Mobile crowd-sensing data collection system, method, electronic device, and storage medium

Abstract

The application discloses a mobile crowd sensing data collection system and method, electronic equipment and a storage medium, comprising: a data protection subsystem, a data quality evaluation subsystem and a data reward distribution subsystem; the data protection subsystem is used for differentiating encryption of user positions of sensing users in user data, setting random disturbance on an encryption key of the user positions, and uploading the encrypted user positions; the data quality evaluation subsystem is used for determining user abnormal conditions of the sensing users, determining a reputation parameter according to a user historical participation frequency of the sensing users when the user abnormal conditions are normal users, and evaluating user data quality according to the reputation parameter and a similarity determined by comparison of the user data and target data; and the data reward distribution subsystem is used for determining a task reward corresponding to the user data quality, and deploying the task reward. The application can solve the problem of privacy protection in mobile crowd sensing data collection, can ensure data quality, and improve user participation initiative.

Description

Technical Field

[0001] This invention relates to the field of Internet technology, and in particular to a mobile crowd sensing data collection system, method, electronic device, and storage medium. Background Technology

[0002] In recent years, with the rapid development of 5G communication networks and embedded sensor technology, the sensing and computing capabilities of user mobile smart devices have developed rapidly. Currently, the "Mobile Crowd Sensing (MCS)" model has emerged, using ordinary users' mobile smart devices as baseboard sensing units and completing large-scale, complex social sensing tasks through the mobile internet. MCS can serve many fields, such as environmental pollution monitoring, traffic flow monitoring, and smart city management. Compared with the traditional fixed-deployment sensor network sensing model, the mobile crowd sensing model has stronger sensing capabilities and lower deployment costs, meeting the basic requirements of comprehensive IoT sensing. Data collection is the core of mobile crowd sensing. In the process, after receiving a task request from a requester, the sensing platform assigns the task to the user to collect data. However, current data collection carries the risk of privacy leaks, and the involvement of a wide range of ordinary users makes it difficult to guarantee user reliability, resulting in low usability of the collected data. Furthermore, because data collection consumes users' time and resources, their initiative in data collection is poor. Therefore, data collection faces significant obstacles, making the implementation of mobile crowd sensing difficult. Summary of the Invention

[0003] This invention provides a mobile crowd sensing data collection system, method, electronic device, and storage medium to address the issue of personalized user data security protection during the mobile crowd sensing data collection process, ensure the data quality of sensing users, and enhance the initiative of sensing users in participation.

[0004] According to one aspect of the present invention, a mobile crowd sensing data collection system is provided, wherein the system includes: a data protection subsystem, a data quality assessment subsystem, and a data reward distribution subsystem;

[0005] The data protection subsystem is used to differentially encrypt and sense the user's location within the user data, set a random perturbation for the encryption key of the user location, and upload the encrypted user location.

[0006] The data quality assessment subsystem is used to determine the abnormal user situation of the perceived user. When the abnormal user situation is a normal user, the reputation parameter is determined based on the user's historical participation frequency. The user data quality is assessed according to the reputation parameter and the similarity determined by comparing the user data with the target data.

[0007] The data reward allocation subsystem is used to allocate task rewards corresponding to the quality of the user data.

[0008] According to another aspect of the present invention, a method for collecting mobile crowd sensing data is provided, wherein the method includes:

[0009] In response to sensing task information, a task notification message is generated and transmitted to the sensing user so that the sensing user collects user data corresponding to the sensing task information.

[0010] The user location of the perceived user is differentially encrypted within the user data, and the encryption key of the user location is randomly perturbed, and the encrypted user location is uploaded.

[0011] Determine the user's abnormal situation. When the user's abnormal situation is a normal user, determine the reputation parameter based on the user's historical participation frequency. Evaluate the user data quality according to the reputation parameter and the similarity determined by comparing the user data with the target data.

[0012] Allocate task rewards corresponding to the quality of the user data.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the mobile crowd sensing data collection method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the mobile crowd sensing data collection method according to any embodiment of the present invention.

[0018] The mobile crowdsourced sensing data collection system provided by this invention includes a data protection subsystem, a data quality assessment subsystem, and a data reward subsystem. The data protection subsystem differentially encrypts the location of sensing users, sets random perturbations for the encryption key, and uploads the encrypted user location. The data quality assessment subsystem identifies abnormal user situations. When the abnormal situation is that of a normal user, a reputation parameter is determined based on the user's historical participation frequency. The user data quality is determined based on the similarity between the user data and target data, as well as the reputation parameter. The data reward subsystem determines and deploys task rewards based on the user data quality. This invention solves the user privacy security problem during mobile crowdsourced sensing data collection. Differential encryption of user locations can meet the different levels of privacy needs of sensing users. Evaluating user data quality according to abnormal situations ensures the quality of user data during the mobile crowdsourced sensing data collection process. The data reward subsystem determines task rewards based on user data quality, which can enhance the initiative of sensing users in participating.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a structural example diagram of a mobile crowd sensing data collection system provided in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of the structure of a data protection subsystem provided according to an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of a data quality assessment subsystem provided in an embodiment of the present invention;

[0024] Figure 4 This is an example diagram of a mobile crowd sensing data collection system provided according to an embodiment of the present invention;

[0025] Figure 5 This is an example diagram of a data protection subsystem provided according to an embodiment of the present invention;

[0026] Figure 6 This is an example diagram of a data quality assessment subsystem provided according to an embodiment of the present invention;

[0027] Figure 7 This is an example diagram of a data reward distribution subsystem provided according to an embodiment of the present invention;

