Crowdsourcing task allocation method and related equipment

Abstract

The invention discloses a crowdsourcing task allocation method and related equipment, and the method comprises the steps: verifying an identity signature of a task publisher according to first encryption information and a signature public key, and if the verification is passed, extracting first encryption position information and a credit score threshold value in the first encryption information; verifying an identity signature and a credit signature of the task executor according to the second encryption information and the signature public key, and if the verification is passed, extracting second encryption position information in the second encryption information; based on the embedded road network, determining a candidate task executor set according to the first encryption position information and the second encryption position information; according to the credit score threshold and the current credit score of each candidate task executor, determining a target task executor, and allocating crowdsourcing task information to the target task executor; and enabling the target task executor to execute the task in the crowdsourcing task information. The method can improve the accuracy and execution efficiency of task allocation, and can be widely applied to the technical field of computers.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a crowdsourcing task allocation method and related equipment. Background Technology

[0002] Currently, the main function of location-privacy-protected crowdsourcing task allocation systems is to assign tasks based on participants' geographical location information, while simultaneously protecting participants' location privacy and improving task allocation efficiency. However, in current location-protected mobile crowdsourcing task allocation methods, spatial anonymization and differential privacy, which rely on introducing interference, are not suitable for scenarios requiring precise calculations. Regarding shortest distance matching, the most common method is the secure calculation of Euclidean distance, but measuring travel distance using Euclidean distance does not accurately reflect the true travel cost.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The embodiments of this application aim to at least partially solve one of the technical problems in the related art. Therefore, the main objective of the embodiments of this application is to propose a crowdsourcing task allocation method and related equipment, which can improve the accuracy and efficiency of task allocation.

[0005] To achieve the above objectives, one aspect of this application proposes a crowdsourcing task allocation method, applied to a platform server, the method comprising the following steps: The original urban road network is mapped into an embedded road network using road network hypercube embedding technology. Receive the first encrypted message sent by the task publisher and the second encrypted message sent by the task executor, and receive the signature public key broadcast by the encryption server; The identity signature validity of the task publisher is verified based on the first encrypted information and the signature public key. If the task publisher passes the identity signature verification, the first encrypted location information and credit score threshold are extracted from the first encrypted information. The identity signature validity and credit signature validity of the task executor are verified according to the second encrypted information and the signature public key. If the task executor passes the identity signature verification and credit signature verification, the second encrypted location information in the second encrypted information is extracted. Based on the embedded road network, a set of candidate task executors located in the target area is determined according to the first encrypted location information and the second encrypted location information; wherein, the target area includes the area inside the task area and the external area adjacent to the task area; Based on the credit score threshold and the current credit score corresponding to each candidate task executor in the candidate task executor set, a target task executor is determined from the candidate task executor set, and the crowdsourcing task information published by the task publisher is assigned to the target task executor so that the target task executor can perform the task in the crowdsourcing task information.

[0006] To achieve the above objectives, another aspect of this application proposes a crowdsourcing task allocation method, applied to task publishers, the method comprising the following steps: The system receives a homomorphic encryption public key broadcast by an encryption server, and also receives identity credential information generated by the task publisher during identity registration sent by the encryption server; wherein the identity credential information includes identity identification information, identity identification signature information, credit score information, and credit score signature information. An embedded road network built on a platform server is used to determine the task location and task area of ​​the task publisher; wherein, the task location includes the area boundary vertex and the distance to the vertex; Based on the road network information of the embedded road network, calculate the shortest boundary vertex distance between the task location and the region boundary vertex of the task area; Based on the embedded road network, the initial labels corresponding to the boundary vertices of the region are determined, and the initial labels are packaged using number theory transformation techniques to obtain the target labels; The vertex position distance and the shortest boundary vertex distance are packaged using number theory transformation techniques to obtain the relative boundary distance; Based on the homomorphic encryption public key, the target label and the relative distance of the boundary are encrypted respectively to obtain the encrypted label and the relative distance of the encrypted boundary. Based on the encrypted label, the relative distance of the encrypted boundary and the task area, the first encrypted location information is constructed. Generate a credit score threshold, construct first encrypted information based on the identity information, the identity signature information, the first encrypted location information, and the credit score threshold, and send the first encrypted information to the platform server; The embedded road network is determined according to the crowdsourcing task allocation method applied to the platform server described above.

[0007] To achieve the above objectives, another aspect of this application proposes a crowdsourcing task allocation device applied to a platform server, the device comprising the following modules: An embedded road network building module is used to map the original urban road network into an embedded road network using road network hypercube embedding technology; The first data receiving module is used to receive the first encrypted information sent by the task publisher and the second encrypted information sent by the task executor, and to receive the signature public key broadcast by the encryption server. The first identity validity verification module is used to verify the identity signature validity of the task publisher based on the first encrypted information and the signature public key. If the task publisher passes the identity signature verification, the first encrypted location information and credit score threshold are extracted from the first encrypted information. The second identity validity verification module is used to verify the identity signature validity and credit signature validity of the task executor based on the second encrypted information and the signature public key. If the task executor passes the identity signature verification and credit signature verification, the second encrypted location information in the second encrypted information is extracted. A candidate task executor determination module is used to determine a set of candidate task executors located in a target area based on the embedded road network and according to the first encrypted location information and the second encrypted location information; wherein, the target area includes an area inside the task area and an external area adjacent to the task area; The crowdsourcing task allocation module is used to determine the target task executor from the candidate task executor set based on the credit score threshold and the current credit score corresponding to each candidate task executor in the candidate task executor set, and to allocate the crowdsourcing task information published by the task publisher to the target task executor so that the target task executor can perform the task in the crowdsourcing task information.

[0008] To achieve the above objectives, another aspect of this application proposes a crowdsourcing task allocation device, applied to a task publisher, the device comprising the following modules: The second data receiving module is used to receive the homomorphic encryption public key broadcast by the encryption server, and to receive the identity credential information generated by the task publisher during identity registration sent by the encryption server; wherein, the identity credential information includes identity identification information, identity identification signature information, credit score information, and credit score signature information; The task location region determination module is used to determine the task location and task region of the task publisher based on the embedded road network built by the platform server; wherein, the task location includes the region boundary vertex and the vertex location distance; The shortest boundary vertex distance calculation module is used to calculate the shortest boundary vertex distance between the task location and the regional boundary vertex of the task area based on the road network information of the embedded road network. The target label acquisition module is used to determine the initial label corresponding to the boundary vertex of the region based on the embedded road network, and to package the initial label according to the number theory transformation technique to obtain the target label; The boundary relative distance acquisition module is used to package the vertex position distance and the shortest boundary vertex distance according to the number theory transformation technique to obtain the boundary relative distance; The first encrypted location information construction module is used to encrypt the target tag and the relative distance of the boundary according to the homomorphic encryption public key to obtain the encrypted tag and the relative distance of the encrypted boundary, and construct the first encrypted location information according to the encrypted tag, the relative distance of the encrypted boundary and the task area; The first encrypted information construction module is used to generate a credit score threshold, construct first encrypted information based on the identity information, the identity signature information, the first encrypted location information and the credit score threshold, and send the first encrypted information to the platform server. The embedded road network is determined according to the crowdsourcing task allocation device applied to the platform server.

