CN117455546A - Performance supervision method and device based on blockchain, storage medium and electronic equipment - Google Patents
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Abstract
The invention provides a block chain-based performance supervision method, a device, a storage medium and electronic equipment, wherein the block chain-based performance supervision method comprises the following steps: collecting participation intention voting data of a user on a target performance; predicting the fare of the target performance according to the participation intention voting data; receiving a ticket buying request of the target performance according to the ticket price, generating a presentation ticket, constructing a mapping relation between the presentation ticket and user identification information carried in the ticket buying request, and storing the mapping relation into a blockchain; scanning a ticket of a ticket purchasing user aiming at the target performance, and acquiring user identification information mapped by the ticket from the blockchain; and acquiring the user information of the ticket buying user, and checking the ticket based on the user identification information of the presentation ticket mapping and the user information. The supervision efficiency of the performance ticketing can be improved.
Description
Technical Field
The invention relates to the field of electronic ticket processing, in particular to a block chain-based performance supervision method, a block chain-based performance supervision device, a storage medium and electronic equipment.
Background
With the rapid development of the current market, more and more people begin to pay attention to and participate in various performance activities. In order to avoid crowding and confusing on-site ticket purchases for popular or popular shows, ticket sales for the shows need to be monitored.
Disclosure of Invention
In view of the above, the present invention provides a blockchain-based performance supervision method, device, storage medium and electronic apparatus.
Specifically, the invention is realized by the following technical scheme:
according to a first aspect of the present invention there is provided a blockchain-based performance supervision method, the method comprising:
collecting participation intention voting data of a user on a target performance; predicting the fare of the target performance according to the participation intention voting data; receiving a ticket buying request of the target performance according to the ticket price, generating a presentation ticket, constructing a mapping relation between the presentation ticket and user identification information carried in the ticket buying request, and storing the mapping relation into a blockchain; scanning a ticket of a ticket purchasing user aiming at the target performance, and acquiring user identification information mapped by the ticket from the blockchain; and acquiring the user information of the ticket buying user, and checking the ticket based on the user identification information of the presentation ticket mapping and the user information.
According to the blockchain-based performance supervision method, the blockchain technology is utilized, and the number of participants in the performance is predicted by combining data analysis, so that the ticket price of the performance is reasonably set. And (3) carrying out identity verification on the users participating in ticket purchase, and verifying the ticket holding users according to the user identification information of ticket purchase when the performance enters, so that illegal ticket purchase operation is timely discovered and prevented through ticket selling and entering electronic ticket playing authentication, fairness of electronic ticket selling is guaranteed, and supervision efficiency of performance ticket selling is improved.
According to a second aspect of the present invention, there is provided a blockchain-based performance supervision apparatus, the performance supervision apparatus comprising:
the voting collection module is used for collecting the participation intention voting data of the user on the target performance;
the fare prediction module is used for predicting the fare of the target performance according to the participation intention voting data;
the ticket selling module is used for receiving a ticket buying request of the target performance according to the ticket price, generating a presentation ticket, constructing a mapping relation of the presentation ticket and user identification information carried in the ticket buying request, and storing the mapping relation into a blockchain;
the ticket selling verification module is used for scanning a ticket of the ticket purchasing user aiming at the target performance and acquiring user identification information mapped by the ticket from the blockchain;
And the ticket checking module acquires the user information of the ticket purchasing user, and checks the ticket based on the user identification information mapped by the presentation ticket and the user information.
According to a third aspect of the present invention there is provided a storage medium having stored thereon a computer program which when executed by a processor implements the steps of the blockchain-based performance supervision method in any possible implementation of the first aspect.
According to a fourth aspect of the present invention there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the blockchain-based performance supervision method in any possible implementation of the first aspect when the program is executed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the related art will be briefly described below, and it will be apparent to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a performance supervision method based on blockchain according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a performance monitor device based on blockchain according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the related technology, the on-chain storage of the electronic tickets based on the blockchain technology is realized, but the technology cannot solve the problems of ticket robbery and ticket resale.
Referring to fig. 1, an embodiment of the present invention provides a blockchain-based performance supervision method, which may be applied to sales and supervision of electronic tickets when a performance is held, and the method may include the following steps:
S101, collecting participation intention voting data of a user on a target performance;
in this embodiment, as an optional embodiment, the step of collecting the willingness voting data of the user to participate in the target performance includes:
publishing activity information of the target performance on a blockchain;
receiving participation intention voting data of a user for the target performance, wherein the participation intention voting data comprises identity information of the user;
and verifying the identity information, and storing the verified participation intention voting data into the blockchain.
In this embodiment, as an alternative embodiment, the sponsor may post the activity information for the target performance on the blockchain. The published activity information is recorded in one block of the blockchain and has a time stamp, ensuring transparency and non-tamper ability of the information.
In this embodiment, as an alternative embodiment, a user voting system based on a blockchain may be established, and the user may provide personal information in the voting system to register, thereby completing authentication. The voting system verifies and records the identity information of the user and generates a unique blockchain identity identifier for the user.
In this embodiment, as an alternative embodiment, the user may participate in voting before purchasing the ticket, expressing the interest and willingness to participate in the target performance. The user's participation will vote data is encrypted and recorded in a new block on the blockchain.