[0028] Figure 8 This is a flowchart of a mobile crowd sensing data collection method provided according to an embodiment of the present invention;

[0029] Figure 9 This is an example diagram of a mobile crowd sensing data collection method provided by an embodiment of the present invention;

[0030] Figure 10 This is a schematic diagram of the structure of an electronic device that implements the mobile crowd sensing data collection system of this invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] Figure 1 This is a structural example diagram of a mobile crowd sensing data collection system according to an embodiment of the present invention. This embodiment is applicable to mobile crowd sensing data collection. The system can be implemented in hardware and / or software, and can be configured in a server or server cluster. Figure 1As shown, the system includes: a data protection subsystem 10, a data quality assessment subsystem 11, and a data reward allocation subsystem 12. The data protection subsystem 10 is used to differentially encrypt the user's location within the user data, set random perturbations for the encryption key of the user location, and upload the encrypted user location. The data quality assessment subsystem 11 is used to determine the user's abnormal situation. When the user's abnormal situation is a normal user, it determines the reputation parameter based on the user's historical participation frequency, and assesses the user data quality according to the reputation parameter and the similarity determined by comparing the user data with the target data. The data reward allocation subsystem 13 is used to allocate the task reward corresponding to the user data quality.

[0034] In this embodiment of the invention, the data protection subsystem 10 can implement user data security functions. The data protection subsystem 10 can encrypt the user data of the sensing user. This user data may be data generated by the sensing user participating in mobile crowd sensing. The user location in the user data may be user privacy information. To ensure user privacy, the user location in the user data can be encrypted to prevent leakage of the sensing user's privacy. Furthermore, since the transaction protocol for user data during mobile crowd sensing requires encryption / decryption and user signature, and keys need to be stored during this process, these keys are at risk of leakage. To ensure user data security, the data protection subsystem 10 can also randomly scramble the keys used to encrypt the user location during the above process. This random scrambling process may include adding random perturbation parameters to the key to improve the security of key storage. This embodiment of the invention can upload the encrypted user location. In addition, the data protection subsystem can also upload the encrypted user data via blockchain, allowing the publisher of the mobile crowd sensing task to obtain the user data. The method by which user data is transmitted to other sensing users or task publishers is not limited here. The above scheme is only an example. Other information in the user data, except for location information, can also be uploaded to the blockchain by the data protection subsystem without encryption. Alternatively, the other information can be transmitted to other sensing users or task publishers by a specified communication protocol.

[0035] Specifically, the mobile crowd-sensing data collection system can call the data quality assessment subsystem 11 to evaluate the quality of user data. The user data quality assessment in the data quality assessment subsystem 11 can include three dimensions: user evaluation, user reputation assessment, and evaluation of the user data itself. Specifically, user evaluation in the data quality assessment subsystem can determine whether a perceived user is a normal participant in data collection. User evaluation can be implemented using the DPOS consensus algorithm or the PBFT consensus algorithm. User reputation assessment can be determined based on user anomalies. When a perceived user has a historical reputation, the current reputation parameter generated by judging the historical reputation and user anomalies can be used to jointly determine the user's reputation. The data quality assessment subsystem 11 can also evaluate the user data itself. The evaluation method can be determined by comparing the similarity between the user data and the target data. The user data quality can be determined by comprehensively considering the above user evaluation, user reputation assessment, and evaluation of the user data itself.

[0036] In this embodiment of the invention, the data reward allocation subsystem 12 of the mobile crowd-sensing data collection system is used to determine the task reward based on the user data quality determined by the user data, and to deploy the task reward so that the sensing user can obtain the task reward. The deployment method may include notifying the corresponding sensing user of the task reward through a communication message, or configuring the task reward into a smart contract on the blockchain. Each sensing user can determine the task reward obtained by participating in the mobile crowd-sensing data collection through the smart contract, thereby enabling the sensing user to actively participate in the mobile crowd-sensing data collection.

[0037] The mobile crowd-sensing data collection system provided by this invention includes a data protection subsystem, a data quality assessment subsystem, and a data reward subsystem. The data protection subsystem differentially encrypts the user location within the user data of the sensed users, using a randomly perturbed encryption key, and uploads the encrypted user location. The data quality assessment subsystem identifies user anomalies and determines the user's reputation parameters based on these anomalies. It also determines the user data quality based on the similarity between the user data and target data, as well as the reputation parameters. The data reward subsystem determines task rewards based on the user data quality and deploys these rewards. This addresses the user privacy protection issue during mobile crowd-sensing data collection, achieving personalized security for user data. Evaluation of user data across different dimensions improves data quality during mobile crowd-sensing data collection, and task rewards enhance user participation.

[0038] Furthermore, based on the above embodiments of the invention, see also... Figure 2The data protection subsystem 10 includes a data protection module 101 and a key storage module 102. Based on the above embodiments, the data protection subsystem 10 differentially encrypts the user's location within the user data, and after setting a random perturbation on the encryption key for the user location, uploads the encrypted user location to the blockchain. Specifically, the data protection module 101 determines the preset privacy protection level of the perceived user, calls a preset order-preserving encryption rule to encrypt the user location in the user data, and stores the user location in a hidden area corresponding to the preset privacy protection level. It can be understood that this hidden area can be a portion of the user data or separately generated interference data. The key storage module 102 sets a random perturbation on the encryption key, stores the randomly perturbed key in the perceived user's user terminal, and uploads the location information of the hidden area, the ciphertext containing the user location, and the perceived user's signature information to the blockchain.

