Crowd-sensing data quality assessment method based on weighted voting on blockchain

Abstract

A blockchain-based method for assessing the quality of collectively-driven sensing data comprises the following steps: constructing a user information contract, constructing a data collection contract, participants joining the sensing task, data quality assessment, uploading to the blockchain, and updating the user information contract. This invention constructs a blockchain-based data quality assessment model, uses edge servers to assist in data quality assessment, decentralizing the process; employs a weighted voting method to calculate the data quality of the sensing task, improving the accuracy of the assessment; uses the blockchain to store the assessment process, ensuring traceability; and verifies the assessment results, enhancing security.

Description

Technical Field

[0001] This invention belongs to the technical field of crowd sensing network applications, specifically relating to a crowd sensing data quality assessment method based on weighted voting on a blockchain. Background Technology

[0002] Crowdsourcing sensing is a novel data acquisition model born from emerging technologies such as 5G communication, the Internet of Things (IoT), and artificial intelligence, combined with sociology, game theory, and blockchain. It recruits a large number of sensors or mobile devices as basic sensing units, using IoT and mobile internet to distribute sensing tasks and efficiently collect sensing data. The widespread adoption of smartphones provides mobile participants and device hardware, but also places higher demands on platform security and fairness. Most existing methods employ centralized platform architectures, which are vulnerable to DDoS attacks, node failures, data loss and leakage, and platform untrustworthiness. Blockchain technology has potential advantages in addressing these risks. Its core advantage is decentralization, enabling peer-to-peer transactions and collaboration based on decentralized trust in a distributed system where nodes do not need to trust each other, through data encryption, timestamps, and distributed consensus and incentives. Applying blockchain technology to crowdsourcing sensing can, to some extent, solve problems related to trust, incentives, and decentralization.

[0003] The quality of the perceived data directly impacts the perception effect; therefore, quantifying the quality of data submitted by participants and their perception capabilities is crucial. Due to differences in sensor accuracy and participants' proficiency in completing perception tasks, the collected perceived data will vary due to systematic and random errors. Malicious participants—those who submit perceived data randomly to obtain rewards without actually collecting data—will submit false perceived data. If the authenticity of the perceived data cannot be determined, it will not only affect the accuracy of the perceived data but also the fairness of the incentive mechanism. Therefore, a current technical problem to be solved is to provide a fair, secure, and decentralized method for evaluating the quality of collective intelligent perception data. Summary of the Invention

[0004] The technical problem to be solved by this invention is to overcome the shortcomings of the above-mentioned technical problems and provide a method for assessing the quality of collectively intelligent sensing data based on weighted voting on a decentralized, secure and fair blockchain.

[0005] The technical solution adopted to solve the above technical problems consists of the following steps:

[0006] (1) Constructing a user information contract

[0007] Participant p i When registering on the Crowd Intelligence Sensing Platform, the platform will generate a User Information Contract (UIC) unique to each participant.

[0008] UIC i =(I i ,P i ,R i A i (1)

[0009] Among them, I i A unique identifier for a participant, P i Indicating participants' preferences, R i Indicating the participant's credibility, A i Let p represent the blockchain account address of the participant, i be the participant's index, i∈[1,N], and N be a finite positive integer. i Upload your personal information to the blockchain.

[0010] (2) Constructing a data collection contract

[0011] When a data requester issues a collective sensing task, it needs to use a Data Collection Contract (DCC) to establish a tamper-proof protocol on the blockchain. The Data Collection Contract (DCC) is shown in equation (2):

[0012] DCC = (O, N) r W s ,R s N w ,C w ,C c (2)

[0013] W s ={p1,p2,...,p N}

[0014] Where O represents the contract creator, N r W represents the minimum reputation required for the task. s Let R represent the set of participants. s Store the results of data quality assessment of the perceptual data submitted by each participant, N w N represents the number of participants required for the task. w The value can be a finite number of positive integers, C w C represents the current number of participants in the task. w The value can be a finite number of positive integers, C c C represents the number of participants who have completed the task. c The value can be a finite number of positive integers.

