CN110866777B - Tourism big data business platform system - Google Patents
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- 238000013507 mapping Methods 0.000 claims description 33
- 230000002776 aggregation Effects 0.000 claims description 20
- 238000004220 aggregation Methods 0.000 claims description 20
- 210000002569 neuron Anatomy 0.000 claims description 20
- 230000002860 competitive effect Effects 0.000 claims description 12
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- 238000013528 artificial neural network Methods 0.000 claims description 6
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0281—Customer communication at a business location, e.g. providing product or service information, consulting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/14—Travel agencies
Abstract
The invention relates to a business platform system of big data of tourism, solve the insufficient technical problem of visitor's security evaluation prevention, through adopting including at least 1 central server that is connected with each other wirelessly, at least 2 mobile user terminals; the central server calculates a path track according to the position information and the speed information contained in the uploading information of the mobile user terminal, and counts the time t of the sub-region in the scenic spot i Calculating the number of real-time tourists in the subarea, screening out control area information of which the real-time flow exceeds a threshold according to a predefined flow threshold, and publishing the control area information to the mobile user terminal; the mobile user terminal sends the safety business technical proposal containing the distress emergency information to the central server and the adjacent mobile user terminals, which better solves the problem and can be used in travel application.
Description
Technical Field
The invention relates to the field of big travel data, in particular to a business platform system for big travel data.
Background
In recent years, every day before and after major festival celebration, people's air rising situation can appear in all scenic spots (scenic spots) in the whole country, but we can see some news reports about the scenic spot tourists being full, road congestion and some scenic spot safety accidents caused by the road congestion, and the negative reports make the tourist industry become the focus of public opinion attention, bring about wider and violent discussion of the public society, and become a ' pain spot ' for the sustainable development of the health of the tourist industry. With the popularization of internet technology, the network searching and the attention of the security of the scenic spots are in an increasing trend, and as the activity is completed before the tourists travel, the network searching and the attention has obvious precursor effect relative to the peak period of travel.
The invention provides a tourist big data business platform system, which can solve the technical problem that the safety estimation of tourists is insufficient.
Disclosure of Invention
The invention aims to solve the technical problem of insufficient tourist safety estimation in the prior art. The novel tourist 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 adopted is as follows:
a travel big data business platform system comprising at least 1 central server, and at least 2 mobile user terminals; the mobile user terminal is connected with the wireless network of the central server and also connected with the wireless network of the adjacent mobile user terminal, and the two wireless network connection modes are alternated;
the central server calculates a path track according to the position information and the speed information contained in the uploading information of the mobile user terminal, and counts the time t of the sub-region in the scenic spot i Calculating the number of real-time tourists in the subarea, screening out control area information of which the real-time flow exceeds a threshold according to a predefined flow threshold, and publishing the control area information to the mobile user terminal;
the mobile user terminal sends the security service containing the distress emergency information to the central server and the adjacent mobile user terminals.
In the above scheme, for optimization, further, the number of mobile user terminals=the number of real-time tourists in the subarea is defined, and the definition that the repetition probability of the mobile user terminal position information exceeds the predefined repetition rate threshold is defined as repeated mobile user terminals and removed.
Further, the mobile terminal collects safety source data of facts around the tourist, as shown in fig. 2, and the central server performs security assessment of the tourist according to the safety source data by adopting the following assessment 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 levels of cascaded self-organizing feature mapping neural sub-networks, and the ratio of the numbers of self-organizing feature mapping neural network units of adjacent levels of self-organizing feature mapping neural sub-networks is 2;
step 2, defining the number of guest safety evaluation grades, namely judging a threshold value R of merging or splitting of competing layer neurons in a neural network unit, inputting safety source data, training a self-organizing feature mapping learning algorithm by a 1 st self-organizing feature mapping neural sub-network, and calculating initial clustering center values of various classes representing the guest safety evaluation grades;
step 3, judging the quantity of the safety source data corresponding to the competitive layer neurons, and deleting the competitive layer neurons with the quantity of the corresponding safety source data lower than a threshold value R;
step 4, calculating the mapping coefficient H=1 of the neurons of the competitive layer/the corresponding guest security assessment grade number, and executing step 5 if H is smaller than 1; otherwise, defining the initial cluster center in the step 2 as a corrected cluster center, executing the step 6,
step 5, invoking the next-level self-organizing feature mapping neural sub-network to carry out self-organizing feature mapping learning algorithm training, calculating the corrected cluster centers of all classes as initial cluster center values, and returning to execute the step 3;
and 6, defining a corrected clustering center as a final clustering center value, calculating Euclidean distance between each 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 competing layer neurons corresponding to the number of the safety source data being lower than the threshold R are:
inter-class distance D j Threshold value R, inter-class distance D j ＝||m j -m j+1 ||，j＝1,2,3，……c-1，m j A cluster center value of the j-th class;
the mapping coefficient H < 1 of the competitive layer neurons is:
average distance d in class j The value of the sum of the values is greater than the threshold value R,j＝1，2,3……c，x i for the secure source data value corresponding to the input layer neuron i, n j Is the amount of secure source data within the class.