[0009] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0010] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0011] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0012] The embodiments of this application include at least the following beneficial effects: This application provides a crowdsourcing task allocation method and related equipment. This scheme maps the original urban road network into an embedded road network by adopting road network hypercube embedding technology; receives first encrypted information sent by the task publisher and second encrypted information sent by the task executor, and receives the signature public key broadcast by the encryption server; verifies the validity of the task publisher's identity signature based on the first encrypted information and the signature public key; if the task publisher passes the identity signature verification, extracts the first encrypted location information and credit score threshold from the first encrypted information; and verifies the validity of the task executor's identity signature and credit signature based on the second encrypted information and the signature public key. If the task executor passes identity signature verification and credit signature verification, the second encrypted location information is extracted from the second encrypted information. Based on the embedded road network, a set of candidate task executors located in the target area is determined according to the first encrypted location information and the second encrypted location information. The target area includes the area inside the task area and the external area adjacent to the task area. According to the credit score threshold and the current credit score corresponding to each candidate task executor in the candidate task executor set, the target task executor is determined from the candidate task executor set, and the crowdsourcing task information published by the task publisher is assigned to the target task executor so that the target task executor can perform the task in the crowdsourcing task information. This application embodiment utilizes hypercube embedding technology to structurally process the original road network, transforming complex geospatial information into computable data in a high-dimensional vector space. This reduces the topological complexity of the road network and improves the efficiency and accuracy of subsequent location information matching and regional range calculation. Simultaneously, the embedded network more efficiently supports algorithmic analysis of spatial relationships, providing a technical foundation for the accurate delineation of target areas. By leveraging the efficient spatial computing capabilities of the embedded road network, target areas, including both internal and adjacent external regions, are accurately delineated. This ensures the spatial correlation between candidates and the task area while expanding the candidate range by incorporating neighboring areas, avoiding the problem of unaccepted tasks due to overly strict regional restrictions, thus improving the flexibility and success rate of task allocation. Furthermore, credit score threshold screening ensures that the credit level of the target executor meets the requirements of the task issuer, reducing credit risk during task execution. The optimal executor is accurately matched from the candidate set, achieving dual screening of spatial adaptation and creditworthiness, thereby improving the accuracy and efficiency of task allocation. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating a crowdsourcing task allocation method provided in this application embodiment when applied to a platform server; Figure 2 This is a flowchart illustrating a crowdsourcing task allocation method provided in this application embodiment when applied to a task publisher; Figure 3 This is a simplified road network diagram provided in an embodiment of this application; Figure 4 This is a schematic diagram of alternating cutting provided in an embodiment of this application; Figure 5 This is a schematic diagram of alternating cut data provided in an embodiment of this application; Figure 6 This is a schematic diagram of road network embedding vectors provided in an embodiment of this application; Figure 7 This is a schematic diagram of a hypercube provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of a crowdsourcing task allocation device provided in this application embodiment when applied to a platform server; Figure 9 This is a schematic diagram of the structure of a crowdsourcing task allocation device provided in this application embodiment when applied to a task publisher; Figure 10 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0015] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0016] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0018] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0019] (1) Crowdsourcing: Crowdsourcing is a work model that outsources tasks to a large number of non-specific people through an internet platform. It refers to companies or organizations outsourcing tasks that were originally completed by employees to an unspecified and usually large group of people through open solicitation, such as food delivery platforms and ride-hailing platforms.

[0020] (2) Crowdsourcing tasks: This application focuses on location-based crowdsourcing tasks. The content of crowdsourcing tasks in this application mainly consists of location information, task information, identity credentials and credit information.

[0021] (3) Crowdsourcing task allocation system structure: The task allocation system consists of four parts: task publisher, task executor, platform server, and encryption server. The encryption server is responsible for generating public parameters and registering users, the platform server is responsible for receiving and allocating tasks, the task publisher publishes tasks to the platform server and scores them, and the task executor publishes location information to the platform server and executes the received tasks.

[0022] (4) Homomorphic encryption: Homomorphic encryption is an encryption method that allows specific computations to be performed on encrypted data without decryption. Homomorphic encryption allows a third party to perform specific computable operations on encrypted data without knowing the plaintext content of the data, while maintaining the functionality and structure of the data.

[0023] (5) Differential privacy: Differential privacy is a privacy protection technique that aims to protect information by adding noise to the data while allowing useful aggregate statistics to be extracted from the dataset.

[0024] (6) Semi-honest security: Semi-honest security is a cryptographic model used to describe the security of a protocol against a semi-honest adversary. A semi-honest adversary will abide by and execute the protocol, but will attempt to steal more information from the protocol's data. In this application, the platform server and task executor are semi-honest adversaries, while the cryptographic server and task publisher are assumed to be completely trustworthy.

[0025] The main function of a location-privacy-protected crowdsourcing task allocation system is to allocate tasks based on participants' geographical location information, while simultaneously protecting participants' location privacy and improving task allocation efficiency. Current location-protected mobile crowdsourcing task allocation methods can be categorized into unencrypted and encrypted methods. Unencrypted methods include spatial anonymization, spoofing, location offsetting and ambiguity, and differential privacy. These methods strive to balance privacy protection strength with service quality and are highly efficient, but they offer only a limited level of location protection, and generalization or noise mechanisms can affect service accuracy. Conversely, encrypted methods offer a higher level of privacy protection without compromising service accuracy, but they incur greater computational overhead. Encrypted privacy protection methods include privacy information retrieval, secure multi-party computation, privacy intersection computation, and homomorphic encryption. Furthermore, among the shortest distance matching algorithms for crowdsourced task allocation, the most common is Euclidean distance calculation. Secure calculation of Euclidean distance often utilizes homomorphic encryption algorithms and multi-party computation protocols. These methods are relatively mature and efficient, with their greatest advantage being high efficiency and support for large-scale concurrent computation. However, measuring travel costs using Euclidean distance ignores the crucial characteristic of vehicles traveling along the road network, as considered in this application, making it unable to accurately reflect the true travel costs. Meanwhile, in recent years, some efficiency-enhancing shortest path algorithms have been proposed for large-scale graph structures. For example, using homomorphic encryption algorithms and Order Preserving Encryption (ORE) algorithms to achieve secure queries for approximate shortest distances on encrypted graph structures, but ORE only provides limited privacy protection. Another approach involves embedding the road network into a high-dimensional space using road network embedding technology, and then using partially homomorphic encryption algorithms and Yao's protocol to calculate approximate network distances in the high-dimensional space. This method requires a dual-server scenario where they do not communicate, and the computational and communication overhead on the server side is high. The methods described above only calculate the approximate shortest path network distance, and their accuracy is often proportional to the computational and communication overhead. That is, higher accuracy requirements lead to higher overhead. In crowdsourcing, the platform connects a large number of task publishers and task executors. Due to information asymmetry between them, ensuring high-quality task completion and incentivizing outstanding individuals to continue participating are key challenges. To address this, reputation management schemes have been proposed. Current reputation management schemes can resist reputation link attacks and greedy user attacks, but because they rely on traditional public-key cryptography, they cannot resist quantum attacks.

