A regional intangible cultural heritage management method based on big data

Abstract

The present invention relates to the technical field of intangible cultural heritage publicity and management, and in particular to a regional intangible cultural heritage management method based on big data, comprising: step S1, collecting intangible cultural heritage information in the management area, and periodically collecting publicity information and browsing information; step S2, storing intangible cultural heritage information, publicity information and browsing information, and constructing management parameters; step S3, classifying the collected publicity information to obtain information categories; step S4, analyzing activity frequency parameters and normal characteristics; step S5, analyzing the remaining time of holding, and analyzing publicity characteristics according to the remaining time of holding, publicity information, information category and browsing information; step S6, analyzing browsing characteristics; step S7, analyzing the popularity of intangible cultural heritage; step S8, analyzing and outputting publicity suggestions. The method realizes accurate management of intangible cultural heritage publicity.

Description

Technical Field

[0001] The present invention relates to the technical field of intangible cultural heritage publicity and management, and in particular to a regional intangible cultural heritage management method based on big data. Background Art

[0002] The publicity and management of intangible cultural heritage mainly relies on modern digital technology and new media platforms. Through official websites, social media, digital platforms and other channels, the knowledge and value of intangible cultural heritage can be efficiently disseminated, the public's awareness and participation can be enhanced, and the protection and inheritance of intangible cultural heritage can be promoted.

[0003] China Patent Publication No.: CN116596473A discloses a regional intangible cultural heritage collaborative management method and system based on big data, including, relying on the intangible cultural heritage industry standard specification, customizing and developing a data acquisition module to collect and process intangible cultural heritage data elements and data attributes, collecting intangible cultural heritage business management data and various types of intangible cultural heritage electronic data within the specified area, and processing the collected data according to the needs of the regional intangible cultural heritage management business line. The invention realizes the classification and processing of intangible cultural heritage resources in the region, but does not realize the publicity, analysis and management of intangible cultural heritage activities. There are problems such as low efficiency in intangible cultural heritage data analysis and inaccurate publicity and management of intangible cultural heritage. Summary of the invention

[0004] The purpose of the present invention is to provide a regional intangible cultural heritage management method based on big data to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A regional intangible cultural heritage management method based on big data, comprising:

[0007] Step S1, collecting intangible cultural heritage information within the management area, and periodically collecting publicity information and browsing information;

[0008] Step S2, storing the intangible cultural heritage information, publicity information and browsing information, and constructing management parameters according to the stored intangible cultural heritage information, publicity information and browsing information;

[0009] Step S3, classifying the collected publicity information according to the intangible cultural heritage information to obtain information categories;

[0010] Step S4, analyzing the activity frequency parameters and normality characteristics according to the stored intangible cultural heritage information, publicity information, management parameters and information categories;

[0011] Step S5, analyzing the remaining duration of the event according to the intangible cultural heritage information, and analyzing the publicity features according to the remaining duration of the event, publicity information, information category and browsing information;

[0012] Step S6, analyzing browsing characteristics according to the promotional information, information category and browsing information;

[0013] Step S7, analyzing the popularity of intangible cultural heritage according to browsing characteristics, publicity characteristics, normal characteristics and activity frequency parameters;

[0014] Step S8, analyzing and outputting the publicity suggestions according to the popularity of the intangible cultural heritage.

[0015] Furthermore, the step S3 matches the article subject and article content with the name of the intangible cultural heritage, and determines the information category of the promotional information according to the matching result, and the information category includes one category, two categories and three categories.

[0016] Furthermore, the step S4 comprises:

[0017] Step S401, analyzing the activity frequency parameters according to the stored event dates;

[0018] Step S402, extracting the stored promotional information according to the activity frequency parameter and the information category to obtain normal promotional information;

[0019] Step S403, analyzing the normal characteristics according to the normal promotion information and activity frequency parameters.

[0020] Furthermore, the step S401 counts the number of dates on which the same intangible cultural heritage information is held within a year as an activity frequency parameter P(i);

[0021] The step S402 extracts the stored promotional information according to the extraction parameters L1(i) and L2(i), extracting the promotional information of the information category of the first and second categories, and the promotional information whose release time is between L1(i) and L2(i) days before the date of the intangible cultural heritage information as the normal promotional information;

[0022] The step S403 counts the number of normal publicity information as the normal publicity number NL1(i), counts the number of publicity information that successfully matches the intangible cultural heritage information with the publicity information within a year as the information publicity number NL2(i), and analyzes the normal characteristics based on the normal publicity number and the publicity number to obtain the set normal characteristics A(i).

