Tourist attraction guiding system comprehensive evaluation method based on big data

Abstract

The invention relates to the field of tourism, and particularly discloses a tourist attraction guiding system comprehensive evaluation method based on big data, comprising the following steps: step 1, collecting various data of a tourist attraction guiding system; the data is preprocessed; calculating an overall quality evaluation value Q after data preprocessing; step 2, on the basis of the step 1, further collecting operation data of the guiding system in real time; according to the method, the accuracy of data processing and the objectivity of system performance evaluation are ensured in a scientific quantification mode by calculating the overall quality evaluation value after data preprocessing and the performance evaluation value of real-time data analysis. According to the method, the deviation of subjective judgment is avoided, and the accuracy and credibility of evaluation are improved; the method not only pays attention to processing and quality evaluation of static data, but also realizes real-time evaluation and dynamic monitoring of system performance by collecting operation data of the guiding system in real time and performing rapid analysis by using a deep learning algorithm.

Description

Technical Field

[0001] The invention belongs to the field of tourism, and specifically is a comprehensive evaluation method for a tourist attraction guidance system based on big data. Background Art

[0002] With the rapid development of tourism, tourist attraction guidance systems are an important tool for tourists to obtain navigation, guidance and information services in scenic spots. Their performance and quality have a significant impact on tourists' experience and satisfaction. However, traditional evaluation methods for tourist attraction guidance systems often rely on subjective means such as manual surveys and questionnaires, which have problems such as long evaluation cycles and results that are greatly affected by human factors.

[0003] Traditional technologies often rely on subjective means such as manual surveys, questionnaires or expert scoring when evaluating tourist attraction guidance systems. These methods are easily affected by personal experience, preferences or emotions, resulting in biased evaluation results. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a comprehensive evaluation method for a tourist attraction guidance system based on big data, so as to solve the problem that in the prior art, when evaluating the tourist attraction guidance system, the evaluation method often relies on subjective means such as manual investigation, questionnaire survey or expert scoring. These methods are easily affected by personal experience, preference or emotion, resulting in deviations in the evaluation results.

[0005] A comprehensive evaluation method for a tourist attraction guidance system based on big data comprises the following steps:

[0006] Step 1: Collect various data of tourist attraction guidance system;

[0007] Preprocessing the data;

[0008] Calculate the overall quality assessment value Q after data preprocessing;

[0009] Step 2: Based on step 1, further collect the operation data of the guidance system in real time;

[0010] Use deep learning algorithms to quickly analyze operational data;

[0011] Calculate the performance evaluation value P of real-time data analysis;

[0012] Step 3: Set evaluation indicators according to the characteristics and evaluation requirements of the tourist attraction guidance system;

[0013] Using the analytic hierarchy process, weights are assigned to the indicators;

[0014] Calculate the total score S of the comprehensive evaluation index;

[0015] Step 4: Based on the results of steps 1, 2, and 3, analyze the overall performance of the tourist attraction guidance system, identify the strengths and weaknesses of the system, and put forward corresponding improvement suggestions.

[0016] Preferably, in step one, the various types of data of the tourist attraction guidance system include tourist flow, tourist behavior path, frequency of use of guidance signs, and tourist feedback and evaluation.

[0017] Preferably, in step one, preprocessing the data specifically includes data cleaning, data integration and formatting.

[0018] Preferably, in step 1, the calculation formula of the overall quality evaluation value Q is as follows:

[0019]

[0020] Among them, Q is the overall quality assessment value after data preprocessing;

[0021] N is the total number of data items;

[0022] E i (t) is the preprocessed value of the i-th data at time t;

[0023] D i (t) is the original value of the i-th data at time t;

[0024] T is the time frame for data collection;

[0025] λ is the adjustment coefficient of quality assessment, which is used to adjust the impact of variance on quality assessment;

[0026] Var(E i ) is the variance of the i-th data after preprocessing;

[0027] The value range is [0,1]. The closer the Q value is to 1, the higher the quality of the data after preprocessing.

