CN110866777A - Tourism big data business platform system - Google Patents
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Abstract
The invention relates to a tourism big data business platform system, which solves the technical problem of insufficient safety evaluation and prevention of tourists, and adopts at least 1 central server and at least 2 mobile user terminals which are mutually wirelessly connected; the central server calculates the path track according to the position information and the speed information contained in the uploaded information of the mobile user terminal, and counts the sub-areas in the scenic spot at the moment tiThe number of the mobile user terminals in the sub-area is calculated, and according to a predefined people flow threshold value, the information of a control area with real-time people flow exceeding the threshold value is screened out and issued to the mobile user terminals; the technical scheme that the mobile user terminal sends the safety service containing the distress emergency information to the central server and the adjacent mobile user terminal better solves the problem and can be used in tourism application.
Description
Technical Field
The invention relates to the field of tourism big data, in particular to a tourism big data business platform system.
Background
In recent years, every day before and after a major festival day, every large scenic spot (scenic spot) in China has a situation of rising popularity, but people can also see news reports about the explosion of tourists in the scenic spot (scenic spot), road congestion and safety accidents in the scenic spot caused by the explosion, the road congestion and the safety accidents in the scenic spot, and the negative reports enable the tourism industry to become the focus of social public opinion attention, lead the social public to discuss more extensively and intensely, and become a pain point for the healthy and sustainable development of the tourism industry. With the popularization of internet technology, the network search and attention degree related to scenic spot safety is in a growing trend, and the activity is completed before the trip of the tourist, so that the activity has obvious precursor effect relative to the peak period of the tour.
The invention provides a tourism big data business platform system, which can solve the technical problem that the safety estimation of tourists is insufficient in the conventional tourism big data business platform system.
Disclosure of Invention
The invention aims to solve the technical problem of insufficient tourist safety estimation in the prior art. A new travel big data business platform system is provided, and the travel big data business platform system has the characteristics of effectively estimating the safety of tourists and warning.
In order to solve the technical problems, the technical scheme is as follows:
a big data business platform system of tourism, the said big data business platform system of tourism includes at least 1 central server, and at least 2 mobile user terminals; the mobile user terminal is connected with the central server in a wireless network way and also connected with the adjacent mobile user terminal in a wireless network way, and the two wireless network connection modes are alternated;
the central server calculates the path track according to the position information and the speed information contained in the uploaded information of the mobile user terminal, and counts the sub-areas in the scenic spot at the moment tiThe number of the mobile user terminals in the sub-area is calculated, and according to a predefined people flow threshold value, the information of a control area with real-time people flow exceeding the threshold value is screened out and issued to the mobile user terminals;
and the mobile user terminal sends a safety service containing the distress emergency information to the central server and the adjacent mobile user terminal.
In the above scheme, for optimization, further, the number of the mobile user terminals is defined as the number of real-time tourists in a sub-area, and the mobile user terminals with the position information repetition probability exceeding a predefined repetition rate threshold are defined as the repeated mobile user terminals and removed.
Further, the mobile terminal collects security source data of facts around the visitor, as shown in fig. 2, and the central server performs visitor security evaluation according to the security source data by using the following evaluation method;
step 1, constructing a self-organizing feature mapping neural network model, wherein the self-organizing feature mapping neural network model is composed of at least 2 cascaded self-organizing feature mapping neural sub-networks, and the number ratio of self-organizing feature mapping neural network units of adjacent self-organizing feature mapping neural sub-networks is 2;
step 2, defining tourist safety evaluation grade numbers for judging a threshold value R of neuron merging or splitting of a competition layer in a neural network unit, inputting safety source data, performing self-organization characteristic mapping learning algorithm training by a 1 st self-organization characteristic mapping neural subnetwork, and calculating initial clustering center values of various classes representing tourist safety evaluation grades;
step 4, calculating the mapping coefficient H of the neuron of the competition layer as 1/the corresponding guest safety assessment grade number, and if H is less than 1, executing step 5; otherwise, defining the initial clustering center of the step 2 as a modified clustering center, executing the step 6,
step 5, calling a next-level self-organizing feature mapping neural sub-network to perform self-organizing feature mapping learning algorithm training, calculating a corrected clustering center of each class as an initial clustering center value, and returning to execute the step 3;
and 6, defining the modified clustering center as a final clustering center value, calculating Euclidean distances between each piece of safety source data and the final clustering center value, and taking the final clustering center value with the minimum Euclidean distance as a classification result of the safety source data.