[0026] Therefore, the shortcomings of the existing related technologies and the technical problems that this application needs to solve are as follows: (1) In location protection methods, spatial concealment and differential privacy are both methods that achieve location protection by adding interference terms, which are not suitable for scenarios that require precise calculation. However, the homomorphic encryption used in this application can ensure the accuracy of the matching results without disclosing the original location data to the defense rights. Therefore, homomorphic encryption can meet the requirements of precise matching and quality of service.

[0027] (2) Among the current shortest distance matching algorithms, the most common is the safe calculation of Euclidean distance. However, measuring the travel cost using Euclidean distance ignores the key feature of the vehicle traveling along the road network, which is considered in this application, making it unable to accurately reflect the real travel cost. This application will use road network embedding technology combined with privacy protection methods to calculate the shortest distance.

[0028] (3) While current reputation management schemes can resist reputation link attacks and greedy user attacks, they cannot resist quantum attacks because they rely on traditional public-key cryptography. This application will introduce a design to counter quantum computer attacks by using the Dilithium signature scheme.

[0029] In view of this, this application provides a crowdsourcing task allocation method and related equipment. This scheme uses hypercube embedding technology to structurally process the original road network, which can transform complex geospatial information into computable data in a high-dimensional vector space, reducing the topological complexity of the road network and improving the efficiency and accuracy of subsequent location information matching and regional range calculation. At the same time, the embedded network can more efficiently support the algorithmic analysis of spatial relationships, providing a technical foundation for the accurate delineation of target areas. By leveraging the efficient spatial computing capabilities of the embedded road network, the target area, including the internal and adjacent external areas, is accurately delineated. This ensures the spatial correlation between candidates and task areas, and expands the candidate range by including neighboring areas, avoiding the problem of no one accepting tasks due to overly strict regional restrictions, thus improving the flexibility and success rate of task allocation. Through credit score threshold screening, the credit level of the target executor is ensured to meet the requirements of the task issuer, reducing credit risk in task execution. The optimal executor is accurately matched from the candidate set, achieving dual screening of spatial adaptation and credit compliance, thereby improving the accuracy and efficiency of task allocation.

[0030] The crowdsourcing task allocation method provided in this application relates to the field of computer technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the crowdsourcing task allocation method, but is not limited to the above forms.

[0031] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0032] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0033] Please see Figure 1 , Figure 1This is an optional flowchart of the crowdsourcing task allocation method provided in this application embodiment applied to a platform server. Figure 1 The method may include, but is not limited to, steps S101 to S106.

[0034] Step S101: Using road network hypercube embedding technology, the original urban road network is mapped into an embedded road network; Step S102: Receive the first encrypted information sent by the task publisher and the second encrypted information sent by the task executor, and receive the signature public key broadcast by the encryption server; The first encrypted information is the encrypted task text (containing location information, task information, and identity credentials, etc.) that the task publisher sends to the platform server after generating the space crowdsourcing task.

[0035] The second encrypted information is the information that the task executor sends to the platform server in encrypted form, including their location information, credit score, and identity credentials.

[0036] Optionally, the signature public key broadcast by the encryption server is generated by the encryption server when generating the private and public keys during system initialization, and the encryption server will broadcast the public key to all participants.

[0037] Step S103: Verify the validity of the task publisher's identity signature based on the first encrypted information and the signature public key. If the task publisher passes the identity signature verification, extract the first encrypted location information and credit score threshold from the first encrypted information. Step S104: Verify the validity of the task executor's identity signature and credit signature based on the second encrypted information and the signature public key. If the task executor passes the identity signature verification and credit signature verification, extract the second encrypted location information from the second encrypted information. Step S105: Based on the embedded road network, determine a set of candidate task executors located in the target area according to the first encrypted location information and the second encrypted location information; wherein, the target area includes the area inside the task area and the external area adjacent to the task area; In some embodiments, step S105 may include: calculating the internal road distance between a task executor and a task location within the task area based on the embedded road network, according to the first encrypted location information and the second encrypted location information; calculating the external road distance between a task executor and a task location in an external area adjacent to the task area based on the embedded road network, according to the first encrypted location information and the second encrypted location information; and determining a set of candidate task executors located in the target area based on the internal road distance and the external road distance.

[0038] In some specific embodiments, the step of determining the set of candidate task executors located in the target area based on the internal road distance and the external road distance may include: blinding the internal road distance and the external road distance to obtain blinded internal road distance and blinded external road distance, and sending the blinded internal road distance and blinded external road distance to an encryption server; enabling the encryption server to decrypt the blinded internal road distance and blinded external road distance using a homomorphic encryption private key, and calculate a first security comparison protocol parameter based on the blinded internal road distance and blinded external road distance; calculating a second security comparison protocol parameter based on a blinding factor, and sending the second security comparison protocol parameter to the encryption server; enabling the encryption server to compare the first security comparison protocol parameter and the second security comparison protocol parameter to obtain a decryption comparison result, and sending the decryption comparison result to the platform server; and determining the set of candidate task executors located in the target area based on the decryption comparison result.

[0039] Step S106: Based on the credit score threshold and the current credit score corresponding to each candidate task executor in the candidate task executor set, determine the target task executor from the candidate task executor set, and assign the crowdsourcing task information published by the task publisher to the target task executor; so that the target task executor executes the task in the crowdsourcing task information.