[0028] Preferably, in step 2, the operating data of the guidance system includes current tourist flow, real-time location of tourists and real-time usage status of guidance signs.

[0029] Preferably, in step 2, the calculation formula of the performance evaluation value P is as follows:

[0030]

[0031] in:

[0032] P is the performance evaluation value of real-time data analysis;

[0033] M is the total number of real-time data items;

[0034] wj is the weight of the jth real-time data;

[0035] A j (t) is the analysis result of the j-th real-time data at time t;

[0036] R j (t) is the actual value of the j-th real-time data at time t;

[0037] t c is the current time;

[0038] μ is the adjustment coefficient of performance evaluation, which is used to adjust the impact of mean square error on performance evaluation;

[0039] MSE(A j ,R j ) is the mean square error between the analysis result and the actual value of the j-th real-time data;

[0040] The value range is [0,1]. The closer the P value is to 1, the better the performance of real-time data analysis.

[0041] Preferably, in step three, the evaluation indicators include accuracy, clarity, readability, update frequency, coverage and personalized recommendation effect of the guidance information.

[0042] Preferably, in step three, the calculation formula of the total score S of the comprehensive evaluation index is as follows:

[0043]

[0044] in:

[0045] S is the total score of the comprehensive evaluation index;

[0046] L is the total number of evaluation indicators;

[0047] w k is the weight of the kth evaluation index;

[0048] I k is the quantitative value of the kth evaluation index;

[0049] min(I k ) and max(I k ) are the minimum and maximum values ​​of the quantized values ​​of the kth evaluation index respectively;

[0050] T k is the timeliness evaluation value of the kth evaluation indicator;

[0051] v and τ are the adjustment coefficients of the timeliness effect;

[0052] The value range is [0, +∞), but according to the actual situation, the score S will be limited to a reasonable range to facilitate intuitive comparison and analysis. The higher the S value, the better the performance of the comprehensive evaluation index.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] By calculating the overall quality evaluation value after data preprocessing and the performance evaluation value of real-time data analysis, the accuracy of data processing and the objectivity of system performance evaluation are ensured in a scientific and quantitative way. This method avoids the bias of subjective judgment and improves the accuracy and credibility of the evaluation;

[0055] The present invention not only focuses on the processing and quality assessment of static data, but also realizes real-time evaluation and dynamic monitoring of system performance by collecting the operating data of the guidance system in real time and using deep learning algorithms for rapid analysis, which helps to promptly discover and solve problems in the system and improve the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a system schematic diagram of the present invention. DETAILED DESCRIPTION

[0057] 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.

[0058] like Figure 1 As shown:

[0059] Embodiment 1: The present invention provides a comprehensive evaluation method for a tourist attraction guidance system based on big data, comprising the following steps:

[0060] Step 1: Collect various data of tourist attraction guidance system;

[0061] Preprocessing the data;

[0062] Calculate the overall quality assessment value Q after data preprocessing;

[0063] Step 2: Based on step 1, further collect the operation data of the guidance system in real time;

[0064] Use deep learning algorithms to quickly analyze operational data;

[0065] Calculate the performance evaluation value P of real-time data analysis;

[0066] Step 3: Set evaluation indicators according to the characteristics and evaluation requirements of the tourist attraction guidance system;

[0067] Using the analytic hierarchy process, weights are assigned to the indicators;

[0068] Calculate the total score S of the comprehensive evaluation index;

[0069] Step 4: Based on the results of steps 1, 2, and 3, analyze the overall performance of the tourist attraction guidance system, identify the strengths and weaknesses of the system, and put forward corresponding improvement suggestions.

[0070] Specifically, in step one, the various types of data of the tourist attraction guidance system include tourist flow, tourist behavior path, frequency of use of guidance signs, and tourist feedback and evaluation.

[0071] Specifically, in step one, the data is preprocessed including data cleaning, data integration and formatting.