[0015] (3) Participants engage in sensory tasks

[0016] The timeliness Tp of the participant's task completion is determined by equation (3):

[0017]

[0018] Where r represents the participant's task completion time, f represents the task time limit threshold, and T represents the task requirement completion time. Participants should complete the perception task and submit it to the data collection contract DCC within the time period [Tf, T+f].

[0019] Determine the location Loc for the perception task according to formula (4):

[0020] Loc=(Lng,Lat) (4)

[0021] Where Lng represents longitude and Lat represents latitude, participants must participate in the sensing task at the sensing task location Loc. Participants complete the sensing information collection task and submit sensing data Data to the data collection contract DCC, which is determined by the following formula:

[0022] Data = (T, Loc, D)

[0023] Where T is the task completion time, Loc is the sensing task location, and D is the sensor data, with D being a rational number.

[0024] (4) Data quality assessment

[0025] 1) Construct a weighted voting model based on participant reputation

[0026] Construct a weighted voting model D based on participant reputation according to formula (5). g :

[0027]

[0028] Where N represents the number of participants submitting data, and N is a finite positive integer, D i p represents the i-th participant. i Submitted data, R i p represents the i-th participant. i Reputation.

[0029] 2) Data quality assessment for crowd-sensing

[0030] The edge server uses a weighted voting model based on participant reputation. g The quality of the collective sensing data is evaluated, and the sensing data quality evaluation results are obtained.

[0031] (5) Upload to blockchain

[0032] The edge server uploads the assessment results of the perceived data to the blockchain.

[0033] (6) Update User Information Contract

[0034] The Data Collection Contract (DCC) calculates the perceived data quality of each participant based on the data quality assessment results and submits it to the User Information Contract (UIC). The User Information Contract (UIC) updates the User Information Contract based on the perceived data quality submitted by the participants in the current task and applies the participant reputation value update model.

[0035] In step (1) of the present invention, the user information contract is constructed according to formula (1), wherein R... i R represents the credibility of the participants. i ∈(0,1).

[0036] In step (2) of the present invention, the N... w N represents the number of participants required for the task. w The value range is 10 to 50, C w C represents the current number of participants in the task. w The value range is 10 to 50, C c C represents the number of participants who have completed the task. c The value range is 10 to 50.

[0037] In step (3) of this invention, where the participant joins the perception task, T represents the task completion time, and T∈[8,18]. In step (4), Lng represents longitude, and Lat represents latitude. The value range of Lng is 104°~116°, and the value range of Lat is 30°~39°.

[0038] In step (4) of the present invention, the data quality assessment formula (5) represents the number of participants who submitted data, and the value of N ranges from 10 to 50.

[0039] In step (4) of the present invention, the data quality assessment formula (5) represents the number of participants who submitted data, and the optimal value of N is 32.

[0040] In step (6) of the present invention, the participant reputation value update model is as follows: the data collection contract DCC determines the relative task data quality v of the participant according to formula (6). r :

[0041]

[0042] Where v represents the quality of the participant's task data, v a v represents the average task data quality. i p represents the i-th participant. i Data quality, where N represents the total number of participants; D g For real data, D iFor the data submitted by participant i, S D R is the weighted standard deviation of the data. i For the i-th participant p i The reputation of T i For the i-th participant p i Perceived noise.

[0043] The User Information Contract (UIC) updates the participant's reputation R according to formula (7):

[0044]

[0045] Among them, R l Represents the reputation before the update, k represents the update rate, and v r This indicates the relative quality of the task data.

[0046] Upload the updated participant's reputation to the User Information Contract (UIC) and save it.

[0047] In equation (7), the R mentioned l Indicates the reputation before the update, R l∈ [0,1], k represents the update rate, k∈(0,1], v r Indicates relative task data quality, v r ∈[-1,1].

[0048] In equation (7), the R mentioned l Indicates the reputation before the update, R l The optimal value for k is 0.5, where k represents the update rate, and the optimal value for k is 0.5. r Indicates relative task data quality, v r The optimal value is 0.

