A smart scenic spot data analysis system and method based on artificial intelligence

Abstract

The present invention discloses a smart scenic spot data analysis system and method based on artificial intelligence. The present invention relates to the technical field of scenic spot data analysis, and solves the technical problem that insufficient data analysis and failure to formulate effective improvement measures result in decreased tourist satisfaction. The present invention analyzes data from different areas of a scenic spot, combines passenger flow data and complaint data for comprehensive evaluation during analysis, and reasonably classifies complaint data according to the same complaint type. In the process of data processing, corresponding improvement measures are generated in combination with corresponding historical data and changes in passenger flow data. On the one hand, the efficiency of overall data analysis can be improved, and the situation of insufficient data analysis can be reduced. On the other hand, reasonable improvement measures can be formulated according to the situation reflected by the data, thereby achieving the purpose of improving tourist satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of scenic spot data analysis, and in particular to a smart scenic spot data analysis system and method based on artificial intelligence. Background Art

[0002] With the improvement of people's living standards, people's requirements for spiritual life are developing rapidly. The tourism industry has also developed rapidly in modern life. The number of tourists during holidays has exceeded year by year, and the number of tourists each year is very huge. The number of tourists received by each scenic spot has increased year by year, causing congestion in the scenic area, while the passenger flow of some scenic spots is not large, resulting in uneven distribution of tourists in various scenic spots, and it is not possible for tourists to reasonably choose suitable tourist attractions.

[0003] According to the application number CN201910021254.4, a tourism management platform based on big data is disclosed, including a big data storage center, multiple scenic area management systems, a service management platform, a background management system and a mobile service terminal. The present invention performs big data analysis on tourist attractions and tourist information based on big data, and a scenic area management system is provided in the scenic area. The scenic area data information is collected and stored through the scenic area management system to form a scenic area database, and the scenic area data is shared to the big data storage center. The service management platform uniformly manages each scenic spot, obtains data from each scenic area management system and the big data storage center, organizes and publishes the obtained data, and the mobile service terminal collects tourist information, displays tourist information and scenic spot information, and feeds back tourist experience information to the service management platform.

[0004] Some existing scenic spot data analysis systems are not fully understood when used. Even if the analysis results are obtained, many scenic spots lack the ability to convert them into actual operational strategies. Secondly, the scenic spots discovered the problem of declining tourist satisfaction through data analysis, but failed to formulate effective improvement measures, resulting in the problem remaining unsolved for a long time. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a smart scenic spot data analysis system and method based on artificial intelligence, which solves the problem of insufficient data analysis, failure to formulate effective improvement measures, and decreased tourist satisfaction.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a smart scenic spot data analysis system based on artificial intelligence, comprising:

[0007] The scenic spot information collection module is used to collect scenic spot information of the smart scenic spot and transmit the collected scenic spot information to the scenic spot information analysis module, wherein the acquired scenic spot information includes passenger flow data and complaint data;

[0008] The scenic spot information analysis module is used to identify and analyze the smart scenic spot according to the scenic spot information transmitted by the scenic spot information collection module, and generate corresponding relationship analysis information by judging the relationship between the passenger flow data and the complaint data, wherein the relationship analysis information includes: correlation signals and non-correlation signals, and transmit the generated relationship analysis information to the information comprehensive processing module;

[0009] The information comprehensive processing module is used to process the acquired relationship analysis information, analyze the relationship analysis information by combining historical data, and make a comprehensive judgment based on the data type corresponding to the complaint data to generate comprehensive processing information, and the comprehensive processing information includes specific cause information and secondary analysis signals, and transmits the specific cause information to the analysis information output module, and transmits the secondary analysis signal to the secondary analysis processing module;

[0010] The secondary analysis processing module is used to process the acquired secondary analysis signals and generate secondary analysis information by combining the regional passenger flow data and complaint data within the analysis object for comprehensive analysis. The secondary analysis information includes specific cause information and normal monitoring signals.

[0011] Analysis information output module, which is used to display the acquired specific cause information and normal monitoring signals to the corresponding operators.