[0072] Specifically, in step 1, the calculation formula of the overall quality evaluation value Q is as follows:

[0073]

[0074] Among them, Q is the overall quality assessment value after data preprocessing;

[0075] N is the total number of data items;

[0076] E i (t) is the preprocessed value of the i-th data at time t;

[0077] D i (t) is the original value of the i-th data at time t;

[0078] T is the time frame for data collection;

[0079] λ is the adjustment coefficient of quality assessment, which is used to adjust the impact of variance on quality assessment;

[0080] Var(E i ) is the variance of the i-th data after preprocessing;

[0081] The value range is [0,1]. The closer the Q value is to 1, the higher the quality of the data after preprocessing.

[0082] The specific application process of the above formula is as follows:

[0083] 1. Data Preparation

[0084] Data Collection:

[0085] Collect various types of data from tourist attraction guidance systems, including tourist flow, tourist behavior paths, frequency of use of guide signs, and tourist feedback and evaluation.

[0086] Ensure that the data collected is complete and current to allow for accurate quality assessment.

[0087] Data preprocessing:

[0088] Clean the collected data to remove duplicate, missing or abnormal data.

[0089] The data were integrated and formatted to make them suitable for subsequent analysis.

[0090] 2. Formula Application

[0091] Determine the parameters:

[0092] Determine the total number of data items N, that is, the number of data items that need to be evaluated.

[0093] Determine the time frame T for data collection, ensuring that data from all relevant time points are considered in the evaluation.

[0094] Determine the adjustment coefficient λ of quality assessment and adjust it according to the specific application scenario and data characteristics.

[0095] Calculate the difference between the preprocessed value and the original value:

[0096] For each data item i, calculate its preprocessed value E at time t i (t) and the original value D i (t) The difference between

[0097] Use integration to calculate the sum of the differences over the entire time range T and divide by the sum of the original values ​​to get the relative difference.

[0098] Calculate the variance:

[0099] For each data item i, calculate its variance Var(E i ).

[0100] Variance reflects the degree of discreteness after data preprocessing and is one of the important indicators for evaluating data quality.

[0101] The quality assessment value is calculated using the formula:

[0102] Substitute the above calculation results into the formula to calculate the overall quality assessment value Q after data preprocessing.

[0103] The closer the Q value is to 1, the higher the quality of the data after preprocessing.

[0104] As can be seen from the above, this method comprehensively evaluates the system performance through multiple steps; first, various data of the guidance system are collected and preprocessed, including tourist flow, behavioral paths, frequency of use of guidance signs, and tourist feedback, to ensure the integrity and timeliness of the data; then, the deep learning algorithm is used to analyze the system operation data in real time, and the performance evaluation value of real-time data analysis is calculated; then, the evaluation indicators are set according to the system characteristics and evaluation requirements, and the hierarchical analysis method is used for weight allocation to calculate the total score of the comprehensive evaluation indicators; finally, the overall performance of the system is analyzed by combining the data preprocessing quality evaluation value, the real-time data analysis performance evaluation value and the comprehensive evaluation indicator score, to identify the advantages and disadvantages and put forward improvement suggestions; throughout the process, special attention is paid to the quality evaluation of data preprocessing. By accurately calculating the difference and variance between the preprocessed data and the original data, the quality evaluation value is obtained using a specific formula, and the accuracy of data processing and the optimization of system performance are ensured in a scientific and quantitative way.

[0105] Embodiment 2: This embodiment is basically the same as the previous embodiment, except that, in step 2, the operating data of the guidance system includes the current tourist flow, the real-time location of the tourists and the real-time usage status of the guidance signs.

[0106] Specifically, in step 2, the calculation formula of the performance evaluation value P is as follows:

[0107]

[0108] in:

[0109] P is the performance evaluation value of real-time data analysis;

[0110] M is the total number of real-time data items;

[0111] wj is the weight of the jth real-time data;

[0112] A j (t) is the analysis result of the j-th real-time data at time t;

[0113] R j (t) is the actual value of the j-th real-time data at time t;

[0114] t c is the current time;

[0115] μ is the adjustment coefficient of performance evaluation, which is used to adjust the impact of mean square error on performance evaluation;

[0116] MSE(A j ,R j ) is the mean square error between the analysis result and the actual value of the j-th real-time data;

[0117] The value range is [0,1]. The closer the P value is to 1, the better the performance of real-time data analysis.