[0039] In this embodiment of the invention, the data protection subsystem 10 may include a data protection module 101 and a key storage module 102. The data protection module 101 can set different preset privacy protection levels for each perceived user. These preset privacy protection levels can be set according to the privacy protection needs of different perceived users. Different preset privacy protection levels can implement user security protection strategies with different strengths. For example, different hidden areas or different encryption algorithms can be configured under different preset privacy protection levels. The data protection module 101 can determine the preset privacy protection level corresponding to a perceived user and call a specified preset order-preserving encryption rule to encrypt the user's location in the user data, making the user location in the user data ciphertext. This user location can be stored in the hidden area corresponding to the preset privacy protection level. It is understood that the higher the preset privacy protection level, the larger the corresponding hidden area can be, thus lowering the risk of user location leakage. The key storage module 102 can randomly scramble the key for encrypting the user's location, thereby improving the security level of the key. The randomly scrambled key can be stored on the user terminal of the sensing user, which can reduce the risk of key leakage. The key storage module 102 can upload the location information of the sensing user's hidden area, the hidden area containing the encrypted text of the user's location, and the sensing user's signature information to the blockchain, so that the task publisher of the mobile crowd-sensing data collection can use the user data to assist the sensing user in completing the sensing task.

[0040] In some embodiments of the invention, the mobile crowd sensing data collection system obtains the privacy setting parameters of the sensing user in the data protection module 101, and adjusts the preset privacy protection level according to the privacy setting parameters.

[0041] In this embodiment of the invention, the perceived user can set a preset privacy protection level. The preset privacy protection level can be adjusted by configuring the privacy setting parameters, thereby setting different sizes of the hidden area according to the differences in the perceived user's sensitivity to privacy, and achieving different levels of user data security protection.

[0042] Furthermore, based on the above embodiments of the invention, see also... Figure 3 The data quality assessment subsystem 11 includes an abnormal user identification module 111, a reputation assessment module 112, and a similarity matching module 113. The data quality assessment subsystem is used to determine abnormal user situations, determine the reputation parameters of the perceived users based on the abnormal user situations, and assess the quality of user data according to the reputation parameters and the similarity determined by comparing user data with target data. This includes:

[0043] The abnormal user identification module 111 is used to determine the learning ability type of the perceived user and to determine the abnormal situation of the perceived user according to the learning ability type; the reputation evaluation module 112 is used to determine the current reputation value of the perceived user according to a preset weighted evaluation rule when the perceived user is a normal user. If the perceived user's historical participation count exists, the perceived user's historical reputation value and current reputation value are fitted according to the Richard curve to become the reputation parameter. If the perceived user's historical participation count does not exist, the current reputation value is used as the reputation parameter.

[0044] In this embodiment of the invention, the reputation assessment module 112 can obtain the abnormal user information determined by the abnormal user identification module 111 before assessing the user's reputation. The reputation assessment module 112 only assesses the user's reputation parameters when it determines that the user is a normal user, thereby reducing the data processing load of the reputation assessment module 112. If the reputation assessment module 112 detects that a user is an abnormal user, it can choose not to assess the reputation parameters of the abnormal user and discard the user data.

[0045] The weighted evaluation rule can be a processing rule that quantifies user anomalies. The preset weighted evaluation rule can be executed by participants in the mobile crowdsourcing sensing activity. In one exemplary implementation, the preset weighted evaluation rule can specifically be a neural network model. Participants can use their own trained neural network models to analyze user anomalies and generate their own evaluation values. The current reputation value of a specific sensing user can be determined according to the weight coefficients corresponding to each participant and the evaluation values. In another exemplary implementation, participants can score a user with a specific characteristic, and the average of the scores can be used as the current reputation value.

[0046] Furthermore, when there is a perceived user's historical reputation value, the historical reputation value and the current reputation value are fitted according to a Richard curve as reputation parameters. Specifically, the historical reputation value and the current reputation value can be fitted according to a Richard curve, and the saturation value of the fitted curve can be used as the reputation parameter. When there is no perceived user's historical reputation value, the current reputation value is used as the reputation parameter. The similarity matching module 113 is used to determine the dimensional features of user data and target data in at least one preset dimension, determine the similarity between dimensional features of the same preset dimension, and determine the user data quality according to each similarity and reputation parameter. The preset dimension includes at least one of the following: response time, travel distance, resource consumption, number of transactions, throughput, and transaction frequency.

[0047] In this embodiment of the invention, the abnormal user identification module 111 can identify abnormal user situations in different ways according to the learning ability type of the perceived user. The learning ability type can refer to the situation where the perceived user can receive result feedback and improve learning. The learning ability type can be determined by the terminal configuration of the perceived user. For example, if the perceived user does not select the result feedback option when participating in the mobile crowd intelligence sensing data collection, then the perceived user does not have learning ability. If the perceived user selects the result feedback option when participating in the mobile crowd intelligence sensing data collection, then the perceived user has learning ability.

[0048] In some embodiments of the invention, the abnormal user identification module 111 is specifically used for:

[0049] Determine whether the attribute information of the perceived user has set result feedback; if the attribute information has set result feedback, then determine that the learning ability type of the perceived user is "has learning ability", call the DPoS consensus processing rule to determine the user's abnormal situation, and provide the perceived user with the function of modifying user data when the user's abnormal situation is an abnormal user; if the attribute information has not set result feedback, then determine that the learning ability type of the perceived user is "does not have learning ability", and call the PBFT consensus processing rule to determine the user's abnormal situation.