Further, the neurons in the competition layer corresponding to the amount of the secure source data lower than the threshold R are:
distance between classes Dj< threshold R, inter-class distance Dj＝||mj-mj+1||，j＝1,2,3，……c-1，mjThe cluster center value of the jth class;
the mapping coefficient H < 1 for the competition layer neurons is:
average distance d in classjIs greater than the threshold value R and the threshold value R,
j＝1，2,3……c，xifor input layer neuron i corresponding to a secure source data value, njIs the amount of secure source data within the class.
Further, the safety source data comprises a terrain risk score, a weather risk score, a people flow risk score and a team centrifugal distance value, and the sum of the weights of all the safety source data is 1.
Further, the step of calculating the number of the real-time tourists in the sub-area further comprises estimating a time ti+1The number of real-time guests in the sub-region of (1).
Furthermore, the uploaded information of the mobile user terminal also comprises time information corresponding to the position information, and the mobile user terminal records the information flow of the service or the shared service;
and the central server classifies the preference of the tourists corresponding to the mobile user terminal by using the user preference model according to the received information, selects a corresponding path mode according to the classification result to generate a tour path and feeds the tour path back to the corresponding mobile user terminal.
Further, the classification result determines a recommendation pattern, including a preference pattern, a de-duplication pattern, and an aggregation pattern.
And further, the classification result determines a recommendation mode, when the mode is an aggregation mode, the mobile user terminal sets an aggregation place and an aggregation time, and when a subsequent path of the scenic spot is generated and recommended, path planning is carried out by taking the aggregation place and the aggregation time as the highest priority.
The invention has the beneficial effects that: according to the invention, through the position information collection of the tourist mobile terminal, the pedestrian volume in a certain scenic region is counted and analyzed, and the safety problem caused by the saturation of the pedestrian volume can be considered. Meanwhile, through the expansion connection of the tourist mobile terminal, emergency help seeking can be realized when the mobile terminal and the central server are unsmooth in network. In view of the complexity of scenic spots, the objective of recent tourist cooperation can also be achieved. On the basis, the accuracy is further improved through the mobile terminal and visitor relevance error elimination. By collecting information around the tourists, a safety evaluation model is built, the real-time safety factors of the tourists are comprehensively evaluated, the safety levels are graded, and the tourists can be warned or distress information and warning information can be issued to a central server and an adjacent mobile terminal when the levels exceed a threshold value. Further preferably, in order to solve the problems that scenic spot tourists are difficult to gather and the tourists are not easy to control the tourism integrity and efficiency under the condition of limited tourism time, the gathering place, the gathering time and the tourists preference are used as input parameters, the weight value of the parameters is used as the highest priority on the basis of the existing path planning algorithm, after the existing path planning algorithm is used for path planning, the solidified path is displayed according to the priority, and parameters such as the path, the remaining time and the remaining distance from the gathering place are updated in real time.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a schematic diagram of a travel big data business platform system in embodiment 1.