Further, the safety source data comprises a terrain risk score, a weather risk score, a traffic risk score and a team centrifugal distance value, and the sum of the weights of all the safety source data is 1.
Further, the calculating the real-time tourist number in the subarea further comprises estimating the time t i+1 Real-time guest numbers in a sub-area of (a).
Further, the uploading information of the mobile user terminal further 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 favorites of tourists corresponding to the mobile user terminals by using a 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 terminals.
Further, the classification results determine recommended patterns, including bias-preference patterns, deduplication patterns, and aggregations patterns.
Further, the classification result determines a recommendation mode, when the recommendation mode is the aggregation mode, the mobile user terminal sets aggregation and aggregation time, and when the subsequent paths of the scenic spots are generated and recommended, the aggregation and aggregation time is used as the highest priority to conduct path planning.
The invention has the beneficial effects that: according to the invention, through the position information acquisition of the tourist mobile terminal, the people flow in a certain scenic area is counted and analyzed, and the safety problem caused by people flow saturation can be considered. Meanwhile, through the expansion connection of the tourist mobile terminal, the emergency help seeking can be realized when the mobile terminal is not smooth with the central server network. In view of the complexity of scenic spots, the goal of the interaction of the nearest tourists can also be achieved. On the basis, accuracy is further improved through eliminating the relevance error between the mobile terminal and the tourist. The safety evaluation model is constructed by collecting surrounding information of tourists, real-time safety coefficients of the tourists are comprehensively evaluated, safety grades are graded, and the tourists can be warned or distress information and warning information can be issued to a central server and adjacent mobile terminals when the grades exceed a threshold value. Further preferably, in order to solve the difficulty of scenic spot tourist collection and the condition that the control of the integrity and the efficiency of tourist is not good under the condition of limited tourist time, by taking the collection place, the collection time and the tourist preference as input parameters, the weight value of the parameters is taken as the highest priority on the basis of the existing path planning algorithm, after the path planning is carried out by using the existing path planning algorithm, the solidification path is displayed according to the priority, and parameters such as the path, the residual time, the residual distance from the collection place and the like are updated in real time.
Drawings
The invention will be further described with reference to the drawings and examples.
FIG. 1 is a schematic diagram of a travel big data commercial platform system in example 1.
Fig. 2 is a flow chart for evaluating the guest safety index in example 1.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
The embodiment provides a travel big data business platform system, as shown in fig. 1, which comprises at least 1 central server and at least 2 mobile user terminals; the mobile user terminal is connected with the wireless network of the central server and also connected with the wireless network of the adjacent mobile user terminal, and the two wireless network connection modes are alternated;
the central server calculates a path track according to the position information and the speed information contained in the uploading information of the mobile user terminal, and counts the time t of the sub-region in the scenic spot i Calculating the number of real-time tourists in the subarea, screening out control area information of which the real-time flow exceeds a threshold according to a predefined flow threshold, and publishing the control area information to the mobile user terminal;
the mobile user terminal sends the security service containing the distress emergency information to the central server and the adjacent mobile user terminals. At the moment, the central server can directly respond and send out scenic spot staff, and the tourists corresponding to the adjacent mobile user terminals can finish emergency rescue in 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 microwave communication, WIFI or 4G mobile network connection mode.