[0050] In this embodiment of the invention, the abnormal user identification module 111 can extract the attribute information of the sensing user. This attribute information may be information configured by the sensing user for participating in the mobile collective intelligent sensing data collection. It can be determined whether the result feedback has been set in the attribute information. It is understood that the attribute information can be obtained from the sensing user's terminal or the host server. The attribute information may include at least the configuration parameters for result feedback. For example, if the sensing user selects result feedback during the mobile collective intelligent sensing data collection process, the configuration parameter for result feedback in the sensing user's attribute information is 1. If the sensing user does not select result feedback during the mobile collective intelligent sensing data collection process, the configuration parameter for result feedback in the sensing user's attribute information is 0. In this embodiment of the invention, the learning ability type can be determined by judging the attribute information of the sensing user. For sensing users whose learning ability type is "learning ability", the DPoS consensus processing rule can be invoked to judge the user's abnormal situation. This DPoS consensus processing rule can be implemented based on the blockchain's DPOS consensus algorithm. For example, the DPoS consensus processing rule can include each sensing user participating in mobile collective intelligence sensing being able to jointly elect an abnormal sensing user based on their own ability and the abnormal situation of the sensing user as the evaluation target, and marking the abnormal situation of the sensing user as abnormal. Furthermore, when a sensing user is marked as abnormal, it can be considered a user with learning ability. The system provides user data modification functionality, allowing perceived users to modify their data and thus improve the severity of perceived user anomalies. For perceived users with no learning ability, the PBFT consensus processing rules can be invoked to determine user anomalies. These PBFT consensus processing rules can be implemented using the PBFT consensus algorithm. For example, the PBFT consensus processing rules could include each perceived user participating in mobile crowdsourcing sensing judging the perceived user's anomalies based on the target perceived user's anomalies. The system could statistically analyze the judgment results of all perceived users for a given perceived user, and mark the perceived user's anomaly as abnormal if more than half of the judgment results are abnormal. It is understood that the above DPoS and PBFT consensus processing rules are merely examples and not limiting; other user anomaly judgment rules conforming to the DPoS or PBFT consensus algorithms are also within the scope of protection. The scope of user anomalies mentioned above is not limited here, and includes, but is not limited to, perceived user identity anomalies, perceived user network anomalies, and user data anomalies.

[0051] Specifically, the reputation assessment module 112 can evaluate the current reputation value of a perceived user through a preset weighted assessment rule. This preset weighted assessment rule can set different weight values ​​for different indicators of the perceived user. For example, indicators may include network status, number of historical task participations, historical data quality, historical reputation value, the perceived user's occupation, and the region where the perceived user is located. The current reputation value of the perceived user can be determined through each weight value. Furthermore, the preset weighted assessment rule can be implemented using blockchain, and each perceived user can have their own preset weighted assessment rule. The indicators and weight settings for each perceived user can be different. Further, since a user's reputation value is affected not only by the current situation of the perceived user but also by their honesty in previous data collection activities, when a perceived user has historical reputation values, the reputation assessment module 112 can collect those historical reputation values. It can then fit each historical reputation value and the current reputation value to a Richard curve, using the saturation value of the fitted Richard curve as the perceived user's reputation parameter. If no historical reputation value exists for the perceived user, the determined current reputation value is used as the reputation parameter.

[0052] In this embodiment of the invention, the similarity matching module 113 is used to perform quality evaluation on the user data itself. It can determine the dimensional features of user data and target data in different dimensions. User data can be data generated by a user for a data collection task, while target data can be sample data configured for the data collection task. It is understood that the aforementioned dimensional features can be indicator data on different dimensions. These dimensional features can include conventional indicators and blockchain indicators. For example, conventional indicators can include user data quality, the time of user data collection, and user data upload time, while blockchain indicators can include the transmission network status of user data, the storage usage of user data on the blockchain, and the upload and download speed of user data on the blockchain. In some embodiments, dimensional features can include values ​​for response time, travel distance, resource consumption, number of transactions, throughput, and transaction frequency. The similarity between user data and target data can be calculated for the same dimensional features. The user data quality can be determined by combining the similarity between the aforementioned different dimensional features and reputation parameters.

[0053] In some embodiments of the invention, determining the quality of the user data according to the aforementioned similarity and the reputation parameter includes:

[0054] The similarity value corresponding to the reputation parameter is adjusted; the user data corresponding to the similarity value is arranged into a user data sequence, and the sequence order within the user data sequence is used as the user data quality of the user data.

[0055] In this embodiment of the invention, the similarity between each user data and the target data can be determined. It is understood that when user data has multiple similarities corresponding to multiple dimensions of features, the weighted sum of the multiple similarities can be used as the similarity of the user data. For the similarity of each user data, the corresponding reputation parameter can be used to adjust the similarity. For example, different reputation parameters can correspond to different weight coefficients, and the product of the weight coefficient and the similarity can be used as the adjusted similarity. Each user data can be sorted according to the adjusted similarity to generate a user data sequence. The order of each user data in the user data sequence can represent the order of the quality of the user data. For example, the first user data in the user data sequence may have the best data quality, and the sequence order of each user data in the user data sequence can be used as the user data quality.

[0056] Furthermore, in some embodiments of the invention, the data reward allocation subsystem 12 includes an allocation type determination module, a task reward determination module, and a reward allocation module: the data reward allocation subsystem 12 is used to determine the reward allocation type corresponding to the user data; when the reward allocation type is user-center allocation, a first reward allocation mechanism based on evolutionary game rules is invoked to determine the task reward corresponding to the user data; when the reward allocation type is task-center allocation, a second reward allocation mechanism based on multi-attribute auction rules is invoked to determine the task reward corresponding to the user data of the user data quality; the task allocation module is used to allocate task rewards.