Fig. 2 is a flow chart of evaluation of the guest safety index in embodiment 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The present embodiment provides a business platform system for big data of travel, as shown in fig. 1, where the business platform system for big data of travel includes at least 1 central server and at least 2 mobile user terminals; the mobile user terminal is connected with the central server in a wireless network way and also connected with the adjacent mobile user terminal in a wireless network way, and the two wireless network connection modes are alternated;
the central server is based on the mobile user terminal up-signalingCalculating the path track by the position information and the speed information contained in the information, and counting the sub-areas in the scenic region at the time tiThe number of the mobile user terminals in the sub-area is calculated, and according to a predefined people flow threshold value, the information of a control area with real-time people flow exceeding the threshold value is screened out and issued to the mobile user terminals;
and the mobile user terminal sends a safety service containing the distress emergency information to the central server and the adjacent mobile user terminal. At the moment, the central server can send the scenic spot workers after directly responding, and the tourists corresponding to the adjacent mobile user terminals can complete emergency rescue in case of emergency, so that the safety of the tourists is improved.
The wireless connection of the embodiment is completed by adopting the prior art, such as a microwave communication, WIFI or 4G mobile network connection mode.
Specifically, the number of the mobile user terminals is defined as the number of real-time tourists in the sub-area, and the mobile user terminals with the position information repetition probability exceeding a predefined repetition rate threshold are defined as the repeated mobile user terminals and eliminated. The present embodiment adopts the above-described deduplication based on the case where sometimes one guest carries 2 mobile terminals. Based on the condition that some tourists do not have mobile terminals, the tourists can be actually positioned by adopting the existing video system or the Internet of things system in the scenic spot and are matched with the position information of the mobile terminals according to real-time positioning. And the unmatched tourist mobile terminals can be virtually set by the central server according to the tourist data collected by the scenic spot video system or the Internet of things system.
Preferably, in order to evaluate the security index of the tourist conveniently, for the mobile terminal to collect security source data of facts around the tourist, the central server performs safety evaluation on the tourist by adopting the following evaluation method according to the security source data;
step 1, constructing a self-organizing feature mapping neural network model, wherein the self-organizing feature mapping neural network model is composed of at least 2 cascaded self-organizing feature mapping neural sub-networks, and the number ratio of self-organizing feature mapping neural network units of adjacent self-organizing feature mapping neural sub-networks is 2;
step 2, defining tourist safety evaluation grade numbers for judging a threshold value R of neuron merging or splitting of a competition layer in a neural network unit, inputting safety source data, performing self-organization characteristic mapping learning algorithm training by a 1 st self-organization characteristic mapping neural subnetwork, and calculating initial clustering center values of various classes representing tourist safety evaluation grades;
step 4, calculating the mapping coefficient H of the neuron of the competition layer as 1/the corresponding guest safety assessment grade number, and if H is less than 1, executing step 5; otherwise, defining the initial clustering center of the step 2 as a modified clustering center, executing the step 6,
step 5, calling a next-level self-organizing feature mapping neural sub-network to perform self-organizing feature mapping learning algorithm training, calculating a corrected clustering center of each class as an initial clustering center value, and returning to execute the step 3;
and 6, defining the modified clustering center as a final clustering center value, calculating Euclidean distances between each piece of safety source data and the final clustering center value, and taking the final clustering center value with the minimum Euclidean distance as a classification result of the safety source data.
Specifically, the neurons in the competition layer corresponding to the secure source data quantity lower than the threshold value R are:
distance between classes Dj< threshold R, inter-class distance Dj＝‖mj-mj+1||，j＝1,2,3，……c-1，mjThe cluster center value of the jth class;
the mapping coefficient H < 1 for the competition layer neurons is:
average distance d in classjIs greater than the threshold value R and the threshold value R,
j＝1，2,3……c，xifor input layer neuron i corresponding to a secure source data value, njIs the amount of secure source data within the class.
The safety source data comprise a terrain risk value, a weather risk value, a people flow risk value and a team centrifugal distance value, and the sum of the weights of all the safety source data is 1. And the safety source data value is used as a numerical value according to the sum of the products of each value and the weighted value. In order to perform a gradual change, normalization processing can be performed.
