Scenic area data operation management method and system based on artificial intelligence

Abstract

The invention provides a scenic spot data operation management method and system based on artificial intelligence, and belongs to the technical field of operation management, and the method comprises the steps: determining a data analysis strategy corresponding to monitoring data based on a data type corresponding to the monitoring data, the monitoring data being data collected by monitoring equipment in a target scenic spot; based on the data analysis strategy corresponding to each type of monitoring data, analyzing the type of monitoring data to obtain multiple types of analysis results; and determining an operation management strategy of the target scenic area based on the multi-class analysis result, and performing operation management on the target scenic area based on the operation management strategy. According to the scenic spot data operation management method and system based on artificial intelligence, pertinence of scenic spot data operation management can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of operation management. More specifically, it relates to a scenic area data operation management method and system based on artificial intelligence. Background Art

[0002] With the rapid development of the tourism industry, scenic areas are facing challenges in many aspects such as tourist flow management, service quality improvement, and reasonable resource allocation. Traditional scenic area management methods lack effective utilization of a large amount of data, making it difficult to accurately grasp tourist needs and optimize scenic area operations.

[0003] Therefore, there is an urgent need for a targeted scenic area data operation management method. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a scenic area data operation management method and system based on artificial intelligence to improve the pertinence of scenic area data operation management.

[0005] In the first aspect of the embodiments of the present disclosure, a scenic area data operation management method based on artificial intelligence is provided, including: Determining an analysis strategy corresponding to the monitoring data based on the data type of the monitoring data, where there are multiple types of monitoring data, and the monitoring data is data collected by monitoring devices in the target scenic area; Analyzing each type of monitoring data based on the analysis strategy corresponding to the monitoring data to obtain multiple types of analysis results; Determining an operation management strategy for the target scenic area based on the multiple types of analysis results, and performing operation management on the target scenic area based on the operation management strategy.

[0006] In the second aspect of the embodiments of the present disclosure, a scenic area data operation management system based on artificial intelligence is provided, including: An analysis strategy determination module for determining an analysis strategy corresponding to the monitoring data based on the data type of the monitoring data, where there are multiple types of monitoring data, and the monitoring data is data collected by monitoring devices in the target scenic area; An analysis module for analyzing each type of monitoring data based on the analysis strategy corresponding to the monitoring data to obtain multiple types of analysis results; An operation management module for determining an operation management strategy for the target scenic area based on the multiple types of analysis results, and performing operation management on the target scenic area based on the operation management strategy.

[0007] In a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned method for operating and managing scenic area data based on artificial intelligence are implemented.

[0008] In a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for operating and managing scenic area data based on artificial intelligence are implemented.

[0009] The beneficial effects of the method and system for operating and managing scenic area data based on artificial intelligence provided by the embodiments of the present disclosure are as follows: By comprehensively acquiring various types of monitoring data of the target scenic area, the present disclosure can achieve all-round and real-time monitoring of the operating status of the scenic area. Secondly, for different types of monitoring data, personalized data analysis strategies are adopted to ensure the accuracy and effectiveness of the analysis, thereby enhancing the practical value of the analysis results. At the same time, the present disclosure can comprehensively integrate various types of analysis results to formulate more accurate and reasonable operation and management strategies, which not only helps to optimize the allocation of scenic area resources, improve operation efficiency, but also can effectively respond to various emergencies to ensure the safe and orderly operation of the scenic area. Therefore, the present disclosure can improve the pertinence of scenic area data operation and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0011] Figure 1 It is a flowchart of a method for operating and managing scenic area data based on artificial intelligence provided by an embodiment of the present disclosure; Figure 2 It is a block diagram of the structure of a system for operating and managing scenic area data based on artificial intelligence provided by an embodiment of the present disclosure; Figure 3 It is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present disclosure. However, those skilled in the art should clearly understand that the present disclosure can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present disclosure.

[0013] To make the objectives, technical solutions, and advantages of the present disclosure clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a scenic area data operation and management method based on artificial intelligence provided for an embodiment of the present disclosure. The method includes: S101: Determine the data analysis strategy corresponding to the monitoring data based on the data type of the monitoring data. There are multiple types of monitoring data, and the monitoring data is the data collected by the monitoring devices in the target scenic area.

[0015] In this embodiment, the target scenic area is the scenic area that needs to be operated and managed. The monitoring data is various data of the target scenic area in terms of operation, which can reflect the operation status of the target scenic area from different dimensions.