In this embodiment, as an alternative embodiment, the user's participation intention vote data may be verified by a vote management contract. The voting management contract can be used to verify the user's identity information and the validity of the voting options and to check if repeated votes or other cheating actions exist. If the participation will voting data is verified to be in compliance with the specification, the voting management contract adds the participation will voting data to the blocks on the blockchain, and updates the voting counter.
As an alternative embodiment, the step of vote verification includes: accessing user information and participation intention voting data stored on the blockchain, including a boolean type voting field voted, determining whether the account voting option is valid by evaluating the field, and if so, incrementing a voting counter for recording the total number of votes.
In this embodiment, the blockchain technique may ensure the security and confidentiality of the voting results. Each participation intention voting data is encrypted and stored in a distributed mode in the blockchain network, and is protected by the blockchain, so that the participation intention voting data is difficult to tamper or leak.
In this embodiment, as an optional embodiment, the blockchain records each participation intention voting data including the identity information of the user and the voting options. The sponsor can estimate the number of persons participating in the ticket purchase according to the participation intention voting data. Through analyzing the participation intention voting data, the interests and participation intention of the user are known, and more accurate people number prediction is performed according to the interests and participation intention.
In this embodiment, the sponsor may predict the number of ticket buyers in advance before the performance, and better plan the sites, ticket sales and other related resources. Meanwhile, the participation wish voting data of the user can provide knowledge of market demands and user preferences for sponsors to make more accurate decisions and marketing strategies.
In this embodiment, as an alternative embodiment, the implementation of the voting management contract interface and the description of the functions in the user voting system are shown in table 1.
TABLE 1
S102, predicting the fare of the target performance according to the participation intention voting data;
in this embodiment, as an optional embodiment, the sponsor may predict the fare of the target performance according to the participation intention voting data, and the specific steps may include:
and acquiring historical behavior data of the user corresponding to each participation intention voting data, wherein the historical behavior data comprises: voting data and ticket buying records of participation will of the historical performance;
based on the participation intention voting data corresponding to each user, the participation intention voting data of the historical performance and the ticket purchasing record, acquiring the number of the pre-purchased tickets of the target performance by using a machine learning algorithm;
And determining the fare of the target performance based on the number of pre-purchased tickets, the cost of the target performance at the place of play, the estimated marketing cost of the target performance and other costs of the target performance.
In this embodiment, as an alternative embodiment, the machine learning algorithm may include: a deep learning algorithm, a cluster analysis algorithm, an association rule analysis algorithm and a random forest algorithm.
In this embodiment, the blockchain technique is combined with big data analysis to obtain historical behavior data of the user, so as to predict interests and participation will of the user. The number of the participants in the performance can be estimated in advance, and more accurate decision support and marketing strategies are provided for sponsors, including setting of the performance occasions, arrangement of the sites, setting of the performance duration and the like. As an alternative embodiment, the blockchain is utilized to collect historical behavioral data of the user, including personal information of the user, transaction records, voting records, and the like. As an alternative embodiment, because the collected historical behavior data of the user may have noise and inconsistency, preprocessing can be performed on the historical behavior data of the user, including removing duplicate data, processing missing values, outliers and the like, so as to ensure the accuracy and consistency of the data.
In this embodiment, as an optional embodiment, based on participation intention voting data corresponding to each user, participation intention voting data for a historical performance, and a ticket purchasing record, a machine learning algorithm such as a deep learning algorithm, a cluster analysis algorithm, an association rule analysis algorithm, and a random forest algorithm is used to obtain the number of pre-purchased tickets for the target performance.
In this embodiment, as an alternative embodiment, taking a machine learning algorithm as a cluster analysis algorithm as an example: historical behavioral data of the user is input to analyze interests and willingness to participate of the user, and the user is divided into different clusters or groups, each cluster representing a group of users with similar characteristics. The specific implementation process of the cluster analysis algorithm comprises the following steps:
a11, initializing the number K of the selected clusters, and randomly initializing K cluster center points. The clustering center point can randomly select K sample points in the data set, and each sample point corresponds to historical behavior data of a user;
a12, distributing data points which are sample points except the sample points in the data set, calculating the distance between each data point and each cluster center point, and distributing the data points to clusters corresponding to the closest cluster center points.
In this embodiment, euclidean distance is used as the distance measure for two data points x and yThe distance calculation formula is as follows:wherein x is 1 …x n And y 1 …y n Values of data points x and y in each dimension are represented separately;
a13, updating the clustering center point, calculating the average value of the data points in each cluster, and taking the average value as a new clustering center point. The clustering center updating formula is shown as follows, namely, in each iteration process, the average value of data points of each cluster is calculated, the average value is used as a new clustering center point, the data point set in the cluster is assumed to be C, and the clustering center point is assumed to be M:wherein x is 1 …x k A value representing each data point in the cluster;
a14, repeating the step A12 and the step A13 until the mean change of the cluster center points updated before and after is smaller than a preset mean change threshold value or the maximum iteration number is reached.