[0023] Furthermore, the step S5 comprises:

[0024] Step S501: Obtain the current analysis time, where the current analysis time is the time when analyzing the publicity features.

[0025] Step S502: Analyze the remaining duration of the event based on the current analysis time and the event holding date.

[0026] Step S503: Analyze the publicity features based on the remaining duration of the event, publicity information, information category, and browsing information.

[0027] Further, in step S502, the time interval between the current analysis time and the next event holding date in the intangible cultural heritage information in chronological order is used as the remaining duration of the event T(i).

[0028] In step S503, the number of publicity information that matches the intangible cultural heritage information within the time period of t1≤T(i)≤t2 is counted as the nearby publicity quantity NL3(i), and the publicity features are analyzed based on the nearby publicity quantity, information publicity quantity, and browsing information to obtain the publicity feature B(i), where t1 represents the first statistical parameter and t2 represents the second statistical parameter.

[0029] Further, in step S6, the browsing features are analyzed based on the publicity and browsing information of information categories one and two to obtain the browsing feature C(i,j).

[0030] Further, step S7 includes:

[0031] Step S701: Analyze the popularity of the intangible cultural heritage based on the publicity features and the event frequency parameter.

[0032] Step S702: Adjust the analysis process of the popularity of the intangible cultural heritage based on the browsing features.

[0033] Step S703: Optimize the adjustment process of the popularity of the intangible cultural heritage based on the normal features.

[0034] Further, in step S701, the popularity of the intangible cultural heritage is analyzed based on the publicity features and the event frequency parameter to obtain the popularity of the intangible cultural heritage as F(i).

[0035] In step S702, the browsing feature C(i) is compared with the browsing comparison threshold c, and the analysis process of the popularity of the intangible cultural heritage is adjusted according to the comparison result. When the browsing feature does not meet the threshold, the analysis process of the popularity of the intangible cultural heritage is adjusted, and the adjusted popularity of the intangible cultural heritage is F1(i).

[0036] The step S703 compares the normal feature A(i) with the normal comparison threshold a, and optimizes the adjustment process of the intangible cultural heritage heat according to the comparison result. When the normal feature does not meet the threshold, the adjustment process of the intangible cultural heritage heat is optimized, and the optimized intangible cultural heritage heat is F2(i).

[0037] Furthermore, the step S8 compares the intangible cultural heritage heat F(i) with the heat thresholds f1 and f2, and analyzes the publicity suggestions according to the comparison results to obtain publicity suggestions, which include a first suggestion, a second suggestion and a third suggestion.

[0038] The beneficial effects of the present invention are as follows: by collecting intangible cultural heritage information, publicity information and browsing information within the management area, and analyzing the collected information, it is possible to analyze the popularity of intangible cultural heritage publicity based on the characteristic relationship between publicity data, user browsing data and data during the activity period within the comprehensive management area, thereby analyzing publicity suggestions, realizing the management of intangible cultural heritage publicity, and then improving the efficiency of analyzing intangible cultural heritage publicity data and improving the accuracy of publicity management. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a structural diagram of the regional intangible cultural heritage management method based on big data in this embodiment.

[0041] Figure 2 Flow chart of the activity frequency parameter and normal characteristic analysis method of this embodiment.

[0042] Figure 3 This is a flow chart of the feature analysis method for this embodiment.

[0043] Figure 4 Flow chart of the intangible cultural heritage heat analysis method of this embodiment. DETAILED DESCRIPTION

[0044] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0045] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0046] See also Figure 1 As shown, it is a regional intangible cultural heritage management method based on big data in this embodiment, including:

[0047] Step S1, collect intangible cultural heritage information within the management area, and periodically collect publicity information and browsing information, the intangible cultural heritage information includes the name of the intangible cultural heritage, the type of intangible cultural heritage and the date of holding, the type of intangible cultural heritage includes but is not limited to traditional festivals and traditional art performances and other intangible cultural heritage performing arts or social practices, rituals, and festival activities, the publicity information includes article topics, article content and release time, the browsing information is the number of user interactions, the number of user interactions is the sum of user views, likes and comments, the browsing information is data within a collection period, the publicity information is article information published by the media or institutions, the intangible cultural heritage information is obtained by user interactive input, and the publicity information and browsing information are obtained by importing data from the publicity work management platform of the media or institutions.

