A digital city tour method and system based on big data

Abstract

The present invention provides a digital city tour method and system based on big data, wherein the method comprises: step S1: obtaining at least one first city that a first user wants to visit input; step S2: constructing a city-city scan database based on big data technology; step S3: determining the target city scan data corresponding to the first city based on the city-city scan database, and constructing a digital city based on the target city scan data; step S4: obtaining a target tour strategy for the digital city, and leading the first user into the digital city for a tour based on the target tour strategy. The digital city tour method and system based on big data of the present invention constructs a digital city based on the city that the first user wants to visit input, improves the user's selectivity, obtains a suitable target tour strategy to guide the user to tour, improves the experience, and at the same time reduces economic consumption and time expenditure, is more convenient, and is particularly suitable for special situations such as epidemics.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a digital city tour method and system based on big data. Background Art

[0002] At present, people are often restricted in many aspects when they want to travel to tourist cities. For example, the target city is too far away and the travel cost is too high; in addition, because of work, people find it difficult to take a whole block of time to travel, and there will be travel peaks during holidays.

[0003] Therefore, a solution is urgently needed. Summary of the invention

[0004] The present invention provides a digital city tour method and system based on big data, which constructs a digital city based on the city that a first user wants to visit, thereby improving the user's selectivity, obtaining a suitable target tour guide to guide the user's tour, and improving the experience. At the same time, it reduces economic consumption and time expenditure, and is more convenient.

[0005] The present invention provides a digital city tour method based on big data, comprising:

[0006] Step S1: obtaining at least one first city that a first user wants to visit and inputs;

[0007] Step S2: Building a city-city scanning database based on big data technology;

[0008] Step S3: determining the target city scan data corresponding to the first city based on the city-city scan database, and constructing a digital city based on the target city scan data;

[0009] Step S4: obtaining a target tour guide for the digital city, and leading the first user to enter the digital city for a tour based on the target tour guide.

[0010] Preferably, step S2: constructing a city-city scanning database based on big data technology, including:

[0011] Based on big data technology, multiple first-city scan data sets are obtained;

[0012] Obtaining a real-time value corresponding to the first city scan data set;

[0013] If the real-time value is greater than or equal to a preset real-time value threshold, the corresponding first city scan data set is used as a second city scan data set;

[0014] Acquire a collection method corresponding to the second city scan data set, where the collection method includes: active collection or passive collection;

[0015] When the acquisition mode is active acquisition, acquiring an acquisition scene image of the corresponding second city scan data set;

[0016] Based on the behavior recognition technology, the collection behavior of the on-site collection personnel who are performing data collection is extracted from the collection scene image;

[0017] Based on a preset collection behavior normative analysis model, a normative analysis is performed on the collection behavior of the collection personnel to obtain a normative value, and the normative value is associated with the corresponding second city scan data set;

[0018] Accumulating and calculating the standard values ​​associated with the second city scan data set to obtain a standard value sum;

[0019] If the standard value and the threshold are greater than or equal to the preset standard value and the threshold, the corresponding second city scan data set is used as the third city scan data set;

[0020] When the acquisition mode is passive acquisition, obtaining the capture scene corresponding to the passive acquisition;

[0021] Acquire historical evaluation information of the capture scene;

[0022] Parsing the historical evaluation information to obtain a first evaluation value, and at the same time, obtaining an evaluation dimension of the historical evaluation information;

[0023] Querying a preset evaluation dimension-reference weight library to determine the reference weight of the evaluation dimension;

[0024] Assigning the reference weight to the first evaluation value to determine a second evaluation value corresponding to the second city scan data set:

[0025] If the second evaluation value is greater than or equal to a preset second evaluation value threshold, the corresponding second city scan data set is used as a fourth city scan data set;

[0026] The third city scan data set and the fourth city scan data set are collectively split to obtain a plurality of city scan data and associated pairing groups corresponding to associated cities, and the associated pairing groups are stored in a preset blank database;

[0027] When all the associated pairing groups to be stored are stored in the blank database, the blank database is used as a city-city scanning database to complete the construction.

[0028] Preferably, step S3: determining target city scan data corresponding to the first city based on the city-city scan database, and constructing a digital city based on the target city scan data, comprises:

[0029] Matching the first city with a second city in the city-city scan database, and if a match is found, using the city scan data corresponding to the second city that matches the match as the target city scan data;

[0030] Based on GIS technology and / or BIM technology and / or virtual reality technology, a digital city is constructed according to the scanning data of the target city.

[0031] Preferably, a digital city tour method based on big data, step S4: obtaining a target tour guide for the digital city, and based on the target tour guide, leading the first user to enter the digital city for tour, comprises:

[0032] Based on big data technology, obtaining a plurality of first tour guides corresponding to the digital city;

[0033] Obtaining a source type of the first travel guide source, wherein the source type includes: a personal source and a platform source;

[0034] When the source type is a personal source, obtaining a provider who publishes the first travel guide source;

[0035] Get the historical arrival cities of the provider;

[0036] Determine whether the target city exists in the historical arrival cities;

[0037] If it exists, obtain the time length between the first moment when the provider goes to the target city and the second moment when the provider publishes the first travel guide;

[0038] Based on a preset time length-reliability value library, obtaining a reliability value corresponding to the first tour guide provided by the provider;

[0039] If the reliability value is greater than or equal to a preset reliability value threshold, the first tour guide is used as the second tour guide;

[0040] When the source type is a platform source, obtaining a providing platform corresponding to the first tour guide;

[0041] Attempting to obtain user evaluation information in a comment area corresponding to the first travel guide in the providing platform;

[0042] If the acquisition attempt is successful, scoring the first tour guide according to the user evaluation information based on a preset scoring rule to obtain the evaluation score corresponding to the first tour guide;

[0043] If the evaluation score is greater than or equal to a preset evaluation score threshold, the first tour guide will be used as the second tour guide;

[0044] Sending the second tour strategy to a preset technical node, and obtaining reply information of the technical node to the second tour strategy;

[0045] Parsing the reply information, obtaining the degree of restorability of the second tour guide, and taking the second tour guide corresponding to the second tour guide with the highest degree of restorability as the target tour guide;

[0046] Based on the target tour strategy, the first user is led to enter the digital city for tour.