[0118] The specific application process of the above formula is as follows:

[0119] 1. Preparation

[0120] Clarify the assessment objectives:

[0121] Determine the real-time data items that need to be evaluated, such as tourist flow, tourist behavior paths, and the usage status of guide signs.

[0122] Specify the time range of the evaluation, that is, determine the current time t c and the historical time period that needs to be evaluated.

[0123] Collect data:

[0124] The analysis results of the target data items collected in real time from the tourist attraction guidance system A j (t) and the actual value R j (t).

[0125] Ensure data accuracy and completeness for subsequent performance evaluation.

[0126] Determine the weights:

[0127] According to the importance of the data item, a weight w is assigned to each real-time data item. j .

[0128] The weights should reflect how much the data item contributes to the overall performance evaluation.

[0129] To set the adjustment factor:

[0130] According to the evaluation requirements and data characteristics, the adjustment coefficient μ of the performance evaluation is set.

[0131] The adjustment coefficient is used to adjust the impact of the mean square error (MSE) on the performance evaluation results.

[0132] 2. Calculation Phase

[0133] Calculate the mean square error MSE:

[0134] For each real-time data j, calculate its analysis result A j (t) and the actual value R j (t) between the mean square error MSE (A j ,R j ).

[0135] The mean square error reflects the degree of difference between the analysis result and the actual value, and is one of the important indicators for evaluating performance.

[0136] Calculate the relative error:

[0137] Using the integral part of the formula, calculate the time range of each real-time data [0,t c ] within the relative error.

[0138] The relative error reflects the degree of deviation between the analysis result and the actual value.

[0139] The performance evaluation value P is calculated using the formula:

[0140] Substitute the above calculation results into the formula to calculate the performance evaluation value P of real-time data analysis.

[0141] As can be seen from the above, this embodiment pays special attention to the operating data of the guidance system, including the current tourist flow, the real-time location of tourists and the real-time usage status of the guidance signs; in the evaluation process, firstly, the evaluation target and time range are clarified, the analysis results and actual values ​​of the target data items are collected in real time, and the accuracy and completeness of the data are ensured; then, weights are assigned according to the importance of the data items, and the adjustment coefficient of the performance evaluation is set to adjust the influence of the mean square error on the evaluation results; in the calculation stage, the mean square error and relative error of each real-time data are calculated, and these indicators reflect the difference and degree of deviation between the analysis results and the actual values; finally, the performance evaluation value of the real-time data analysis is calculated using a specific formula, and the closer the value is to 1, the better the performance; through precise calculation and analysis, this method can comprehensively and objectively evaluate the performance of the real-time data analysis of the tourist attraction guidance system, and provide strong support for system optimization and improvement.

[0142] Embodiment 3: This embodiment is basically the same as the previous embodiment, except that, in step 3, the evaluation indicators include the accuracy, clarity, readability, update frequency, coverage and personalized recommendation effect of the guidance information.

[0143] Specifically, in step 3, the calculation formula of the total score S of the comprehensive evaluation index is as follows:

[0144]

[0145] in:

[0146] S is the total score of the comprehensive evaluation index;

[0147] L is the total number of evaluation indicators;

[0148] w k is the weight of the kth evaluation index;

[0149] I k is the quantitative value of the kth evaluation index;

[0150] min(I k ) and max(Ik ) are the minimum and maximum values ​​of the quantized values ​​of the kth evaluation index respectively;

[0151] T k is the timeliness evaluation value of the kth evaluation indicator;

[0152] v and τ are the adjustment coefficients of the timeliness effect;

[0153] The value range is [0, +∞), but according to the actual situation, the score S will be limited to a reasonable range to facilitate intuitive comparison and analysis. The higher the S value, the better the performance of the comprehensive evaluation index.