[0048] Specifically, this embodiment is applied to the intangible cultural heritage publicity management system in the management area, and by acquiring and analyzing the intangible cultural heritage publicity data and user browsing data in the management area, the analysis of the intangible cultural heritage publicity heat is realized to ensure the smooth operation of the intangible cultural heritage publicity work. The management area includes but is not limited to towns and provinces.

[0049] Please continue reading Figure 1 As shown, the regional intangible cultural heritage management method based on big data also includes:

[0050] Step S2, storing the intangible cultural heritage information, publicity information and browsing information, and constructing management parameters according to the stored intangible cultural heritage information, publicity information and browsing information, wherein the management parameters include the intangible cultural heritage number, article number and date number.

[0051] Specifically, step S2 in this embodiment sequentially numbers the intangible cultural heritage information in the order of collection time to obtain the intangible cultural heritage number, and sets the intangible cultural heritage number to i, i∈N + , number the promotional information in the order of collection time to get the article number, and set the article number to j, j∈N+ , number the browsing information in the order of the collection period to obtain the date number d, d∈N + .

[0052] Specifically, in this embodiment, the intangible cultural heritage information, publicity information and browsing information are stored through step S2 to construct management parameters to distinguish the collected data and ensure that the data is complete and unified when the collected data is subsequently analyzed, thereby improving the analysis efficiency of the intangible cultural heritage publicity data and the accuracy of publicity management.

[0053] Please continue reading Figure 1 As shown, the regional intangible cultural heritage management method based on big data also includes:

[0054] Step S3, classifying the collected publicity information according to the intangible cultural heritage information to obtain information categories.

[0055] Specifically, step S3 described in this embodiment matches the article subject and article content with the name of the intangible cultural heritage, and determines the information category of the promotional information based on the matching result. If both the article subject and the article content contain words that are the same as the name of the intangible cultural heritage, the information category of the currently analyzed article information is set to category one. If neither the article subject nor the article content contains words that are the same as the name of the intangible cultural heritage, the information category of the currently analyzed article information is set to category three. Otherwise, the information category of the currently analyzed article information is set to category two.

[0056] Specifically, in this embodiment, the promotional information is classified through step S3 to divide the promotional information into three categories according to the degree of relevance to the intangible cultural heritage information, so as to extract the promotional information about the intangible cultural heritage, thereby improving the analysis efficiency of the intangible cultural heritage promotional data and improving the accuracy of promotional management.

[0057] Please continue reading Figure 1 As shown, the regional intangible cultural heritage management method based on big data also includes:

[0058] Step S4, analyzing the activity frequency parameters and normal characteristics based on the stored intangible cultural heritage information, publicity information, management parameters and information categories.

[0059] See also Figure 2 As shown, it is a flow chart of the activity frequency parameter and normal characteristic analysis method, including:

[0060] Step S401, analyzing the activity frequency parameters according to the stored event dates.

[0061] Specifically, step S401 described in this embodiment counts the number of dates on which the same intangible cultural heritage information is held in a year as an activity frequency parameter, and uses the activity frequency parameter to represent the number of intangible cultural heritage activities in each year, so as to determine the activity frequency of intangible cultural heritage, thereby improving the efficiency of analyzing intangible cultural heritage publicity data and improving the accuracy of publicity management.

[0062] Please continue reading Figure 2 As shown, it is a flow chart of the activity frequency parameter and normal characteristic analysis method, which also includes:

[0063] Step S402, extracting the stored publicity information according to the activity frequency parameter and the information category to obtain normal publicity information, where the normal publicity information represents the publicity information during the intangible cultural heritage activity and in a period of time that is relatively long from the intangible cultural heritage activity.