[0047] Preferably, leading the first user to tour includes:

[0048] Before starting to lead the first user on a tour, reminding the first user to wear a first experience device;

[0049] Acquire a first scene in the digital city that the first user is currently touring based on the first experience device;

[0050] Obtaining introduction information of the first scenario;

[0051] Based on the introduction information and according to preset voice introduction rules, a voice introduction is performed to the first user through the first experience device.

[0052] Preferably, the digital city tour method based on big data further includes:

[0053] Determining whether there is an interactive item in the first scene;

[0054] If yes, obtaining first user information of the first user;

[0055] Based on the first user information and the interactive item, judging whether the interactive item is suitable for the first user according to a preset suitability judgment rule;

[0056] If so, inviting the appropriate user to interact with the interactive project;

[0057] Obtaining response information of the appropriate user;

[0058] Obtaining an invitation determination rule corresponding to the interactive project;

[0059] Based on the invitation determination rule, judging whether the suitable user accepts the interactive invitation according to the response information;

[0060] If yes, based on a preset reminder rule, remind the first user to take the second experience device required for the interactive project;

[0061] After the suitable user takes the second experience device, a preset second experience device-usage method library is queried to determine the usage method of the second experience device;

[0062] Based on the usage method, according to a preset usage method display rule, displaying the usage method corresponding to the second experience device to the suitable user through the first experience device;

[0063] After the first experience device has completed displaying the usage method to be displayed, based on a preset inquiry rule, the appropriate user is asked whether to start the interactive project;

[0064] If so, start the interactive project;

[0065] Based on the interactive items, the suitable users perform corresponding interactions;

[0066] After the interactive project is completed, determining whether the first user has reached the end of the tour route;

[0067] If so, stop the tour;

[0068] If not, the tour continues until the first user reaches the destination.

[0069] Preferably, the digital city tour method based on big data further includes:

[0070] When the first user arrives at the destination, obtaining at least one evaluation information of the first user on the tour process;

[0071] Analyze the evaluation information to obtain at least one suggestion for improvement;

[0072] Sending the suggestions to be improved to a preset background node, and obtaining an improvement plan of the background node for the suggestions to be improved;

[0073] Sending the improved solution to a target node;

[0074] Obtaining the recognition value of the target node for the improved solution;

[0075] Query a preset target node-node weight library to determine the node weight of the target node;

[0076] Based on the recognition value corresponding to the target node and the corresponding node weight, obtaining the recognition degree of the improvement plan;

[0077] If the recognition degree is greater than or equal to the preset recognition degree threshold, the corresponding improvement plan will be used as the implementation plan;

[0078] Based on the implementation scheme, the corresponding tour process is modified.

[0079] Preferably, the digital city tour method based on big data further includes:

[0080] A linkage module, configured to determine whether the first user accepts the linkage request when the second user sends the linkage request to the first user, and if so, allow the corresponding second user who sends the linkage request to access the digital city;

[0081] The linkage module performs the following operations:

[0082] When the second user sends the linkage request, obtaining feedback information of the first user;

[0083] parsing the feedback information to determine whether the first user accepts the linkage request;

[0084] If so, the second user is allowed to access the digital city, and the first user and the second user conduct a joint tour.

[0085] Preferably, the digital city tour method based on big data further includes:

[0086] When the first user and the second user conduct a linked browsing, dynamically obtain the conversation information between the first user and the second user;

[0087] Based on the semantic analysis technology, obtaining a plurality of first semantics and the generation time of the first semantics according to the dialogue information;

[0088] Get the preset time axis;

[0089] Based on the generation time of the first semantics, expanding the first semantics on the time axis according to the chronological order of the generation time;

[0090] According to the order of the generation time, the first semantics are traversed in sequence, and the first semantics corresponding to each traversal are used as the second semantics;

[0091] Querying a preset conditional semantic library, and matching the second semantics with the conditional semantics in the conditional semantic library;

[0092] If there is a match, stop traversing, and use the second semantics currently traversed as the third semantics;

[0093] Acquire a preset number of the first semantics after the third semantics on the time axis;

[0094] Based on the corresponding first semantics, judging whether the third semantics is effective according to a preset conditional semantics effectiveness judgment rule;

[0095] If so, the operation instruction corresponding to the third semantics is executed in the digital city.

[0096] The present invention provides a digital city tour system based on big data, comprising:

[0097] An acquisition module, used for acquiring at least one first city that a first user wants to visit and input;

[0098] The first building module is used to build a city-city scanning database based on big data technology;

[0099] A second construction module is used to determine target city scan data corresponding to the first city based on the city-city scan database, and to construct a digital city based on the target city scan data;

[0100] The guiding module is used to obtain the target tour strategy of the digital city of the first user, and guide the first user to enter the digital city for tour based on the target tour strategy.

[0101] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0102] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0104] Figure 1 This is a flow chart of a digital city tour method based on big data in an embodiment of the present invention;

[0105] Figure 2 Schematic diagram of a digital city tour system based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0106] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0107] The present invention provides a digital city tour method based on big data, such as Figure 1 As shown, including:

[0108] Step S1: obtaining at least one first city that a first user wants to visit and inputs;

[0109] Step S2: Building a city-city scanning database based on big data technology;

[0110] Step S3: based on the first city and the constructed city-city scan database, determining the target city scan data corresponding to the city, and constructing a digital city based on the target city scan data;

[0111] Step S4: obtaining a target tour guide for the digital city, and leading the first user to enter the digital city for a tour based on the target tour guide.