[0012] As a further solution of the present invention: the specific method of generating relationship analysis information by the scenic spot information analysis module is:

[0013] Different areas of the scenic area are labeled as i, and i=1, 2, ..., j, where j represents the number of scenic area areas. At the same time, the change of passenger flow data corresponding to the scenic area i within time t is obtained, and change information is generated, where the change information includes: increase change and decrease change. Then, the complaint data is analyzed according to the change information of passenger flow data;

[0014] The corresponding complaint data changes are obtained when the passenger flow data is increasing. If the corresponding complaint data changes are increasing, a correlation signal is generated. Otherwise, if the corresponding complaint data changes are not increasing, a non-correlation signal is generated.

[0015] As a further solution of the present invention: the information comprehensive processing module analyzes the correlation signal in the following manner:

[0016] Take any scenic area i as the analysis object, then obtain the complaint data corresponding to the analysis object, and classify the complaint data according to the complaint type to obtain the complaint type denoted as a, where a=1, 2, ..., b, and b represents the type of complaint type;

[0017] Obtain the number corresponding to complaint type a and record it as La. Then obtain the change of the number La corresponding to complaint type a within the time period t, and select the complaint types corresponding to the increase in the number La as the types to be analyzed. Then process the obtained types to be analyzed. Specifically, the case where the number of complaint types decreases will not be processed.

[0018] The type to be analyzed is labeled as a1, and a1=1, 2, ..., b1, and b1 represents the type of the type to be analyzed. Then, the increase proportion of the type to be analyzed within the time period t is calculated, and the increase proportion is compared with the preset value. When the increase proportion is greater than the preset value, the specific cause information is generated. Conversely, when the increase proportion is less than the preset value, a secondary analysis signal is generated.

[0019] As a further solution of the present invention: the information comprehensive processing module analyzes the non-correlated signals as follows:

[0020] Take any scenic area i as the analysis object, then obtain the complaint data corresponding to the analysis object, and classify the complaint data according to the complaint type to obtain the complaint type denoted as a, where a=1, 2, ..., b, and b represents the type of complaint type;

[0021] The complaint type corresponding to the largest quantity La is obtained and recorded as the target type. At the same time, the historical data corresponding to the target type is obtained, and the quantity change of the target type is judged in combination with the historical data. If the quantity change of the target type corresponding to the historical data is an increase, an increase signal is generated. If the quantity change of the target type corresponding to the historical data is a decrease, a decrease signal is generated, and the generated increase signal and decrease signal are processed at the same time.

[0022] As a further solution of the present invention: the specific way in which the information comprehensive processing module processes the increased signal and the decreased signal is:

[0023] Analyze the generated enlargement signal, take time t1 as the segmentation node, obtain the increase number of the target type in the same segmentation time period, and establish the corresponding relationship diagram. According to the relationship diagram, the change trend of the increase number is judged. If the increase number is stable, the specific reason information is generated based on the target type and transmitted to the analysis information output module. If the increase number is unstable, a secondary analysis signal is generated.

[0024] The generated decreasing signal is analyzed to obtain the change of passenger flow data within the analysis object time period t. If the passenger flow data increases, the specific reason information is generated; if the passenger flow data decreases, a secondary analysis signal is generated.

[0025] As a further solution of the present invention: the specific manner in which the secondary analysis processing module processes the secondary analysis signal is:

[0026] Obtain the regional passenger flow data of the analysis object, and determine the change of the regional passenger flow data. If the regional passenger flow data is an increase, generate specific reason information, and transmit the specific reason information to the analysis information output module. If the regional passenger flow data is a decrease, obtain the increase change amount of the target type within the time period t, and calculate the corresponding increase change ratio;

[0027] Then the calculated increase change ratio value is compared with the preset value. When the increase change ratio value is greater than the preset value, an early warning signal is generated, and specific cause information is generated with the corresponding target type. Conversely, when the increase ratio value is less than the preset value, a normal monitoring signal is generated.