[0049] This invention leverages the decentralized and immutable characteristics of blockchain, combined with user information contracts and data collection contracts, to achieve effective management and traceability of information throughout the entire process of collective intelligence sensing tasks. By introducing a weighted voting model based on participant reputation or ability, the objectivity and accuracy of data quality assessment are improved. Dynamically updating participants' reputation or ability based on the assessment results enhances the fairness and security of the entire system and effectively incentivizes participants to submit high-quality data. Attached Figure Description

[0050] Figure 1 This is a flowchart of Embodiment 1 of the present invention.

[0051] Figure 2 This is a comparison chart of the output results of the data quality assessment method and the actual results.

[0052] Figure 3These are the reputation update curves for five different participants. Detailed Implementation

[0053] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments, but the present invention is not limited to these embodiments.

[0054] Example 1

[0055] The blockchain-based weighted voting method for assessing the quality of crowd-sensing data in this embodiment consists of the following steps (see...). Figure 1 ):

[0056] (1) Constructing a user information contract

[0057] When participant p registers on the crowdsensing platform, the platform will generate a user information contract UIC for the participant as shown in equation (1):

[0058] UIC i =(I i ,P i ,R i A i (1)

[0059] Among them, I i A unique identifier for a participant, P i Indicating participants' preferences, R i R represents the credibility of the participants. i ∈(0,1], R in this embodiment i The value is 0.5, A i Let p represent the blockchain account address of the participant, i be the participant's index, i∈[1,N], and N be a finite positive integer. i Upload your personal information to the blockchain.

[0060] (2) Constructing a data collection contract

[0061] When a data requester issues a collective sensing task, it needs to use a Data Collection Contract (DCC) to establish a tamper-proof protocol on the blockchain. The Data Collection Contract (DCC) is shown in equation (2):

[0062] DCC = (O, N) r W s ,R s N w ,C w ,C c (2)

[0063] W s ={p1,p2,...,p N}

[0064] Where O represents the contract creator, N r W represents the minimum reputation required for the task. s Let R represent the set of participants. s Store the results of data quality assessment of the perceptual data submitted by each participant, N w N represents the number of participants required for the task. w The value ranges from 10 to 50. In this embodiment, N... w The value is 32, C w C represents the current number of participants in the task. w The value range is 10 to 50, and C in this embodiment... w The value is 32, C c C represents the number of participants who have completed the task. c The value range is 10 to 50, and C in this embodiment... c The value is 32, p N Let N represent the number of participants.

[0065] (3) Participants engage in sensory tasks

[0066] The timeliness Tp of the participant's task completion is determined by equation (3):

[0067]

[0068] Where r represents the participant's task completion time, f represents the task time limit threshold, T represents the task requirement completion time, T∈[8,18], and in this embodiment, T is 13. The participant should complete the perception task and submit it to the data collection contract DCC within the time period [Tf,T+f].

[0069] Determine the location Loc for the perception task according to formula (4):

[0070] Loc=(Lng,Lat) (4)

[0071] Where Lng represents longitude and Lat represents latitude, the value of Lng ranges from 104° to 116°, and in this embodiment, Lng is 110°. The value of Lat ranges from 30° to 39°, and in this embodiment, Lat is 35°. Participants need to participate in the sensing task at the sensing task location Loc. After completing the sensing information collection task, participants submit sensing data Data to the Data Collection Contract DCC, which is determined by the following formula:

[0072] Data = (T, Loc, D)

[0073] Where T is the task completion time, Loc is the sensing task location, and D is the sensor data, with D being a rational number.

[0074] (4) Data quality assessment

[0075] 1) Construct a weighted voting model based on participant reputation

[0076] Construct a weighted voting model D based on participant reputation according to formula (5). g :

[0077]

[0078] Where N represents the number of participants submitting data, and the value of N ranges from 10 to 50. In this embodiment, the value of N is 32. i R represents the data submitted by the i-th participant p. i p represents the i-th participant. i Reputation.