[0112] The working principle and beneficial effects of the above technical solution are:

[0113] A first city (for example, Xiamen) that the first user wants to visit is obtained, and based on big data technology (big data technology belongs to the prior art, and its principle is not repeated here), a city-city scanning database (a database that stores multiple cities obtained through big data and 3D scanning data corresponding to the cities) is constructed, target city scanning data of the city that the first user wants to visit is determined, and a digital city corresponding to the target city scanning data is constructed (for example, a 3D simulated city model of Xiamen), and based on virtual reality technology (virtual reality technology belongs to the prior art, and its principle is not repeated here), the first user wears a first experience device (for example, VR glasses) to tour the digital city;

[0114] The embodiment of the present invention builds a digital city based on the city that the first user wants to visit, thereby improving the user's selectivity, obtaining a suitable target tour guide to guide the user's tour, improving the experience, and at the same time reducing economic consumption and time expenditure, which is more convenient.

[0115] The present invention provides a digital city tour method based on big data, step S2: based on big data technology, building a city-city scanning database, including:

[0116] Based on big data technology, multiple first-city scan data sets are obtained;

[0117] Obtaining a real-time value corresponding to the first city scan data set;

[0118] If the real-time value is greater than or equal to a preset real-time value threshold, the corresponding first city scan data set is used as a second city scan data set;

[0119] Acquire a collection method corresponding to the second city scan data set, where the collection method includes: active collection or passive collection;

[0120] When the acquisition mode is active acquisition, acquiring an acquisition scene image of the corresponding second city scan data set;

[0121] Based on the behavior recognition technology, the collection behavior of the on-site collection personnel who are performing data collection is extracted from the collection scene image;

[0122] Based on a preset collection behavior normative analysis model, a normative analysis is performed on the collection behavior of the collection personnel to obtain a normative value, and the normative value is associated with the corresponding second city scan data set;

[0123] Accumulating and calculating the standard values ​​associated with the second city scan data set to obtain a standard value sum;

[0124] If the standard value and the threshold are greater than or equal to the preset standard value and the threshold, the corresponding second city scan data set is used as the third city scan data set;

[0125] When the acquisition mode is passive acquisition, obtaining the capture scene corresponding to the passive acquisition;

[0126] Acquire historical evaluation information of the capture scene;

[0127] Parsing the historical evaluation information to obtain a first evaluation value, and at the same time, obtaining an evaluation dimension of the historical evaluation information;

[0128] Querying a preset evaluation dimension-reference weight library to determine the reference weight of the evaluation dimension;

[0129] Assigning the reference weight to the first evaluation value to determine a second evaluation value corresponding to the second city scan data set:

[0130] If the second evaluation value is greater than or equal to a preset second evaluation value threshold, the corresponding second city scan data set is used as a fourth city scan data set;

[0131] The third city scan data set and the fourth city scan data set are collectively split to obtain a plurality of city scan data and associated pairing groups corresponding to associated cities, and the associated pairing groups are stored in a preset blank database;

[0132] When all the associated pairing groups to be stored are stored in the blank database, the blank database is used as a city-city scanning database to complete the construction.

[0133] The working principle and beneficial effects of the above technical solution are:

[0134] When constructing city-to-city scan data, multiple city scan data can be obtained through the big data platform, but not all city scan data are reliable (for example, some city scan data are not updated in time, resulting in the loss of urban landscape scan data or inaccurate uploaded data, causing the restored model to be not realistic and accurate enough); therefore, a solution is urgently needed;

[0135] Using big data technology, multiple first city scan data sets are obtained (a collection of 3D scan data of all cities obtained through big data), and real-time values ​​corresponding to the first city scan data sets are obtained (the larger the real-time value, the newer the acquired data, and the higher the degree of restoration of the constructed city, for example, 80). If the first city scan data set has a real-time value greater than or equal to a preset real-time value threshold (for example, 95), it is used as the second city scan data set (city scan data with a high degree of data update);

[0136] Obtaining a collection method of the city scan data corresponding to the second city scan data set, the collection methods including: active collection (for example, dispatching on-site collection personnel to collect data on-site) and passive collection (for example, crawling city scan data from other platforms);

[0137] When the collection method is active collection, obtain the collection site image of the second city scan data set (which can be obtained through the work recorder worn by the collector), based on the behavior recognition technology (the behavior recognition technology belongs to the existing technology, and its principle is not repeated here), based on the preset collection behavior normative analysis model of the collection personnel (using multiple records of manual normative analysis of the collection behavior to train the neural network model until the neural network model converges), determine the normative value of the collection behavior, sum the normative values ​​to obtain the normative value sum (the larger the normative value sum, the more reliable the corresponding second city scan data set), and obtain the third city scan data set whose normative value sum is greater than or equal to the preset normative value and threshold (for example: 260);

[0138] When the collection method is passive collection, obtain the corresponding capture scene (for example: a cloud platform that provides urban data), obtain the historical evaluation information of the collector who collects data on the platform (for example: accurate information provided), parse the evaluation information, and obtain a first evaluation value (for example: good platform service, corresponding evaluation value: 90). At the same time, obtain the evaluation dimensions of the historical evaluation information (for example: platform service, data accuracy, etc.), and determine the reference weight of the evaluation information based on the preset evaluation dimension-reference weight library (database, storing evaluation dimensions and corresponding reference weights), and then determine the second evaluation value according to the first evaluation value (assign the reference weight corresponding to the first evaluation value, when assigning, the first evaluation value and the corresponding reference weight are multiplied, the larger the second evaluation value, the more available the data provided by the corresponding platform), if the second evaluation value is greater than or equal to the preset second evaluation value threshold (for example: 92), use the corresponding second city scan data set as the fourth city scan data set;

[0139] The third city scan data set and the fourth city scan data set are set-splitting to obtain multiple city scan data and corresponding city associated pairing groups, and after storing all associated pairing groups in a blank database, the construction of the city-city scan database is completed;

[0140] The embodiment of the present invention determines to collect a second city scan data set in time based on the real-time nature of data collection of the first city scan data set, and obtains a third city scan data set and a fourth city scan data set in time and in a reliable manner based on different city scan data collection methods, thereby improving the rationality of constructing a city-city scan database.