[0028] A method for analyzing data of a smart scenic spot based on artificial intelligence, the method specifically comprises the following steps:

[0029] Step 1: Collect passenger flow data and complaint data of smart scenic spots;

[0030] Step 2: Perform correlation analysis on the collected passenger flow data and complaint data, and generate correlation signals and non-correlation signals;

[0031] Step 3: Analyze the correlation signal by analyzing the increase and change of the complaint type within the time period, and calculate the increase ratio and compare them to generate specific cause information and secondary analysis signals;

[0032] Step 4: Analyze the non-correlated signals, analyze the complaint types in combination with historical data, and establish a corresponding relationship diagram to comprehensively judge and generate specific cause information and secondary analysis signals;

[0033] Step 5: Process the obtained secondary analysis signal, and generate specific cause information and normal monitoring signals based on the passenger flow data and complaint data of the scenic area.

[0034] The present invention provides a smart scenic spot data analysis system and method based on artificial intelligence. Compared with the prior art, it has the following beneficial effects:

[0035] The present invention analyzes data from different areas of a scenic spot, combines passenger flow data and complaint data for comprehensive evaluation during the analysis, and reasonably classifies complaint data according to the same complaint type. In the process of data processing, corresponding improvement measures are generated in combination with corresponding historical data and changes in passenger flow data. On the one hand, the efficiency of overall data analysis can be improved and the situation of insufficient data analysis can be reduced. On the other hand, reasonable improvement measures can be formulated according to the situation reflected by the data, thereby achieving the purpose of improving tourist satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a block diagram of the system principle of the present invention;

[0037] Figure 2 Figure 2 is a diagram of the method of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] For example, see Figure 1 The present application provides an artificial intelligence-based smart scenic spot data analysis system, including a scenic spot information collection module, a scenic spot information analysis module, an information comprehensive processing module, a secondary analysis processing module and an analysis information output module, and the signal transmission between the above modules is a one-way wire connection.

[0040] The scenic spot information collection module is used to collect scenic spot information of the smart scenic spot, and transmit the collected scenic spot information to the scenic spot information analysis module, where the acquired scenic spot information includes passenger flow data and complaint data.

[0041] The scenic spot information analysis module is used to identify and analyze the smart scenic spot based on the acquired scenic spot information, and to generate corresponding relationship analysis information by judging the relationship between the passenger flow data and the complaint data, wherein the relationship analysis information includes: correlation signals and non-correlation signals, and transmit the generated relationship analysis information to the information comprehensive processing module at the same time, and the specific method of generating the relationship analysis information is:

[0042] Different areas of the scenic spot are labeled as i, and i=1, 2, ..., j, where j represents the number of scenic area areas. At the same time, the change of the passenger flow data corresponding to the scenic area i within the time t is obtained, and the specific value of the time t is set by the operator. The passenger flow data represents the number of people playing in the corresponding scenic area, and the change information is generated, wherein the change information includes: increase change and decrease change. Then, the complaint data is analyzed according to the change information of the passenger flow data. The specific analysis method is as follows:

[0043] The corresponding change in complaint data is obtained when the passenger flow data is increasing. If the corresponding change in complaint data is increasing, it means that there is a correlation between the passenger flow data and the complaint data, and a correlation signal is generated. Otherwise, if the corresponding change in complaint data is non-increasing, it means that there is no correlation between the passenger flow data and the complaint data, and a non-correlation signal is generated.

[0044] Suppose a scenic spot launches a new tourist attraction during the peak season, which leads to a significant increase in the number of tourists. At the same time, the scenic spot management also noticed that the number of complaints about the project has also increased significantly, especially about the problem of long queues. By analyzing the changing relationship between passenger flow data and complaint data, it is found that on days when the number of tourists increases significantly, related complaints also increase, showing a positive correlation;

[0045] In this case, the data analysis system automatically generates a correlation signal based on the above correlation logic, prompting managers to note that the increase in passenger flow may directly affect tourists' satisfaction and lead to more complaints. Based on this signal, the management can take measures, such as adding staff, optimizing the queuing process, or preparing for tourist diversion in advance, to reduce the negative impact caused by the increase in passenger flow.