[0040] In some embodiments, step S106 may include: caching the current credit score corresponding to the task executor when performing the step of verifying the validity of the credit signature of the task executor based on the second encryption information and the signature public key; wherein, if the task executor is determined as a candidate task executor in the credit signature validity verification stage, the current credit score corresponding to the candidate task executor is the current credit score corresponding to the task executor in the credit signature validity verification stage; traversing the set of candidate task executors, and filtering out target candidates that meet the credit score threshold based on the current credit scores corresponding to each candidate task executor in the set of candidate task executors; wherein, the number of target candidates is one or more; determining the target task executor from the target candidates, and allocating the crowdsourcing task information published by the task publisher to the target task executor.

[0041] In some embodiments, after step S106, the method may further include: receiving task feedback data sent by a task publisher; wherein the task feedback data includes the task publisher's identity information, the target task executor's identity information, and the task evaluation score given by the task publisher to the target task executor after performing the task; verifying the validity of the task publisher's identity based on the task publisher's identity information, and verifying the validity of the target task executor's identity based on the task executor's identity information; if both the task publisher and the target task executor pass the identity verification, then sending the task feedback data to an encryption server; so that the encryption server calculates the target task executor's new credit score and new credit signature based on the task evaluation score in the task feedback data, and sends the new credit score and new credit signature to the target task executor; so that the target task executor updates its current credit score based on the new credit score, and updates its current credit signature based on the new credit signature.

[0042] Steps S101 to S106, as illustrated in this embodiment, utilize hypercube embedding technology to structurally process the original road network. This transforms complex geospatial information into computable data in a high-dimensional vector space, reducing the topological complexity of the road network and improving the efficiency and accuracy of subsequent location information matching and regional range calculation. Simultaneously, the embedded network more efficiently supports algorithmic analysis of spatial relationships, providing a technical foundation for the accurate delineation of target areas. By leveraging the efficient spatial computing capabilities of the embedded road network, the target area, including both internal and adjacent external regions, is accurately delineated. This ensures the spatial correlation between candidates and the task area while expanding the candidate range by incorporating neighboring areas, avoiding the problem of unaccepted tasks due to overly strict regional restrictions, thus improving the flexibility and success rate of task allocation. Furthermore, credit score threshold screening ensures that the credit level of the target executor meets the requirements of the task issuer, reducing credit risk during task execution. The optimal executor is accurately matched from the candidate set, achieving dual screening of spatial adaptation and creditworthiness, thereby improving the accuracy and efficiency of task allocation.

[0043] Please see Figure 2 , Figure 2 This is an optional flowchart of the crowdsourcing task allocation method provided in this application embodiment applied to a task publisher. Figure 2 The method may include, but is not limited to, steps S201 to S207.

[0044] Step S201: Receive the homomorphic encryption public key broadcast by the encryption server, and receive the identity credential information generated by the task publisher during identity registration sent by the encryption server; wherein, the identity credential information includes identity identification information, identity identification signature information, credit score information, and credit score signature information; Among them, the embedded road network is based on Figure 1 The crowdsourcing task allocation method is determined.

[0045] Step S202: Based on the embedded road network constructed by the platform server, determine the task location and task area of ​​the task publisher; wherein, the task location includes the area boundary vertex and the vertex location distance; Step S203: Calculate the shortest boundary vertex distance between the task location and the region boundary vertex of the task area based on the road network information of the embedded road network; Step S204: Based on the embedded road network, determine the initial labels corresponding to the boundary vertices of the region, and package the initial labels according to number theory transformation techniques to obtain the target labels; Step S205: The vertex position distance and the shortest boundary vertex distance are packaged according to the number theory transformation technique to obtain the boundary relative distance; Step S206: Based on the homomorphic encryption public key, encrypt the target tag and the relative distance of the boundary respectively to obtain the encrypted tag and the relative distance of the encrypted boundary, and construct the first encrypted location information based on the encrypted tag, the relative distance of the encrypted boundary and the task area; Step S207: Generate a credit score threshold. Based on the identity information, the identity signature information, the first encrypted location information, and the credit score threshold, construct first encrypted information and send the first encrypted information to the platform server.

[0046] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0047] To further understand the specific implementation of the method proposed in this application's embodiments, the following will explain it from five aspects: system architecture, threat model, homomorphic encryption, shortest path distance calculation on ciphertext, and Dilithium signature mechanism. The specific content is as follows: (1) System architecture: Mobile space crowdsourcing is a business model where enterprises or organizations outsource tasks originally performed by their internal employees to a non-specific public group via the internet on a voluntary basis. In this embodiment of the application, the system architecture consists of four modules: a platform server, an encryption server, a task publisher, and a task executor. The platform server, also known as the space crowdsourcing platform server, is a core component of mobile space crowdsourcing task allocation. It is mainly responsible for calculating the distance between the task publisher and the task executor in the encrypted location, filtering out suitable task executors, and assigning tasks to suitable task executors.

[0048] The encryption server, a security system managed and maintained by a third-party organization, is responsible for generating keys and signatures in mobile space crowdsourcing and distributing them to users who wish to participate. When a user wants to participate in space crowdsourcing, they first request a key from the encryption server. Next, the user encrypts their real location locally using the obtained key and then sends their encrypted information and signature to the platform server. Furthermore, the platform server and the encryption server collaborate to verify the signature; if verification is successful, the platform server begins task allocation. In other words, the encryption server ensures the security and reliability of key distribution without requiring additional information, preventing the key from being illegally obtained or misused, thus guaranteeing the security of the entire encryption process.

[0049] For task publishers, who are users who post various location-related task requests on space crowdsourcing platforms, they hope to find nearby and trustworthy task executors to complete the tasks.

[0050] For task executors, they are a group of users who receive and complete tasks through a space crowdsourcing platform, hoping to earn rewards and credit points by completing nearby tasks.

[0051] (2) Threat Model: Regarding the threat model, in the application embodiment, it is assumed that the two servers are not colluding (between the platform server and the encryption server). The main threats originate from the platform server and the task executor, both of whom are considered honest but curious attackers. Specifically: The platform server will honestly provide services to users according to the system rules, but may curiously obtain users' location signals or other sensitive information from encrypted data and profit from this private data. Therefore, on the one hand, the platform server will honestly execute the mobile space crowdsourcing service; on the other hand, it may use some algorithm to obtain users' sensitive information from the stored information, such as daily habits, social relationships, and residential addresses. Furthermore, there is a risk that the platform server's maintenance personnel may obtain and utilize users' sensitive information. Meanwhile, the task executor may be interested in the task publisher's private information. During the execution of the space crowdsourcing task, the task executor may want to learn more about the task publisher's private information. In addition, the task executor may greedily profit from the space crowdsourcing service, even attempting to tamper with their own credit score to increase the probability of being assigned tasks. In addition, the task publisher and the encryption server are considered honest and trustworthy parties in this application embodiment. The task publisher is a beneficiary of the space crowdsourcing service and has no need to cheat during the task allocation period, nor will it know the information of other unrelated tasks or task executors. The encryption server is operated by an independent and trustworthy third-party organization that is unwilling to bear the risk of colluding with the platform server to steal information.