A15: and outputting a clustering result to obtain a final clustering center point and clusters to which each data point belongs, namely finishing cluster analysis.
In this embodiment, as an alternative embodiment, through the result of the cluster analysis, a deep knowledge of the user population can be obtained. For example, a group of people of particular interest to an artist may be identified, or a group of users participating in multiple activities may be found. These results may provide decision support and marketing strategies for the sponsor, including: more accurately predicts the number of people buying tickets in advance, establishes a personalized popularization scheme and improves the participation degree and success rate of the event.
In this embodiment, as an alternative embodiment, the machine learning algorithm may further include an association rule analysis algorithm. Through association rule mining, the association relationship between ticket buying user attributes and performance attributes can be known. Such as the relationship between a particular attribute of the ticket purchaser and participation in a certain type of performance. And further, the sponsor can be assisted in formulating more effective marketing strategies, such as pushing related event information, targeted advertising, etc., based on the interests of the ticketing user.
Association rule mining may reveal the degree of association between ticket-buying user attributes and performance attributes. Through analyzing indexes such as confidence coefficient, support degree and the like in the association rule, a sponsor can know the preference degree of ticket purchasing users for different types of performances, so that reasonable fare strategies are formulated for different performances.
Association rule mining may divide ticket purchasers into different groups or categories. For example, ticket users may be classified into a group of popular rock, a group of popular music, etc. based on the ticket user attributes and the results of the association rules. The sponsor may provide personalized services and customized campaign experiences based on these classification results.
Abnormal association rules or unreasonable ticket purchasing behaviors can be found through association rule mining. The sponsor can use the association rules to identify potential false orders or cheating behaviors and monitor the user operation, so that the credibility and fairness of the ticket purchasing process are improved.
In this embodiment, as an optional embodiment, the step of analyzing by using the association rule analysis algorithm includes:
a21, initializing and defining a minimum support threshold (min_support) and a minimum confidence threshold (min_confidence). While initializing the candidate set list and the frequent item set list to be empty. The support is used for measuring the probability that the frequent item set A and the frequent item set B occur simultaneously, and the confidence is used for measuring whether the frequent item set A occurs or how much probability the frequent item set B occurs. The support and confidence calculation formula is as follows:
support degree: support (A- > B) =P (A U.B)
Confidence level: confidence (A- > B) =P (A|B)
A22, generating a candidate 1-item set and scanning a data set: frequent item sets of all individual items are generated with each of the different attributes of the ticket buyer and the show, and candidate item sets comprising two or more items are generated by combining the frequent item sets. By counting the support count of each item based on the historical behavior data of the user, i.e. the proportion of the item set contained in all data. And screening out item sets with the support degree larger than or equal to the threshold according to the minimum support degree threshold to serve as candidate 1-item sets.
A23, generating frequent item sets in an iteration mode, generating candidate 2-item sets by using the candidate 1-item sets, scanning the data sets, counting the support again, and screening out items with the support greater than or equal to a threshold according to the minimum support threshold. This process is repeated until no more frequent item sets can be generated, generating frequent k-item sets. And finally, adding the frequent k-item set into a frequent item set list.
A24, generating association rules, namely generating a non-empty subset S of each frequent item set F. Confidence (confidence) of the association rule is then calculated. And finally, screening out the association rule with the confidence coefficient larger than or equal to the threshold value according to the minimum confidence coefficient threshold value.
In this embodiment, as an alternative embodiment, the association analysis may be performed on music preference, artist preference, performance location, performance time, price, and ticket purchase intention. Taking the music preference as an example, it can be further divided into 5 subclasses of popularity, rock, ballad, rap and classical. The association rule mining can help sponsors to deeply understand the association relationship between ticket purchaser attributes and performance attributes so as to formulate more effective marketing strategies and ticket pricing strategies, provide personalized services and identify potential false orders. The participation degree and the user satisfaction degree of the concert are improved, and the operation effect of the concert is optimized to the greatest extent.
In this embodiment, as an alternative embodiment, the machine learning algorithm may further include a random forest algorithm. The random forest algorithm can provide a high-accuracy prediction result, has the advantages of robustness, feature importance evaluation and the like, and is suitable for processing a great amount of current historical data and complex feature relations. As an alternative embodiment, the steps of the random forest algorithm include:
A31: and preparing data, namely acquiring historical data and behavior data of the user from the blockchain data, and simultaneously acquiring the favorite degree of the user on activities and the willingness degree of the user to participate in the voting process. And determines the predicted goal of the model, i.e., the activity fare.
A32: the pretreatment process of the data mainly cleans the data, and comprises the steps of removing repeated data and processing missing values. And selecting proper characteristics according to the predicted targets and extracting the characteristics, such as characteristic values of music types, artist types, performance sites, performance time and the like. Finally, the features are encoded, for example, by single-hot encoding the features.
A33: the data set is divided into a training set and a test set, wherein 70% of data is usually used as the training set and 30% of data is used as the test set.
A34: the training process of the random forest model mainly comprises training the random forest model by using a training set. Wherein the random forest is an integrated learning algorithm, which is composed of a plurality of decision trees. Each decision tree is trained based on randomly selected samples and features. And obtaining a final prediction result by averaging or voting the prediction result of each decision tree.