Specifically, defining the number of mobile user terminals = the number of real-time tourists in the subarea, defining the mobile user terminal position information repetition probability exceeding a predefined repetition rate threshold as repeated mobile user terminals and eliminating. Based on the situation that one tourist has 2 mobile terminals sometimes, the embodiment adopts the mode of de-duplication as above. Based on the fact that some tourists do not have mobile terminals, the existing video system or the Internet of things system of scenic spots can be adopted to locate the fact of the tourists, and the fact is matched with the position information of the mobile terminals according to the real-time location. The mobile terminal of the tourist can be virtually set by the center server according to the tourist data acquired 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 the security source data of the facts around the tourist, the central server adopts the following evaluation method to evaluate the security of the tourist 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 levels of cascaded self-organizing feature mapping neural sub-networks, and the ratio of the numbers of self-organizing feature mapping neural network units of adjacent levels of self-organizing feature mapping neural sub-networks is 2;
step 2, defining the number of guest safety evaluation grades, namely judging a threshold value R of merging or splitting of competing layer neurons in a neural network unit, inputting safety source data, training a self-organizing feature mapping learning algorithm by a 1 st self-organizing feature mapping neural sub-network, and calculating initial clustering center values of various classes representing the guest safety evaluation grades;
step 3, judging the quantity of the safety source data corresponding to the competitive layer neurons, and deleting the competitive layer neurons with the quantity of the corresponding safety source data lower than a threshold value R;
step 4, calculating the mapping coefficient H=1 of the neurons of the competitive layer/the corresponding guest security assessment grade number, and executing step 5 if H is smaller than 1; otherwise, defining the initial cluster center in the step 2 as a corrected cluster center, executing the step 6,
step 5, invoking the next-level self-organizing feature mapping neural sub-network to carry out self-organizing feature mapping learning algorithm training, calculating the corrected cluster centers of all classes as initial cluster center values, and returning to execute the step 3;
and 6, defining a corrected clustering center as a final clustering center value, calculating Euclidean distance between each 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 competing layer neurons corresponding to the number of secure source data below the threshold R are:
inter-class distance D j Threshold value R, inter-class distance D j ＝‖m j -m j+1 ||，j＝1,2,3，……c-1，m j A cluster center value of the j-th class;
the mapping coefficient H < 1 of the competitive layer neurons is:
average distance d in class j The value of the sum of the values is greater than the threshold value R,j＝1，2,3……c，x i for the secure source data value corresponding to the input layer neuron i, n j Is the amount of secure source data within the class.
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. The secure source data value is a numerical value based on the sum of the products of the values and the weight values. Normalization may also be performed for a gradual transition.
Preferably, in order to further improve the security predictability, the calculating the real-time tourist number in the subarea further includes estimating the time t i+1 Real-time guest numbers in a sub-area of (a). The estimation algorithm adopted at this time can be a field support vector machine people flow estimation algorithm or the like.
Preferably, the uploading information of the mobile user terminal further includes time information corresponding to the location information, and the mobile user terminal records the information flow of the service or the sharing service;
and the central server classifies the favorites of tourists corresponding to the mobile user terminals by using a 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 terminals.
The user preference is used as a path planning basis. On the basis, in order to solve the problem that tourists scatter in scenic spots after being combined, and finally, when the tourists gather in a certain place, the tourists cannot gather in time, can be guaranteed in quality and quantity, and can gather according to the appointed place. The embodiment selects the paths by taking the aggregation place and the aggregation time parameter as the highest priority on the basis of the existing path planning method. Meanwhile, the tour progress and planning of the rest members can be transferred through the central server, so that the high efficiency of scenic spot tour is realized to the maximum extent.
Specifically, the classification result determines recommended modes including a bias preference mode, a deduplication mode, and an aggregation mode.
In detail, the classification result determines a recommendation mode, when the recommendation mode is the aggregation mode, the mobile user terminal sets aggregation and aggregation time, and when the subsequent paths of the scenic spots are generated and recommended, the aggregation and aggregation time is used as the highest priority to carry out path planning.
According to the embodiment, through the position information acquisition of the tourist mobile terminal, the people flow in a certain scenic area is counted and analyzed, and the safety problem caused by people flow saturation can be considered. Meanwhile, through the expansion connection of the tourist mobile terminal, the emergency help seeking can be realized when the mobile terminal is not smooth with the central server network. In view of the complexity of scenic spots, the goal of the interaction of the nearest tourists can also be achieved. On the basis, accuracy is further improved through eliminating the relevance error between the mobile terminal and the tourist. The safety evaluation model is constructed by collecting surrounding information of tourists, real-time safety coefficients of the tourists are comprehensively evaluated, safety grades are graded, and the tourists can be warned or distress information and warning information can be issued to a central server and adjacent mobile terminals when the grades exceed a threshold value. Further preferably, in order to solve the difficulty of scenic spot tourist collection and the condition that the control of the integrity and the efficiency of tourist is not good under the condition of limited tourist time, by taking the collection place, the collection time and the tourist preference as input parameters, the weight value of the parameters is taken as the highest priority on the basis of the existing path planning algorithm, after the path planning is carried out by using the existing path planning algorithm, the solidification path is displayed according to the priority, and parameters such as the path, the residual time, the residual distance from the collection place and the like are updated in real time.