[0046] In short, if there is no increase in complaint data with the increase in passenger flow, for example, complaints are mainly concentrated on the quality of catering services in the scenic area and have no direct relationship with the number of tourists, the system will generate a non-correlation signal, indicating that the increase in passenger flow is not a direct factor affecting the increase in complaints.

[0047] The information comprehensive processing module is used to process the acquired relationship analysis information. The relationship analysis information is analyzed by combining historical data, and the data type corresponding to the complaint data is comprehensively judged to generate comprehensive processing information. The comprehensive processing information includes specific cause information and secondary analysis signals. At the same time, the specific cause information is transmitted to the analysis information output module, and the secondary analysis signal is transmitted to the secondary analysis processing module. The specific method of generating the comprehensive processing information is:

[0048] Take any scenic area i as the analysis object, then obtain the complaint data corresponding to the analysis object, and classify the complaint data according to the complaint type to obtain the complaint type denoted as a, where a=1, 2, ..., b, and b represents the type of complaint type;

[0049] Analyze the correlation signal in the relationship analysis information: obtain the number corresponding to complaint type a and record it as La, then obtain the change of the number La corresponding to complaint type a within the time period t, and select the complaint types corresponding to the increase in the number La as the types to be analyzed. The time period t here is the same as the time t mentioned above, and both represent the same time period. Then process the obtained types to be analyzed, and do not process the specific situation where the number of complaint types decreases;

[0050] The type to be analyzed is labeled as a1, and a1=1, 2, ..., b1, and b1 represents the type of the type to be analyzed. Then, the increase ratio of the type to be analyzed in the time period t is calculated, and the increase ratio is compared with the preset value. When the increase ratio is greater than the preset value, specific cause information is generated, and the specific cause information here represents the specific cause of the complaint corresponding to the type to be analyzed. At the same time, the value of the preset value is set by the operator. On the contrary, when the increase ratio is less than the preset value, a secondary analysis signal is generated. In this way, all scenic areas are analyzed and the corresponding comprehensive processing information is generated.

[0051] Analyze the non-correlated signals in the relationship analysis information: obtain the complaint type corresponding to the largest quantity La and record it as the target type, and at the same time obtain the historical data corresponding to the target type, and judge the change in the quantity of the target type based on the historical data. If the change in the quantity of the target type corresponding to the historical data is an increase, then generate an increase signal; if the change in the quantity of the target type corresponding to the historical data is a decrease, then generate a decrease signal;

[0052] The generated increasing signal is analyzed, and time t1 is used as the segmentation node to obtain the increase number of the target type in the same segmentation time period. At the same time, a corresponding relationship diagram is established, and the change trend of the increase number is judged according to the relationship diagram. If the increase number is a stable increase, specific cause information is generated based on the target type, and it is transmitted to the analysis information output module. If the increase number is an unstable increase, a secondary analysis signal is generated. The specific method of judging stability and instability here is to calculate the difference between the increase numbers in two adjacent segmentation time periods, and compare the calculated difference with the threshold. If the difference is greater than the threshold, it indicates instability. Otherwise, if the difference is less than the threshold, it indicates stability. When judging the whole, if there is instability in any segmentation time period, it indicates that the whole is unstable. Otherwise, if all are stable, it indicates that the whole is stable.

[0053] The generated decreasing signal is analyzed to obtain the change of passenger flow data within the analysis object time period t. If the passenger flow data increases, the specific reason information is generated; if the passenger flow data decreases, a secondary analysis signal is generated.

[0054] An analysis information output module is used to display the acquired specific cause information to the corresponding operator.

[0055] Embodiment 2, as embodiment 2 of the present invention, is implemented on the basis of embodiment 1, and differs from embodiment 1 in that.