[0057] The reward allocation type can be a pre-configured type of information that determines the allocation focus of task rewards. The reward allocation type is divided according to different data collection situations. The reward allocation type can include user-center allocation and task-center allocation, etc. User-center allocation can be a reward allocation based on the premise of perceiving users to obtain more benefits, aiming to improve the provision of comprehensive and accurate data by users. Task-center allocation can be an allocation aimed at maximizing the benefits of data collection tasks, reducing task reward expenditures while obtaining high-quality data. Different reward allocation types can be configured with different reward allocation mechanisms.

[0058] In this embodiment of the invention, the reward allocation subsystem 12 is used to determine the reward allocation type of user data. This reward allocation type can be pre-configured within the system. The publisher of the data collection task can pre-configure the reward classification type within the reward allocation subsystem 12 according to its own needs. When the reward allocation type of user data is determined to be user-centered allocation, the first reward allocation mechanism of the evolutionary game rules within the data reward allocation subsystem 12 can be invoked. This first reward allocation mechanism can be a reward allocation mechanism implemented using evolutionary game theory, which can realize the acquisition of user rewards. The evolutionary game rules within the first reward allocation mechanism can model the user reward allocation problem as a user-publisher evolutionary game model. By solving the evolutionary stable strategy solution and stability analysis, the optimal user reward acquisition strategy for different perceived users under corresponding attribute conditions in the above user-publisher evolutionary game model can be obtained. The attribute conditions can be determined by the specific circumstances of the perceived user's participation in the mobile collective intelligent perception data collection. The above optimal reward acquisition strategy can be used as the allocation strategy of the first reward allocation mechanism, thereby ensuring that participating users obtain as much benefit as possible. When the reward allocation type for user data is determined to be task center allocation, a second reward allocation mechanism within the data reward allocation subsystem 12 can be invoked. This second reward allocation mechanism can be implemented based on multi-attribute auction rules. Perceiving users can obtain task rewards by auctioning each attribute rule requirement within the multi-attribute rules. These multi-attribute rules can include rules based on dimensions such as reward price, user data quality, and user reputation. Furthermore, the multi-attribute rules can be iterated multiple times to improve user data quality and ensure fair distribution of task rewards. Specifically, the reward allocation module can allocate the determined task rewards. These rewards can be directly distributed to users participating in mobile crowdsourcing perception, or the task rewards can be published on the platform, allowing participating users to actively obtain them within the platform. In some exemplary embodiments, after determining the task reward corresponding to the user data through the first or second reward allocation mechanism, the task reward can be uploaded to a smart contract on a preset blockchain, enabling perceiving users to obtain task rewards through the smart contract on the preset blockchain.

[0059] In some exemplary implementations, Figure 4 This is an example diagram of a mobile crowd sensing data collection system provided according to an embodiment of the present invention. See also... Figure 4In this embodiment of the invention, the mobile crowd-sensing data collection system can be implemented based on blockchain. It can provide different services to sensing users and task publishers. For sensing users, it protects user privacy and obtains a final reward system based on uploaded data. For task publishers, it allows them to publish sensing tasks, obtain task requirement data, and mine valuable information, ultimately providing services for smart city integration, environmental pollution monitoring, and collaborative computing. The system provided in this embodiment mainly consists of three parts: a data protection subsystem, a data quality assessment subsystem, and a data reward distribution subsystem. First, the data protection subsystem protects data privacy and stores large-scale keys to ensure user data security. Second, the data quality assessment subsystem identifies abnormal users, assesses user reputation, and matches data, improving the accuracy of data quality assessment. Finally, the data reward distribution subsystem implements a reward distribution method centered on different roles to ensure fairness in reward distribution. See also... Figure 5 The data protection subsystem implements data protection functions and consists of a data protection module and a key storage module. The data protection module provides personalized location privacy protection based on differences in user privacy sensitivities, ensuring user data security. The key storage module stores encryption keys to prevent key privacy leaks. The execution process of the data protection subsystem includes: before users upload data, the data protection module within the subsystem securely protects user data. Since the quality of user data is positively correlated with user location, the data protection module protects user location privacy by encrypting the location using order-preserving encryption, generating a ciphertext hash value containing the location. Simultaneously, based on differences in user privacy sensitivities, different sizes of hidden areas are set; different privacy sensitivity requirements correspond to different sizes of hidden areas, achieving varying levels of user data security protection. In this embodiment, order-preserving encryption is used to protect user location data, but the encrypted key is at risk of leakage, and there may be correlations between ciphertexts. Therefore, the key storage module improves the encryption key by adding random perturbation parameters to ensure key security. Ultimately, participating users upload their encrypted hash values ​​containing their location, anonymous location, and personal signature to the blockchain, while the remaining data is stored on the user's own device, achieving distributed storage "on-chain + off-chain" and ensuring the security of participating user data.