[0052] (3) Homomorphic encryption: For finite homomorphic encryption, this application employs a quantum-safe finite homomorphic encryption scheme, specifically FV homomorphic encryption, to protect the privacy of sensitive data such as user geolocation during computation. FV homomorphic encryption is a homomorphic encryption built upon ring-based fault-tolerant learning, where all operations are performed within a polynomial ring. The plaintext data before encryption is encoded into a polynomial ring. An element in the text means that the plaintext is less than a certain number of times. coefficient pair The modulo polynomial, where, It is a power of 2; the encrypted ciphertext data is composed of a polynomial ring. express, It is a large prime number and greater than In these polynomial rings, the most important operational rules are... This causes the result of arbitrary polynomial multiplication to exhibit... All items will be replaced with This ensures that the number of operations on all operational structures remains constant. Within this range. The following is a brief overview of the core algorithm: For the key generation step: ,in, It is the degree of the polynomial. It is the ciphertext modulus. Plaintext Modulus It is the noise distribution. It is the base.

[0053] Generate private key From the polynomial ring Randomly sample a secret polynomial , .

[0054] Generate public key From the polynomial ring Randomly sample a polynomial From the noise distribution Sampling a small noise polynomial ,calculate This yields a public key consisting of two polynomials. .

[0055] Generate relinear keys To support multiplication between ciphertexts, an additional key needs to be generated. The function of this key is to convert the multiplication process... Item conversion A new term of linear dependence. Basis analysis of the ciphertext product result. Decomposition, number of terms in the decomposition Ciphertext Modulus The bit length is determined by the formula: The system enters a loop, index. from Traversing to In each iteration of the loop, perform the following steps: from the polynomial ring Random sampling of new polynomials and from noise distribution New noise polynomial in mid-sampling Based on the above parameters and private key calculate: The final generated contains A set of elements .

[0056] The final output public key and relinearized key The private key is made public to the entire system. It is kept strictly confidential by the generator.

[0057] The steps for encrypting plaintext are as follows: ,in It is a plaintext polynomial. It is a public key. Specifically, from the polynomial random sampling bivariate polynomial From the noise distribution Two noise polynomials were randomly sampled. and Based on the parameters and public key mentioned above calculate: ,in It is a scaling factor used to scale plaintext. from Mapped to Finally, the ciphertext is output. .

[0058] The steps for decrypting ciphertext are as follows: ,in, It is ciphertext. It's the private key. Specifically, the core step is to compute the polynomial. ,according to Substituting, we can get .because The above formula is approximately equal to ;calculate The final output is plaintext. .

[0059] For homomorphic addition: ,in, There are two ciphertexts. Specifically, the corresponding polynomials of the two ciphertexts are added together, i.e. The result of the calculation is a new ciphertext. When this ciphertext is decrypted, the result is equivalent to the sum of the two original plaintexts. The same principle applies to homomorphic subtraction, and the embodiments in this application will not be described in detail here.

[0060] For homomorphic multiplication: ,in, There are two ciphertexts. Specifically, first, the polynomials in the two ciphertexts are cross-multiplied to obtain a polynomial containing three polynomials. This is a temporary result, at which point the ciphertext structure becomes complex, and its association with the private key changes. Then, using relinearization techniques, with the help of... Will include Conversion of complex ciphertext back to A linearly dependent polynomial ciphertext structure. Its computational result is a new standard ciphertext, which, after decryption, is equivalent to the product of the two original plaintexts. .

[0061] (4) Calculation of the shortest path distance on the ciphertext: Shortest road distance calculation is a key operation in crowdsourcing task allocation in this application embodiment, used to address the lack of real road condition calculation in related privacy-preserving crowdsourcing task allocation systems. In the scheme of this application embodiment, the shortest road distance calculation on encrypted text is achieved through the following steps: First, road network hypercube embedding technology is used to map the road network into a high-dimensional hypercube space, thereby transforming the shortest road distance calculation into Hamming distance calculation; then, the encrypted Hamming distance is calculated on the ciphertext after homomorphic encryption, i.e., the road distance between the task executor and the task location within the target road network region is calculated; finally, the shortest road distance calculation is extended to handle road network partitioning, i.e., the road distance between the task executor and the task location corresponding to the external region adjacent to the target road network region is calculated, to achieve more comprehensive crowdsourcing task allocation.

[0062] The specific implementation process is as follows: 1) Road network embedded in hypercube space: This stage forms the foundation for subsequent privacy-preserving computations, and its goal is to embed a complex road network graph using road network hypercube embedding technology. (i.e., the original urban road network mentioned above) is mapped onto a regular hypercubic space. In this process, an embedded road network is obtained, thereby transforming the shortest road distance calculation into a Hamming distance calculation for the embedded road network.

[0063] First, assume the road network is an undirected weighted planar graph. It contains a set of vertices (intersections). set of edges (roads) Weight (road length) set .exist Hypercube Space In, such as Figure 7 As shown, each vertex consists of one A dimensional Boolean vector (a vector consisting of 0s and 1s) indicates that two vertices are adjacent if they differ in exactly one dimension.

[0064] In the road network Mapped to a higher-dimensional hypercube space Before that, it's worth mentioning that: the inner side It is a picture A finite connected region in the graph. The outside is an infinite region; odd-numbered and even-numbered faces refer to a face consisting of an odd or even number of edges (roads); if the inner face... There are two sides ( and If two sides are a pair of opposite edges, then the distance between corresponding points on the two edges is the inner surface distance. diameter Each edge of an even-numbered face has a unique opposite edge, and each edge of an odd-numbered face has two opposite edges; cut It is a set of edges It also has the following attributes: and It must be the outer edge, otherwise it must be the same edge, requiring a cut. Middle and edge On the same inner surface The edges are mutually opposite. If cut The edges in the graph If deleted, the diagram Divided into two subgraphs and Alternating cuts refer to alternating cuts on odd-numbered faces. That is, if the right (or left) part of the current odd-numbered face is cut into an odd-numbered face, then the left (or right) part will be cut into an odd-numbered face the next time an odd-numbered face is encountered.

[0065] like Figure 3 As shown, Figure 3 For a simplified road network diagram, by... Figure 3 By dividing the road into distance units and inserting virtual vertices between real vertices, we can obtain results such as... Figure 4 The points shown are kept at equal distances. Next, based on the above method of finding opposite edges in the road network graph, we can obtain the following... Figure 5 shown Alternating cuts. Then through the... Figure 5 Subgraph obtained by alternating cuts and , will be in the subgraph The vertices are marked as 0 in the subgraph. The vertex is marked as 1, resulting in the following: Figure 6 The dimensions shown are The embedding vector.

