Weighted voting method-based crowd sensing data quality evaluation method on block chain

Abstract

The invention discloses a weighted voting method-based crowd sensing data quality evaluation method on a block chain. The method comprises the steps of constructing a user information contract, constructing a data collection contract, adding a sensing task into a participant, evaluating data quality, uploading the block chain and updating the user information contract. According to the method, a block chain-based data quality evaluation model is constructed, an edge server is used for assisting data quality evaluation, and a data quality evaluation process is decentralized; the weighted voting method is used for calculating the perception task data quality, and the accuracy of data quality evaluation is improved; the block chain is used for storing the data quality evaluation process, so that the data quality evaluation process is traceable; and the data quality evaluation result is verified, so that the security of data quality evaluation is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of crowd-sensing network applications, and specifically relates to a crowd-sensing data quality assessment method based on a weighted voting method on a blockchain. Background Art

[0002] Crowd sensing is a new data acquisition model spawned by emerging technologies such as 5G communications, the Internet of Things, and artificial intelligence, combined with sociology, game theory, and blockchain. It employs a large number of sensors or mobile devices as basic sensing units, leveraging the Internet of Things and mobile internet to distribute sensing tasks and efficiently collect sensing data. The widespread adoption of smartphones has provided mobile participants and device hardware, but has also placed higher demands on platform security and fairness. Existing approaches mostly employ centralized platform architectures, which are vulnerable to DDoS attacks, node failures, data loss and leakage, and platform untrustworthiness. Blockchain technology offers potential advantages in addressing these risks. Its core advantage is decentralization. Through data encryption, timestamps, and distributed consensus and incentives, it enables decentralized, trust-based peer-to-peer transactions and collaboration in a distributed system where nodes do not need to trust each other. Applying blockchain technology to crowd sensing can, to a certain extent, address issues of trust, incentives, and decentralization.

[0003] The quality of sensor data directly impacts perception outcomes, so quantifying the quality of data submitted by participants and their perception capabilities is crucial. Due to variations in sensor accuracy and participants' proficiency in performing perception tasks, collected perception data can vary due to systematic and random errors. Malicious participants—those who randomly submit perception data for rewards without collecting data—will submit false data. Failure to verify the authenticity of perception data compromises not only its accuracy but also the fairness of the incentive mechanism. Therefore, a current technical challenge is to provide a fair, secure, and decentralized method for assessing the quality of crowdsensing data. Summary of the Invention

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

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

[0006] (1) Building a user information contract

[0007] Participant p i When registering for the crowd intelligence perception platform, the platform will generate a user information contract UIC for the participant:

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

[0009] Among them, I i The unique identifier of the participant, P i represents the participant’s preference, R i Represents the reputation of the participant, A i Represents the blockchain account address of the participant, i is the serial number of the participant, i∈[1,N], N is a finite positive integer, and participant p i Upload your personal information to the blockchain.

[0010] (2) Building a data collection contract

[0011] When the data demander publishes a crowdsensing task, it is necessary to use the data collection contract DCC to establish a tamper-proof protocol on the blockchain. The data collection contract DCC is shown in formula (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] Among them, O represents the contract creator, N r Indicates the minimum reputation required for the task, W s represents the set of participants, R s Store the results of the data quality assessment of the perception data submitted by each participant, N w Indicates the number of participants required for the task, N w The value is a finite positive integer, C w Indicates the current number of participants in the task, C w The value is a finite positive integer, C c Indicates the number of participants who have completed the task, C c The value is a finite positive integer.

[0015] (3) Participants join the perception task

[0016] The timeliness of task completion Tp of participants is determined by formula (3):

[0017]

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

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

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

[0021] Where Lng represents longitude and Lat represents latitude. Participants need to participate in the perception task at the perception task location Loc. After completing the perception information collection task, participants submit the perception data Data to the data collection contract DCC. The perception data Data is determined by the following formula:

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

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

[0024] (4) Data quality assessment

[0025] 1) Build a weighted voting model based on participant credibility

[0026] According to formula (5), the weighted voting model D of the participant's reputation is constructed g :

[0027]

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

[0029] 2) Crowd-sensing data quality assessment

[0030] The edge server adopts a weighted voting model based on the participant’s reputation D g Evaluate the quality of crowd-sensing data and obtain the perception data quality evaluation results.

[0031] (5) Upload blockchain

[0032] The edge server uploads the perception data evaluation results to the blockchain.

[0033] (6) Update User Information Agreement

[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 current task participants and applies the participant reputation value update model.

[0035] In the formula (1) of constructing the user information contract in step (1) of the present invention, the R i Represents the reputation of the participant, R i ∈(0,1].