[0060] See Figure 6The data quality assessment subsystem is used to implement data quality assessment functions and consists of an abnormal user identification module, a reputation assessment module, and a similarity matching module. The abnormal user identification module identifies abnormal users to prevent data forgery; the reputation assessment module provides accurate reputation assessments for different types of participating users and updates user reputations; the similarity matching module assesses user data quality by calculating the similarity between user data and target data. The execution flow of the data quality assessment subsystem includes: after ensuring user data security through the data protection subsystem, considering that the existence of abnormal users can reduce the accuracy of data quality assessment, the subsystem first uses the abnormal user identification module to identify abnormal users to prevent data forgery, and then uses a blockchain consensus algorithm to assess abnormal users. Given a set of participating user perception data... If user data can learn and improve through feedback, i.e., it possesses learning capabilities, then a probabilistic consensus algorithm based on DPoS can be used to identify abnormal users, allowing modifications to the reached consensus, encouraging users to submit normal data, and improving verification speed. If user data cannot be improved through feedback, i.e., it lacks learning capabilities, then a deterministic consensus algorithm based on PBFT can be used to quickly identify abnormal users, meeting the needs of high-frequency trading. Secondly, the data quality assessment subsystem evaluates reputation to ensure the credibility of user data. In the reputation assessment module, the traceability of blockchain data can be used to determine whether a participating user is a new user. If so, only the current reputation assessment is performed on the participating user; if not, a weighted average of the user's historical reputation and current reputation is used to assess the user's reputation. Considering that in practical applications, a user's reputation will slowly rise with a series of honest behaviors, but will rapidly decline due to a very small number of dishonest behaviors, a Richard curve, which conforms to the above pattern, is used for user reputation assessment. Finally, the data quality assessment subsystem matches user data, and in the similarity matching module, it constructs multi-dimensional assessment indicators from both conventional and blockchain dimensions. This includes metrics such as response time, travel distance, resource consumption, number of transactions, throughput, and transaction frequency. Based on these metrics, similarity matching algorithms, such as multidimensional Euclidean distance calculation, are used to obtain the matching degree between user data and target data and sort them to achieve user quality assessment.

[0061] See Figure 7The data reward distribution subsystem is used to implement data reward distribution functions, and consists of a user-based reward distribution module and a task-based reward distribution module. The user-based reward distribution module, centered on the user, combines blockchain technology with various incentive methods to enhance user participation and encourage users to provide comprehensive and accurate data. The task-based reward distribution module aims to maximize the benefits for task publishers by providing the lowest possible reward while obtaining high-quality user data. The execution process of the data reward distribution subsystem includes: after accurate data quality assessment through the data quality assessment subsystem, corresponding rewards are provided to compensate for the resource consumption incurred by participating users in data collection. The data reward distribution subsystem, deployed on blockchain smart contracts, implements strategy initialization and transaction recording. The automatic execution of smart contracts not only improves the utility of data collection but also ensures the fairness of reward distribution. The reward distribution type is determined based on the starting point. If the focus is on the user, the user-based reward distribution module is used; if the focus is on the task publisher, the task-based reward distribution module is used. In the user-centric reward allocation module, a reward allocation mechanism based on evolutionary game theory is used to achieve user reward acquisition. The user reward allocation problem is modeled as a user-publisher evolutionary game model. Through solving for the evolutionary stable strategy and stability analysis, the optimal user reward acquisition strategy under different conditions is obtained, ensuring that participating users obtain as much benefit as possible. In the task publisher-centric reward allocation module, a reward allocation mechanism based on multi-attribute auctions is used to achieve user reward acquisition. Participating users can fully leverage their respective advantages, satisfying the diverse data characteristics of participating users. The task publisher can combine various user factors, including price, quality, reputation, etc., to conduct corresponding competition and negotiation, using multiple iterations to find the optimal solution, ensuring that the task publisher obtains high-quality user data while providing users with reasonable rewards. To improve data collection utility and ensure the fairness of reward allocation.

[0062] Figure 8 This is a flowchart illustrating a mobile crowd sensing data collection method according to an embodiment of the present invention. This embodiment is applicable to mobile crowd sensing data collection. The method is applied to a mobile crowd sensing data collection system, which can be implemented in hardware and / or software. Figure 8 As shown, the method provided in this embodiment of the invention specifically includes the following steps:

[0063] Step 110: In response to the sensing task information, generate a task notification message and transmit it to the sensing user so that the sensing user can collect the user data corresponding to the sensing task information.

[0064] Among them, the perception task information can be the data collection task issued by the task issuer, and the perception task information can include information such as the data collection objectives and data collection requirements.

[0065] In this embodiment of the invention, the task publisher can set up sensing task information according to its own needs and publish the sensing task information to the system. After receiving the sensing task information, the system can encapsulate the sensing task information into a task notification message and notify one or more sensing users through the task notification message, so that one or more sensing users can collect user data according to the sensing task information.

[0066] Step 120: Differentiately encrypt the user's location within the user data, set a random perturbation for the encryption key of the user's location, and upload the encrypted user location.

[0067] In this embodiment of the invention, user data security functions can be implemented. User data of the sensing users can be encrypted. This user data may be data generated by the sensing users participating in mobile crowdsourcing sensing. The user location in the user data may be the sensing users' privacy information. To ensure user privacy, the user location in the user data can be encrypted to prevent leakage. Furthermore, since the transaction protocol for user data during mobile crowdsourcing sensing requires encryption / decryption and user signature, keys need to be stored. These keys are at risk of leakage. To ensure user data security, the keys used to encrypt the user location can be randomly scrambled to improve the security of key storage. This embodiment of the invention can upload the encrypted user location. In addition, the data protection subsystem can also upload the encrypted user data via blockchain, allowing the publisher of the mobile crowdsourcing sensing task to access the user data. The method of transmitting user data to other sensing users or task publishers is not limited here. The above scheme is only an example. Other information in the user data besides location information can also be uploaded to the blockchain by the data protection subsystem without encryption, or the other information can be transmitted to other sensing users or task publishers via a specified communication protocol.