[0066] 2) Calculation of the shortest road distance embedded in the road network: After completing the hypercube embedding, the road distance between any two points can be accurately calculated by performing Hamming distance calculation on the Boolean vector labels with special processing.

[0067] There is a road network and its embedded road network Two vertices The tag is and In this context, vertices are represented by the symbol v, and labels are represented by bv, which is a Boolean vector. Then, two vertices... The formula for calculating the shortest road distance is: ; in, Indicates the shortest road distance. This represents the Hamming distance. For example, according to this calculation formula, Figure 4 It can be calculated .

[0068] Assumption In the road network The position above, where Indicates distance The most recent peak, yes arrive Find the shortest road distance between two locations. and ,but and The shortest road distance between them is: .

[0069] 3) Calculation of the shortest path distance on the ciphertext: This stage is crucial for achieving privacy protection. It involves securely performing the algebraic operations of Stage Two on encrypted data. The FV homomorphic encryption mechanism will be used, which is more efficient for integer computations compared to other homomorphic encryption schemes.

[0070] At this stage, in order to improve computational efficiency, a ciphertext packaging technique will be used to encode the tags. Packed into polynomials The coefficients in the polynomial, and for the polynomial Encryption yields ciphertext For example, for the label The following polynomials exist: ; in, .

[0071] Here This represents the total number of "alternating cuts" required to obtain a region after the system performs road network partitioning. In this embodiment, for ease of recording, the Boolean vector is also m-dimensional. Additionally, each region (e.g.) Each of them has its own embedded network. This embedded network Dimensions Possibly related to embedded networks Dimensions They are different. In the entire system, this application embodiment defines each road network. Dimensions 48.

[0072] Here, 'n' represents packing an m-dimensional Boolean vector into a polynomial. The number of times, also the ring The number of times this occurs is subject to a fundamental constraint in FV homomorphic encryption schemes. Here, 'n' serves to protect privacy; the larger 'n' is, the higher the security and computational costs, and the lower the efficiency. Therefore, dividing a large road network into multiple regions (sub-networks) improves the efficiency of distance calculation to some extent.

[0073] The polynomial ring here Let t be the plaintext space, and t be the plaintext modulus. Let the set of integers be such that all numbers are modulo t, for example, t=10. ={0,1,2,...,9}), The coefficients represent the sources of... All polynomials, For the polynomial used for modulo (as specified in this application) =-1), where n is the polynomial degree. That is, the data to be encrypted (such as the packaged position vector) exists in this plaintext space.

[0074] Similarly, the polynomial ring here Let q be the "ciphertext space," where q is a prime number much larger than t. All encrypted data resides in this ciphertext space. This application sets the security parameters kappa=32, t=2^32, and q to a 61-bit binary prime number.

[0075] label After packaging, the polynomial can be... Encryption was performed, resulting in the following encrypted tags: ; For tags The following polynomials exist: ; in, .

[0076] label After packaging, the polynomial can be... Encryption was performed, resulting in the following encrypted tags: ; Encryption tag and The formula for calculating the shortest road distance is: ; in, It is homomorphic multiplication. It is homomorphic addition. It's a homomorphic subtraction. Then the two positions... and The final formula for calculating the shortest path distance on the ciphertext is: ; in, and The final formula for calculating the shortest road distance. Only four homomorphic additions / subtractions and three homomorphic multiplications are needed to calculate the shortest path distance between two positions on the packed ciphertext.

[0077] 4) Supports calculation of shortest road distance across regions For large-scale cities, embedding them into a dimension of In the hypercube, then This would be extremely large, leading to excessive computational overhead. Therefore, it is advisable to pre-divide the region using a grid. For two positions Road distance calculations can be divided into intra-regional road distance calculations and inter-regional road distance calculations. For intra-regional road distance calculations, the final formula for calculating the shortest road distance can be directly used. The calculation is performed using the method described above.

[0078] For cross-regional road distance calculations, the shortest path must pass through a boundary vertex of the target region, which can be transformed into finding the distance location. The nearest boundary vertex. Specifically: Assuming location ,Location ,in, ,have bit area The set of boundary vertices. Then the formula for calculating the distance between roads across regions is: ; This can be addressed using packaging technology. and The plaintext is obtained by using it as a parameter of the polynomial. The formula for calculating the plaintext is as follows: ; Therefore, polynomial The coefficient represents from arrive The distances on the alternative routes can be used to calculate the cross-regional road distances on the packaged encrypted text. The final cross-regional road distance calculation formula is as follows: .

[0079] (5) Dilithium signature mechanism: To ensure the unforgeability, authenticity, and long-term security of the credit system, this application employs a post-quantum digital signature scheme based on lattice cryptography called CRYSTALS-Dilithium. This scheme aims to resist attacks from both classical and quantum computers, ensuring that credit score certificates issued by trusted institutions in this application are secure and reliable. The Dilithium signature mechanism mainly includes three core algorithms: key generation, signature generation, and signature verification. The specific implementation details are as follows: 1) Key generation: Generate seeds : Securely generate a 256-bit random bit string and random bit string As the generating common matrix Seeds; It is a 256-bit random bit string used in the signature algorithm to generate a "mask" polynomial vector. .

[0080] Generate secret vector Generate two polynomial vectors (dimension is) )and (dimension is) These two vectors are the core of the private key, and the coefficients of the polynomial they form are randomly selected from a very small range.

[0081] Generate common matrix Use an extensible output function to output the common seed. Expand into a matrix Each element in the matrix is ​​a polynomial. Due to the random bit string... It is public; anyone can reconstruct the exact same matrix in the same way. .

[0082] Calculate public value Through matrix and vector multiplication and addition Calculate a new polynomial vector To reduce the public key size, this mechanism does not directly use... Instead, it is broken down into and And generate a public key hash value. , It is used to improve signature efficiency (CRH is a collision-resistant hash function).

[0083] Output key: public key private key .

[0084] 2) Signature generation: Given message Generate message hash value Generate digital signature .

[0085] Entering the loop: The algorithm enters a loop, attempting to generate a signature in each iteration until successful. Let a counter be used. (Kappa) is initially set to 0.

[0086] Generate mask vector Use the secret seed in the loop. hash value and counter Generate mask vector And decompose Obtain its high-order bits and low-order bits The decomposition point is the parameter. ,Right now .

[0087] Calculate commitment Use common matrix calculate And extract its high-order bits to obtain the commitment. .

[0088] Generate Challenge : Hash value of the message and commitment Hash them together to generate sparse polynomials. .