[0016] The monitoring data may include tourist flow data, tourist behavior data, and facility usage data. The tourist flow data can be obtained by installing intelligent turnstiles and cameras at the entrances, popular scenic spots, and main passages of the target scenic area and using image recognition technology to count the number of tourists; it can also be obtained through various ticket purchasing software platforms. By counting the tourist flow data in this embodiment, the number of tourists entering the scenic area and each scenic spot every day and at each time period can be understood, and the distribution and movement of tourists in the target scenic area can be understood.

[0017] The tourist behavior data can be used to obtain information such as the tourist's tour route and stay time in the scenic area by means of the Wi-Fi positioning system, mobile phone signaling data, or intelligent tour guide devices carried by tourists in the scenic area.

[0018] The facility usage data can be obtained by installing sensors on key facilities such as amusement facilities, lighting equipment, and power systems in the scenic area to collect data such as the operating status, energy consumption, and number of faults of the equipment in real time.

[0019] In this embodiment, relevant data in multiple aspects of the target scenic area are collected through different methods and technologies, providing a data basis for the subsequent in-depth analysis of the target scenic area data and the formulation of operation and management strategies.

[0020] Considering the characteristics, internal laws of each type of monitoring data and different analysis goals to be achieved, data analysis strategies adapted to different types of data can be selected and defined. The data analysis strategy can be a machine learning model or algorithm.

[0021] In an embodiment of the present disclosure, the monitoring data includes tourist flow data, tourist behavior data, and facility usage data; Determining the data analysis strategy corresponding to the monitoring data based on the data type of the monitoring data includes: In response to the data type of the monitoring data being tourist flow data, taking the first data analysis strategy as the data analysis strategy corresponding to this type of monitoring data; In response to the data type of the monitoring data being tourist behavior data, taking the second data analysis strategy as the data analysis strategy corresponding to this type of monitoring data; In response to the data type of the monitoring data being facility usage data, taking the third data analysis strategy as the data analysis strategy corresponding to this type of monitoring data.

[0022] In this embodiment, the tourist flow data can record relevant data on the change of the number of tourists in the scenic area over time, including the statistics of the number of tourists in different time periods and different regions. The tourist behavior data can cover a series of behavior information of tourists in the scenic area, such as the tour route, the staying location and duration, consumption behavior, etc. The facility usage data can be the usage situation data of various facilities in the target scenic area, such as amusement facilities, rest seats, toilets, etc., for example, the usage times, usage duration, operation status, and tourist satisfaction of the facilities.

[0023] The first data analysis strategy is used to mine the rules and trends in the tourist flow data; the second data analysis strategy is suitable for analyzing the tourist behavior data to understand the tourist behavior patterns and preferences; the third data analysis strategy is used to analyze the facility usage data to evaluate the facility operation status and usage efficiency, etc.

[0024] Considering the time characteristics and complexity of the tourist flow data, the first data analysis strategy can be a time series analysis module or a support vector machine model; considering that the tourist behavior data includes various types, such as the consumption habits, browsing preferences, and browsing duration of tourists, etc., the second data analysis strategy can be a clustering algorithm for clustering processing; considering that the facility usage data can reflect the operation situation of the equipment and the tourist satisfaction, the third data analysis strategy can establish a fault prediction model to predict the operation status of the facilities, and can also adopt an analytic hierarchy process algorithm for analysis, which is used for subsequent optimization of the facility layout and construction, etc.

[0025] S102: Analyzing each type of monitoring data based on the data analysis strategy corresponding to each type of monitoring data to obtain multiple types of analysis results.

[0026] In this embodiment, after determining the data analysis strategy corresponding to each type of monitoring data in step S101, predicting, clustering, and analyzing the monitoring data based on the corresponding data analysis strategy can obtain different analysis results, that is, conclusions and information in different dimensions.

[0027] In an embodiment of the present disclosure, the first data analysis strategy is a target support vector machine model; the second data analysis strategy is a clustering algorithm; the third data analysis strategy is an analytic hierarchy process algorithm; Analyzing each type of monitoring data based on the corresponding data analysis strategy for that type of monitoring data yields multiple types of analysis results, including: Analyzing the tourist flow data based on the target support vector machine model to obtain tourist flow prediction data; Analyzing the tourist behavior data based on the clustering algorithm to obtain tourist preference characteristics; Analyzing the facility usage data based on the analytic hierarchy process algorithm to obtain facility effectiveness; The multiple types of analysis results include tourist flow prediction data, tourist preference characteristics, and facility effectiveness.

[0028] In this embodiment, the target support vector machine model can classify or regress data by finding an optimal hyperplane and is commonly used to process linearly separable data or data that is linearly separable after being mapped to a high-dimensional space through a kernel function. In scenic area data processing, it is used to analyze tourist flow data and achieve flow prediction.