A35: evaluation of random forest models, namely evaluating the performance and accuracy of the trained random forest models by using a test set. Common evaluation metrics include mean square error (MeanSquared Error, MSE), root mean square error (Root Mean Squared Error, RMSE), mean absolute error (Mean Absolute Error, MAE), and the like. The mean square error is used as an evaluation function, and the formula is:wherein Y is i Represents the true result, mu i Representing the predicted result.
A36: and (3) predicting the rationality of the ticket price of the performance, namely predicting the preprocessed data by using a trained random forest model, and using a prediction result for making the ticket price of the subsequent performance.
In this embodiment, the participation wish voting data and the machine learning algorithm are combined, the number of participants is accurately estimated from multiple dimensions, and the number of pre-purchased tickets for the target performance is obtained. And correspondingly adjusting decision and marketing strategies according to the number of people buying tickets in advance so as to provide better experience and meet the demands of users.
In this embodiment, as an optional embodiment, the fare of the performance is determined based on the number of pre-purchased tickets in the performance and other fees of the performance, so as to effectively meet the requirements and rights and interests of the user.
In this embodiment, as an optional embodiment, according to the reasonable prediction of the price of the event ticket by the machine learning algorithm of the number of pre-purchased tickets of the target performance, the sponsor may combine the estimated marketing cost of the target performance and other costs of the target performance to adjust the decision making and marketing strategies, including adjusting the activity site, pricing strategy, marketing popularization, etc., to determine to meet the user requirement to the maximum extent and improve the ticket sales effect.
In this embodiment, as an optional embodiment, predicting the fare of the target performance further includes:
storing the participation intent vote data to the blockchain;
determining a performance type of the target performance;
acquiring an expanded user which is the same as the participation will in the participation will voting data and does not participate in the target performance will voting from the performance corresponding to the performance type from the blockchain;
the method for obtaining the number of pre-purchased tickets of the target performance by using a machine learning algorithm based on the participation intention voting data corresponding to each user, the participation intention voting data of the historical performance and the ticket purchasing record comprises the following steps:
and acquiring the number of pre-purchased tickets of the target performance by using the machine learning algorithm based on the participation intention voting data corresponding to each user, the participation intention voting data and the ticket purchasing record of the history performance by the expanded user.
In this embodiment, as an optional embodiment, the obtaining, from the blockchain, from the performance corresponding to the performance type, an extended user that is the same as the participation intention in the participation intention voting data and does not participate in the target performance intention vote includes:
Extracting data characteristics of the participation intention voting data corresponding to each user, and acquiring historical participation intention voting data from performances corresponding to the performance types from the blockchain and extracting the data characteristics;
initializing the number of clusters, and randomly initializing a cluster center point of each initialized cluster;
for each willingness voting data, calculating the distance between each willingness voting data and each clustering center point according to the data characteristics of the willingness voting data, and distributing the willingness voting data to the clusters corresponding to the closest clustering center point, wherein the willingness voting data comprises participation willingness voting data and history participation willingness voting data;
updating a cluster center point of the cluster according to willingness voting data contained in the cluster;
distributing each willingness voting data to the cluster corresponding to the updated cluster center point closest to the cluster until the cluster meets the cluster convergence condition set in advance;
and extracting other users except the users corresponding to the participation intention voting data from the finally obtained clusters to obtain the expanded users.
S103, receiving a ticket buying request of the target performance according to the ticket price, generating a presentation ticket, constructing a mapping relation between the presentation ticket and user identification information carried in the ticket buying request, and storing the mapping relation into a blockchain;
In this embodiment, as an optional embodiment, the sponsor may set a ticket management contract through an intelligent contract on the blockchain, where the ticket management contract includes predefined rules and conditions, so as to implement management of the ticketing process. The ticket management contract can receive a ticket buying request of a user, generate a presentation ticket, and the presentation ticket contains a mapping relation of ticket buying user identification information. In this embodiment, as an alternative embodiment, the sponsor may set and sell the ticket for the performance according to the predicted fare of the target performance through the ticket management contract.
In this embodiment, as an alternative embodiment, the ticket management contract may implement permanent recording of the reputation value of the user by using a point, where the point will affect the ticket purchasing process of the subsequent performance of the user. As an alternative embodiment, the event sponsor may also ask the user to provide a certain amount of assurance. If the user finally purchases the ticket, the deposit will be refunded; if the user eventually fails to purchase a ticket, the deposit will be withdrawn. This will help predict the number of pre-tickets in advance and reduce the occurrence of false orders.
In this embodiment, as an optional embodiment, the event sponsor may set a ticket management contract in the blockchain, and the ticket management contract rule may include: the user needs to provide a certain number of digital assets as a deposit. If the user finally successfully purchases the ticket, the security deposit is returned to the user; if the user eventually fails to purchase a ticket, the deposit will be or be partially refunded.