[0064] Specifically, step S402 in this embodiment analyzes the extraction parameters according to the activity frequency parameters, sets the extraction parameters to L1(i) and L2(i), and sets L1(i)={12 / [2×P(i)]-e -P(i)}×365 / P(i), L2(i)={12 / [2×P(i)]+e -R(i)}×365 / P(i), where P(i) represents the activity frequency parameter.

[0065] Specifically, step S402 in this embodiment extracts the stored promotional information according to the extraction parameters, and extracts promotional information of category one and category two, and the promotional information whose release time is between L1(i) and L2(i) days before the date of the intangible cultural heritage information is used as normal promotional information.

[0066] Specifically, in this embodiment, the activity frequency parameters are analyzed in step S402 to analyze the extraction parameters, so as to formulate parameters for extracting promotion information corresponding to the activity frequency of the intangible cultural heritage, thereby extracting normal promotion information, thereby improving the analysis efficiency of the intangible cultural heritage promotion data and the accuracy of promotion management.

[0067] Please continue reading Figure 2 As shown, it is a flow chart of the activity frequency parameter and normal characteristic analysis method, which also includes:

[0068] Step S403, analyzing the normal characteristics according to the normal promotion information and the activity frequency parameters, and using the normal characteristics to represent the characteristic relationship between the promotion data during the non-activity period of the intangible cultural heritage.

[0069] Specifically, step S403 described in this embodiment counts the number of normal publicity information as the normal publicity number, and sets the normal publicity number to NL1(i); counts the number of publicity information that successfully matches the intangible cultural heritage information and publicity information within a year as the information publicity number, and sets the information publicity number to NL2(i); and analyzes the normal characteristics based on the normal publicity number and the publicity number, sets the normal characteristics to A(i), and sets A(i)=NL1(i)×P(i) / NL2(i).

[0070] Specifically, the successful matching of the intangible cultural heritage information and the publicity information indicates the presence of the name of the intangible cultural heritage in the article subject and the article content, that is, the process of determining the information category of the publicity information as one or two.

[0071] Specifically, in this embodiment, the statistics of normal promotion information and promotion information are performed through step S403 to analyze the number of normal promotions and the number of information promotions, so as to realize the statistics of different promotion information within a year, thereby analyzing the normal characteristics, realizing the feature analysis between the extracted data and the promotion data in each year, thereby improving the analysis efficiency of the intangible cultural heritage promotion data and improving the accuracy of the promotion management.

[0072] Please continue reading Figure 1 As shown, the regional intangible cultural heritage management method based on big data also includes:

[0073] Step S5, analyzing the remaining duration of the event based on the intangible cultural heritage information, and analyzing the publicity features based on the remaining duration of the event, publicity information, information category and browsing information.

[0074] See also Figure 3 As shown, it is a flow chart of the publicity feature analysis method, including:

[0075] Step S501, obtaining the current analysis time, where the current analysis time is the time when the promotional features are analyzed.

[0076] Step S502: Analyze the remaining duration of the event according to the current analysis time and the event date, and use the remaining duration of the event to represent the time interval between the current time and the event date of the intangible cultural heritage event.

[0077] Specifically, step S502 in this embodiment takes the time interval between the current analysis time and the next holding date in the chronological order of the intangible cultural heritage information as the remaining holding time, and sets the remaining holding time to T(i), where the unit of the remaining holding time is day.

[0078] Please continue reading Figure 3As shown, it is a flow chart of the publicity feature analysis method, which also includes:

[0079] Step S503, analyzing the promotional features according to the remaining duration of the event, the promotional information, the information category and the browsing information.

[0080] Specifically, step S503 in this embodiment counts the number of promotional information that successfully matches the intangible cultural heritage information and the promotional information within the time period t1≤T(i)≤t2 as the adjacent promotion number, sets the adjacent promotion number as NL3(i), and analyzes the promotional features according to the adjacent promotion number, the information promotion number and the browsing information, sets the promotional features as B(i), and sets , where N(j,d) represents the number of user interactions, U(i) represents the set of article numbers of the publicity information that successfully matches the intangible cultural heritage information with the publicity information, t1 represents the first statistical parameter, 1≤t1≤3, t2 represents the second statistical parameter, 10≤t2≤15; through the analysis of the remaining duration, publicity information, information category and browsing information in step S503, the publicity characteristics are analyzed, and the publicity characteristics are used to represent the characteristic relationship between the publicity data and the browsing data during the temporary construction activities, thereby improving the analysis efficiency of the intangible cultural heritage publicity data and improving the accuracy of publicity management; it can be understood that in this embodiment, the value of the statistical parameter is not specifically limited, and the technicians in this field can freely set it, as long as the analysis of the publicity characteristics is satisfied. The optimal value of the statistical parameter is: t1=1, t2=14. U(i) is the set of article numbers of the publicity information within one year.