While the foregoing describes the illustrative embodiments of the present invention so that those skilled in the art may understand the present invention, the present invention is not limited to the specific embodiments, and all inventive innovations utilizing the inventive concepts are herein within the scope of the present invention as defined and defined by the appended claims, as long as the various changes are within the spirit and scope of the present invention.
Claims (6)
1. A travel big data business platform system, characterized in that: the travel 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 wireless network of the central server and also connected with the wireless network of the adjacent mobile user terminal, 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 uploading information of the mobile user terminal, and counts the subareas in the scenic spot Calculating the number of real-time tourists in the subarea, screening out control area information of which the real-time flow exceeds a threshold according to a predefined flow threshold, and publishing the control area information to the mobile user terminal;
the mobile user terminal sends a security service containing the distress emergency information to a central server and adjacent mobile user terminals;
defining the number of mobile user terminals = the number of real-time tourists in the subarea, defining the repeated probability of the mobile user terminal position information exceeding a predefined repetition rate threshold as repeated mobile user terminals and eliminating;
the mobile user terminal collects safety source data of facts around tourists, and the center server carries out tourist safety assessment according to the safety source data by adopting the following assessment 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 levels of cascaded self-organizing feature mapping neural sub-networks, and the ratio of the numbers of self-organizing feature mapping neural network units of adjacent levels of self-organizing feature mapping neural sub-networks is 2;
step 2, defining the number of guest security evaluation grades for judging the threshold value of merging or splitting of competitive layer neurons in the neural network unitInputting safety source data, training a self-organizing feature mapping learning algorithm by a level 1 self-organizing feature mapping neural sub-network, and calculating initial clustering center values of each class representing the tourist safety evaluation level;
step 3, judging the quantity of the safety source data corresponding to the neurons of the competitive layer, and deleting the quantity of the corresponding safety source data to be lower than a threshold valueCompeting layer neurons;
step 4, calculating the mapping coefficient H=1 of the neurons of the competitive layer/the corresponding guest security assessment grade number, and executing step 5 if H is smaller than 1; otherwise, defining the initial cluster center in the step 2 as a corrected cluster center, executing the step 6,
step 5, invoking the next-level self-organizing feature mapping neural sub-network to carry out self-organizing feature mapping learning algorithm training, calculating the corrected cluster centers of all classes as initial cluster center values, and returning to execute the step 3;
step 6, defining a corrected clustering center as a final clustering center value, calculating Euclidean distance between each 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;
the corresponding safe source data quantity is lower than the threshold valueThe competing layer neurons are:
interclass spacing< threshold->Inter-class distance>A cluster center value of the j-th class;
the mapping coefficient H < 1 of the competitive layer neurons is:
average distance in class> threshold R, ">，/>Is input layer neuron->Corresponding secure source data value,/->Is the amount of secure source data within the class.
2. The travel big data commercial platform system according to claim 1, wherein: the safety source data comprise a terrain risk score, a weather risk score, a people flow risk score and a team centrifugal distance value, and the sum of the weight values of all the safety source data is 1.
3. The travel big data commercial platform system according to claim 1, wherein: the calculating the real-time number of visitors in the subarea further comprises estimatingReal-time guest numbers in a sub-area of (a).
4. The travel big data commercial platform system according to 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 favorites of tourists corresponding to the mobile user terminals by using a 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 terminals.
5. The travel big data commercial platform system according to claim 4, wherein: the classification result comprises a bias preference mode, a deduplication mode and an aggregation mode.
6. The travel big data commercial platform system according to claim 5, wherein: when the classification result is the aggregation mode, the mobile user terminal sets aggregation and aggregation time, and when generating and recommending the subsequent paths of the scenic spots, the mobile user terminal takes the aggregation and aggregation time as the highest priority to carry out path planning.
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| CN106027678A (en) * | 2016-07-13 | 2016-10-12 | 桂林电子科技大学 | Scenic region tourist flow real-time statistics and tourist flow over-limit automatic early warning system and method |
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| JP2017111497A (en) * | 2015-12-14 | 2017-06-22 | 株式会社日立システムズ | Traveler position information confirmation system, and traveler position information confirmation method |
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| CN104966134A (en) * | 2015-06-15 | 2015-10-07 | 海南大学 | Scenic spot visitor flow prediction and over-limit emergency system based on mobile terminal |
| JP2017111497A (en) * | 2015-12-14 | 2017-06-22 | 株式会社日立システムズ | Traveler position information confirmation system, and traveler position information confirmation method |
| CN106022614A (en) * | 2016-05-22 | 2016-10-12 | 广州供电局有限公司 | Data mining method of neural network based on nearest neighbor clustering |
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