[0079] 2) Data quality assessment for crowd-sensing

[0080] The edge server uses a weighted voting model based on participant reputation. g The quality of the collective sensing data is evaluated, and the sensing data quality evaluation results are obtained.

[0081] (5) Upload to blockchain

[0082] The edge server uploads the assessment results of the perceived data to the blockchain.

[0083] (6) Update User Information Contract

[0084] The Data Collection Contract (DCC) calculates the perceived data quality of each participant based on the data quality assessment results and submits it to the User Information Contract (UIC). The User Information Contract (UIC) updates the User Information Contract based on the perceived data quality submitted by the participants in the current task and applies the participant reputation value update model.

[0085] The participant reputation update model is as follows: the data collection contract DCC determines the relative task data quality v of the participants according to equation (6). r :

[0086]

[0087] Where v represents the quality of the participant's task data, v a v represents the average task data quality. i p represents the i-th participant. i Data quality, where N represents the total number of participants; D g For real data, D i For the data submitted by participant i, S D R is the weighted standard deviation of the data.i For the i-th participant p i The reputation of T i For the i-th participant p i Perceived noise;

[0088] The User Information Contract (UIC) updates the participant's reputation R according to formula (7):

[0089]

[0090] Among them, R l Indicates the reputation before the update, R l The value range is [0,1]. In this embodiment, R... l The value is 0.5, k represents the update rate, and the value of k ranges from (0,1]. In this embodiment, the value of k is 0.5. r Indicates relative task data quality, v r The value range is [-1, 1]. In this embodiment, v r The value is 0.

[0091] Upload the updated participant's reputation to the User Information Contract (UIC) and save it.

[0092] Complete a data quality assessment method for crowd-sensing data based on weighted voting on the blockchain.

[0093] Example 2

[0094] The blockchain-based collective sensing data quality assessment method using weighted voting in this embodiment consists of the following steps:

[0095] (1) Constructing a user information contract

[0096] When participant p registers on the crowdsensing platform, the platform will generate a user information contract UIC for the participant as shown in equation (1):

[0097] The expression for the User Information Contract (UIC) is the same as in Example 1.

[0098] In equation (1), R i R represents the credibility of the participants. i ∈(0,1], R in this embodiment i The value is 0.1, and the other parameters, variables, and value ranges are the same as in Example 1.

[0099] (2) Constructing a data collection contract

[0100] When a data requester issues a collective sensing task, it needs to use a Data Collection Contract (DCC) to establish a tamper-proof protocol on the blockchain. The Data Collection Contract (DCC) is shown in equation (2):

[0101] The expression (2) of the data collection contract DCC is the same as that in Example 1.

[0102] In equation (2), N w N represents the number of participants required for the task. w The value range is 10 to 50, and N in this embodiment... w The value is 10, C w C represents the current number of participants in the task. w The value range is 10 to 50, and C in this embodiment... w The value is 10, C c C represents the number of participants who have completed the task. c The value range is 10 to 50, and C in this embodiment... c The value is 10. Other parameters, variables, and their ranges are the same as in Example 1.

[0103] (3) Participants engage in sensory tasks

[0104] The timeliness Tp of the participant's task completion is determined by equation (3):

[0105] The expression of equation (3) is the same as that in Example 1.

[0106] In equation (3), T represents the task completion time, T∈[8,18], and the value of T in this embodiment is 8. Other parameters and variables, as well as their value ranges, are the same as in embodiment 1.

[0107] Determine the location Loc for the perception task according to formula (4):

[0108] The expression of equation (4) is the same as that in Example 1.

[0109] In equation (4), Lng represents longitude and Lat represents latitude. The value of Lng ranges from 104° to 116°, and in this embodiment, Lng is 104°. The value of Lat ranges from 30° to 39°, and in this embodiment, Lat is 30°. Other parameters, variables, and their value ranges are the same as in embodiment 1.

[0110] (4) Data quality assessment

[0111] Construct a weighted voting model D based on participant reputation according to formula (5). g :

[0112] The expression of equation (5) is the same as that in Example 1.

[0113] In equation (5), N represents the number of participants who submit data. The value of N ranges from 10 to 50. In this embodiment, the value of N is 10.