[0029] The clustering algorithm is an unsupervised learning algorithm that aims to divide the data points in a dataset into different clusters according to similarity, so that the data points within the same cluster have high similarity and the data points in different clusters have low similarity, and can be used to analyze tourist behavior data and mine tourists' preference characteristics.

[0030] The analytic hierarchy process algorithm is a multi-criteria decision-making method that combines qualitative and quantitative analysis. By decomposing a complex problem into multiple levels, constructing a judgment matrix, and calculating the relative weights of the elements at each level, a comprehensive evaluation result can be obtained, and it can be used to analyze facility usage data and evaluate facility effectiveness.

[0031] The tourist flow prediction data can be the data of the number of tourists in different future time periods predicted after analyzing the historical tourist flow data through the target support vector machine model.

[0032] The tourist preference characteristics can be obtained by using the clustering algorithm to analyze tourist behavior data (such as tour routes, consumption behaviors, etc.) and mining the behavior patterns and preference characteristics of tourists in the scenic area.

[0033] The effectiveness of facilities can be evaluated by using the analytic hierarchy process algorithm. After analyzing the facility usage data while comprehensively considering various factors such as the usage frequency, operating status, and maintenance cost of the facilities, the effectiveness performance of the facilities in the scenic area operation can be evaluated.

[0034] S103: Determine the operation management strategy of the target scenic area based on various analysis results, and conduct operation management on the target scenic area based on the operation management strategy.

[0035] In this embodiment, the various analysis results obtained from S102 may include tourist flow prediction data, tourist preference characteristics, and facility effectiveness, etc. The operation management strategy is a series of decisions, measures, and action plans formulated based on various analysis results to achieve goals such as the efficient operation of the target scenic area and the improvement of the tourist experience. Operation management is used to organize, coordinate, control, and optimize the daily operation activities of the target scenic area, covering aspects such as tourist service, facility maintenance, resource allocation, and marketing.

[0036] This embodiment comprehensively considers various results obtained through different data analyses, and determines the operation management strategy suitable for the target scenic area based on the information such as the scenic area operation status, tourist needs, and facility status reflected by these results. Then, according to the determined operation management strategy, various operation management tasks are actually carried out in the target scenic area to put the strategy into practice to achieve the good operation of the scenic area.

[0037] Determining the operation management strategy of the target scenic area based on various analysis results includes: Formulating an operation strategy based on the tourist flow prediction data; Optimizing the service strategy based on the tourist preference characteristics; Improving the facility management strategy based on the facility effectiveness evaluation.

[0038] Exemplarily, if the tourist flow prediction data in the multi-category analysis results shows that the tourist flow in the scenic area will increase significantly next weekend and is expected to reach twice that of weekdays. Based on this, the scenic area formulates the following operation and management strategies: In terms of personnel allocation, arrange more staff in advance at ticket windows, ticket checkpoints, and popular scenic spots to maintain order and provide services; in terms of material preparation, increase the procurement volume of catering raw materials to ensure sufficient catering supply; in terms of facility guarantee, check and maintain amusement facilities in advance to ensure their safe operation under high load. If the analysis results show that the tourist preference characteristics are liking cultural experience projects and special local foods. The operation and management strategies of the scenic area can be: increase the number of cultural performances and cultural experience activities in the scenic area, such as traditional handicraft making experiences; develop more special local foods and set up food streets in areas where tourists gather. When the analysis results of facility efficiency show that the utilization rate of rest seats in several remote areas is very low, while the rest seats near popular scenic spots are often in short supply. The operation and management strategy formulated by the scenic area is: relocate some rest seats in remote areas to the periphery of popular scenic spots; regularly clean and maintain the rest seats around popular scenic spots to ensure their good condition for use.

[0039] After the operation and management of the target scenic area based on the operation and management strategy, it further includes: Obtain tourist feedback data; Evaluate the operation and management strategy based on the tourist feedback data to obtain an evaluation result; Adjust the operation and management strategy based on the evaluation result.

[0040] In this embodiment, the tourist feedback data is information such as opinions, suggestions, and evaluations expressed by tourists through various channels during the process of visiting the scenic area regarding the scenic area experience, service, facilities, etc., and can be written questionnaires or online reviews.

[0041] Analyze the collected tourist feedback data to evaluate the implementation effect of the operation and management strategy and obtain corresponding evaluation results. According to the problems and deficiencies pointed out in the evaluation results, make targeted adjustments to the operation and management strategy to improve the effectiveness and adaptability of the strategy.

