CN113344404A - Reputation sharing dual-stage data quality evaluation method based on block chain - Google Patents

Reputation sharing dual-stage data quality evaluation method based on block chain Download PDF

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CN113344404A
CN113344404A CN202110677792.6A CN202110677792A CN113344404A CN 113344404 A CN113344404 A CN 113344404A CN 202110677792 A CN202110677792 A CN 202110677792A CN 113344404 A CN113344404 A CN 113344404A
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王利娥
马士乾
李先贤
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Abstract

The invention discloses a reputation sharing double-stage data quality evaluation method based on a block chain, which utilizes a reputation sharing mechanism based on the block chain to store historical data of a task participant, and can utilize reputation values of the task participant in other fields as a judgment basis for the reliability of the task participant for task participants who do not participate in a task, thereby not only improving the selection quality of the task participants, but also solving the problems of storage and cold start of the historical data in a dynamic environment. Aiming at the pre-estimated true phase problem in the initial stage in the true phase discovery algorithm, a true phase discovery algorithm based on machine learning is provided, data collected from task participants are subjected to preliminary pre-judgment through a machine learning model, a pre-estimated value which is more accurate than a randomly determined pre-true phase is obtained and serves as an initial value, the deviation between the pre-estimated value and a correct answer is reduced, and the execution efficiency of the true phase discovery algorithm and the accuracy of a sensing result can be effectively improved.

Description

Reputation sharing dual-stage data quality evaluation method based on block chain
Technical Field
The invention relates to the technical field of crowd sensing, in particular to a reputation sharing dual-stage data quality evaluation method based on a block chain.
Background
With the rapid development of embedded devices, wireless sensor networks, internet of things intelligent mobile terminals and the like, ubiquitous intelligent systems integrating sensing, computing and communication capabilities are being widely deployed and gradually integrated into the daily living environment of people. A new data collection and processing model called crowd sensing (CrowdSensing) has become a research focus in recent years and has been deployed in a number of fields such as environmental monitoring, indoor positioning, public safety, personalized recommendations, traffic planning, etc.
The basic idea of crowd-sourcing perception is to solve various complicated problems by using crowd-sourcing, and the system model mainly comprises: task publishers, perceptual task platforms and task participants on blockchains. The task publisher publishes a corresponding task to recruit task participants to serve the task through the perception task platform on the blockchain, the task participants upload the perception task platform or the task publisher on the blockchain after collecting data, and the task publisher receives the data and then issues corresponding rewards to the task participants and the perception task platform on the blockchain.
However, due to factors such as differences of various mobile devices, different cognition of different people on the same task, inherent noise and the like, the quality of perception data obtained by task participants after the task is executed is uneven, results provided by different task participants for the same task are differentiated, and the quality of data provided by the task participants directly influences the final perception result. Therefore, how to effectively manage and utilize the perception data provided by the task participants is a difficult problem.
Disclosure of Invention
The invention aims to solve the problem of low perceived data quality in the conventional crowd sensing and provides a credit sharing dual-stage data quality evaluation method based on a block chain.
In order to solve the problems, the invention is realized by the following technical scheme:
the reputation sharing double-stage data quality evaluation method based on the block chain comprises the following steps:
step 1, a task participant applies for registration to a perception task platform on a block chain and requests to join a perception network to execute a perception task; in the process, the task participant sends personal information of the task participant to a perception task platform on the block chain in an encryption mode, and authorizes the perception task platform on the block chain to inquire credit values of the task participant in various fields;
step 2, a task publisher publishes a task requirement to a perception task platform on the block chain;
step 3, the perception task platforms on all the block chains on the block chains issue perception tasks to task participants according to task requirements, and if the task participants agree to join the task, the perception task platforms on the block chains submit personal information to the task participants as a proof of task participation;
step 4, the perception task platform on the block chain takes the personal information of the task participant as input, inquires the credit values of the task participant and the fields related to the task requirement, and calculates the comprehensive credit values of the fields; selecting task participants with higher comprehensive reputation value as formal task participants according to the number of people in the task requirement, and distributing public keys and identity certificates to the formal task participants;
step 5, after receiving the public key and the identity certificate, the formal task participant executes the perception task according to the task requirement and collects perception data;
step 6, after the formal task participants finish the collection of the perception data, the public key distributed by the perception task platform on the block chain is used for encryption to obtain encrypted perception data, and the encrypted perception data is sent to the perception task platform on the block chain;
step 7, decrypting the encrypted sensing data sent by the formal task participants by the sensing task platform on the block chain to obtain sensing data, and evaluating the sensing data by using a truth value discovery algorithm based on machine learning to obtain an evaluated sensing data set;
and 8, the perception task platform on the block chain sends the evaluated perception data set to the task publisher, the task publisher obtains the required data, and the execution process of the whole task is finished.