[0114] Other parameters and variables, as well as their value ranges, are the same as in Example 1.

[0115] (5) Upload to blockchain

[0116] The steps are the same as in Example 1.

[0117] (6) Update User Information Contract

[0118] The User Information Contract (UIC) updates the participant's reputation R according to formula (7):

[0119] The expression of equation (7) is the same as that in Example 1.

[0120] In equation (7), R l Indicates the reputation before the update, R l The value range is [0,1]. In this embodiment, R... l The value is 0, k represents the update rate, and the value of k ranges from (0,1]. In this embodiment, the value of k is 0.1. r Indicates relative task data quality, v r The value range is [-1, 1]. In this embodiment, v r The value is -1.

[0121] Upload the updated participant's reputation to the User Information Contract (UIC) and save it.

[0122] The other steps in this procedure are the same as in Example 1.

[0123] The other steps are the same as in Example 1. A method for assessing the quality of crowd-sensing data based on weighted voting on the blockchain is completed.

[0124] Example 3

[0125] The blockchain-based collective sensing data quality assessment method using weighted voting in this embodiment consists of the following steps:

[0126] (1) Constructing a user information contract

[0127] When participant p registers on the crowdsensing platform, the platform will generate a user information contract UIC for the participant as shown in equation (1):

[0128] The expression for the User Information Contract (UIC) is the same as in Example 1.

[0129] In equation (1), R i R represents the credibility of the participants. i ∈(0,1], R in this embodiment i The value is 1, and the other parameters, variables, and value ranges are the same as in Example 1.

[0130] (2) Constructing a data collection contract

[0131] When a data requester issues a collective sensing task, it needs to use a Data Collection Contract (DCC) to establish a tamper-proof protocol on the blockchain. The Data Collection Contract (DCC) is shown in equation (2):

[0132] The expression (2) of the data collection contract DCC is the same as that in Example 1.

[0133] In equation (2), N w N represents the number of participants required for the task. w The value range is 10 to 50, and N in this embodiment... w The value is 50, C w C represents the current number of participants in the task. w The value range is 10 to 50, and C in this embodiment... w The value is 50, C c C represents the number of participants who have completed the task. c The value range is 10 to 50, and C in this embodiment... c The value is 50. Other parameters, variables, and value ranges are the same as in Example 1.

[0134] (3) Participants engage in sensory tasks

[0135] The timeliness Tp of the participant's task completion is determined by equation (3):

[0136] The expression of equation (3) is the same as that in Example 1.

[0137] In equation (3), T represents the task completion time, T∈[8,18], and the value of T in this embodiment is 18. Other parameters and variables, as well as their value ranges, are the same as in embodiment 1.

[0138] Determine the location Loc for the perception task according to formula (4):

[0139] The expression of equation (4) is the same as that in Example 1.

[0140] In equation (4), Lng represents longitude and Lat represents latitude. The value of Lng ranges from 104° to 116°, and in this embodiment, Lng is 116°. The value of Lat ranges from 30° to 39°, and in this embodiment, Lat is 39°. Other parameters, variables, and their value ranges are the same as in embodiment 1.

[0141] (4) Data quality assessment

[0142] Construct a weighted voting model D based on participant reputation according to formula (5). g :

[0143] The expression of equation (5) is the same as that in Example 1.

[0144] In equation (5), N represents the number of participants who submit data. The value of N ranges from 10 to 50. In this embodiment, the value of N is 50.

[0145] Other parameters and variables, as well as their value ranges, are the same as in Example 1.

[0146] (5) Upload to blockchain

[0147] The steps are the same as in Example 1.

[0148] (6) Update User Information Contract

[0149] The User Information Contract (UIC) updates the participant's reputation R according to formula (7):

[0150] The expression of equation (7) is the same as that in Example 1.

[0151] In equation (7), R l Indicates the reputation before the update, R l The value range is [0,1]. In this embodiment, R... l The value is 1, k represents the update rate, and the value of k ranges from (0,1]. In this embodiment, the value of k is 1. r Indicates relative task data quality, v r The value range is [-1, 1]. In this embodiment, v r The value is 1.