[0042] Exemplarily, compare the tourist feedback data before and after the implementation of the operation and management strategy, analyze the changes in various indicators, and can compare indicators such as tourist satisfaction scores, complaint rates, and tourist flows to evaluate the improvement degree of the scenic area operation status after the implementation of the strategy.

[0043] This embodiment not only ensures that the operation and management strategy can closely meet the tourist needs and improve tourist satisfaction, but also realizes the dynamic optimization of the strategy through continuous evaluation and adjustment. This embodiment helps the scenic area to timely discover and solve problems in operation and ensure the effectiveness and adaptability of the management strategy.

[0044] As can be seen from the above, by comprehensively obtaining various types of monitoring data of the target scenic area, the present disclosure can achieve all-round and real-time monitoring of the operating status of the scenic area. Secondly, for different types of monitoring data, personalized data analysis strategies are adopted to ensure the accuracy and effectiveness of the analysis, thereby enhancing the practical value of the analysis results. At the same time, the present disclosure can comprehensively integrate various types of analysis results to formulate more accurate and reasonable operation management strategies, which not only helps to optimize the allocation of scenic area resources, improve operation efficiency, but also can effectively respond to various emergencies to ensure the safe and orderly operation of the scenic area. Therefore, the present disclosure can improve the pertinence of scenic area data operation management.

[0045] In an embodiment of the present disclosure, the target support vector machine model includes a kernel function and an objective function; The method for operating and managing scenic area data based on artificial intelligence further includes: In response to the absolute value of the difference between the historical tourist flow characteristics and the correlation coefficient of the influencing factors being greater than or equal to the first difference threshold, increase the parameter of the kernel function ; In response to the absolute value of the difference between the historical tourist flow characteristics and the correlation coefficient of the influencing factors being less than the first difference threshold, decrease the parameter of the kernel function ; In response to the prediction accuracy requirement being greater than or equal to the first accuracy threshold, increase the penalty parameter of the objective function; In response to the prediction accuracy requirement being less than the first accuracy threshold, decrease the penalty parameter of the objective function.

[0046] In this embodiment, the support vector machine model is trained based on historical tourist flow data, the kernel function, and the objective function to obtain the target support vector machine model.

[0047] The kernel function is used to map low-dimensional data to a high-dimensional space, making the data that is linearly inseparable in the low-dimensional space become linearly separable in the high-dimensional space, and its parameter affects the mapping effect and the complexity of the model. The kernel function in this embodiment can be a Gaussian kernel function.

[0048] The objective function is a function defined for the support vector machine model to achieve the goal of classification or regression, and the parameters of the model are determined by optimizing the objective function. The penalty parameter is an important parameter in the objective function, which controls the penalty degree for misclassified samples.

[0049] The historical tourist flow characteristics are the characteristics extracted from the historical tourist flow data, such as the mean, peak value of the daily tourist flow, and the flow proportion in different time periods, etc., which are used for model training and analysis. The influencing factors are various factors that affect the tourist flow, such as seasons, weather conditions, holidays, scenic area activities, etc.

[0050] Calculate the absolute value of the difference in correlation coefficients between historical tourist flow characteristics and influencing factors, including: Calculate the first correlation coefficient between historical tourist flow characteristics and influencing factors based on the first formula; Calculate the second correlation coefficient between historical tourist flow characteristics and influencing factors based on the second formula; Determine the absolute value of the difference in correlation coefficients between historical tourist flow characteristics and influencing factors based on the first correlation coefficient and the second correlation coefficient.

[0051] Let the historical tourist flow characteristics be variable , and the influencing factor be variable ; The first formula is:

[0052] Among them, represents the -th monitoring value of variable , represents the -th monitoring value of variable , represents 's mean value, represents 's mean value, represents the number of monitoring values, represents the first correlation coefficient, represents the -th monitoring value's time decay factor, represents the decay coefficient ( ), represents the time interval between the data point and the current time.

[0053] In response to the time interval being greater than the first time interval threshold, increase the decay coefficient; In response to the time interval being less than or equal to the first time interval threshold, decrease the decay coefficient.

[0054] The above adjustment means that when calculating the first correlation between tourist flow and influencing factors, more importance is attached to recent data, which can better reflect the current actual situation. The first time interval threshold is a preset value and can be set according to experience.