[0081] Please continue reading Figure 1 As shown, the regional intangible cultural heritage management method based on big data also includes:

[0082] Step S6, analyzing the browsing characteristics according to the promotional information, information category and browsing information.

[0083] Specifically, step S6 in this embodiment analyzes the browsing characteristics according to the promotion information and browsing information of the information categories of the first and second categories, sets the browsing characteristics as C(i, j), and sets , where v represents the feature analysis parameter, 0≤v≤V, v∈N, and V represents the feature analysis threshold, 5≤V≤10. It can be understood that the value of the force feature analysis threshold in this embodiment is specifically limited, and those skilled in the art can freely set it as long as it satisfies the analysis of the browsing features. The optimal value of the feature analysis threshold is: V=7.

[0084] Specifically, in this embodiment, the step S6 analyzes the promotional information, information categories and browsing information to analyze browsing characteristics, and uses browsing characteristics to represent the changing characteristics of browsing data of promotional information related to intangible cultural heritage over a period of time, thereby improving the analysis efficiency of intangible cultural heritage promotional data and improving the accuracy of promotional management.

[0085] Please continue reading Figure 1 As shown, the regional intangible cultural heritage management method based on big data also includes:

[0086] Step S7, analyzing the popularity of intangible cultural heritage according to browsing characteristics, publicity characteristics, normal characteristics and activity frequency parameters.

[0087] See also Figure 4 As shown, it is a flow chart of the intangible cultural heritage heat analysis method, including:

[0088] Step S701, analyzing the popularity of intangible cultural heritage according to publicity characteristics and activity frequency parameters.

[0089] Specifically, step S701 in this embodiment analyzes the popularity of intangible cultural heritage according to the publicity characteristics and activity frequency parameters, sets the popularity of intangible cultural heritage to F(i), and sets F(i)=B(i) / e P(i) / NL3(i) .

[0090] Specifically, in this embodiment, the publicity characteristics and activity frequency parameters are analyzed through step S701 to analyze the popularity of the intangible cultural heritage, and the intangible cultural heritage popularity is used to represent the popularity characteristics of the intangible cultural heritage publicity data during the publicity period, so as to judge whether the intangible cultural heritage publicity efforts are qualified, thereby improving the analysis efficiency of the intangible cultural heritage publicity data and improving the accuracy of publicity management.

[0091] Please continue reading Figure 4 As shown, it is a flow chart of the intangible cultural heritage heat analysis method, which also includes:

[0092] Step S702, adjusting the analysis process of the intangible cultural heritage popularity according to the browsing characteristics, so that the adjusted intangible cultural heritage popularity is related to the interactive characteristics of the users on the promotional data within a period of time.

[0093] Specifically, step S702 in this embodiment compares the browsing feature with the browsing comparison threshold, and adjusts the analysis process of the intangible cultural heritage heat according to the comparison result. If AvgC(i)≤c, it is determined that the browsing feature does not meet the threshold, and the analysis process of the intangible cultural heritage heat is adjusted. The adjusted intangible cultural heritage heat is F1(i), and F1(i)=F(i)×e-AvgC(i) ; If AvgC(i)>c, it is determined that the browsing feature meets the threshold, and the analysis process of the popularity of intangible cultural heritage is not adjusted, where AvgC(i) represents the mean value of the browsing feature, , c represents the browsing comparison threshold, 0.1≤c≤0.15; through the analysis of browsing features in step S702, the analysis process of the heat of intangible cultural heritage is adjusted. When the browsing data of the publicity information fluctuates less within a period of time, the analysis process of the heat of intangible cultural heritage is adjusted, thereby improving the analysis efficiency of the intangible cultural heritage publicity data and improving the accuracy of publicity management; it can be understood that the value of the browsing comparison threshold is not specifically limited in this embodiment, and those skilled in the art can set it freely, as long as the adjustment of the heat analysis process of intangible cultural heritage is satisfied. The optimal value of the browsing comparison threshold is: c=0.1.