In this embodiment, as an optional embodiment, setting the ticket management contract includes:
setting ticket management contract rules, and determining conditions of intelligent contracts, including deposit amount, refund rules, and forfeiting rules;
creating ticket management contracts, writing intelligent contract codes by using a blockchain platform, and deploying the intelligent contract codes to a blockchain network;
the user interacts with the ticket management contract in the ticket purchasing process, and can be required to provide the security as a certificate for participating in the event;
if the ticket purchase of the user is successful, the ticket management contract automatically returns the guarantee deposit to the user, and if the ticket purchase of the user fails, the guarantee deposit is processed according to the ticket management contract rule;
ticket management contracts may be automatically executed according to set rules and recorded on the blockchain. The user and the event sponsor can oversee execution of the ticket management contract by transaction history and contract status on the blockchain.
In this embodiment, as an optional embodiment, the implementation and the functional description of the ticket management contract interface are shown in table 2.
TABLE 2
S104, scanning a ticket of the ticket purchasing user aiming at the target performance, and acquiring user identification information mapped by the ticket from the blockchain;
In this embodiment, as an optional embodiment, the ticket of the ticket buyer is scanned, and the user identification information of the ticket mapping may be obtained from the blockchain. The ticket purchasing situation of the user in the ticket management contract can be monitored in real time by using the blockchain technology, and abnormal situations and cheating behaviors can be found timely. If an abnormal condition is found, immediately taking corresponding measures including, but not limited to: prevents the ticket purchasing behavior of cheaters, thereby ensuring the fairness and credibility of ticket purchasing.
In this embodiment, as an optional embodiment, the sponsor may establish a blockchain-based monitoring system, and implement supervision on the ticket purchasing process of the user through blockchain real-time monitoring. The sponsor can effectively prevent the problems of repeated ticket purchasing, ticket purchasing exceeding limit, false order purchasing and the like by utilizing the monitoring system, provide a fair and transparent ticket purchasing environment for the user, and enhance the management and control capability of the sponsor on performances.
In this embodiment, as an alternative embodiment, the monitoring system may monitor the related information stored on the blockchain in real time, and the method of real-time monitoring may include, but is not limited to, an abnormal situation monitoring method, a real-time data monitoring method, and a data authenticity monitoring method.
In this embodiment, as an alternative embodiment, the sponsor may implement real-time high quality supervision of the performance through the monitoring system:
abnormal condition monitoring: different types of anomalies are defined, such as repeat ticket purchases, exceeding ticket purchase limits, illegal transactions, etc. These anomalies may be determined based on the sponsor's specifications and needs. Based on the defined abnormal situation, the monitoring system can detect the occurrence of the abnormal situation through a data analysis and rule matching mode. If an abnormal situation is found, the system will trigger a corresponding alarm or processing mechanism. Once an abnormal situation is found, the monitoring system takes corresponding measures to process. Measures taken may include preventing the continuation of the abnormal behavior, notifying relevant personnel for further investigation, freezing relevant transactions, etc.;
and (3) real-time data monitoring: the monitoring system collects data related to ticket buying in real time, including user information, ticket buying records, transaction details and the like. The data can be recorded on the blockchain to ensure the safety and the credibility of the blockchain, and after the performance is finished, the number of participants in the whole performance can be counted accurately according to the sales condition of the performance tickets stored in the blockchain;
Data authenticity monitoring: the monitoring system verifies and compares the acquired data to ensure the consistency and accuracy of the data. For example, verifying the validity of the identity information of the ticket purchaser, comparing whether the number of tickets purchased meets the regulations, and the like.
In this embodiment, as an optional embodiment, the monitoring system may notify the event sponsor and related personnel of the abnormal situation and the processing result in time by notifying and generating a report in real time. Thus, actions can be taken in time, and effective monitoring of the ticket purchasing system is maintained.
S105, acquiring user information of the ticket buying user, and checking tickets based on the user identification information mapped by the presentation ticket and the user information.
In this embodiment, as an optional embodiment, the host may obtain the user information of the ticket purchasing user through the presentation ticket, and compare with the user identification information mapped by the presentation ticket, thereby completing ticket checking.
In this embodiment, as an optional embodiment, the sponsor may also count the entire performance by using the data of the ticket management contract in the blockchain, and perform visual analysis on the attributes of the users in the entire performance. As an alternative embodiment, visual analysis of user attributes may be implemented using the Sang Ji graph approach.
In this embodiment, as an optional embodiment, the performance supervision based on the blockchain further includes:
acquiring the actual number of users of the target performance from the blockchain;
and adjusting parameters of the machine learning algorithm based on the participation intention voting data corresponding to the actual user, the participation intention voting data of the historical performance and the ticket purchasing record.
In the embodiment, the user is used for collecting the participation intention voting data of the target performance; predicting the fare of the target performance according to the participation intention voting data; receiving a ticket buying request of the target performance according to the ticket price, generating a presentation ticket, constructing a mapping relation between the presentation ticket and user identification information carried in the ticket buying request, and storing the mapping relation into a blockchain; scanning a ticket of a ticket purchasing user aiming at the target performance, and acquiring user identification information mapped by the ticket from the blockchain; and acquiring the user information of the ticket buying user, and checking the ticket based on the user identification information of the presentation ticket mapping and the user information. Therefore, through data acquisition and data analysis, the estimation of the number of participants and the setting of ticket prices are realized, and a targeted marketing scheme is formulated. Meanwhile, unified credit management is carried out on the user accounts on the blockchain, so that a supervision effect is achieved, and fairness and high efficiency of electronic ticket sales are realized.