[0055] The second formula is:

[0056] Among them, represents the second correlation coefficient, represents 's rank, represents 's rank, represents the mean of the ranks represents the mean of the ranks represents the weight of the nth data point. The weight can increase or decrease as the standard deviation of historical tourist flow data increases or decreases during each time period

[0057]

[0058] where represents the absolute value of the difference between the correlation coefficient of historical tourist flow characteristics and influencing factors

[0059] The first difference threshold is a preset numerical standard used to judge the magnitude of the correlation coefficient difference, thereby determining the adjustment direction of the kernel function parameter

[0060] The prediction accuracy requirement is the expected degree of the accuracy of the target support vector machine model in predicting tourist flow, measuring the closeness between the model prediction value and the actual value. The first accuracy threshold is a preset prediction accuracy standard used to judge whether to adjust the penalty parameter of the objective function

[0061] If the absolute value of the difference between the correlation coefficient of historical tourist flow characteristics and influencing factors is greater than or equal to the first difference threshold, increase the parameter of the kernel function by the first step size ; If the absolute value of the difference between the correlation coefficient of historical tourist flow characteristics and influencing factors is less than the first difference threshold, decrease the parameter of the kernel function by the second step size ; where the first step size and the second step size can be set according to experience

[0062] When the difference in the correlation coefficient between historical tourist flow characteristics and influencing factors is large, it means that the degree of association between the two changes greatly. At this time, increase the parameter of the kernel function to make the model more complex to capture this change. If the difference in the correlation coefficient is small, it means that the association between the two is relatively stable, and decrease the parameter of the kernel function to simplify the model and prevent overfitting

[0063] If the prediction accuracy requirement is greater than or equal to the first accuracy threshold, increase the penalty parameter of the objective function by the third step size; If the prediction accuracy requirement is less than the first accuracy threshold, decrease the penalty parameter of the objective function by the fourth step size; where the third step size and the fourth step size can be set according to experience

[0064] When the requirement for prediction accuracy is high, increase the penalty parameter to intensify the penalty for misclassified samples, making the model pay more attention to accuracy. If the requirement for prediction accuracy is not high, reduce the penalty parameter to allow the model to tolerate classification errors to a certain extent and reduce the model complexity.

[0065] As can be seen from the above, in this embodiment, by dynamically adjusting the kernel function parameters, the model can more flexibly capture the complex relationship between historical tourist flow characteristics and influencing factors, improving the adaptability of the model to data. At the same time, adjusting the penalty parameter of the objective function according to the prediction accuracy requirement ensures the performance stability of the model under different accuracy requirements. This embodiment not only improves the prediction accuracy of the model but also enhances its generalization ability, enabling the model to perform well in different scenarios.

[0066] In an embodiment of the present disclosure, based on the clustering algorithm, analyze the tourist behavior data to obtain tourist preference characteristics, including: Determine multiple cluster centers; Based on the first metric method, determine the distances between the tourist behavior data and the tourist behavior data of multiple cluster centers to obtain multiple first distances; Based on the multiple first distances, determine the tourist preference characteristics.

[0067] In this embodiment, the clustering algorithm can divide the data points in the dataset into different clusters (classes) according to similarity. The data points within the same cluster have high similarity, while the data points in different clusters have low similarity.

[0068] The cluster center is the representative data point of each cluster and can be a certain statistical center of all data points within the cluster, such as the mean, which can be understood as the "core" of a cluster.

[0069] The first metric method is used to measure the formula for the distance between the tourist behavior data and the tourist behavior data of the cluster center.

[0070] The formula of the first metric method is:

[0071] Among them, the tourist behavior data and the cluster center are both dimensional vectors, , ; represents the covariance, , represents the correlation between the dimensional tourist behavior data and the dimensional cluster center; represents the weight of the dimension, represents the The weights of dimensions are used to adjust the importance of different dimensions in distance calculation.

[0072] The first distance can be the distance value between the tourist behavior data calculated by the first metric method and the tourist behavior data of each cluster center.

[0073] The tourist preference characteristics can be the behavioral preference characteristics of tourists during the tourism process summarized after clustering analysis of tourist behavior data, such as the preferred scenic spot type, playing time, consumption items, etc.

[0074] First, determine the number of clusters and find the starting center position of each cluster. Different initialization methods of cluster centers will affect the final clustering results. Then, using the first metric method, calculate the distance from each tourist behavior data point to each cluster center one by one. These distance values reflect the similarity between the tourist behavior data and different cluster centers. The closer the distance, the higher the similarity.

[0075] Finally, according to the calculated distances, divide each tourist behavior data into the cluster where the nearest cluster center is located, and then analyze and summarize the data within each cluster to obtain the preference characteristics of different tourist groups.

[0076] It can be concluded from the above that in this embodiment, by determining multiple cluster centers, a large amount of tourist behavior data can be effectively classified, and then based on the first metric method, the distance between tourist behavior and the cluster center can be accurately calculated to obtain multiple first distances with reference value. The above distance data is beneficial to more accurately grasp the behavioral characteristics and preference trends of tourists by deeply analyzing tourist preferences.