[0068] Step 130: Determine the user's abnormal situation. When the user's abnormal situation is a normal user, determine the reputation parameter based on the user's historical participation frequency. Evaluate the user data quality according to the reputation parameter and the similarity determined by comparing the user data with the target data.

[0069] In this embodiment of the invention, the quality of user data can be evaluated. The evaluation of user data quality can include three dimensions: user evaluation, user reputation evaluation, and evaluation of the user data itself. User evaluation can be used to judge the situation of perceived users and determine whether they are normal users participating in data collection. User evaluation can be implemented using the DPOS consensus algorithm or the PBFT consensus algorithm. User reputation evaluation can be used to judge abnormal user situations. When a perceived user has a historical reputation, the current reputation parameter generated by judging the historical reputation and the user's abnormal situation can be used to jointly determine the user's reputation. Evaluation can also be performed on the user data itself. The evaluation method can be determined by comparing the similarity between the user data and the target data. The quality of user data can be determined by comprehensively considering the above user evaluation, user reputation evaluation, and evaluation of the user data itself.

[0070] Step 140: Allocate task rewards corresponding to user data quality.

[0071] Specifically, task rewards can be determined based on the quality of user data identified by user data, and then distributed so that the sensing users can receive the rewards. This distribution method can include notifying the corresponding sensing users of the task rewards via communication messages, or configuring the task rewards into a blockchain smart contract. Each sensing user can determine the task rewards for participating in the mobile collective intelligent sensing data collection through the smart contract, thereby encouraging sensing users to actively participate in the mobile collective intelligent sensing data collection.

[0072] In one exemplary implementation, Figure 9 This is an example diagram of a mobile crowd-sensing data collection method according to an embodiment of the present invention. Taking road congestion monitoring as an example, participating users include ordinary users carrying smart mobile devices, vehicles using in-vehicle navigation, public service personnel (such as police officers, bus drivers, taxi drivers, etc.), etc., and the task issuer is a smart navigation APP provider. See also Figure 9 The mobile crowd sensing data collection process may include the following steps:

[0073] Step 1: Upload Task and Reward. The task publisher, based on actual needs, such as monitoring traffic congestion on a certain road segment between 2 PM and 4 PM, uploads the sensing task and related reward to the blockchain-based mobile crowdsourcing sensing data collection system. The reward information for the task publisher is stored in blockchain format to ensure authenticity.

[0074] Step 2: Task Issuance. After receiving a task request from the task issuer, the blockchain-based mobile crowd-sensing data collection system categorizes and issues the task requirements, informing participating users via open calls.

[0075] Step 3: Task Confirmation. After receiving the sensing task, participating users decide whether to participate in the sensing activity based on their own circumstances and confirm the task with the system. Once a user confirms participation, they can collect data using their own devices, such as smart tablets, smart wearable devices, smartphones, and vehicle sensors, submitting various types of data including photos, text descriptions, and vehicle time.

[0076] Step 4: Data Protection. The blockchain-based mobile crowd-sensing data collection system, based on the task confirmation information of participating users and combined with blockchain technology, ensures the privacy and security of participating users. Utilizing a data protection subsystem, the system performs differentiated encryption on the location based on the differences in user privacy sensitivities, ensuring user data security. This includes obtaining the encrypted hash value of the location, the anonymous location, and the personal signature. The remaining data is stored on the user's own device, achieving distributed storage and data security protection for participating users through a "on-chain + off-chain" approach.

[0077] Step 5: Quality Assessment. While ensuring user privacy and security, the blockchain-based mobile crowd-sensing data collection system utilizes a data quality assessment subsystem to identify abnormal users, evaluate user reputation, and match similarity, providing user data quality assessment results to ensure the acquisition of high-quality user data.

[0078] Step 6: Reward Distribution. After completing the quality assessment, to compensate for the resource consumption incurred by participating users in data collection, rewards are provided through the data reward distribution subsystem. Different reward distribution methods are designed for different centers based on different concerns to encourage active participation from a large number of users, ensure the fairness of reward distribution, and ultimately improve the effectiveness of the blockchain-based mobile collective intelligence sensing data collection system.

[0079] Step 7: Data Feedback. The blockchain-based mobile crowd sensing data collection system feeds back user data that meets the requirements to the task publisher. The task publisher can then provide various convenient services driven by mobile crowd sensing big data, including real-time route navigation planning and traffic accident monitoring, avoiding traffic congestion to save time, fuel, and money, thus providing convenience for users' travel.

[0080] Figure 10A schematic diagram of an electronic device 20 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0081] like Figure 10 As shown, the electronic device 20 includes at least one processor 21 and a memory, such as a read-only memory (ROM) 22 or a random access memory (RAM) 123, communicatively connected to the at least one processor 21. The memory stores computer programs executable by the at least one processor. The processor 21 can perform various appropriate actions and processes based on the computer program stored in the ROM 22 or loaded from storage unit 28 into the RAM 23. The RAM 23 can also store various programs and data required for the operation of the electronic device 20. The processor 21, ROM 22, and RAM 23 are interconnected via a bus 24. An input / output (I / O) interface 25 is also connected to the bus 24.