[0141] The present invention provides a digital city tour method based on big data, step S3: based on the city-city scan database, determining the target city scan data corresponding to the first city, and at the same time, constructing a digital city based on the target city scan data, including:

[0142] Matching the first city with a second city in the city-city scan database, and if a match is found, using the city scan data corresponding to the second city that matches the match as the target city scan data;

[0143] Based on GIS technology and / or BIM technology and / or virtual reality technology, a digital city is constructed according to the scanning data of the target city.

[0144] The working principle and beneficial effects of the above technical solution are:

[0145] The first city that the first user wants to visit (for example, Xiamen) is matched with the second city in the city-city scan database to obtain the target city scan data that matches the corresponding second city (for example, the city scan data of Xiamen), and then a digital city (for example, a three-dimensional city model of Xiamen) is constructed through GIS technology and / or BIM technology and / or virtual reality technology (GIS technology, BIM technology and virtual reality technology belong to existing technologies and their principles are not repeated here), thereby improving the rationality of digital city construction.

[0146] The present invention provides a digital city tour method based on big data, step S4: obtaining a target tour strategy of the digital city, and leading the first user to enter the digital city for tour based on the target tour strategy, including:

[0147] Based on big data technology, obtaining a plurality of first tour guides corresponding to the digital city;

[0148] Obtaining a source type of the first travel guide source, wherein the source type includes: a personal source and a platform source;

[0149] When the source type is a personal source, obtaining a provider who publishes the first travel guide source;

[0150] Get the historical arrival cities of the provider;

[0151] Determine whether the target city exists in the historical arrival cities;

[0152] If it exists, obtain the time length between the first moment when the provider goes to the target city and the second moment when the provider publishes the first travel guide;

[0153] Based on a preset time length-reliability value library, obtaining a reliability value corresponding to the first tour guide provided by the provider;

[0154] If the reliability value is greater than or equal to a preset reliability value threshold, the first tour guide is used as the second tour guide;

[0155] When the source type is a platform source, obtaining a providing platform corresponding to the first tour guide;

[0156] Attempting to obtain user evaluation information in a comment area corresponding to the first travel guide in the providing platform;

[0157] If the acquisition attempt is successful, scoring the first tour guide according to the user evaluation information based on a preset scoring rule to obtain the evaluation score corresponding to the first tour guide;

[0158] If the evaluation score is greater than or equal to a preset evaluation score threshold, the first tour guide will be used as the second tour guide;

[0159] Sending the second tour strategy to a preset technical node, and obtaining reply information of the technical node to the second tour strategy;

[0160] Parsing the reply information, obtaining the degree of restorability of the second tour guide, and taking the second tour guide corresponding to the second tour guide with the highest degree of restorability as the target tour guide;

[0161] Based on the target tour strategy, the first user is led to enter the digital city for tour.

[0162] The working principle and beneficial effects of the above technical solution are:

[0163] When obtaining tour guides through big data, not all tour guides are suitable (for example, the time of the tour guide published by an individual is too long from the time when the person goes to the corresponding city, the time span is long, and the timeliness of the tour guide is low. At the same time, when the target tour guide is applied to the digital city, due to the design of some tourism projects, play scenes, etc., corresponding technical means are required. Some tour guides may be applicable in actual tourism, but the restoration effect in the digital city is poor and they are not suitable for the application of the digital city; therefore, it is urgent to solve this problem;

[0164] Obtain the first tour guide of the target city (for example, go to XX scenic spot and play XX project) through big data technology, obtain the source type of the source node corresponding to the first tour guide, including: personal source (for example, the first tour guide of the target city informed by friends) and platform source (for example, the first tour guide published on the travel website). When the source type is personal source, obtain the provider's historical arrival city, determine whether the provider has been to the target city, and obtain the time length from the time when the provider went to the target city to the time when the provider published the corresponding first tour guide. Due to the development of urban tourism, the first tour guide generated by the travel experience closer to the current time is more reliable. Query the preset time length-reliability value library (database, storing the corresponding relationship between time length and reliability value, the shorter the time length, the greater the reliability value), and use the first tour guide with a reliability value greater than or equal to the preset reliability value threshold (for example: 90) as the second tour guide;

[0165] If it is the first tour guide provided on the platform, determine the comment area on the platform corresponding to the first tour guide, obtain the evaluation information of the first user in the comment area (for example: XX first user commented that the tour guide arrangement is suitable and the experience is good), and determine the evaluation score corresponding to the first tour guide based on the preset evaluation score scoring rule (using multiple manually recorded records of scoring the first tour guide to train the neural network model until the neural network model converges);

[0166] Filter out the first tour guide whose threshold is greater than or equal to a preset evaluation score threshold (for example, 85) as the second tour guide;

[0167] Send the second tour guide to a preset technical node (e.g., technical department), parse the reply information of the technical department, determine the restoreability of the second tour guide (the restoreability of the second tour guide in the digital city, for example, the restoreability of experiencing local food is lower than that of viewing scenery), determine the second tour guide with the highest restoreability as the target tour guide, and guide the first user to tour based on the target tour guide;

[0168] The embodiment of the present invention determines to provide a reliable and highly rated second tour guide based on the historical arrival cities of the first tour guide provider and the first tour guide providing platform, and determines a target tour guide suitable for digital city application based on the restorability of the second tour guide, thereby improving rationality and suitability.