[0089] Calculate the response : .

[0090] Perform rejection sampling: check Does the coefficient exceed a preset safety boundary? ( yes (upper bound); check Has the low-order bit exceeded the safety boundary? ( yes (Number of 1s in the middle). If any check fails, abort the attempt, discard all intermediate values, and return from the generated mask vector. To generate a response The steps are repeated, with the aim of ensuring that the response z does not reveal the secret vector s1, but generates the hash value of message M. It remains unchanged.

[0091] Output signature: The final tuple consisting of three parts This will be the output of the signature result.

[0092] 3) Verify signature: Verifying a signature when only the public key is known. Is the message correct? A valid signature.

[0093] Reconstructed matrix From public key Extracting seeds And generate a common matrix. .

[0094] Calculate the hash value of the message : .

[0095] Usage Tips Refactoring Challenge: The validator uses the signature in... and and the public key Combined with the prompts to reply to the original promise ,calculate Restoring the Challenge .

[0096] Verify signature: Check Is it smaller than the safety boundary? ,examine Whether or not Equal, check Is the number of bits 1 less than or equal to 1? If all checks pass, the signature is valid.

[0097] The following is a detailed description and explanation of the solutions in the embodiments of the present invention, using specific application examples: In this application embodiment, a privacy-preserving task matching scheme is provided that supports semi-honest servers against quantum attacks and combines precise distance calculation and verifiable credit, addressing current privacy-preserving crowdsourcing task schemes. The overall framework of the crowdsourcing task allocation method provided in this application embodiment is summarized as follows: The first step is initialization: The platform server divides the original urban road network into regions and constructs an embedded road network, while generating initial parameters. A private key and public key are generated via an encryption server, and the public key is broadcast to all participants.

[0098] The second step is user registration: an encrypted server generates initial credit scores and anonymous credentials for all registered users (including task publishers and task executors).

[0099] The third step is task publishing: the task publisher generates a space crowdsourcing task and sends the encrypted task (containing location information, task information and identity credentials) to the platform server.

[0100] The fourth step is location update: the task executor sends their location information, credit score, and identity credentials to the platform server in encrypted form.

[0101] Step 5, Task Allocation: The platform server processes the received information. First, it verifies the identity information using zero-knowledge proof. If the verification is successful, the platform server filters based on the region where the task is located, selects a suitable group of task executors, and calculates the shortest distance from the task executor's location to the task location. Finally, it calculates the task executor's credit score based on the shortest distance, obtains the optimal task executor, allocates the task, and sends the task information to the optimal task executor.

[0102] Step 6, Credit Update: When a task executor completes the task, the task publisher calculates the executor's credit score based on the task result and sends their identity credentials to the platform server. The platform server verifies the identity credentials; if successful, it calculates a new credit score and a new identity credential for the task executor and sends them to the task executor. Upon receiving the information, the task executor updates their credit score and identity credential.

[0103] In the specific implementation, the specific implementation process of each step includes the following: (1) The specific implementation process of initialization is as follows: This stage is jointly completed by the encryption server and the platform server, which builds the foundation for the privacy protection and operating environment of the entire system.

[0104] 1) Key system generation for the encryption server: A master key seed generated internally by the encryption server and stored with the highest level of security. This is the unique root of trust for all encryption and signing keys in the entire system; (Key Derivation Function): A standard key derivation function (e.g., ); Encryption server usage A seed derived from the master key, specifically used for homomorphic encryption; its calculation method is as follows: ; Encryption server usage A seed derived from the master key, specifically used for digital signatures; its calculation method is as follows: ; Specific parameters of the homomorphic encryption scheme ( For polynomial degree, For ciphertext modulus, For plaintext modulus, (For the error distribution), determined by the dimension of the embedded road network. (Right now ,and The decision is based on factors such as security requirements.

[0105] Encryption server usage Using the random source, the key generation algorithm of the FV homomorphic encryption scheme is run to obtain the secret polynomial. Noise polynomial Secret polynomial and cryptographic polynomial Homomorphic public key and homomorphic encryption private key Homomorphic encryption public key public, It is kept secret by an encrypted server.

[0106] Encryption server usage The secret vector is obtained by running the key generation algorithm of the CRYSTALS-Dilithium scheme as a random source. public matrix and public value public key for signing and signing private key Signature public key Public, signing private key It is kept secret by an encrypted server.

[0107] 2) Generation of road network and service parameters for the platform server: Original urban road network map; : The grid size of the road network partition; A collection of road network zones; : Dimensions (bit length) of the hypercube label; A collection of embedded road networks (hypercube labels).

[0108] (2) The specific implementation process of user registration is as follows: When a new user (task publisher and task executor) registers, the new user interacts with the encryption server through a secure channel and submits some identity information (such as mobile phone number, email address, etc.). The encryption server generates a basic credit score and an unforgeable credit certificate for the user.

[0109] 1) The encryption server begins generating basic user parameters: The encryption server uses the user's unique identity information to generate an identity identifier. and generate initial scores. .

[0110] The encryption server generates identity identifiers for users. and initial score signature The encryption server uses a signing private key. Restore the common matrix Generate mask vector Calculate commitment And extract the commitment of the high-order bits. Calculate the identity identifiers separately and initial score hash value and generate identity identifiers. and initial score Challenges Generate identity identifier and initial score response Generate identity identifier and initial score Tips A valid identity signature is obtained through rejection sampling technology. and the signature of the initial score .

[0111] After the encryption server generates the user's basic parameters, it sends them through a secure channel. For users, encrypted server storage Here it is assumed that the user will It is stored in the read-only memory of the mobile device.

[0112] The verifier (platform server) can use the public key when verifying the signature. Seeds ,user Identity identifier And credit score Generate a common matrix And message digest (i.e., the hash value of the message) The platform server uses signatures. middle and the public key Combined with the prompts To reply with your identity information and initial score The original commitment That is, calculation Restoring the challenge ,examine and Check the correctness. and and and That is, inspection Is it smaller than the safety boundary? ,examine Whether or not Equal, check Is the number of bits 1 less than or equal to 1? If all checks pass, the signature is valid.

[0113] (3) The specific implementation process for task publishing is as follows: The tuple is obtained after the task publisher completes user registration. , where, This refers to the identity identifier of the task issuer. This refers to the initial credit score of the task poster. This refers to the signature identifying the task issuer. This refers to the initial credit score signature of the task publisher; when the task publisher needs to publish a task, the task publisher generates a space crowdsourcing task and sends the encrypted task ciphertext (containing location information, task information and identity credentials) to the platform server.