Preferably, in order to further improve the safety predictability, in this embodiment, the calculating the number of real-time visitors in the sub-area further includes estimating a time ti+1The number of real-time guests in the sub-region of (1). The estimation algorithm adopted at the moment can adopt a field support vector robot flow estimation algorithm and the like.
Preferably, the uploaded information of the mobile user terminal further includes time information corresponding to the location information, and the mobile user terminal records an information stream of a service or a shared service;
and the central server classifies the preference of the tourists corresponding to the mobile user terminal by using the user preference model according to the received information, selects a corresponding path mode according to the classification result to generate a tour path and feeds the tour path back to the corresponding mobile user terminal.
The user preference is used as the basis for path planning. On the basis, in order to solve the problem that some tourists cannot collect at appointed places on time and with guaranteed quality when the tourists scatter at scenic spots after being combined and finally collect at certain places. In this embodiment, based on the existing path planning method, the aggregation location and the aggregation time parameter are used as the highest priority to select the path. Meanwhile, the tour schedule and the plan of other members can be transferred through the central server, and the high efficiency of scenic spot tour is realized to the maximum extent.
Specifically, the classification results determine recommended patterns including a preference pattern, a deduplication pattern, and an aggregation pattern.
In detail, the classification result determines a recommendation mode, when the mode is the gathering place mode, the mobile user terminal sets the gathering place and the gathering time, and when a subsequent path of the scenic spot is generated and recommended, path planning is carried out by taking the gathering place and the gathering time as the highest priority.
According to the embodiment, the passenger flow in a certain scenic area is counted and analyzed through the position information collection of the tourist mobile terminal, and the safety problem caused by the saturation of the passenger flow can be considered. Meanwhile, through the expansion connection of the tourist mobile terminal, emergency help seeking can be realized when the mobile terminal and the central server are unsmooth in network. In view of the complexity of scenic spots, the objective of recent tourist cooperation can also be achieved. On the basis, the accuracy is further improved through the mobile terminal and visitor relevance error elimination. By collecting information around the tourists, a safety evaluation model is built, the real-time safety factors of the tourists are comprehensively evaluated, the safety levels are graded, and the tourists can be warned or distress information and warning information can be issued to a central server and an adjacent mobile terminal when the levels exceed a threshold value. Further preferably, in order to solve the problems that scenic spot tourists are difficult to gather and the tourists are not easy to control the tourism integrity and efficiency under the condition of limited tourism time, the gathering place, the gathering time and the tourists preference are used as input parameters, the weight value of the parameters is used as the highest priority on the basis of the existing path planning algorithm, after the existing path planning algorithm is used for path planning, the solidified path is displayed according to the priority, and parameters such as the path, the remaining time and the remaining distance from the gathering place are updated in real time.
Although the illustrative embodiments of the present invention have been described above to enable those skilled in the art to understand the present invention, the present invention is not limited to the scope of the embodiments, and it is apparent to those skilled in the art that all the inventive concepts using the present invention are protected as long as they can be changed within the spirit and scope of the present invention as defined and defined by the appended claims.
Claims (9)
1. A tourism big data business platform system is characterized in that: the tourism big data business platform system comprises at least 1 central server and at least 2 mobile user terminals; the mobile user terminal is connected with the central server in a wireless network way and also connected with the adjacent mobile user terminal in a wireless network way, and the two wireless network connection modes are alternated;
the central server calculates the path track according to the position information and the speed information contained in the uploaded information of the mobile user terminal, and counts the sub-areas in the scenic spot at the moment tiThe number of the mobile user terminals in the sub-area is calculated, and according to a predefined people flow threshold value, the information of a control area with real-time people flow exceeding the threshold value is screened out and issued to the mobile user terminals;
and the mobile user terminal sends a safety service containing the distress emergency information to the central server and the adjacent mobile user terminal.