Based on the same inventive concept, as shown in fig. 2, the embodiment of the invention further provides a performance supervision device based on a blockchain, which comprises:
the voting collection module 201 is used for collecting the participation intention voting data of the user on the target performance;
in this embodiment, as an optional embodiment, the step of collecting the willingness voting data of the user to participate in the target performance includes:
publishing activity information of the target performance on a blockchain;
receiving participation intention voting data of a user for the target performance, wherein the participation intention voting data comprises identity information of the user;
and verifying the identity information, and storing the verified participation intention voting data into the blockchain.
In this embodiment, as an alternative embodiment, the sponsor may post the activity information for the target performance on the blockchain. The user registers by providing personal information to complete the authentication. Each user has a unique blockchain identity.
In this embodiment, as an optional embodiment, the user may participate in voting before purchasing the ticket, express an interest in the target performance and a participation intention, and the participation intention voting data includes identity information of the corresponding user. As an alternative embodiment, participation in willingness voting data may be verified by intelligent contracts on the blockchain, including verifying the user's identity information and validity of the voting options, and checking if repeated voting or other cheating actions are present. The participation intention voting data meeting the specification is verified and added to the blocks on the blockchain.
In this embodiment, as an optional embodiment, the blockchain records each participation intention voting data including the identity information of the user and the voting options. And (5) accurately predicting the number of participants by analyzing the participation intention voting data.
A fare prediction module 202, configured to predict a fare of the target performance according to the participation intention voting data;
in this embodiment, as an optional embodiment, the sponsor may predict the fare of the target performance according to the participation intention voting data, and the specific steps may include:
and acquiring historical behavior data of the user corresponding to each participation intention voting data, wherein the historical behavior data comprises: voting data and ticket buying records of participation will of the historical performance;
based on the participation intention voting data corresponding to each user, the participation intention voting data of the historical performance and the ticket purchasing record, acquiring the number of the pre-purchased tickets of the target performance by using a machine learning algorithm;
and determining the fare of the target performance based on the number of pre-purchased tickets, the cost of the target performance at the place of play, the estimated marketing cost of the target performance and other costs of the target performance.
In this embodiment, as an alternative embodiment, the machine learning algorithm may include: a deep learning algorithm, a cluster analysis algorithm, an association rule analysis algorithm and a random forest algorithm.
In this embodiment, as an alternative embodiment, the blockchain is used to collect historical behavior data of the user, including personal information, transaction records, voting records, and the like of the user. As an alternative embodiment, the historical behavior data of the user can be preprocessed, including removing duplicate data, processing missing values, outliers and the like, so as to ensure the accuracy and consistency of the data.
In this embodiment, as an optional embodiment, based on participation intention voting data corresponding to each user, participation intention voting data for a historical performance, and a ticket purchasing record, a machine learning algorithm such as a deep learning algorithm, a cluster analysis algorithm, an association rule analysis algorithm, and a random forest algorithm is used to obtain the number of pre-purchased tickets for the target performance.
In this embodiment, the participation wish voting data and the machine learning algorithm are combined, the number of participants is accurately estimated from multiple dimensions, and the number of pre-purchased tickets for the target performance is obtained. And correspondingly adjusting decision and marketing strategies according to the number of people buying tickets in advance so as to provide better experience and meet the demands of users.
In this embodiment, as an optional embodiment, according to a reasonable prediction of the ticket price of the event by the machine learning algorithm of the number of pre-purchased tickets of the target performance, the sponsor may combine the estimated marketing cost of the target performance and other costs of the target performance to adjust the decision making and marketing strategies.
In this embodiment, as an optional embodiment, predicting the fare of the target performance further includes:
storing the participation intent vote data to the blockchain;
determining a performance type of the target performance;
acquiring an expanded user which is the same as the participation will in the participation will voting data and does not participate in the target performance will voting from the performance corresponding to the performance type from the blockchain;
the method for obtaining the number of pre-purchased tickets of the target performance by using a machine learning algorithm based on the participation intention voting data corresponding to each user, the participation intention voting data of the historical performance and the ticket purchasing record comprises the following steps:
and acquiring the number of pre-purchased tickets of the target performance by using the machine learning algorithm based on the participation intention voting data corresponding to each user, the participation intention voting data and the ticket purchasing record of the history performance by the expanded user.
In this embodiment, as an optional embodiment, the obtaining, from the blockchain, from the performance corresponding to the performance type, an extended user that is the same as the participation intention in the participation intention voting data and does not participate in the target performance intention vote includes:
extracting data characteristics of the participation intention voting data corresponding to each user, and acquiring historical participation intention voting data from performances corresponding to the performance types from the blockchain and extracting the data characteristics;
initializing the number of clusters, and randomly initializing a cluster center point of each initialized cluster;
for each willingness voting data, calculating the distance between each willingness voting data and each clustering center point according to the data characteristics of the willingness voting data, and distributing the willingness voting data to the clusters corresponding to the closest clustering center point, wherein the willingness voting data comprises participation willingness voting data and history participation willingness voting data;
updating a cluster center point of the cluster according to willingness voting data contained in the cluster;
distributing each willingness voting data to the cluster corresponding to the updated cluster center point closest to the cluster until the cluster meets the cluster convergence condition set in advance;
And extracting other users except the users corresponding to the participation intention voting data from the finally obtained clusters to obtain the expanded users.