In step 4, the comprehensive reputation value C is:
Figure BDA0003121501800000021
in the formula, RiReputation value for ith domain; xiIs a weight of the i-th domain,
Figure BDA0003121501800000022
n is the number of selected domains.
In step 7, the sensing data is evaluated by using a truth finding algorithm based on machine learning, and the specific process of obtaining the evaluated sensing data is as follows:
7.1, performing off-line training on the decision tree by using given sensing data with quality grade marks to obtain a trained decision tree model;
7.2, sending the perception data sent by all formal task participants into a trained decision tree model to obtain the quality grade of each perception data;
7.3, respectively selecting perception data from the perception data of different quality grades to form a primary screening perception data set based on the given quality grade proportion;
and 7.4, performing iterative computation by taking the initially screened sensing data set as an initial true value of a true value discovery algorithm to obtain an evaluated sensing data set.
Compared with the prior art, the invention has the following characteristics:
1. the reputation sharing mechanism based on the block chain is used for storing the historical data of the task participants, the reputation values of the task participants in other fields can be used as the judgment basis of the reliability of the task participants for the task participants who do not participate in the task (such as sesame credits in treasures for payment, the task participants can be used for free deposit rental cars, free deposit lodging in hotels, shopping privileges and the like), and the similar principle is used for selecting the task participants, so that the selection quality of the task participants can be improved, and the problems of historical data storage and cold start under the dynamic environment can be solved.
2. Aiming at the pre-estimated true phase problem at the initial stage in the true phase Discovery algorithm, a machine learning-based true phase Discovery algorithm (ML Truth Discovery) is provided, data collected from task participants are subjected to preliminary prejudgment through a machine learning model, a preestimation value which is more accurate than a randomly determined true phase is obtained as an initial value, the deviation between the preestimation value and a correct answer is reduced, and the execution efficiency of the true phase Discovery algorithm and the accuracy of a sensing result can be effectively improved.
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FIG. 1 is a schematic diagram of a blockchain-based reputation sharing dual-phase data quality assessment method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to specific examples.
The crowd sensing system of the invention consists of a task publisher, a task participant, a sensing task platform and a block chain.
The task publisher: if data in a certain field needs to be used, the task publisher sends a requirement for certain specific data to the perception task platform, the platform replaces the requirement for recruitment of people and data preference, and the task publisher only needs to pay corresponding remuneration to the perception task platform and the task participants to enable the perception task platform and the task participants to continue to provide data for work. Rewards are typically stored in a cognitive task platform, with the platform rewarding task participants based on the quality of completion of the final data.
The task participant: the task participants use various terminal devices (mobile phones, bracelets and the like) at their sides to collect data of the received tasks. The selected task participant typically has a higher reputation value, or superior data quality completion in past tasks. For the task participants (without history records or data quality certificates) participating in the tasks of the crowd sensing system for the first time, the task participants can be preliminarily judged and audited through the cross-domain reputation value through the proposed reputation sharing mechanism, namely whether the task participants can be added into the crowd sensing system as the task participants by evaluating some habitual behaviors and data of the task participants in other domains.