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

[0037] In step (3) of the present invention, in formula (3) for participants to join the perception task, T represents the task completion time, T∈[8,18]. In formula (4) of this step, Lng represents longitude, and Lat represents latitude. The value range of Lng is 104° to 116°, and the value range of Lat is 30° to 39°.

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

[0039] In the formula (5) of the data quality assessment in step (4) of the present invention, the N 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: the data collection contract DCC determines the relative task data quality v of the participant according to formula (6) r :

[0041]

[0042] Among them, v represents the quality of the participant's task data, v a represents the average task data quality, v i represents the i-th participant p i The data quality of N is the total number of participants; D g is the real data, D iThe data submitted by participant i, S D is the weighted standard deviation of the data, R i For the i-th participant p i Reputation, T i For the i-th participant p i of 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 update, k represents the update rate, v r Indicates the relative mission data quality.

[0046] Upload the updated participant's reputation to the user information contract UIC and save.

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

[0048] In formula (7), the R l represents the reputation before the update, R l The best value is 0.5, k represents the update rate, the best value of k is 0.5, v r Indicates the relative task data quality, v r The best value is 0.

[0049] This invention utilizes the decentralized, public, and tamper-proof characteristics of blockchain, combined with the design of user information contracts and data collection contracts, to achieve effective management and traceability of information throughout the entire process of crowd perception tasks; by introducing a weighted voting model based on the reputation or ability of participants, the quality of perception data is evaluated, thereby improving the objectivity and accuracy of data quality assessment; dynamically updating the reputation or ability of participants based on the evaluation results, enhancing the fairness and security of the entire system, and effectively incentivizing participants to submit high-quality data. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0052] Figure 3is the reputation update curve of five different participants. DETAILED DESCRIPTION

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

[0054] Example 1

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

[0056] (1) Building a user information contract

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

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

[0059] Among them, I i The unique identifier of the participant, P i represents the participant’s preference, R i Represents the reputation of the participant, R i ∈(0,1], R in this embodiment i The value is 0.5, A i Represents the blockchain account address of the participant, i is the serial number of the participant, i∈[1,N], N is a finite positive integer, and participant p i Upload your personal information to the blockchain.

[0060] (2) Building a data collection contract

[0061] When the data demander publishes a crowdsensing task, it is necessary to use the data collection contract DCC to establish a tamper-proof protocol on the blockchain. The data collection contract DCC is shown in formula (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] Among them, O represents the contract creator, N r Indicates the minimum reputation required for the task, W s represents the set of participants, R s Store the results of the data quality assessment of the perception data submitted by each participant, N w Indicates the number of participants required for the task, N w The value ranges from 10 to 50. In this embodiment, N w The value is 32, C w Indicates the current number of participants in the task, C w The value range is 10 to 50. In this embodiment, C w The value is 32, C c Indicates the number of participants who have completed the task, C c The value range is 10 to 50. In this embodiment, C c The value is 32, p N represents the participant, and N represents the number of participants.

[0065] (3) Participants join the perception task

[0066] The timeliness of task completion Tp of participants is determined by formula (3):

[0067]

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

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

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

[0071] Wherein, Lng represents longitude, Lat represents latitude, and the value range of Lng is 104° to 116°. In this embodiment, the value of Lng is 110°. The value range of Lat is 30° to 39°. In this embodiment, the value of Lat is 35°. Participants need to participate in the perception task at the perception task location Loc. Participants complete the perception information collection task and submit the perception data Data to the data collection contract DCC. The perception data Data is determined according to the following formula:

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

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

[0074] (4) Data quality assessment

[0075] 1) Build a weighted voting model based on participant credibility

[0076] According to formula (5), the weighted voting model D of the participant's reputation is constructed g :

[0077]

[0078] Wherein, N represents the number of participants who submitted data. The value range of N is 10 to 50. In this embodiment, the value range of N is 32. i represents the data submitted by the i-th participant p, R i represents the i-th participant p i credibility.

[0079] 2) Crowd-sensing data quality assessment

[0080] The edge server adopts a weighted voting model based on the participant’s reputation D g Evaluate the quality of crowd-sensing data and obtain the perception data quality evaluation results.

[0081] (5) Upload blockchain

[0082] The edge server uploads the perception data evaluation results to the blockchain.

[0083] (6) Update User Information Agreement

[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 current task participants 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 participant according to formula (6): r :

[0086]

[0087] Among them, v represents the quality of the participant's task data, v a represents the average task data quality, v i represents the i-th participant p i The data quality of N is the total number of participants; D g is the real data, D i The data submitted by participant i, S D is the weighted standard deviation of the data, Ri For the i-th participant p i Reputation, 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 represents 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 range of k is (0,1]. In this embodiment, the value of k is 0.5, v r Indicates the 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.