[0152] Upload the updated participant's reputation to the User Information Contract (UIC) and save it.

[0153] The other steps in this procedure are the same as in Example 1.

[0154] The other steps are the same as in Example 1. A method for assessing the quality of crowd-sensing data based on weighted voting on the blockchain is completed.

[0155] To verify the beneficial effects of the present invention, a computer simulation experiment was conducted on 30 days of temperature monitoring in Beijing using the method of Embodiment 1 of the present invention. During the experiment, 10 participants were assigned to each of three groups (high, medium, and low) of data quality assessment; two groups (one participant each) were assigned to provide abnormal data. A total of 32 participants participated in the experiment, with the abnormal data providers uploading data that significantly deviated from reality.

[0156] After 30 days of temperature monitoring, a line graph showing the experimental results versus actual temperatures was obtained, as shown below. Figure 2 As shown in the figure, the ground truth is the actual temperature, and the aggregate data is the experimental results. It is evident that this invention can effectively obtain data that closely approximates real-world conditions.

[0157] After each temperature monitoring, the reputation score was updated in real time based on the quality of the tasks submitted by the participants. The experimental results are as follows: Figure 3 As shown. In Figure 3 In the diagram, the noise1 curve represents the reputation update curve of a high-quality crowd perception participant, the noise2 curve represents the reputation update curve of a medium-quality crowd perception participant, the noise3 curve represents the reputation update curve of a low-quality crowd perception participant, the malice1 curve represents the reputation update curve of an anomalous data provider, and the malice2 curve represents the reputation update curve of another anomalous data provider. Figure 3 As can be seen, the reputation scores of the two anomalous data providers continued to decline, indicating that the present invention can effectively distinguish between crowd-aware participants with different perceived data qualities and anomalous data providers. The reputation scores of the three crowd-aware participants with different data qualities approached 0.7, 0.6, and 0.5, respectively, while the reputation score of the anomalous data provider approached 0, demonstrating that the present invention can assign corresponding reputation scores to crowd-aware participants with different data qualities and anomalous data providers, which is consistent with the actual situation.

Claims

1. A method for assessing the quality of crowd-sensing data based on weighted voting on a blockchain, characterized in that... It consists of the following steps: (1) Constructing a user information contract Participant p i When registering on the Crowd Intelligence Sensing Platform, the platform will generate a User Information Contract (UIC) unique to each participant. UIC i =(I i ,P i ,R i ,A i ) (1) Among them, I i A unique identifier for a participant, P i Indicating participants' preferences, R i Indicating the participant's credibility, A i Let p represent the blockchain account address of the participant, i be the participant's index, i∈[1,N], and N be a finite positive integer. i Upload your personal information to the blockchain; (2) Constructing a data collection contract When a data requester issues a collective sensing task, it needs to use a Data Collection Contract (DCC) to establish a tamper-proof protocol on the blockchain. The Data Collection Contract (DCC) is shown in equation (2): DCC=(O, N r , W s ,R s , N w , C w ,C c ) (2) W s ={p1,p2,...,p N } Where O represents the contract creator, N r W represents the minimum reputation required for the task. s Let R represent the set of participants. s Store the results of data quality assessment of the perceptual data submitted by each participant, N w N represents the number of participants required for the task. w The value can be a finite number of positive integers, C w C represents the current number of participants in the task. w The value can be a finite number of positive integers, C c C represents the number of participants who have completed the task. c The value can be a finite number of positive integers; (3) Participants engage in sensory tasks The timeliness Tp of the participant's task completion is determined by equation (3): Where r represents the participant's task completion time, f represents the task time limit threshold, and T represents the task requirement completion time. Participants should complete the perception task and submit it to the data collection contract DCC within the time period [Tf, T+f]. Determine the location Loc for the perception task according to formula (4): Loc=(Lng, Lat) (4) Where Lng represents longitude and Lat represents latitude, participants must participate in the sensing task at the sensing task location Loc. Participants complete the sensing information collection task and submit sensing data Data to the data collection contract DCC, which is determined by the following formula: Data = (T, Loc, D) Where T is the task completion time, Loc is the location of the sensing task, and D is the sensor data, with D taking the value of a rational number; (4) Data quality assessment 1) Construct a weighted voting model based on participant reputation Construct a weighted voting model D based on participant reputation according to formula (5). g : Where N represents the number of participants submitting data, and N is a finite positive integer, D i p represents the i-th participant. i Submitted data, R i p represents the i-th participant. i Reputation; 2) Data quality assessment for crowd-sensing The edge server uses a weighted voting model based on participant reputation. g The quality of the collective sensing data is evaluated, and the sensing data quality evaluation results are obtained. (5) Upload to blockchain The edge server uploads the assessment results of the perceived data to the blockchain; (6) Update User Information Contract The Data Collection Contract (DCC) calculates the perceived data quality of each participant based on the data quality assessment results and submits it to the User Information Contract (UIC). The User Information Contract (UIC) updates the User Information Contract based on the perceived data quality submitted by the participants in the current task and applies the participant reputation value update model.