[0077] In an embodiment of the present disclosure, the facility usage data includes facility usage frequency, facility failure frequency, and tourist satisfaction. Analyze the facility usage data based on the analytic hierarchy process algorithm to obtain the facility effectiveness, including: Determine the weights of various facility usage data based on the analytic hierarchy process algorithm. Based on various facility usage data and the weights of various facility usage data, perform weighted calculation to determine the facility effectiveness.

[0078] In an embodiment of the present disclosure, based on various facility usage data and the weights of various facility usage data, perform weighted calculation to determine the facility effectiveness, including: Standardize various facility usage data to obtain standardized facility usage data. Based on the standardized facility usage data and the corresponding weights of the standardized facility usage data, perform weighted calculation to determine the facility effectiveness.

[0079] In this embodiment, the facility usage frequency refers to the number of times various facilities in the target scenic area are used within a certain time period, reflecting the busyness of the facilities.

[0080] The facility failure frequency is the number of times the facility fails within the statistical period, reflecting the stability and reliability of the facility.

[0081] The tourist satisfaction can be collected through methods such as questionnaires and online evaluations to obtain the feedback of tourists on the satisfaction of the facilities, usually presented in the form of scores or grades, reflecting the subjective feelings of tourists towards the facilities.

[0082] After comprehensively considering factors such as the facility usage frequency, failure frequency, and tourist satisfaction, the facility effectiveness is a comprehensive evaluation index for the efficiency and effect of the facility in the operation of the scenic area.

[0083] Based on the analytic hierarchy process algorithm, determine the weights of various facility usage data, including: Clarify the hierarchical structure; the hierarchical structure includes the goal layer, criterion layer, and scheme layer; Determine the comparison scale; Based on the elements in the criterion layer, make pairwise comparisons to determine the judgment matrix; Conduct a consistency check on the judgment matrix; If the consistency ratio is less than the first threshold, retain the judgment matrix; If the consistency ratio is greater than or equal to the first threshold, adjust the judgment matrix; Calculate the eigenvector of the judgment matrix and normalize it to determine the weights of various facility usage data.

[0084] Among them, the first threshold is a preset threshold, which can be set according to experience and ranges from 0.01 to 0.2.

[0085] The goal layer is the facility effectiveness evaluation, the criterion layer is the facility usage frequency, facility failure frequency, and tourist satisfaction, and the scheme layer is the specific facilities; Determine that the comparison scale uses the 1-9 scale method to represent the relative importance between two elements, and the specific meanings are as follows: |Scale|Meaning| |----|----| |1|Indicates that when two elements are compared, they have the same importance| |3|Indicates that one element is slightly more important than the other| |5|Indicates that one element is significantly more important than the other| |7|Indicates that one element is strongly more important than the other| |9|Indicates that one element is extremely more important than the other| |2, 4, 6, 8|Respectively represent the intermediate values of the above adjacent judgments.

[0086] Using the analytic hierarchy process algorithm, a judgment matrix is constructed to compare pairwise the facility usage frequency, facility failure frequency, and tourist satisfaction, determine their relative importance in evaluating the facility effectiveness, and obtain their respective weights. According to the weights determined above, combined with the specific facility usage frequency, failure frequency, and tourist satisfaction data, the quantitative value of the facility effectiveness is calculated through weighted calculation to comprehensively evaluate the operation effect of the facility.

[0087] It can be concluded from the above that this embodiment comprehensively considers multiple dimensions such as facility usage frequency, facility failure frequency, and tourist satisfaction. By determining the weights of various types of data, it can comprehensively and objectively reflect the actual effectiveness of the facility.

[0088] Corresponding to the above-mentioned embodiment of a scenic area data operation and management method based on artificial intelligence, Figure 2 This is a structural block diagram of a scenic area data operation and management system based on artificial intelligence provided by an embodiment of the present disclosure. For the sake of illustration, only the parts related to the embodiments of the present disclosure are shown. Refer to Figure 2 This scenic area data operation and management system 20 based on artificial intelligence includes: an analysis strategy determination module 21, an analysis module 22, and an operation and management module 23.

[0089] Among them, the analysis strategy determination module 21 is used to determine the data analysis strategy corresponding to the monitoring data based on the data type of the monitoring data. There are multiple types of monitoring data, and the monitoring data is the data collected by the monitoring devices in the target scenic area; The analysis module 22 is used to analyze each type of monitoring data based on the data analysis strategy corresponding to each type of monitoring data to obtain multiple types of analysis results; The operation and management module 23 is used to determine the operation and management strategy of the target scenic area based on multiple types of analysis results and perform operation and management on the target scenic area based on the operation and management strategy.

