportation utilization rate to the range of 0 to 1. The passenger flow density coefficient, ticket booking trend coefficient, and comprehensive transportation utilization rate coefficient are calculated based on preset weights and used as the load monitoring model. The real-time load index is determined based on the load monitoring model.

3. The method of claim 2, wherein, The steps of constructing a tourist analysis model based on the proportion of overnight tourists, the average spending index per person, and the tourist satisfaction index, and outputting a comprehensive value index, include: Read the entry and exit data from the access control system to determine the proportion of overnight visitors; Based on preset permissions, query the revenue and total number of people, and calculate the average consumption amount per person; Read the tourist satisfaction survey results and determine the average tourist satisfaction. The average spending per person and the average tourist satisfaction rate are normalized to obtain the average spending per person coefficient and the tourist satisfaction rate coefficient. The proportion of overnight tourists, the average spending per person, and the tourist satisfaction rate are calculated based on preset weights to form a tourist analysis model. The comprehensive value index is determined based on a tourist analysis model.

4. The intelligent communication method of tourism promotion data according to claim 3, characterized in that, The normalization process uses a maximum value normalization process, specifically: For any given data, query the maximum value of the same data structure in historical data, calculate the ratio of the data to the maximum value, and obtain the normalized value.

5. The intelligent communication method of tourism promotion data according to claim 1, characterized in that, The steps of constructing an efficiency analysis model based on personnel service saturation, digital facility coverage, and operating cost index, and outputting the operating efficiency index, include: Read the actual number of service personnel and the standard number of service personnel, and calculate the ratio of the actual number of service personnel to the standard number of service personnel as the staff service saturation. Divide the personnel service saturation by the historical best service saturation to obtain the saturation coefficient; The operating cost coefficient is obtained by subtracting the historical lowest operating cost from the actual operating cost and then dividing the difference between the historical highest operating cost and the historical lowest operating cost. Read the digital infrastructure coverage rate, and construct an efficiency analysis model based on the digital infrastructure coverage rate, saturation coefficient, and operating cost coefficient; The operational efficiency index is determined based on the efficiency analysis model.

6. The intelligent dissemination method for cultural tourism promotion data according to claim 1, characterized in that, The steps of constructing an efficiency evaluation model based on the comprehensive value index, real-time load index, and operational efficiency index under the influence of the environmental comfort index, and outputting the operational efficiency index, include: Read the preset environmental comfort level; Read the comprehensive value index, real-time load index, and operational efficiency index, and construct an efficiency evaluation model based on environmental comfort, comprehensive value index, real-time load index, and operational efficiency index to determine the operational efficiency index; Among them, the performance evaluation model is an increasing function of the comprehensive value index, operational efficiency index, and environmental comfort index, and a decreasing function of the real-time load index.

7. A cultural tourism promotion data intelligent dissemination system, the system being used to implement the cultural tourism promotion data intelligent dissemination system as described in any one of claims 1 to 6, characterized in that, The system includes: The real-time load monitoring module is used to build a real-time load monitoring model based on the scenic area's visitor density, ticket booking trend index, and overall transportation utilization rate, and output the real-time load index. The tourist analysis module is used to build a tourist analysis model based on the proportion of overnight tourists, the average spending index per person, and the tourist satisfaction index, and output a comprehensive value index. The resource allocation analysis module is used to build an efficiency analysis model based on personnel service saturation, digital facility coverage, and operating cost index, and output the operating efficiency index. The performance evaluation module is used to construct a performance evaluation model based on the comprehensive value index, real-time load index, and operational efficiency index under the influence of the environmental comfort index, and output the operational performance index. The dynamic allocation module is used to build a budget allocation model based on the current advertising and promotion budget, operational efficiency index, and real-time load index, and output a dynamic promotion budget.

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