2. The method for assessing the quality of crowd-sensing data on a blockchain based on weighted voting as described in claim 1, characterized in that: In step (1), when constructing the user information contract, the R... i R represents the credibility of the participants. i ∈(0,1).

3. The method for assessing the quality of crowd-sensing data on a blockchain based on weighted voting, as described in claim 1, is characterized in that: In step (2), the data collection contract is constructed using equation (2), where N... w N represents the number of participants required for the task. w The value range is 10 to 50, C w C represents the current number of participants in the task. w The value range is 10 to 50, C c C represents the number of participants who have completed the task. c The value range is 10 to 50.

4. The method for assessing the quality of crowd-sensing data on a blockchain based on weighted voting, as described in claim 1, is characterized in that: In step (3), when participants join the perception task, T represents the task completion time, T∈[8,18]; in step (4), Lng represents longitude, Lat represents latitude, Lng ranges from 104° to 116°, and Lat ranges from 30° to 39°.

5. The method for assessing the quality of crowd-sensing data on a blockchain based on weighted voting as described in claim 1, characterized in that: In step (4) of the data quality assessment formula (5), N represents the number of participants who submitted data, and the value of N ranges from 10 to 50.

6. The method for assessing the quality of crowd-sensing data on a blockchain based on weighted voting, as described in claim 5, is characterized in that: In step (4) of the data quality assessment, equation (5) states that N represents the number of participants who submitted data, and N is 32.

7. The method for assessing the quality of crowd-sensing data on a blockchain based on weighted voting, as described in claim 1, is characterized in that... In step (6), the participant reputation value update model is as follows: the data collection contract DCC determines the relative task data quality v of the participants according to equation (6). r : Where v represents the quality of the participant's task data, v a v represents the average task data quality. i p represents the i-th participant. i Data quality, where N represents the total number of participants; D g For real data, D i For the data submitted by participant i, S D R is the weighted standard deviation of the data. i For the i-th participant p i The reputation of T i For the i-th participant p i Perceived noise; The User Information Contract (UIC) updates the participant's reputation R according to formula (7): Among them, R l Represents the reputation before the update, k represents the update rate, and v r Indicates relative task data quality; Upload the updated participant's reputation to the User Information Contract (UIC) and save it.

8. The method for assessing the quality of crowd-sensing data on a blockchain based on weighted voting as described in claim 7, characterized in that: In equation (7), the R mentioned l Indicates the reputation before the update, R l ∈[0,1], k represents the update rate, k∈(0,1], v r Indicates relative task data quality, v r ∈[-1,1].

9. The method for assessing the quality of crowd-sensing data on a blockchain based on weighted voting, as described in claim 7 or 8, is characterized in that: In equation (7), the R mentioned l Indicates the reputation before the update, R l The value is 0.5, k represents the update rate, and k takes a value of 0.

5. r Indicates relative task data quality, v r The value is 0.

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