[0090] In an embodiment of the present disclosure, the monitoring data includes tourist flow data, tourist behavior data, and facility usage data; The analysis strategy determination module 21 is specifically used to, in response to the data type of the monitoring data being tourist flow data, use the first data analysis strategy as the data analysis strategy corresponding to this type of monitoring data; In response to the data type of the monitoring data being tourist behavior data, use the second data analysis strategy as the data analysis strategy corresponding to this type of monitoring data; In response to the data type of the monitoring data being facility usage data, use the third data analysis strategy as the data analysis strategy corresponding to this type of monitoring data.

[0091] In one embodiment of the present disclosure, the first data analysis strategy is a target support vector machine model; the second data analysis strategy is a clustering algorithm; the third data analysis strategy is an analytic hierarchy process algorithm; The analysis module 22 is specifically configured to analyze the tourist flow data based on the target support vector machine model to obtain tourist flow prediction data; Analyze the tourist behavior data based on the clustering algorithm to obtain tourist preference characteristics; Analyze the facility usage data based on the analytic hierarchy process algorithm to obtain facility effectiveness; The multiple types of analysis results include tourist flow prediction data, tourist preference characteristics, and facility effectiveness.

[0092] In one embodiment of the present disclosure, the target support vector machine model includes a kernel function and an objective function; An artificial intelligence-based scenic area data operation and management system 20 further includes: a parameter adjustment module; The parameter adjustment module is configured to increase the parameter of the kernel function in response to the absolute value of the difference between the historical tourist flow characteristics and the correlation coefficient of the influencing factors being greater than or equal to a first difference threshold; ; Reduce the parameter of the kernel function in response to the absolute value of the difference between the historical tourist flow characteristics and the correlation coefficient of the influencing factors being less than the first difference threshold; ; Increase the penalty parameter of the objective function in response to the prediction accuracy requirement being greater than or equal to a first accuracy threshold; Reduce the penalty parameter of the objective function in response to the prediction accuracy requirement being less than the first accuracy threshold.

[0093] In one embodiment of the present disclosure, the analysis module 22 is specifically further configured to determine multiple clustering centers; Determine the distances between the tourist behavior data and the tourist behavior data of the multiple clustering centers based on a first metric method to obtain multiple first distances; Determine tourist preference characteristics based on the multiple first distances.

[0094] In one embodiment of the present disclosure, the facility usage data includes facility usage frequency, facility failure frequency, and tourist satisfaction; The analysis module 22 is specifically further configured to analyze the facility usage data based on the analytic hierarchy process algorithm to obtain facility effectiveness, including: Determine the weights of various types of facility usage data based on the analytic hierarchy process algorithm; Perform weighted calculation based on various types of facility usage data and the weights of various types of facility usage data to determine facility effectiveness.

[0095] In one embodiment of the present disclosure, the analysis module 22 is further specifically configured to standardize various types of facility usage data to obtain standardized facility usage data; Perform weighted calculation based on the standardized facility usage data and the weights corresponding to the standardized facility usage data to determine the facility effectiveness.

[0096] See Figure 3 , Figure 3 is a schematic block diagram of an electronic device provided in an embodiment of the present disclosure. As Figure 3 shown, the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through a communication bus 305. The memory 304 is used to store computer programs, and the computer programs include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned system embodiments, such as Figure 2 the functions of the modules 21 to 23 shown.

[0097] It should be understood that in the embodiments of the present disclosure, the so-called processor 301 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0098] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0099] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0100] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure may execute the implementation manners described in the first and second embodiments of a method for scenic area data operation and management based on artificial intelligence provided by the embodiments of the present disclosure, and may also execute the implementation manner of the electronic device described in the embodiments of the present disclosure, which will not be elaborated herein.

[0101] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the method of the foregoing embodiments are implemented. It may also be completed by instructing relevant hardware through the computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the foregoing method embodiments may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0102] The computer-readable storage medium may be an internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the electronic device. Further, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store the data that has been output or will be output.

[0103] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this disclosure.

[0104] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0105] In several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection to each other can be an indirect coupling or communication connection through some interfaces or units, or can also be in the form of electrical, mechanical, or other connections.

[0106] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can also be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of this disclosure.

[0107] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0108] The above is only the specific implementation manner of this disclosure, but the protection scope of this disclosure is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by this disclosure, and these modifications or substitutions should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be subject to the protection scope of the claims.