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

[0093] Example 2

[0094] The method for evaluating the quality of crowd-sensing data based on weighted voting on the blockchain of this embodiment consists of the following steps:

[0095] (1) Building a user information contract

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

[0097] The expression of the user information contract UIC is the same as that in Example 1.

[0098] In formula (1), R i Represents the reputation of the participant, R 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 those in Example 1.

[0099] (2) Building a data collection contract

[0100] When the data demander publishes a crowdsensing task, it is necessary to use the data collection contract DCC to establish a tamper-proof protocol on the blockchain. The data collection contract DCC is shown in formula (2):

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

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

[0103] (3) Participants join the perception task

[0104] The timeliness of task completion Tp of participants is determined by formula (3):

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

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

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

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

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

[0110] (4) Data quality assessment

[0111] According to formula (5), the weighted voting model D of the participant's reputation is constructed g :

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

[0113] In formula (5), N represents the number of participants who submitted data. The value range of N is 10 to 50. In this embodiment, the value range of N is 10.

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

[0115] (5) Upload blockchain

[0116] This step is the same as in Example 1.

[0117] (6) Update User Information Agreement

[0118] The user information contract UIC updates the participant's reputation R according to formula (7):

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

[0120] In formula (7), R l represents 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 range of k is (0,1]. In this embodiment, the value of k is 0.1, v r Indicates the 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.

[0122] The other steps of this step are the same as those in Example 1.

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

[0124] Example 3

[0125] The method for evaluating the quality of crowd-sensing data based on weighted voting on the blockchain of this embodiment consists of the following steps:

[0126] (1) Building a user information contract

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

[0128] The expression of the user information contract UIC is the same as that in Example 1.

[0129] In formula (1), R i Represents the reputation of the participant, R i ∈(0,1], R in this embodiment i The value is 1, and other parameters, variables and value ranges are the same as those in Example 1.

[0130] (2) Building a data collection contract

[0131] When the data demander publishes a crowdsensing task, it is necessary to use the data collection contract DCC to establish a tamper-proof protocol on the blockchain. The data collection contract DCC is shown in formula (2):

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

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

[0134] (3) Participants join the perception task

[0135] The timeliness of task completion Tp of participants is determined by formula (3):

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

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

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

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

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

[0141] (4) Data quality assessment

[0142] According to formula (5), the weighted voting model D of the participant's reputation is constructed g :

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

[0144] In formula (5), N represents the number of participants who submitted data. The value range of N is 10 to 50. In this embodiment, the value range of N is 50.

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

[0146] (5) Upload blockchain

[0147] This step is the same as in Example 1.

[0148] (6) Update User Information Agreement

[0149] The user information contract UIC updates the participant's reputation R according to formula (7):

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

[0151] In formula (7), R l represents 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 range of k is (0,1]. In this embodiment, the value of k is 1, v r Indicates the 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.

[0153] The other steps of this step are the same as those in Example 1.

[0154] The other steps are the same as those in Example 1. The method for evaluating 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 using the method of Example 1 was conducted on 30 days of temperature monitoring in Beijing. The experiment involved three groups of crowd-sensing participants, each consisting of 10 participants with different perceived data quality: high, medium, and low. Two groups of participants, each consisting of one participant, provided abnormal data. A total of 32 participants participated in the experiment, and the abnormal data providers uploaded data that significantly deviated from the actual data.

[0156] After 30 days of temperature monitoring, a line graph of the experimental results and the actual temperature was obtained, as shown in the figure below: Figure 2 As shown in the figure, where ground truth is the actual temperature and aggregate data is the experimental result data. It can be seen that the present invention can effectively obtain data close to the actual situation.

[0157] After each temperature monitoring, the reputation value is updated in real time according to the quality of the tasks submitted by the participants. The experimental results are as follows: Figure 3 As shown. Figure 3 In the above equation, the noise1 curve represents the reputation update curve of high-quality crowd-sensing participants, the noise2 curve represents the reputation update curve of medium-quality crowd-sensing participants, the noise3 curve represents the reputation update curve of low-quality crowd-sensing participants, the malice1 curve represents the reputation update curve of one abnormal data provider, and the malice2 curve represents the reputation update curve of another abnormal data provider. Figure 3 As can be seen, the reputation values of the two outlier data providers continue to decline, demonstrating that the present invention can effectively distinguish between crowdsensing participants and outlier data providers with different perceived data qualities. The reputation values of the three crowdsensing participants with different data qualities approach 0.7, 0.6, and 0.5, respectively, while the reputation of the outlier data provider approaches 0. This demonstrates that the present invention can assign corresponding reputation values to crowdsensing participants and outlier data providers with different data qualities, which is consistent with the actual situation.