[0094] Please continue reading Figure 4 As shown, it is a flow chart of the intangible cultural heritage heat analysis method, which also includes:

[0095] Step S703, optimizing the adjustment process of the intangible cultural heritage heat according to the normal characteristics, so that the optimized intangible cultural heritage heat is related to the characteristics of the publicity data during the intangible cultural heritage activities.

[0096] Specifically, step S703 in this embodiment compares the normal feature with the normal comparison threshold, and optimizes the adjustment process of the intangible cultural heritage heat according to the comparison result. If A(i)<a, it is determined that the normal feature does not meet the threshold, and the adjustment process of the intangible cultural heritage heat is optimized. The optimized intangible cultural heritage heat is F2(i), and F2(i)=F1(i)×e -A(i) ; If A(i)≥a, it is determined that the normal characteristics meet the threshold, and the adjustment process of the intangible cultural heritage heat is not optimized; wherein a represents the normal comparison threshold, 0.6≤a≤0.9; through the analysis of the normal characteristics in step S703, the adjustment process of the intangible cultural heritage heat is optimized. When the normal characteristics are low, the adjustment process of the intangible cultural heritage heat is optimized to achieve the influence of the lower publicity and promotion efforts under normal circumstances on the intangible cultural heritage heat analysis, thereby improving the analysis efficiency of the intangible cultural heritage publicity data and improving the accuracy of publicity management; it can be understood that in this embodiment, the value of the normal comparison threshold is not specifically limited, and the technical personnel in this field can freely set it, as long as the optimization of the intangible cultural heritage heat adjustment process is satisfied. The optimal value of the normal comparison threshold is: a=0.7.

[0097] Please continue reading Figure 1As shown, the regional intangible cultural heritage management method based on big data also includes:

[0098] Step S8, analyzing and outputting the publicity suggestions according to the popularity of the intangible cultural heritage.

[0099] Specifically, step S8 described in this embodiment compares the heat of the intangible cultural heritage with the heat threshold, and analyzes the publicity suggestion based on the comparison result. If F(i)<f1, the publicity suggestion is set as the first suggestion. If f1≤F(i)<f2, the publicity suggestion is set as the second suggestion. If F(i)≥f2, the publicity suggestion is set as the third suggestion, wherein f1 represents the first heat threshold, 0.3≤f1≤0.5, and f2 represents the second heat threshold, 0.8≤f2≤1. It can be understood that the value of the heat threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it meets the analysis of the publicity suggestion. The optimal value of the heat threshold is: f1=0.4, f2=0.8.

[0100] Specifically, in this embodiment, when the publicity suggestion is the first suggestion, it means that the current publicity effort is relatively small and should be increased. For example, the first suggestion can be set to increase the publicity effort. When the publicity suggestion is the second suggestion, it means that the current publicity effort is appropriate and the promotion of high-quality publicity content should be increased. For example, the second suggestion can be set to optimize the publicity content and improve the quality of the publicity content. When the publicity suggestion is the third suggestion, it means that the current publicity effort is relatively large and the quality of the publicity content should be guaranteed while ensuring the publicity effort. For example, the third suggestion can be set to maintain the current publicity effort and ensure the quality of the publicity content.