[0082] Multiple components in electronic device 20 are connected to I / O interface 25, including: input unit 26, such as keyboard, mouse, etc.; output unit 27, such as various types of monitors, speakers, etc.; storage unit 28, such as disk, optical disk, etc.; and communication unit 29, such as network card, modem, wireless transceiver, etc. Communication unit 29 allows electronic device 20 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0083] Processor 21 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 21 performs the various methods and processes described above, such as mobile crowd sensing data collection methods.

[0084] In some embodiments, the mobile crowd sensing data collection system may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 28. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 20 via ROM 22 and / or communication unit 29. When the computer program is loaded into RAM 23 and executed by processor 21, one or more subsystems or modules of the mobile crowd sensing data collection system described above may be executed. Alternatively, in other embodiments, processor 21 may be configured to perform the mobile crowd sensing data collection method by any other suitable means (e.g., by means of firmware).

[0085] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0086] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0087] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0088] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0089] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0090] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0091] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0092] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A mobile crowd sensing data collection system, characterized in that, The system includes: a data protection subsystem, a data quality assessment subsystem, and a data reward distribution subsystem; The data protection subsystem is used to differentially encrypt and sense the user's location within the user data, set a random perturbation for the encryption key of the user location, and upload the encrypted user location. The data quality assessment subsystem is used to determine the abnormal user situation of the perceived user. When the abnormal user situation is a normal user, the reputation parameter is determined based on the user's historical participation frequency. The user data quality is assessed according to the reputation parameter and the similarity determined by comparing the user data with the target data. The data reward allocation subsystem is used to allocate task rewards corresponding to the quality of the user data. The data protection subsystem includes a data protection module and a key storage module; The data protection module is used to determine the preset privacy protection level of the perceived user, call the preset order-preserving encryption rule to encrypt the user location of the user data, and store the ciphertext of the user location in the hidden area corresponding to the preset privacy protection level; The key storage module is used to set a random perturbation for the encryption key and store it in the user terminal of the sensing user, and to upload the location information of the hidden area, the hidden area containing the ciphertext of the user's location, and the signature information of the sensing user.

2. The system according to claim 1, characterized in that, The data quality assessment subsystem includes an abnormal user identification module, a reputation assessment module, and a similarity matching module. The abnormal user identification module is used to determine the learning ability type of the perceived user, and to determine the abnormal user situation of the perceived user according to the learning ability type. The reputation assessment module is used to determine the current reputation value of the perceived user according to a preset weighted assessment rule when the perceived user is a normal user. If the perceived user's historical participation count exists, the historical reputation value of the perceived user and the current reputation value are fitted together according to the Richard curve to form the reputation parameter. If the perceived user's historical participation count does not exist, the current reputation value is used as the reputation parameter. The similarity matching module is used to determine the dimensional features of the user data and the target data in at least one preset dimension, determine the similarity between dimensional features of the same preset dimension, and determine the quality of the user data according to each similarity and the reputation parameter.

3. The system according to claim 2, characterized in that, The abnormal user identification module is used for: Determine whether the attribute information of the perceived user is set to provide a result feedback; If the attribute information has been set to provide feedback, then the learning ability type of the perceived user is determined to be having learning ability. The DPoS consensus processing rules are called to determine the user's abnormal situation. If the user's abnormal situation is an abnormal user, the user's data modification function is provided to the perceived user. If the attribute information does not have a result feedback, then the learning ability type of the perceived user is determined to be no learning ability, and the PBFT consensus processing rules are invoked to determine the user's abnormal situation.

4. The system according to claim 2, characterized in that, Determining the quality of user data based on the aforementioned similarity and reputation parameters includes: Adjust the similarity value corresponding to the similarity according to the reputation parameter; The user data arranged according to the similarity values ​​is a user data sequence, and the sequence order within the user data sequence is used as the user data quality.

5. The system according to claim 1, characterized in that, The data reward allocation subsystem includes an allocation type determination module, a task reward determination module, and a reward allocation module. The allocation type determination module is used to determine the reward allocation type corresponding to the user data; The task reward determination module is used to, when the reward allocation type is user-centered allocation, invoke a first reward allocation mechanism based on evolutionary game rules to determine the task reward corresponding to the user data of the user data quality; and when the reward allocation type is task-centered allocation, invoke a second reward allocation mechanism based on multi-attribute auction rules to determine the task reward corresponding to the user data of the user data quality. The reward allocation module is used to allocate the reward for the task.

6. The system according to claim 1, characterized in that, The data protection module is also used to obtain the privacy setting parameters of the perceived user and adjust the preset privacy protection level according to the privacy setting parameters.

7. A method for collecting mobile crowd sensing data, characterized in that, The method includes: In response to sensing task information, a task notification message is generated and transmitted to the sensing user so that the sensing user collects user data corresponding to the sensing task information. The user location of the perceived user is differentially encrypted within the user data, and the encryption key of the user location is randomly perturbed, and the encrypted user location is uploaded. Determine the preset privacy protection level of the perceived user, call the preset order-preserving encryption rule to encrypt the user location of the user data, and store the ciphertext of the user location in the hidden area corresponding to the preset privacy protection level; The encryption key is set to be randomly perturbed and then stored in the user terminal of the sensing user. The location information of the hidden area, the hidden area containing the ciphertext of the user's location, and the signature information of the sensing user are uploaded. Determine the user's abnormal situation. When the user's abnormal situation is a normal user, determine the reputation parameter based on the user's historical participation frequency. Evaluate the user data quality according to the reputation parameter and the similarity determined by comparing the user data with the target data. Allocate task rewards corresponding to the quality of the user data.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the mobile crowd sensing data collection method of claim 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the mobile crowd sensing data collection method of claim 7.

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