[0169] The present invention provides a digital city tour method based on big data, which leads the first user to tour, including:

[0170] Before starting to lead the first user on a tour, reminding the first user to wear a first experience device;

[0171] Acquire a first scene in the digital city that the first user is currently touring based on the first experience device;

[0172] Obtaining introduction information of the first scenario;

[0173] Based on the introduction information and according to preset voice introduction rules, a voice introduction is performed to the first user through the first experience device.

[0174] The working principle and beneficial effects of the above technical solution are:

[0175] Before the first user starts to visit, the first user is reminded to wear the first experience device (for example, after the first user enters the experience area, the first user is reminded to wear VR glasses through the speaker in the experience area), the first scene that the first user is visiting based on the first experience device (for example, Gulangyu Island, Xiamen) is obtained, the first scene is analyzed to obtain corresponding introduction information (for example, distribution of scenic spots, historical origins, etc.), and based on the introduction information, an introduction is made according to a preset voice introduction rule (for example, voice broadcasting through the speaker built into the VR glasses worn by the first user);

[0176] The embodiment of the present invention obtains the first scene that the first user is browsing, obtains the introduction information of the first scene and performs voice introduction, thereby improving the experience of the first user.

[0177] The present invention provides a digital city tour method based on big data, which also includes:

[0178] Determining whether there is an interactive item in the first scene;

[0179] If yes, obtaining first user information of the first user;

[0180] Based on the first user information and the interactive item, judging whether the interactive item is suitable for the first user according to a preset suitability judgment rule;

[0181] If so, inviting the appropriate user to interact with the interactive project;

[0182] Obtaining response information of the appropriate user;

[0183] Obtaining an invitation determination rule corresponding to the interactive project;

[0184] Based on the invitation determination rule, judging whether the suitable user accepts the interactive invitation according to the response information;

[0185] If yes, based on a preset reminder rule, remind the first user to take the second experience device required for the interactive project;

[0186] After the suitable user takes the second experience device, a preset second experience device-usage method library is queried to determine the usage method of the second experience device;

[0187] Based on the usage method, according to a preset usage method display rule, displaying the usage method corresponding to the second experience device to the suitable user through the first experience device;

[0188] After the first experience device has completed displaying the usage method to be displayed, based on a preset inquiry rule, the appropriate user is asked whether to start the interactive project;

[0189] If so, start the interactive project;

[0190] Based on the interactive items, the suitable users perform corresponding interactions;

[0191] When the interactive project is finished, determining whether the first user has completed browsing;

[0192] If so, stop the tour;

[0193] If not, continue to guide the first user to tour.

[0194] The working principle and beneficial effects of the above technical solution are:

[0195] The first user is touring, and there may be interactive projects in the first scene of the tour. However, based on the properties of the interactive projects, not all interactive projects are suitable for recommendation to the first user (for example, the first user has heart disease, so it is not suitable to recommend intense physical projects to the first user). At the same time, some interactive projects rely on some second experience devices to achieve, such as virtual racing projects that require the first user to go to a fixed racing device, and some projects require some simple small devices. Different projects require different second experience devices, and the first user needs to be guided specifically according to the interactive projects that the first user wants to participate in; therefore, it is urgent to solve this problem;

[0196] Determine whether there is an interactive project (e.g., surfing) in the first scene. If so, obtain the first user information of the first user (e.g., whether the user suffers from high blood pressure, heart disease, or other diseases that are not suitable for virtual reality sports with large sensory stimulation, and age information, etc.), and invite the first user who is determined to be suitable to the activity according to the preset suitability judgment rule (e.g., the surfing project is judged to be unsuitable for patients with heart disease) (e.g., sending a notification in the virtual reality scene: the weather is nice, let's surf together, do you want to join?), obtain the response information of the suitable user (e.g., the first user clicks the OK button on the VR glasses), and prepare to lead the first user to interact based on the obtained invitation determination rule corresponding to the interactive project (e.g., the first user clicks the OK button on the VR glasses, and it is determined that the first user confirms to accept the invitation);

[0197] Parse the interactive project, determine the second experience device (e.g., surfboard) required for the interactive project, and remind the user to take the second experience device based on the preset reminder rules (e.g., the surfboard is on the storage table on your right, please take it). When the first user takes the second experience device, query the preset second experience device-usage method library (database, storing multiple correspondences between second experience devices and usage methods), determine the usage method of the second experience device, and show the usage method of the second experience device to the first user according to the preset usage method display rules (e.g., show the first user an animation tutorial of the usage method of the second experience device through the first experience device). After the display is completed, based on the preset inquiry rules (e.g., display through the first experience device: the usage method has been shown, do you want to start exercising? The corresponding interface provides options: confirm start, replay, and exit);

[0198] If the first user determines to start, the device of the interactive project is started, and the first user experiences the project. After the interactive project is completed, it is determined whether the tour is completed. If so, the tour ends, and if not, the tour continues;

[0199] The embodiment of the present invention invites a suitable first user to an interactive project based on the first user information of the first user, thereby improving suitability and interest; and displays the corresponding usage method of the device to the first user based on the second experience device, thereby improving humanization.

[0200] The present invention provides a digital city tour method based on big data, further comprising:

[0201] When the first user completes the tour, obtaining at least one evaluation information of the first user on the tour process;

[0202] Analyze the evaluation information to obtain at least one suggestion for improvement;

[0203] Sending the suggestions to be improved to a preset background node, and obtaining an improvement plan of the background node for the suggestions to be improved;

[0204] Sending the improved solution to a target node;

[0205] Obtaining the recognition value of the target node for the improved solution;

[0206] Query a preset target node-node weight library to determine the node weight of the target node;

[0207] Based on the recognition value corresponding to the target node and the corresponding node weight, obtaining the recognition degree of the improvement plan;

[0208] If the recognition degree is greater than or equal to the preset recognition degree threshold, the corresponding improvement plan will be used as the implementation plan;

[0209] Based on the implementation scheme, the corresponding tour process is modified.