[0056] The secondary analysis processing module is used to process the acquired secondary analysis signals, and generate secondary analysis information by combining the regional passenger flow data and complaint data within the analysis object for comprehensive analysis. The secondary analysis information includes specific cause information and normal monitoring signals, and transmits the secondary analysis information to the analysis information output module. The specific method of generating the secondary analysis information is:

[0057] The regional passenger flow data of the analysis object is obtained, and the change of the regional passenger flow data is determined. If the regional passenger flow data is an increase, specific reason information is generated, and the specific reason information is transmitted to the analysis information output module. The specific reason information generated here is the target type corresponding to the secondary analysis signal. If the regional passenger flow data is a decrease, the increase change amount of the target type within the time period t is obtained, and the corresponding increase change ratio is calculated. Then, the calculated increase change ratio is compared with the preset value, and the specific value of the preset value is set by the operator. When the increase change ratio is greater than the preset value, an early warning signal is generated, and specific reason information is generated with the corresponding target type. Conversely, when the increase ratio is less than the preset value, a normal monitoring signal is generated.

[0058] Analysis information output module, which is used to display the acquired normal monitoring signals and specific cause information to the corresponding operators.

[0059] Embodiment 3, as the embodiment 3 of the present invention, focuses on combining the implementation processes of embodiment 1 and embodiment 2 for implementation.

[0060] Example 4, please refer to Figure 2 , a smart scenic spot data analysis method based on artificial intelligence, the method specifically comprises the following steps:

[0061] Step 1: Collect passenger flow data and complaint data of smart scenic spots;

[0062] Step 2: Perform correlation analysis on the collected passenger flow data and complaint data, and generate correlation signals and non-correlation signals, and the specific analysis method is the same as the processing method of the scenic spot information analysis module in Example 1;

[0063] Step 3: Analyze the correlation signal by analyzing the increase and change of the complaint type corresponding to the time period, and calculate the increase ratio and compare them to generate specific cause information and secondary analysis signals. The analysis method here is the same as the analysis method of the correlation signal by the information comprehensive processing module in Example 1;

[0064] Step 4: Analyze the non-correlated signals, analyze the complaint types in combination with historical data, and establish a corresponding relationship diagram to comprehensively judge and generate specific cause information and secondary analysis signals. The analysis method here is the same as the analysis method of the information comprehensive processing module for non-correlated signals in Example 1;

[0065] Step 5: Process the obtained secondary analysis signal, and generate specific cause information and normal monitoring signals based on the passenger flow data and complaint data of the scenic area. The specific generation method is the same as the specific processing method of the secondary analysis processing module in Example 1.