Claims

1. A method for evaluating the quality of crowd-sensing data based on weighted voting on blockchain, characterized by It consists of the following steps: (1) Building a user information contract Participant p i When registering for the crowd intelligence perception platform, the platform will generate a user information contract UIC for the participant: UIC i =(I i ,P i ,R i ,A i ) (1) Among them, I i The unique identifier of the participant, P i represents the participant’s preference, R i Represents the reputation of the participant, A i Represents the blockchain account address of the participant, i is the serial number of the participant, i∈[1,N], N is a finite positive integer, and participant p i Upload your personal information to the blockchain; (2) Building a data collection contract When the data demander publishes a crowdsensing task, it is necessary to use the data collection contract DCC to establish a tamper-proof protocol on the blockchain. The data collection contract DCC is shown in formula (2): DCC=(O, N r , W s ,R s , N w , C w ,C c ) (2) W s ={p1,p2,...,p N } Among them, O represents the creator of the contract, N r Indicates the minimum reputation required for the task, W s represents the set of participants, R s Store the results of the data quality assessment of the perception data submitted by each participant, N w Indicates the number of participants required for the task, N w The value is a finite positive integer, C w Indicates the current number of participants in the task, C w The value is a finite positive integer, C c Indicates the number of participants who have completed the task, C c The value is a finite positive integer; (3) Participants join the perception task The timeliness of task completion Tp of participants is determined by formula (3): Where r represents the participant's task completion time, f represents the task time limit threshold, and T represents the task required completion time. The participant should complete the perception task within the period [Tf, T+f] and submit it to the data collection contract DCC; Determine the sensing task location Loc according to formula (4): Loc=(Lng, Lat) (4) Where Lng represents longitude and Lat represents latitude. Participants need to participate in the perception task at the perception task location Loc. After completing the perception information collection task, participants submit the perception data Data to the data collection contract DCC. The perception data Data is determined by the following formula: Data=(T,Loc,D) Where T is the task completion time, Loc is the sensing task location, D is the sensor data, and D is a rational number; (4) Data quality assessment 1) Build a weighted voting model based on participant credibility According to formula (5), the weighted voting model D of the participant's reputation is constructed g : Where N represents the number of participants who submitted data, N is a finite positive integer, D i represents the i-th participant p i Submitted data, R i represents the i-th participant p i credibility; 2) Crowd-sensing data quality assessment The edge server adopts a weighted voting model based on the participant’s reputation D g Evaluate the quality of crowd-sensing data and obtain the perception data quality evaluation results; (5) Upload blockchain The edge server uploads the perception data evaluation results to the blockchain; (6) Update User Information Agreement 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 current task participants and applies the participant reputation value update model.

2. The method for evaluating the quality of crowd-sensing data based on weighted voting on a blockchain according to claim 1 is characterized by: In the formula (1) of constructing the user information contract in step (1), the R i Represents the reputation of the participant, R i ∈(0,1].

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

4. The method for evaluating the quality of crowd-sensing data based on weighted voting on a blockchain according to claim 1 is characterized by: In formula (3) of step (3) when the participant joins the perception task, T represents the task completion time, T∈[8,18]; in formula (4) of this step, Lng represents longitude, Lat represents latitude, and the value range of Lng is 104°~116°, and the value range of Lat is 30°~39°.

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

6. The method for evaluating the quality of crowd-sensing data based on weighted voting on a blockchain according to claim 5 is characterized by: In formula (5) of the data quality assessment in step (4), N represents the number of participants who submitted data, and the value of N is 32.

7. The method for evaluating the quality of crowd-sensing data based on weighted voting on the blockchain according to claim 1 is characterized in that In step (6), the participant reputation value update model is: the data collection contract DCC determines the relative task data quality v of the participant according to formula (6) r : Among them, v represents the quality of the participant's task data, v a represents the average task data quality, v i represents the i-th participant p i The data quality of N is the total number of participants; D g is the real data, D i The data submitted by participant i, S D is the weighted standard deviation of the data, R i For the i-th participant p i Reputation, 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 update, k represents the update rate, v r Indicates relative mission data quality; Upload the updated participant's reputation to the user information contract UIC and save.

8. The method for evaluating the quality of crowd-sensing data based on weighted voting on a blockchain according to claim 7 is characterized by: In formula (7), the R l represents the reputation before update, R l ∈[0,1], k represents the update rate, k∈(0,1], v r Indicates the relative task data quality, v r ∈[-1,1].

9. The method for evaluating the quality of crowd-sensing data based on weighted voting on a blockchain according to claim 7 or 8, characterized in that: In formula (7), the R l represents the reputation before update, R l The value is 0.5, k represents the update rate, k is 0.5, v r Indicates the relative task data quality, v r The value is 0.

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