Claims

1. A scenic area data operation management method based on artificial intelligence, characterized in that Including: Determine the data analysis strategy corresponding to the monitoring data based on the data type of the monitoring data. There are multiple types of the monitoring data, and the monitoring data is the data collected by the monitoring devices in the target scenic area; Analyze each type of the monitoring data based on the data analysis strategy corresponding to the monitoring data of this type to obtain multiple types of analysis results; Determine the operation and management strategy of the target scenic area based on the multiple types of analysis results, and perform operation and management on the target scenic area based on the operation and management strategy.

2. The method for scenic area data operation management based on artificial intelligence according to claim 1, wherein The monitoring data includes tourist flow data, tourist behavior data, and facility usage data; The determining the data analysis strategy corresponding to the monitoring data based on the data type of the monitoring data includes: In response to the data type of the monitoring data being tourist flow data, use the first data analysis strategy as the data analysis strategy corresponding to this type of monitoring data; In response to the data type of the monitoring data being tourist behavior data, use the second data analysis strategy as the data analysis strategy corresponding to this type of monitoring data; In response to the data type of the monitoring data being facility usage data, use the third data analysis strategy as the data analysis strategy corresponding to this type of monitoring data.

3. The method for scenic area data operation management based on artificial intelligence according to claim 2, wherein The first data analysis strategy is a target support vector machine model; the second data analysis strategy is a clustering algorithm; the third data analysis strategy is an analytic hierarchy process algorithm; The analyzing each type of the monitoring data based on the data analysis strategy corresponding to the monitoring data of this type to obtain multiple types of analysis results includes: Analyze the tourist flow data based on the target support vector machine model to obtain tourist flow prediction data; Analyze the tourist behavior data based on the clustering algorithm to obtain tourist preference characteristics; Analyze the facility usage data based on the analytic hierarchy process algorithm to obtain facility effectiveness; The multiple types of analysis results include the tourist flow prediction data, the tourist preference characteristics, and the facility effectiveness.

4. The method for scenic area data operation management based on artificial intelligence according to claim 3, characterized in that The target support vector machine model includes a kernel function and an objective function; The method for operating and managing scenic area data based on artificial intelligence further includes: In response to the absolute value of the difference between the correlation coefficients of historical tourist flow characteristics and influencing factors being greater than or equal to a first difference threshold, increase the parameter of the kernel function ; Reduce the parameter of the kernel function in response to the absolute value of the difference between the correlation coefficients of the historical tourist flow characteristics and influencing factors being less than the first difference threshold ; In response to the prediction accuracy requirement being greater than or equal to the first accuracy threshold, increase the penalty parameter of the objective function; In response to the prediction accuracy requirement being less than the first accuracy threshold, decrease the penalty parameter of the objective function.

5. The method for scenic area data operation management based on artificial intelligence according to claim 3, wherein, The analyzing the tourist behavior data based on the clustering algorithm to obtain tourist preference characteristics includes: Determine multiple cluster centers; Determine the distances between the tourist behavior data and the tourist behavior data of the multiple cluster centers based on the first measurement method to obtain multiple first distances; Determine the tourist preference characteristics based on the multiple first distances.

6. The method for scenic area data operation management based on artificial intelligence according to claim 3, characterized in that The facility usage data includes facility usage frequency, facility failure frequency, and tourist satisfaction; The analyzing the facility usage data based on the analytic hierarchy process algorithm to obtain facility effectiveness includes: Determine the weights of various types of facility usage data based on the analytic hierarchy process algorithm; Perform weighted calculation based on various types of facility usage data and the weights of various types of facility usage data to determine the facility effectiveness.

7. The method for scenic area data operation management based on artificial intelligence according to claim 6, characterized in that, The performing weighted calculation based on various types of facility usage data and the weights of various types of facility usage data to determine the facility effectiveness includes: Standardize the usage data of various facilities to obtain standardized facility usage data; Based on the standardized facility usage data and the weights corresponding to the standardized facility usage data, perform weighted calculations to determine the effectiveness of the facilities.

8. An artificial intelligence-based scenic area data operation and management system, characterized in that, Including: An analysis strategy determination module for determining the data analysis strategy corresponding to the monitoring data based on the data type of the monitoring data. There are multiple types of the monitoring data, and the monitoring data is the data collected by the monitoring devices in the target scenic area; An analysis module for analyzing each type of monitoring data based on the data analysis strategy corresponding to the monitoring data to obtain multiple types of analysis results; An operation management module for determining the operation management strategy of the target scenic area based on the multiple types of analysis results and performing operation management on the target scenic area based on the operation management strategy.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images