[0066] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0067] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A smart scenic spot data analysis system based on artificial intelligence, characterized in that: include: The scenic spot information collection module is used to collect scenic spot information of the smart scenic spot and transmit the collected scenic spot information to the scenic spot information analysis module, wherein the acquired scenic spot information includes passenger flow data and complaint data; The scenic spot information analysis module is used to identify and analyze the smart scenic spot according to the scenic spot information transmitted by the scenic spot information collection module, and generate corresponding relationship analysis information by judging the relationship between the passenger flow data and the complaint data, wherein the relationship analysis information includes: correlation signals and non-correlation signals, and transmit the generated relationship analysis information to the information comprehensive processing module; The information comprehensive processing module is used to process the acquired relationship analysis information, analyze the relationship analysis information by combining historical data, and make a comprehensive judgment based on the data type corresponding to the complaint data to generate comprehensive processing information, and the comprehensive processing information includes specific cause information and secondary analysis signals. At the same time, the specific cause information is transmitted to the analysis information output module, and the secondary analysis signal is transmitted to the secondary analysis processing module. The analysis method of the correlation signal is as follows: Take any scenic area i as the analysis object, then obtain the complaint data corresponding to the analysis object, and classify the complaint data according to the complaint type to obtain the complaint type denoted as a, where a=1, 2, ..., b, and b represents the type of complaint type; Obtain the number corresponding to complaint type a and record it as La. Then obtain the change of the number La corresponding to complaint type a within the time period t, and select the complaint types corresponding to the increase in the number La as the types to be analyzed. Then process the obtained types to be analyzed. Specifically, the case where the number of complaint types decreases will not be processed. The type to be analyzed is labeled as a1, and a1=1, 2, ..., b1, and b1 represents the type of the type to be analyzed. Then, the increase ratio of the type to be analyzed in the time period t is calculated, and the increase ratio is compared with the preset value. When the increase ratio is greater than the preset value, specific cause information is generated. On the contrary, when the increase ratio is less than the preset value, a secondary analysis signal is generated. The analysis of non-correlated signals is: Take any scenic area i as the analysis object, then obtain the complaint data corresponding to the analysis object, and classify the complaint data according to the complaint type to obtain the complaint type denoted as a, where a=1, 2, ..., b, and b represents the type of complaint type; The complaint type corresponding to the largest number La is obtained and recorded as the target type. At the same time, the historical data corresponding to the target type is obtained, and the change in the number of the target type is judged in combination with the historical data. If the change in the number of the target type corresponding to the historical data is an increase, an increase signal is generated. If the change in the number of the target type corresponding to the historical data is a decrease, a decrease signal is generated, and the generated increase signal and decrease signal are processed at the same time; The specific method for processing the increased signal and the decreased signal is as follows: Analyze the generated enlargement signal, take time t1 as the segmentation node, obtain the increase number of the target type in the same segmentation time period, and establish the corresponding relationship diagram. According to the relationship diagram, the change trend of the increase number is judged. If the increase number is stable, the specific reason information is generated based on the target type and transmitted to the analysis information output module. If the increase number is unstable, a secondary analysis signal is generated. Analyze the generated decreasing signal to obtain the change of passenger flow data within the analysis object time period t. If the passenger flow data is increasing, generate specific reason information; if the passenger flow data is decreasing, generate a secondary analysis signal; The secondary analysis processing module is used to process the acquired secondary analysis signal, and generate secondary analysis information by combining the regional passenger flow data and complaint data within the analysis object for comprehensive analysis, and the secondary analysis information includes specific cause information and normal monitoring signals, and the specific method of processing the secondary analysis signal is: Obtain the regional passenger flow data of the analysis object, and determine the change of the regional passenger flow data. If the regional passenger flow data is an increase, generate specific reason information, and transmit the specific reason information to the analysis information output module. If the regional passenger flow data is a decrease, obtain the increase change amount of the target type within the time period t, and calculate the corresponding increase change ratio; Then, the calculated increase change ratio value is compared with the preset value. When the increase change ratio value is greater than the preset value, an early warning signal is generated, and specific cause information is generated with the corresponding target type. Conversely, when the increase ratio value is less than the preset value, a normal monitoring signal is generated. Analysis information output module, which is used to display the acquired specific cause information and normal monitoring signals to the corresponding operators.

2. The artificial intelligence-based smart scenic spot data analysis system according to claim 1 is characterized in that: The specific method of generating relationship analysis information by the scenic spot information analysis module is: Different areas of the scenic area are labeled as i, and i=1, 2, ..., j, where j represents the number of scenic area areas. At the same time, the change of passenger flow data corresponding to the scenic area i within time t is obtained, and change information is generated, where the change information includes: increase change and decrease change. Then, the complaint data is analyzed according to the change information of passenger flow data; The corresponding complaint data changes are obtained when the passenger flow data is increasing. If the corresponding complaint data changes are increasing, a correlation signal is generated. Otherwise, if the corresponding complaint data changes are not increasing, a non-correlation signal is generated.

3. A method for analyzing data of a smart scenic spot based on artificial intelligence, used to execute a system for analyzing data of a smart scenic spot based on artificial intelligence as claimed in any one of claims 1 to 2, characterized in that: The method specifically comprises the following steps: Step 1: Collect passenger flow data and complaint data of smart scenic spots; Step 2: Perform correlation analysis on the collected passenger flow data and complaint data, and generate correlation signals and non-correlation signals; Step 3: Analyze the correlation signal by analyzing the increase and change of the complaint type within the time period, and calculate the increase ratio and compare them to generate specific cause information and secondary analysis signals; Step 4: Analyze the non-correlated signals, analyze the complaint types in combination with historical data, and establish a corresponding relationship diagram to comprehensively judge and generate specific cause information and secondary analysis signals; Step 5: Process the obtained secondary analysis signal, and generate specific cause information and normal monitoring signals based on the passenger flow data and complaint data of the scenic area.

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