[0154] The specific application process of the above formula is as follows:

[0155] 1. Preparation

[0156] Determine the evaluation indicators:

[0157] Determine specific evaluation indicators based on the characteristics and evaluation needs of the tourist attraction guidance system.

[0158] Including the accuracy, clarity, readability, update frequency, coverage, and personalized recommendation effects of the guiding information.

[0159] Collect data:

[0160] For each evaluation indicator, collect relevant data and information.

[0161] Data sources include system logs, user feedback, third-party evaluation reports, etc.

[0162] Data preprocessing:

[0163] Clean, organize and analyze the collected data to ensure its accuracy and reliability.

[0164] According to the characteristics of the evaluation index, the data is quantified to obtain the quantitative value I of each evaluation index. k .

[0165] Determine the weights:

[0166] The weights of evaluation indicators are assigned using hierarchical analysis method, expert survey method or machine learning method.

[0167] Ensure the rationality and scientificity of the weights and reflect the importance of each evaluation indicator in the comprehensive evaluation.

[0168] Set the timeliness evaluation value:

[0169] According to the timeliness characteristics of the evaluation indicators, a timeliness evaluation value T is set for each evaluation indicator. k .

[0170] T k It can reflect the effectiveness or influence of the evaluation indicators at the current time point.

[0171] To set the adjustment factor:

[0172] According to the evaluation requirements and data characteristics, the adjustment coefficients v and τ of timeliness are set.

[0173] These coefficients are used to adjust the degree of influence of the timeliness evaluation value on the total score of the comprehensive evaluation index.

[0174] 2. Calculation Phase

[0175] Calculate the quantized value range:

[0176] For each evaluation index k, calculate its quantitative value I k The minimum value min(I k ) and the maximum value max(I k ).

[0177] These values ​​are used for normalization to ensure comparability between different evaluation indicators.

[0178] Normalization:

[0179] Using the normalization part in the formula, the quantitative value I of each evaluation index is k Convert to normalized values.

[0180] The normalized value reflects the relative performance of the evaluation indicator under the current quantitative value.

[0181] Calculate the timeliness impact:

[0182] The timeliness impact part in the formula is used to calculate the timeliness impact value of each evaluation indicator.

[0183] This value reflects the degree to which the effectiveness or influence of the evaluation indicator at the current time point affects the total score of the comprehensive evaluation indicator.

[0184] Weighted sum:

[0185] Multiply the normalized value and timeliness impact value of each evaluation indicator by the corresponding weight w k , and perform weighted summation.

[0186] The total score S of the comprehensive evaluation index is obtained.

[0187] As can be seen from the above, this method first determines the evaluation indicators including the accuracy, clarity, readability, update frequency, coverage and personalized recommendation effect of the guidance information according to the characteristics of the system; then, relevant data is collected through various channels such as system logs, user feedback and third-party evaluation reports, and pre-processed and quantified; in terms of weight allocation, scientific methods such as hierarchical analysis method, expert survey method or machine learning method are used to ensure the rationality and scientificity of the weights; at the same time, the timeliness evaluation value is set according to the timeliness characteristics of the evaluation indicators, and the influence of timeliness on the comprehensive score is adjusted by the adjustment coefficient; finally, the total score of the comprehensive evaluation index is obtained through the steps of calculating the quantitative value range, normalization, timeliness influence and weighted summation, so as to carry out intuitive comparison and analysis; this method provides a comprehensive and objective basis for the performance evaluation of the tourist attraction guidance system.