Perception task platform: as a task-aware launching mechanism, a task-aware platform issues a task participant required by recruitment of a corresponding task to serve the task through an instruction of a task issuer. After the task is completed, the task participants upload the perceived data to a perception task platform through a corresponding encryption mechanism, the perception task platform executes a Truth Discovery (Truth Discovery) algorithm to screen and evaluate the data quality and accuracy, and then the obtained result is sent to a block chain to be subjected to uplink storage (the result stored in the block chain is the completion quality of the participants, namely the completion quality of a certain perception task, how much the participants complete the quality, and how much weight is in the final result.
Block chains: the block chain has the main function of storing all data transaction records in a distributed mode, and the full block coverage of the stored records is realized by implementing corresponding intelligent contracts and consensus algorithms to achieve consistency. The blockchain is similar to a general hub and is used for storing various transaction information and partial necessary data, wherein each node corresponds to a perception platform and provides the platform with the required transaction data. When a certain record needs to be inquired whether existing or not or whether the record is legal, the inquiry can be completed through a corresponding structure (such as a Merkle Tree) in a block header. According to the characteristics of the block chain, the data stored in the block can not be tampered under the common condition, the decentralization is realized, the single-point fault is prevented, the safety is guaranteed to a certain degree, and the requirement of a sensing system can be met.
The invention provides a reputation sharing double-stage data quality evaluation method based on a block chain, which specifically comprises the following steps as shown in figure 1:
step 1, in an initialization stage, a task participant applies for registration to a perception task platform on a block chain and requests to join a perception network to execute a perception task; in the process, the task participant sends personal information (such as name, gender, identity card number and the like) of the task participant to a perception task platform on the block chain in an encryption mode, and authorizes the perception task platform on the block chain to inquire credit values of the task participant in various fields.
And 2, the task publisher publishes a task requirement to the perception task platform on the block chain.
Task requirements are specific requirements for the requested data, such as: task deadlines, size limits on the amount of data, precision or pixels required for the data, total reward for the task, limits on the number of people participating in the task, etc.
And 3, the perception task platform on the block chain issues a perception task to the task participants to recruit the task participants according to the task requirements, and if the task participants agree to join the execution flow of the task, the perception task platform on the block chain submits personal information (such as photos or identity information) to serve as a participation certificate.
And the perception task platforms on the block chains recruit task participants according to task requirements, and if the task participants receive the tasks of the perception task platforms on one block chain, the same task information of other platforms can not be received. As more than one perception task platform on the blockchain may exist in the same area, for all task participants in a certain area, if the same tasks sent from different platforms in the area are received, the first arriving task is executed according to the sequence of the received tasks, and then the received tasks are discarded without feedback to the corresponding platforms; if the same tasks sent by a plurality of sensing platforms are received at the same time, one sensing platform is randomly selected to execute the tasks; the task participants can only execute one task at a time and can not be interrupted, and other perception tasks can be continuously executed after the task is completed.
Step 4, the perception task platform on the block chain takes the personal information of the task participant as input, inquires the credit values of the task participant and the fields related to the task requirement, and calculates the comprehensive credit values of the fields; and selecting task participants with higher comprehensive reputation values as formal task participants according to the number of people in the task requirements, and distributing public keys and identity certificates to the formal task participants.
After receiving the information of the task participants, the perception task platform on the block chain executes the quality evaluation process of the first stage, namely, by utilizing the evaluation mechanism of credit sharing, the personal information (name, identification number, etc.) of the task participant is used as input, the relevant credit values stored by the task participant on a block chain (such as shopping platforms: Taobao, Tianmao, Jingdong, comment area know, finance APP Paibao, Jingdong finance, etc.) are inquired, the comprehensive credit value is obtained after comprehensive analysis, and evaluating the task participants by using the comprehensive credit value, arranging part of the people before the evaluation in descending order according to the comprehensive credit value as formal task participants, and after the identity verification is finished, the formal task participants receive a public key and an identity certificate distributed by a perception task platform on the block chain so as to encrypt submitted data by the formal task participants.