The ticketing module 203 is configured to receive a ticket purchasing request for the target performance according to the ticket price, generate a presentation ticket, construct a mapping relationship between the presentation ticket and user identification information carried in the ticket purchasing request, and store the mapping relationship in a blockchain;
in this embodiment, as an optional embodiment, the sponsor may set a ticket management contract through an intelligent contract on the blockchain, where the ticket management contract includes predefined rules and conditions, so as to implement management of the ticketing process. The ticket management contract can receive a ticket buying request of a user, generate a presentation ticket, and the presentation ticket contains a mapping relation of ticket buying user identification information.
In this embodiment, as an alternative embodiment, the ticket management contract may use a point manner to implement permanent recording of the reputation value of the user, and may also require the user to provide a certain guarantee. Can help to predict the number of pre-ticket agents in advance and reduce the occurrence of false orders.
In this embodiment, as an alternative embodiment, the ticket management contract may be automatically executed according to a set rule, and recorded on the blockchain. The execution of the ticket management contract may be supervised by transaction history and contract status on the blockchain.
The ticket selling verification module 204 is configured to scan a ticket of the ticket buyer for the target performance, and acquire user identification information mapped by the ticket from the blockchain;
in this embodiment, as an optional embodiment, the ticket of the ticket buyer is scanned, and the user identification information of the ticket mapping may be obtained from the blockchain. And monitoring ticket purchasing conditions of users in ticket management contracts in real time by using a blockchain technology, and timely discovering abnormal conditions and cheating behaviors. If abnormal conditions are found, immediately taking corresponding measures, thereby ensuring fairness and credibility of ticket buying.
In this embodiment, as an optional embodiment, the sponsor may set up a blockchain-based monitoring system, and monitor the ticket purchasing process of the user in real time through blockchain monitoring, so as to prevent the occurrence of problems such as repeated ticket purchasing, ticket purchasing exceeding the limit, false order, etc., provide a fair and transparent ticket purchasing environment for the user, and enhance the management and control capability for the performance.
In this embodiment, as an alternative embodiment, the monitoring system may monitor the related information stored in the blockchain in real time, and the method for real-time monitoring may include an abnormal situation monitoring method, a real-time data monitoring method, and a data authenticity monitoring method. As an alternative embodiment, the monitoring system can also inform the event sponsor and related personnel of the abnormal situation and the processing result in time by means of real-time notification and report generation.
And the ticket checking module 205 acquires the user information of the ticket purchasing user, and performs ticket checking based on the user identification information mapped by the presentation ticket and the user information.
In this embodiment, as an optional embodiment, the host may obtain the user information of the ticket buyer through the presentation ticket, and compare the user information with the user identification information mapped by the presentation ticket, so as to complete ticket checking. As an alternative embodiment, statistics can be performed on the whole performance through the data of the ticket management contracts in the blockchain, and visual analysis can be performed on the attributes of the users in the whole performance. Visual analysis of user attributes is achieved, for example, using the Sang Ji graph.
Based on the same inventive concept, the embodiments of the present invention further provide a storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the blockchain-based performance supervision method in any of the possible implementations described above.
Alternatively, the storage medium may be a non-transitory computer readable storage medium, for example, a ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
Based on the same inventive concept, referring to fig. 3, the embodiment of the present invention further provides an electronic device, including a memory 101 (such as a nonvolatile memory), a processor 102, and a computer program stored in the memory 101 and capable of running on the processor 102, where the steps of the blockchain-based performance supervision method in any of the possible implementations described above are implemented by the processor 102 when the program is executed, and may be equivalent to the blockchain-based performance supervision apparatus as before, and of course, the processor may also be used to process other data or operations. The electronic device may be a PC, server, terminal, etc.
As shown in fig. 3, the electronic device may generally further include: memory 103, network interface 104, and internal bus 105. In addition to these components, other hardware may be included, which is not described in detail.
It should be noted that the above-mentioned blockchain-based performance supervision apparatus may be implemented by software, and is used as a logical apparatus, and is formed by the processor 102 of the electronic device where the blockchain-based performance supervision apparatus is located reading the computer program instructions stored in the nonvolatile memory into the memory 103 and running the computer program instructions.
Embodiments of the subject matter and the functional operations described in this specification can be implemented in: digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and structural equivalents thereof, or a combination of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by, or to control the operation of, data processing apparatus. Alternatively or additionally, the program instructions may be encoded on a manually-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode and transmit information to suitable receiver apparatus for execution by data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.
The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Computers suitable for executing computer programs include, for example, general purpose and/or special purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read only memory and/or a random access memory. The essential elements of a computer include a central processing unit for carrying out or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks, etc. However, a computer does not have to have such a device. Furthermore, the computer may be embedded in another device, such as a mobile phone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device such as a Universal Serial Bus (USB) flash drive, to name a few.
Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disk or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features of specific embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. On the other hand, the various features described in the individual embodiments may also be implemented separately in the various embodiments or in any suitable subcombination. Furthermore, although features may be acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous.
Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings are not necessarily required to be in the particular order shown, or sequential order, to achieve desirable results.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A blockchain-based performance supervision method, comprising:
collecting participation intention voting data of a user on a target performance;
predicting the fare of the target performance according to the participation intention voting data;
receiving a ticket buying request of the target performance according to the ticket price, generating a presentation ticket, constructing a mapping relation between the presentation ticket and user identification information carried in the ticket buying request, and storing the mapping relation into a blockchain;
scanning a ticket of a ticket purchasing user aiming at the target performance, and acquiring user identification information mapped by the ticket from the blockchain;
and acquiring the user information of the ticket buying user, and checking the ticket based on the user identification information of the presentation ticket mapping and the user information.
2. The method of claim 1, wherein the collecting willingness-to-vote data for the user's participation in the target performance comprises:
publishing activity information of the target performance on a blockchain;
receiving participation intention voting data of a user for the target performance, wherein the participation intention voting data comprises identity information of the user;
and verifying the identity information, and storing the verified participation intention voting data into the blockchain.
3. The method of claim 1, wherein predicting the fare for the target performance based on the participation intent vote data comprises:
and acquiring historical behavior data of the user corresponding to each participation intention voting data, wherein the historical behavior data comprises: voting data and ticket buying records of participation will of the historical performance;
based on the participation intention voting data corresponding to each user, the participation intention voting data of the historical performance and the ticket purchasing record, acquiring the number of the pre-purchased tickets of the target performance by using a machine learning algorithm;
and determining the fare of the target performance based on the number of pre-purchased tickets, the cost of the target performance at the place of play, the estimated marketing cost of the target performance and other costs of the target performance.
4. A method according to claim 3, wherein the machine learning algorithm comprises: a deep learning algorithm, a cluster analysis algorithm, an association rule analysis algorithm and a random forest algorithm.
5. A method according to claim 3, characterized in that the method further comprises:
storing the participation intent vote data to the blockchain;
determining a performance type of the target performance;
acquiring an expanded user which is the same as the participation will in the participation will voting data and does not participate in the target performance will voting from the performance corresponding to the performance type from the blockchain;
the method for obtaining the number of pre-purchased tickets of the target performance by using a machine learning algorithm based on the participation intention voting data corresponding to each user, the participation intention voting data of the historical performance and the ticket purchasing record comprises the following steps:
and acquiring the number of pre-purchased tickets of the target performance by using the machine learning algorithm based on the participation intention voting data corresponding to each user, the participation intention voting data and the ticket purchasing record of the history performance by the expanded user.
6. The method of claim 5 wherein the obtaining, from the blockchain, from the performance corresponding to the performance type, expanded users that are the same as the participation in the willingness voting data and that do not participate in the target performance willingness vote comprises:
extracting data characteristics of the participation intention voting data corresponding to each user, and acquiring historical participation intention voting data from performances corresponding to the performance types from the blockchain and extracting the data characteristics;
initializing the number of clusters, and randomly initializing a cluster center point of each initialized cluster;
for each willingness voting data, calculating the distance between each willingness voting data and each clustering center point according to the data characteristics of the willingness voting data, and distributing the willingness voting data to the clusters corresponding to the closest clustering center point, wherein the willingness voting data comprises participation willingness voting data and history participation willingness voting data;
updating a cluster center point of the cluster according to willingness voting data contained in the cluster;
distributing each willingness voting data to the cluster corresponding to the updated cluster center point closest to the cluster until the cluster meets the cluster convergence condition set in advance;
And extracting other users except the users corresponding to the participation intention voting data from the finally obtained clusters to obtain the expanded users.
7. The method of claim 6, wherein the method further comprises:
acquiring the actual number of users of the target performance from the blockchain;
and adjusting parameters of the machine learning algorithm based on the participation intention voting data corresponding to the actual user, the participation intention voting data of the historical performance and the ticket purchasing record.
8. A blockchain-based performance monitoring device, wherein the blockchain-based performance monitoring device comprises:
the voting collection module is used for collecting the participation intention voting data of the user on the target performance;
the fare prediction module is used for predicting the fare of the target performance according to the participation intention voting data;
the ticket selling module is used for receiving a ticket buying request of the target performance according to the ticket price, generating a presentation ticket, constructing a mapping relation of the presentation ticket and user identification information carried in the ticket buying request, and storing the mapping relation into a blockchain;
the ticket selling verification module is used for scanning a ticket of the ticket purchasing user aiming at the target performance and acquiring user identification information mapped by the ticket from the blockchain;
And the ticket checking module acquires the user information of the ticket purchasing user, and checks the ticket based on the user identification information mapped by the presentation ticket and the user information.
9. A storage medium having stored thereon a program or instructions which when executed by a processor perform the steps of the blockchain-based performance supervision method of any of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the blockchain-based performance supervision method of any of claims 1 to 7 when the program is executed by the processor.
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