[0188] The standard parts used in the present invention can all be purchased from the market, and the special-shaped parts can be customized according to the description and the drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. The machinery, parts and equipment all adopt conventional models in the prior art, and the circuit connection adopts the conventional connection method in the prior art, which will not be described in detail here. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0189] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0190] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0191] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0192] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0193] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0194] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for tourist attraction guidance system based on big data, characterized in that: The following steps are involved: Step 1: Collect various data of tourist attraction guidance system; Preprocessing the data; Calculate the overall quality assessment value Q after data preprocessing; Step 2: Based on step 1, further collect the operation data of the guidance system in real time; Use deep learning algorithms to quickly analyze operational data; Calculate the performance evaluation value P of real-time data analysis; Step 3: Set evaluation indicators according to the characteristics and evaluation requirements of the tourist attraction guidance system; Using the analytic hierarchy process, weights are assigned to the indicators; Calculate the total score S of the comprehensive evaluation index; Step 4: Based on the results of steps 1, 2, and 3, analyze the overall performance of the tourist attraction guidance system, identify the strengths and weaknesses of the system, and put forward corresponding improvement suggestions.

2. A comprehensive evaluation method for tourist attraction guidance system based on big data as claimed in claim 1, characterized in that: In step one, various data of the tourist attraction guidance system include tourist flow, tourist behavior path, frequency of use of guidance signs, and tourist feedback and evaluation.

3. A comprehensive evaluation method for tourist attraction guidance system based on big data as claimed in claim 2, characterized in that: In step one, the data is preprocessed, including data cleaning, data integration and formatting.

4. A comprehensive evaluation method for tourist attraction guidance system based on big data as claimed in claim 3, characterized in that: In step 1, the calculation formula of the overall quality evaluation value Q is as follows: Among them, Q is the overall quality assessment value after data preprocessing; N is the total number of data items; E i (t) is the preprocessed value of the i-th data at time t; D i (t) is the original value of the i-th data at time t; T is the time frame for data collection; λ is the adjustment coefficient of quality assessment, which is used to adjust the impact of variance on quality assessment; Var(E i ) is the variance of the i-th data after preprocessing; The value range is [0,1]. The closer the Q value is to 1, the higher the quality of the data after preprocessing.

5. A comprehensive evaluation method for tourist attraction guidance system based on big data as claimed in claim 1, characterized in that: In step 2, the operating data of the guidance system includes the current tourist flow, the real-time location of tourists and the real-time usage status of the guidance signs.

6. A comprehensive evaluation method for tourist attraction guidance system based on big data as claimed in claim 5, characterized in that: In step 2, the calculation formula of the performance evaluation value P is as follows: in: P is the performance evaluation value of real-time data analysis; M is the total number of real-time data items; wj is the weight of the jth real-time data; A j (t) is the analysis result of the j-th real-time data at time t; R j (t) is the actual value of the j-th real-time data at time t; t c is the current time; μ is the adjustment coefficient of performance evaluation, which is used to adjust the impact of mean square error on performance evaluation; MSE(A j ,R j ) is the mean square error between the analysis result and the actual value of the j-th real-time data; The value range is [0,1]. The closer the P value is to 1, the better the performance of real-time data analysis.

7. A comprehensive evaluation method for tourist attraction guidance system based on big data as claimed in claim 1, characterized in that: In step three, the evaluation indicators include the accuracy, clarity, readability, update frequency, coverage and personalized recommendation effect of the guidance information.

8. A comprehensive evaluation method for tourist attraction guidance system based on big data as claimed in claim 7, characterized in that: In step 3, the calculation formula of the total score S of the comprehensive evaluation index is as follows: in: S is the total score of the comprehensive evaluation index; L is the total number of evaluation indicators; w k is the weight of the kth evaluation index; I k is the quantitative value of the kth evaluation index; min(I k ) and max(I k ) are the minimum and maximum values ​​of the quantized values ​​of the kth evaluation index respectively; T k is the timeliness evaluation value of the kth evaluation indicator; v and τ are the adjustment coefficients of the timeliness effect; The value range is [0, +∞), but according to the actual situation, the score S will be limited to a reasonable range to facilitate intuitive comparison and analysis. The higher the S value, the better the performance of the comprehensive evaluation index.

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