[0210] The working principle and beneficial effects of the above technical solution are:

[0211] After the first user completes the tour, the first user's corresponding evaluation (for example, the experience is average) can be obtained, and corresponding suggestions for improvement (for example, vibration feedback should be used when waves hit the surfboard) can be formulated based on the evaluation information, and the suggestions for improvement can be sent to a preset background node (for example, the design department), and the design department's improvement plan for the suggestions for improvement can be obtained (for example, adding a vibration feedback device to the surfboard in the interactive project, and triggering vibration feedback when the virtual waves hit the board surface), and the improvement plan can be sent to the target node (for example, the target user of the somatosensory game, the deep user of the somatosensory game, and other interactive project designers), and the target node's recognition value for the improvement plan can be obtained (the higher the recognition value, the more the target node recognizes the improvement plan, and the preset target node-node weight library (database, storing the corresponding relationship between nodes and weights) is queried, and the recognition of the improvement plan is obtained based on the recognition value corresponding to the target node and the corresponding node weight;

[0212] The recognition degree corresponding to the improvement plan is obtained by the following formula:

[0213]

[0214] Among them, α is the recognition degree, β t is the recognition value of the tth node for the improved solution, γ t is the node weight corresponding to the t-th node, and n is the total number of nodes;

[0215] The improvement plan with a recognition degree greater than or equal to a preset recognition degree threshold (for example, 90) is used as an implementation plan to modify the tour process;

[0216] The embodiment of the present invention obtains the improvement plan of the backend department for the evaluation information based on the evaluation information of the first user on this tour, and through the recognition of the improvement plan by nodes with different weights, determines the improvement plan with high recognition to modify, thereby improving rationality.

[0217] The present invention provides a digital city tour method based on big data, further comprising:

[0218] The linkage module is used to determine whether the first user accepts the linkage request when the second user sends the linkage request to the first user. If yes, the second user who sends the linkage request is allowed to access the digital city. The linkage module performs the following operations:

[0219] When the second user sends the linkage request, obtaining feedback information of the first user;

[0220] parsing the feedback information to determine whether the first user accepts the linkage request;

[0221] If so, the second user is allowed to access the digital city, and the first user and the second user conduct a joint tour.

[0222] The working principle and beneficial effects of the above technical solution are:

[0223] When the second user sends a linkage request to the first user, the first user's device will display a prompt message (for example: User XX applies to tour with you, would you like to let him join?). If the first user agrees to the second user's application, the first user's feedback information is obtained (for example: pressing the "OK" button on the VR device). If the first user allows, the second user can also join.

[0224] The present invention implements the method of determining whether to allow the second user to tour together through the feedback information of the first user, thereby improving rationality.

[0225] The present invention provides a digital city tour method based on big data, wherein a first user and a second user conduct a linked tour, comprising:

[0226] When the first user and the second user conduct a linked browsing, dynamically obtain the conversation information between the first user and the second user;

[0227] Based on the semantic analysis technology, obtaining a plurality of first semantics and the generation time of the first semantics according to the dialogue information;

[0228] Get the preset time axis;

[0229] Based on the generation time of the first semantics, expanding the first semantics on the time axis according to the chronological order of the generation time;

[0230] According to the order of the generation time, the first semantics are traversed in sequence, and the first semantics corresponding to each traversal are used as the second semantics;

[0231] Querying a preset conditional semantic library, and matching the second semantics with the conditional semantics in the conditional semantic library;

[0232] If there is a match, stop traversing, and use the second semantics currently traversed as the third semantics;

[0233] Acquire a preset number of the first semantics after the third semantics on the time axis;

[0234] Based on the corresponding first semantics, judging whether the third semantics is effective according to a preset conditional semantics effectiveness judgment rule;

[0235] If so, the operation instruction corresponding to the third semantics is executed in the digital city.

[0236] The working principle and beneficial effects of the above technical solution are:

[0237] In the existing digital city tour system, when the user wants to change the current play scene, it is often necessary to give clear instructions to the system, and the user cannot immerse himself in the tour process itself. Therefore, the user's first semantics (for example, going to Tiananmen Square for a tour) can be obtained through communication between users to determine the place the user wants to visit next. However, after obtaining the first semantics, it is often not possible to immediately determine the scene switch, because the user sometimes only mentions the first semantics (for example, user A proposes to go to Tiananmen Square for a tour, user B suggests to go to the Great Wall for a tour, and user A agrees with B's plan), which is not certain; therefore, a solution is urgently needed;

[0238] After the second user joins, the conversation information between the first user and the second user (the conversation content between the first user and the second user) is dynamically obtained. Based on the semantic analysis technology (the semantic analysis technology belongs to the existing technology and its principle is not repeated here), multiple first semantics (for example: go to the Great Wall for fun) and the generation time of the first semantics of the conversation between the two are obtained. Based on the generation time of the first semantics, the first semantics on the first time axis are traversed in sequence according to the order of the generation time. The traversed first semantics are used as the second semantics, and the preset conditional semantic library (database, storing multiple conditional semantics, for example: go to XX city) is queried. The second semantics are matched with the conditional semantics in the conditional semantic library. If there is a match, the traversal is stopped, and the second semantics currently traversed are used as the third semantics. The preset number (for example: 5) of first semantics after the third semantic on the time axis are obtained. Based on the preset conditional semantic effectiveness judgment rule (for example: the first semantic corresponding to the third semantic is about the travel strategy of the city mentioned by the third semantic, then the corresponding conditional semantic is judged to be effective). If effective, the operation instruction corresponding to the third semantic is executed (for example: the system performs scene rendering of the Great Wall);

[0239] The embodiment of the present invention introduces a first semantic, matches the first semantic with a conditional semantic in a conditional semantic library, obtains a third semantic that matches the match, and determines the effectiveness of the conditional semantic on the first semantic after the third semantic. A scenario is constructed based on the third semantic that is determined to be effective, thereby improving the accuracy of the semantic determination. The user does not need to manually execute the desired instructions, thereby improving the user experience.