[0101] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A regional intangible cultural heritage management method based on big data, characterized in that: include: Step S1, collecting intangible cultural heritage information within the management area, and periodically collecting publicity information and browsing information; Step S2, storing the intangible cultural heritage information, publicity information and browsing information, and constructing management parameters according to the stored intangible cultural heritage information, publicity information and browsing information; Step S3, classifying the collected publicity information according to the intangible cultural heritage information to obtain information categories; Step S4, analyzing the activity frequency parameters and normality characteristics according to the stored intangible cultural heritage information, publicity information, management parameters and information categories; The step S4 comprises: Step S401, analyzing the activity frequency parameters according to the stored event dates; Step S402, extracting the stored promotional information according to the activity frequency parameter and the information category to obtain normal promotional information; Step S403, analyzing the normal characteristics according to the normal promotion information and activity frequency parameters; The step S401 counts the number of dates on which the same intangible cultural heritage information is held within a year as an activity frequency parameter P(i); The step S402 extracts the stored promotional information according to the extraction parameters L1(i) and L2(i), extracting the promotional information of the information category of the first and second categories, and the promotional information whose release time is between L1(i) and L2(i) days before the date of the intangible cultural heritage information as the normal promotional information; The step S403 counts the number of normal publicity information as the normal publicity number NL1(i), counts the number of publicity information that successfully matches each intangible cultural heritage information with the publicity information within one year as the information publicity number NL2(i), and analyzes the normal characteristics according to the normal publicity number and the publicity number to obtain the set normal characteristics A(i); Step S5, analyzing the remaining duration of the event according to the intangible cultural heritage information, and analyzing the publicity features according to the remaining duration of the event, publicity information, information category and browsing information; The step S5 comprises: Step S501, obtaining the current analysis time, where the current analysis time is the time when the promotional features are analyzed; Step S502, analyzing the remaining duration of the event according to the current analysis time and the event date; Step S503, analyzing the promotional features according to the remaining duration of the event, the promotional information, the information category and the browsing information; The step S502 takes the time interval between the current analysis time and the next event date in the chronological order of the intangible cultural heritage information as the remaining event time T(i); The step S503 counts the number of promotional information that successfully matches the intangible cultural heritage information with the promotional information within the time period t1≤T(i)≤t2 as the adjacent promotion number NL3(i), and analyzes the promotional features according to the adjacent promotion number, the information promotion number and the browsing information to obtain the promotional features B(i), where t1 represents the first statistical parameter and t2 represents the second statistical parameter; Step S6, analyzing browsing characteristics according to the promotional information, information category and browsing information; Step S7, analyzing the popularity of intangible cultural heritage according to browsing characteristics, publicity characteristics, normality characteristics and activity frequency parameters; Step S8, analyzing and outputting the publicity suggestions according to the popularity of the intangible cultural heritage.

2. The regional intangible cultural heritage management method based on big data according to claim 1 is characterized in that: The step S3 matches the article subject and article content with the name of the intangible cultural heritage, and determines the information category of the promotional information according to the matching result, and the information category includes one category, two categories and three categories.

3. The regional intangible cultural heritage management method based on big data according to claim 1 is characterized in that: The step S6 analyzes the browsing characteristics according to the promotional information and browsing information of the first and second information categories to obtain the browsing characteristics C(i, j).

4. The regional intangible cultural heritage management method based on big data according to claim 1 is characterized in that: The step S7 comprises: Step S701, analyzing the popularity of intangible cultural heritage according to publicity characteristics and activity frequency parameters; Step S702, adjusting the analysis process of the popularity of the intangible cultural heritage according to the browsing characteristics; Step S703, optimizing the adjustment process of the intangible cultural heritage popularity according to the normal characteristics.

5. The regional intangible cultural heritage management method based on big data according to claim 4 is characterized in that: The step S701 analyzes the popularity of the intangible cultural heritage according to the publicity characteristics and the activity frequency parameters to obtain the popularity of the intangible cultural heritage as F(i); The step S702 compares the browsing feature C(i) with the browsing comparison threshold c, and adjusts the analysis process of the intangible cultural heritage heat according to the comparison result. When the browsing feature does not meet the threshold, the analysis process of the intangible cultural heritage heat is adjusted, and the adjusted intangible cultural heritage heat is F1(i); The step S703 compares the normal feature A(i) with the normal comparison threshold a, and optimizes the adjustment process of the intangible cultural heritage heat according to the comparison result. When the normal feature does not meet the threshold, the adjustment process of the intangible cultural heritage heat is optimized, and the optimized intangible cultural heritage heat is F2(i).

6. The regional intangible cultural heritage management method based on big data according to claim 5 is characterized in that: The step S8 compares the intangible cultural heritage heat F(i) with the heat thresholds f1 and f2, and analyzes the publicity suggestions according to the comparison results to obtain publicity suggestions, which include the first suggestion, the second suggestion and the third suggestion.

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