The specific formula for the composite reputation value C is defined as follows:
Figure BDA0003121501800000041
in the formula, XiIs a weight of the i-th domain,
Figure BDA0003121501800000051
Riand n is the credit value of the ith domain, and the number of the selected domains.
The block chain stores transaction information and personal data of participants, namely the block chain is used as a total hub consisting of a plurality of nodes (perception task platforms) to assist the perception platform, data are called from the chain to be verified and audited, then all nodes in the block chain execute corresponding intelligent contracts and consensus algorithms, and the task participant information is stored in the blocks to achieve consistency, so that the overload problem of some nodes can be effectively avoided, and single point faults can be avoided.
For the task participants who are added into the crowd sensing system for the first time, corresponding information such as reputation values of the task participants is stored, so that historical data which can be inquired about the task participants who are added and quitted are available in the subsequent sensing task execution process; in addition, for the calculation of the reputation value of the initial participant, a given calculation formula is utilized to obtain a result according to the reputation value of the initial participant in the applications of Paibao, Jingdong finance, Tianmao and the like as a first reputation value in the crowd sensing system, and the later reputation value updating is respectively based on reputation values of different degrees according to the level of the quality of the finished task. For the task participants who join the crowd sensing system again, if the task participants join the crowd sensing system again within one month after exiting, the task participants can inherit the last credit value; if the withdrawal exceeds one month, the withdrawal is sequentially decreased at a rate of 20 points per month.
And 5, after receiving the public key and the identity certificate, the formal task participant executes a perception task according to the task requirement and collects data.
For example, when data collection of urban traffic is performed, task participants need to collect the following information: the traffic light display condition of each traffic main road in a city during the peak of going to work and off work, the congestion condition of each road junction in different time periods, the road planning and building layout condition near the main road branch, and the number limiting condition of vehicles in different time periods.
And 6, after the formal task participants finish the collection of the perception data, encrypting the perception data by using a public key distributed by the perception task platform on the block chain to obtain encrypted perception data, and sending the encrypted perception data to the perception task platform on the block chain.
Step 7, after receiving the perception data of the formal task participants on a certain specific task, the perception task platform on the block chain executes a Truth Discovery (ML try Discovery) algorithm based on machine learning to perform a second-stage quality evaluation on the data, specifically:
and 7.1, performing off-line training on the decision tree by using given sensing data with quality grade marks to obtain a trained decision tree model.
For the collected perception data, a primary accuracy judgment is firstly made through a learning model obtained through training, and the learning model can be obtained through off-line training according to a large amount of traffic data, such as a decision tree. Performing offline training on the decision tree by using given sensing data (such as a large amount of traffic sensing data) with quality grade marks to obtain a decision tree model; considering that the generalization capability of the decision tree model directly obtained through the training set is limited, the obtained decision tree model is pruned and optimized by using two stages of pre-pruning and post-pruning, so that the accuracy and the accuracy are improved, and the trained decision tree model is obtained.
And 7.2, sending the perception data sent by all formal task participants into the trained decision tree model to obtain the quality grade of each perception data.
And 7.3, selecting the perception data from the perception data of different quality grades respectively based on the given quality grade proportion to form a primary screening perception data set.
In the invention, the higher the quality grade is, the greater the allocated proportion is, the greater the proportion of the sensing data of the quality grade in the primary screening sensing data set is; the lower the quality level, the smaller the assigned weight, and the smaller the weight of the sensing data of the quality level in the prescreened sensing data set.
For example, the quality levels of the sensing data are classified into A, B, C grades, wherein the grade a is the highest, the assigned specific gravity is 60%, the grade B is the medium, the assigned specific gravity is 40%, the grade C is the lowest, and the assigned specific gravity is 20%, so that in the prescreened sensing data set, 60% of the sensing data are sensing data with the quality level of grade a, 40% of the sensing data are sensing data with the quality level of grade B, and 20% of the sensing data are sensing data with the quality level of grade C.