[0240] The present invention provides a digital city tour system based on big data, such as Figure 2 As shown, including:

[0241] An acquisition module 1 is used to acquire at least one first city that a first user wants to visit and input;

[0242] The first construction module 2 is used to construct a city-city scanning database based on big data technology;

[0243] A second construction module 3 is used to determine the target city scan data corresponding to the first city based on the city-city scan database, and to construct a digital city based on the target city scan data;

[0244] The guiding module 4 is used to obtain the target sightseeing strategy of the digital city of the first user, and guide the first user to enter the digital city for sightseeing based on the target sightseeing strategy.

[0245] The working principle and beneficial effects of the above technical solution have been described in the method claims and will not be repeated here.

[0246] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A digital city tour method based on big data, characterized in that: include: Step S1: obtaining at least one first city that a first user wants to visit and inputs; Step S2: Building a city-city scanning database based on big data technology; Step S3: determining the target city scan data corresponding to the first city based on the city-city scan database, and constructing a digital city based on the target city scan data; Step S4: obtaining a target tour guide for the digital city, and leading the first user to enter the digital city for a tour based on the target tour guide; The step S2: constructing a city-city scanning database based on big data technology, includes: Based on big data technology, multiple first-city scan data sets are obtained; Obtaining a real-time value corresponding to the first city scan data set; If the real-time value is greater than or equal to a preset real-time value threshold, the corresponding first city scan data set is used as a second city scan data set; Acquire a collection method corresponding to the second city scan data set, where the collection method includes: active collection or passive collection; When the acquisition mode is active acquisition, acquiring an acquisition scene image of the corresponding second city scan data set; Based on the behavior recognition technology, the collection behavior of the on-site collection personnel who are performing data collection is extracted from the collection scene image; Based on a preset collection behavior normative analysis model, a normative analysis is performed on the collection behavior of the collection personnel to obtain a normative value, and the normative value is associated with the corresponding second city scan data set; Accumulating and calculating the standard values ​​associated with the second city scan data set to obtain a standard value sum; If the standard value and the threshold are greater than or equal to the preset standard value and the threshold, the corresponding second city scan data set is used as the third city scan data set; When the acquisition mode is passive acquisition, obtaining the capture scene corresponding to the passive acquisition; Acquire historical evaluation information of the capture scene; Parsing the historical evaluation information to obtain a first evaluation value, and at the same time, obtaining an evaluation dimension of the historical evaluation information; Querying a preset evaluation dimension-reference weight library to determine the reference weight of the evaluation dimension; Assigning the reference weight to the first evaluation value to determine a second evaluation value corresponding to the second city scan data set: If the second evaluation value is greater than or equal to a preset second evaluation value threshold, the corresponding second city scan data set is used as a fourth city scan data set; The third city scan data set and the fourth city scan data set are collectively split to obtain a plurality of city scan data and associated pairing groups corresponding to associated cities, and the associated pairing groups are stored in a preset blank database; When all the associated pairing groups to be stored are stored in the blank database, the blank database is used as a city-city scanning database to complete the construction.

2. A digital city tour method based on big data as claimed in claim 1, characterized in that: The step S3: determining the target city scan data corresponding to the first city based on the city-city scan database, and constructing a digital city based on the target city scan data, including: Matching the first city with a second city in the city-city scan database, and if a match is found, using the city scan data corresponding to the second city that matches the match as the target city scan data; Based on GIS technology and / or BIM technology and / or virtual reality technology, a digital city is constructed according to the scanning data of the target city.

3. A digital city tour method based on big data as claimed in claim 1, characterized in that: The step S4: obtaining a target tour guide for the digital city, and leading the first user to enter the digital city for a tour based on the target tour guide, comprises: Based on big data technology, obtaining a plurality of first tour guides corresponding to the digital city; Obtaining a source type of the first travel guide source, wherein the source type includes: a personal source and a platform source; When the source type is a personal source, obtaining a provider who publishes the first travel guide source; Get the historical arrival cities of the provider; Determine whether the target city exists in the historical arrival cities; If it exists, obtain the time length between the first moment when the provider goes to the target city and the second moment when the provider publishes the first travel guide; Based on a preset time length-reliability value library, obtaining a reliability value corresponding to the first tour guide provided by the provider; If the reliability value is greater than or equal to a preset reliability value threshold, the first tour guide is used as the second tour guide; When the source type is a platform source, obtaining a providing platform corresponding to the first tour guide; Attempting to obtain user evaluation information in a comment area corresponding to the first travel guide in the providing platform; If the acquisition attempt is successful, scoring the first tour guide according to the user evaluation information based on a preset evaluation score scoring rule to obtain an evaluation score corresponding to the first tour guide; If the evaluation score is greater than or equal to a preset evaluation score threshold, the first tour guide will be used as the second tour guide; Sending the second tour strategy to a preset technical node, and obtaining reply information of the technical node to the second tour strategy; Parsing the reply information, obtaining the degree of restorability of the second tour guide, and taking the second tour guide corresponding to the second tour guide with the highest degree of restorability as the target tour guide; Based on the target tour strategy, the first user is led to enter the digital city for tour.

4. The method for touring a digital city based on big data according to claim 3, wherein the step of leading the first user to touring comprises: Before starting to lead the first user on a tour, reminding the first user to wear a first experience device; Acquire a first scene in the digital city that the first user is currently touring based on the first experience device; Obtaining introduction information of the first scenario; Based on the introduction information and according to preset voice introduction rules, a voice introduction is performed to the first user through the first experience device.