2. The travel big data business platform system of claim 1, wherein: and defining the number of the mobile user terminals as the number of real-time tourists in the sub-area, defining the position information repetition probability of the mobile user terminals exceeding a predefined repetition rate threshold as the repeated mobile user terminals, and removing the repeated mobile user terminals.
3. The travel big data business platform system of claim 2, wherein: the mobile terminal collects security source data of facts around the tourist, and the central server carries out safety evaluation on the tourist by adopting the following evaluation method according to the security source data;
step 1, constructing a self-organizing feature mapping neural network model, wherein the self-organizing feature mapping neural network model is composed of at least 2 cascaded self-organizing feature mapping neural sub-networks, and the number ratio of self-organizing feature mapping neural network units of adjacent self-organizing feature mapping neural sub-networks is 2;
step 2, defining tourist safety evaluation grade numbers for judging a threshold value R of neuron merging or splitting of a competition layer in a neural network unit, inputting safety source data, performing self-organization characteristic mapping learning algorithm training by a 1 st self-organization characteristic mapping neural subnetwork, and calculating initial clustering center values of various classes representing tourist safety evaluation grades;
step 3, judging the quantity of the security source data corresponding to the neuron of the competition layer, and deleting the neuron of the competition layer, the quantity of which is lower than a threshold value R, corresponding to the security source data;
step 4, calculating the mapping coefficient H of the neuron of the competition layer as 1/the corresponding guest safety assessment grade number, and if H is less than 1, executing step 5; otherwise, defining the initial clustering center of the step 2 as a modified clustering center, executing the step 6,
step 5, calling a next-level self-organizing feature mapping neural sub-network to perform self-organizing feature mapping learning algorithm training, calculating a corrected clustering center of each class as an initial clustering center value, and returning to execute the step 3;
and 6, defining the modified clustering center as a final clustering center value, calculating Euclidean distances between each piece of safety source data and the final clustering center value, and taking the final clustering center value with the minimum Euclidean distance as a classification result of the safety source data.
4. The travel big data business platform system of claim 3, wherein: the neurons of the competition layer corresponding to the safe source data with the quantity lower than the threshold value R are as follows:
distance between classes Dj< threshold R, inter-class distance Dj＝||mj-mj+1||，j＝1,2,3，……c-1，mjThe cluster center value of the jth class;
the mapping coefficient H < 1 for the competition layer neurons is:
average distance d in classjIs greater than the threshold value R and the threshold value R,
xifor input layer neuron i corresponding to a secure source data value, njIs the amount of secure source data within the class.
5. The travel big data business platform system of claim 3, wherein: the safety source data comprises a terrain risk score, a weather risk score, a people flow risk score and a team centrifugal distance value, and the sum of the weights of all the safety source data is 1.
6. The travel big data business of claim 2Platform system, its characterized in that: the step of calculating the number of the real-time tourists in the sub-area further comprises estimating the time ti+1The number of real-time guests in the sub-region of (1).
7. The travel big data business platform system of claim 1, wherein: the uploading information of the mobile user terminal also comprises time information corresponding to the position information, and the mobile user terminal records the information flow of the service or the sharing service;
and the central server classifies the preference of the tourists corresponding to the mobile user terminal by using the user preference model according to the received information, selects a corresponding path mode according to the classification result to generate a tour path and feeds the tour path back to the corresponding mobile user terminal.
8. The travel big data business platform system of claim 7, wherein: the classification result comprises a preference mode, a de-duplication mode and an aggregation mode.
9. The travel big data business platform system of claim 8, wherein: and when the classification result is in the gathering place mode, the mobile user terminal sets the gathering place and the gathering time, and performs path planning by taking the gathering place and the gathering time as the highest priority when generating and recommending subsequent paths of scenic spots.
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| CN112837188A (en) * | 2021-03-10 | 2021-05-25 | 科豆(福州)教育科技有限公司 | Research and travel intelligent planning method based on transfer learning and clustering algorithm |
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