And 7.4, performing iterative computation by taking the initially screened sensing data set as an initial true value of a true value discovery algorithm, namely performing weight and true value estimation in iteration, and obtaining the evaluated sensing data when the iteration times reach a certain number of conditions or the result obtained after iterative computation meets the requirements.
In a conventional truth finding algorithm, an initial truth value of the algorithm is usually given by a task platform according to task requirements, namely a predicted value is used as the start of algorithm execution; however, since the initial value is usually rough and only related to a task, rather than an answer required by the task, the initial value at this time is not large in reference value and is not favorable for the result of the subsequent algorithm execution. The quality grade of the sensing result is determined through learning, and the data with different quality grades are selected from a plurality of sensing data based on the quality grade to serve as the initial true value, so that the initial value of the algorithm is screened according to the corresponding task, the optimization selection is realized, and the accuracy of the sensing result is improved.
And 8, the perception task platform on the block chain sends the evaluated perception data to the task publisher, the task publisher obtains the required data, and the execution process of the whole task is finished.
It should be noted that, although the above-mentioned embodiments of the present invention are illustrative, the present invention is not limited thereto, and thus the present invention is not limited to the above-mentioned embodiments. Other embodiments, which can be made by those skilled in the art in light of the teachings of the present invention, are considered to be within the scope of the present invention without departing from its principles.

Claims (3)

1. A reputation sharing double-stage data quality evaluation method based on a block chain is characterized by comprising the following steps:
step 1, a task participant applies for registration to a perception task platform on a block chain and requests to join a perception network to execute a perception task; in the process, the task participant sends personal information of the task participant to a perception task platform on the block chain in an encryption mode, and authorizes the perception task platform on the block chain to inquire credit values of the task participant in various fields;
step 2, a task publisher publishes a task requirement to a perception task platform on the block chain;
step 3, the perception task platforms on all the block chains on the block chains issue perception tasks to task participants according to task requirements, and if the task participants agree to join the task, the perception task platforms on the block chains submit personal information to the task participants as a proof of task participation;
step 4, the perception task platform on the block chain takes the personal information of the task participant as input, inquires the credit values of the task participant and the fields related to the task requirement, and calculates the comprehensive credit values of the fields; selecting task participants with higher comprehensive reputation value as formal task participants according to the number of people in the task requirement, and distributing public keys and identity certificates to the formal task participants;
step 5, after receiving the public key and the identity certificate, the formal task participant executes the perception task according to the task requirement and collects perception data;
step 6, after the formal task participants finish the collection of the perception data, the public key distributed by the perception task platform on the block chain is used for encryption to obtain encrypted perception data, and the encrypted perception data is sent to the perception task platform on the block chain;
step 7, decrypting the encrypted sensing data sent by the formal task participants by the sensing task platform on the block chain to obtain sensing data, and evaluating the sensing data by using a truth value discovery algorithm based on machine learning to obtain an evaluated sensing data set;
and 8, the perception task platform on the block chain sends the evaluated perception data set to the task publisher, the task publisher obtains the required data, and the execution process of the whole task is finished.
2. The reputation sharing dual-stage data quality assessment method based on a blockchain according to claim 1, wherein in step 4, the integrated reputation value C is:
Figure FDA0003121501790000011
in the formula, RiReputation as i-th domainA value; xiIs a weight of the i-th domain,
Figure FDA0003121501790000012
n is the number of selected domains.
3. The reputation sharing dual-stage data quality assessment method based on blockchains according to claim 1, wherein in step 7, the perception data is assessed by using a machine learning-based truth finding algorithm, and the specific process of obtaining the assessed perception data is as follows:
7.1, performing off-line training on the decision tree by using given sensing data with quality grade marks to obtain a trained decision tree model;
7.2, sending the perception data sent by all formal task participants into a trained decision tree model to obtain the quality grade of each perception data;
7.3, respectively selecting perception data from the perception data of different quality grades to form a primary screening perception data set based on the given quality grade proportion;
and 7.4, performing iterative computation by taking the initially screened sensing data set as an initial true value of a true value discovery algorithm to obtain an evaluated sensing data set.
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