5. A digital city tour method based on big data as claimed in claim 4, characterized in that: Also includes: Determining whether there is an interactive item in the first scene; If yes, obtaining first user information of the first user; Based on the first user information and the interactive item, judging whether the interactive item is suitable for the first user according to a preset suitability judgment rule; If so, inviting the appropriate user to interact with the interactive project; Obtaining response information of the appropriate user; Obtaining an invitation determination rule corresponding to the interactive project; Based on the invitation determination rule, judging whether the suitable user accepts the interactive invitation according to the response information; If yes, based on a preset reminder rule, remind the first user to take the second experience device required for the interactive project; After the suitable user takes the second experience device, a preset second experience device-usage method library is queried to determine the usage method of the second experience device; Based on the usage method, according to a preset usage method display rule, displaying the usage method corresponding to the second experience device to the suitable user through the first experience device; After the first experience device has completed displaying the usage method to be displayed, based on a preset inquiry rule, the appropriate user is asked whether to start the interactive project; If so, start the interactive project; Based on the interactive items, the suitable users perform corresponding interactions; When the interactive project is finished, determining whether the first user has completed browsing; If so, stop the tour; If not, continue to guide the first user to tour.

6. A digital city tour method based on big data as claimed in claim 5, characterized in that: Also includes: When the first user completes the tour, obtaining at least one evaluation information of the first user on the tour process; Analyze the evaluation information to obtain at least one suggestion for improvement; Sending the suggestions to be improved to a preset background node, and obtaining an improvement plan of the background node for the suggestions to be improved; Sending the improved solution to a target node; Obtaining the recognition value of the target node for the improved solution; Query a preset target node-node weight library to determine the node weight of the target node; Based on the recognition value corresponding to the target node and the corresponding node weight, obtaining the recognition degree of the improvement plan; If the recognition degree is greater than or equal to the preset recognition degree threshold, the corresponding improvement plan will be used as the implementation plan; Based on the implementation scheme, the corresponding tour process is modified.

7. A digital city tour method based on big data as claimed in claim 1, characterized in that: Also includes: A linkage module, configured to determine whether the first user accepts the linkage request when the second user sends the linkage request to the first user, and if so, allow the corresponding second user who sends the linkage request to access the digital city; The linkage module performs the following operations: When the second user sends the linkage request, obtaining feedback information of the first user; parsing the feedback information to determine whether the first user accepts the linkage request; If so, the second user is allowed to access the digital city, and the first user and the second user conduct a joint tour.

8. A digital city tour method based on big data as claimed in claim 7, characterized in that: Also includes: When the first user and the second user conduct a linked browsing, dynamically obtain the conversation information between the first user and the second user; Based on the semantic analysis technology, obtaining a plurality of first semantics and the generation time of the first semantics according to the dialogue information; Get the preset time axis; Based on the generation time of the first semantics, expanding the first semantics on the time axis according to the chronological order of the generation time; According to the order of the generation time, the first semantics are traversed in sequence, and the first semantics corresponding to each traversal are used as the second semantics; Querying a preset conditional semantic library, and matching the second semantics with the conditional semantics in the conditional semantic library; If there is a match, stop traversing, and use the second semantics currently traversed as the third semantics; Acquire a preset number of the first semantics after the third semantics on the time axis; Based on the corresponding first semantics, judging whether the third semantics is effective according to a preset conditional semantics effectiveness judgment rule; If so, the operation instruction corresponding to the third semantics is executed in the digital city.

9. A digital city tour system based on big data, characterized in that: include: An acquisition module, used for acquiring at least one first city that a first user wants to visit and input; The first building module is used to build a city-city scanning database based on big data technology; A second construction module is used to determine target city scan data corresponding to the first city based on the city-city scan database, and to construct a digital city based on the target city scan data; A guiding module, configured to obtain a target tour guide of the digital city from the first user, and guide the first user to enter the digital city for a tour based on the target tour guide; The first building module performs the following operations: Based on big data technology, multiple first-city scan data sets are obtained; Obtaining a real-time value corresponding to the first city scan data set; If the real-time value is greater than or equal to a preset real-time value threshold, the corresponding first city scan data set is used as a second city scan data set; Acquire a collection method corresponding to the second city scan data set, where the collection method includes: active collection or passive collection; When the acquisition mode is active acquisition, acquiring an acquisition scene image of the corresponding second city scan data set; Based on the behavior recognition technology, the collection behavior of the on-site collection personnel who are performing data collection is extracted from the collection scene image; Based on a preset collection behavior normative analysis model, a normative analysis is performed on the collection behavior of the collection personnel to obtain a normative value, and the normative value is associated with the corresponding second city scan data set; Accumulating and calculating the standard values ​​associated with the second city scan data set to obtain a standard value sum; If the standard value and the threshold are greater than or equal to the preset standard value and the threshold, the corresponding second city scan data set is used as the third city scan data set; When the acquisition mode is passive acquisition, obtaining the capture scene corresponding to the passive acquisition; Acquire historical evaluation information of the capture scene; Parsing the historical evaluation information to obtain a first evaluation value, and at the same time, obtaining an evaluation dimension of the historical evaluation information; Querying a preset evaluation dimension-reference weight library to determine the reference weight of the evaluation dimension; Assigning the reference weight to the first evaluation value to determine a second evaluation value corresponding to the second city scan data set: If the second evaluation value is greater than or equal to a preset second evaluation value threshold, the corresponding second city scan data set is used as a fourth city scan data set; The third city scan data set and the fourth city scan data set are collectively split to obtain a plurality of city scan data and associated pairing groups corresponding to associated cities, and the associated pairing groups are stored in a preset blank database; When all the associated pairing groups to be stored are stored in the blank database, the blank database is used as a city-city scanning database to complete the construction.

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