[0114] 1) The relevant parameters generated by the task publisher are as follows: First, the task publisher and task executor use the road network parameters (i.e., the embedded road network set) published by the platform server. and region set Get the mission location and the area Then, the task issuer calculates the task location based on the road network information. With the region boundary vertex (region) Boundary vertex set The shortest boundary vertex distance set ,gather Total One element; then, the task publisher, based on the embedded network... Get the vertex tags The packaged labeled polynomials were obtained using NTT (Number-Theoretic Transform) technology. This yields the target label; to save overhead, the vertex position distance can be calculated. Distance to shortest boundary vertex set Pack them together to get the distance Having obtained the aforementioned relative distances between boundaries, here we will use the shortest boundary vertex distance... set Packing eight times is to divide the area The eight neighboring regions are all packaged into a polynomial, thus reducing computational complexity; then, the task issuer uses a homomorphic encrypted public key. Encrypted packaged labels Obtain the encrypted tag Re-encrypt the relative distance of the boundary Obtain the relative distance of the encrypted boundary Finally, the encrypted task location information was obtained. That is, the first encrypted location information mentioned above.

[0115] in, Geographical location in plain text and The shortest distance between, where, Includes the tags of the user located in this area. Distance from its label , Similarly, I will not elaborate further. In the calculation, Hamming distance is used to calculate the distance between the two labels. and The distance between them, plus the distance between the two geographical locations and the tag, is... and Optionally, It's about the shortest distance. The set has a total of One element, The label represents the boundary vertex of the region where user tp is located (the label also refers to the vertex itself), but the geographical location of the boundary vertex is not specified. Therefore, the shortest distance formula can be used directly for calculation.

[0116] in, Geographical location on the ciphertext and The shortest distance between them, and the encryption technology of this application is FV homomorphic encryption technology, which is to package the plaintext (an integer polynomial modulo t) into ciphertext (an integer polynomial modulo q) and perform polynomial calculation, that is, the encrypted tag of user s located in this area is used in the calculation. And its encrypted distance label .

[0117] The task publisher generates a credit score threshold. During task matching, the platform server uses this threshold to select suitable task executors.

[0118] 2) The final task issuer will The task is sent to the platform server for crowdsourcing task matching. This refers to the identity identifier of the task issuer. This refers to the signature identifying the task issuer. This refers to the encrypted task location information provided by the task publisher. This refers to the credit score threshold. It is included in the first encrypted information.

[0119] (4) The specific implementation process of location update is as follows: After completing user registration and obtaining basic information, the task executor then initiates the process of searching for crowdsourced tasks. This process is executed periodically on their mobile device. All task executors in the system participate in this process. The encrypted location information and credit score are sent to the platform server. The encrypted location information of the task executor is compared with the encrypted location information generated by the task issuer in the previous step. The encryption process is similar.

[0120] 1) The relevant parameters generated by the task executor are as follows: First, the task executor also finds its real-time location through road network parameters. and the area Then, the task executor calculates its real-time location based on the road network information. With the region boundary vertex (region) Boundary vertex set The shortest distance To facilitate the calculation of the shortest distance between the current location and each neighboring area. And use NTT technology to package and obtain distance ,in It is a region The number of adjacent areas; subsequently, the task executor uses an encryption method similar to that used by the task issuer to obtain encrypted location information. .

[0121] 2) Finally, the task executor will update the encrypted location information. Send to the platform server, where, This refers to the identification of the person performing the task. This refers to the signature identifying the person performing the task. This refers to the credit score signature of the task executor. This refers to the encrypted location information of the task executor. It is included in the second encrypted information.

[0122] (5) The specific implementation process of task allocation is as follows: During the task allocation phase, the platform server, encryption server, and task executors collaborate. The platform server first verifies the validity of the identity signatures of the task publisher and the task executor, and the validity of the credit signature of the task executor. Then, the platform server filters out task publishers and task executors with valid signatures and interacts with the encryption server to select a batch of suitable task executors based on the shortest path distance of the received task locations. Finally, from this batch of task executors, the best task executor is selected through interactive filtering based on a credit threshold.

[0123] 1) The platform server determines whether the signature is valid: Among them, according to the first encrypted information and signing public key Verify the validity of the task publisher's identity signature. If the task publisher passes the identity signature verification, extract the first encrypted location information from the first encrypted information. and credit score threshold The specific steps are as follows: The platform server received information from the task publisher. That is, identity information Identity signature information Encrypted task location information and credit score threshold First, extract the identity signature information. Verify the identity signature information of the task issuer using zero-knowledge proof. If valid, retain the encrypted task location information from the task publisher. and credit score threshold .

[0124] Among them, according to the second encrypted information and signing public key Verify the validity of the task executor's identity signature and credit signature. If the task executor passes the identity signature and credit signature verification, extract the second encrypted location information from the second encrypted information. The specific steps are as follows: The platform server received information from the task executor. That is, identity information Identity signature information Credit signature information and encrypted location information First, extract the identity signature information. and credit signature information Verification using zero-knowledge proofs If valid, retain the location information of the task executor. .

[0125] 2) The platform server and the encryption server interact to calculate the shortest path distance and select a group of suitable task executors (i.e., determine the set of candidate task executors located inside the task area and in the external area adjacent to the task area): First, the platform server determines the region from the received task location information. Filter out adjacent task executors and mark them. It is located in the region The internal set of task executors; It is located in the region The group of task executors in the nearby area, i.e. It is a collection of task executors across regions.

[0126] Then, the platform server calculates in the region. The internal road distance between the task executor and the task location, i.e., the internal road distance mentioned above. Specifically, the platform server will... Location information With the mission location Substituting into the final formula for calculating the shortest path distance between two locations in the ciphertext (i.e.) To calculate and will and Replace with and , obtained in the region The set of road distances between the internal task executor and the task location ,Right now Let R represent the set of distances within the region, and let R represent the set of blinding factors. The platform server calculates the shortest road distance within the region as follows: Its encrypted form is .

[0127] Meanwhile, the platform server computes in the region The road distance between the task executor and the task location in the vicinity of the external area is the aforementioned external road distance. Specifically, the platform server will... Location information With the mission location Substitute into the final cross-regional road distance calculation formula (i.e. )calculate and will and Replace with and , obtained in the region Set of road distances between the task executor and the task location in the vicinity of the external area The platform server calculates the shortest road distance across regions as follows: Its encrypted form is .

[0128] Next, in order to interact with the encryption server to find the group of task executors closest to the task location, the platform server needs to... Each element is blinded before communication. For elements in the region... The set of road distances between the internal task executor and the task location Blinding only requires a sampling length of random integers (Because of the ciphertext) Integers are all For length, simply add a length of... Blinding can be achieved using random integers, where For all Obtained through homomorphic addition Storing blinding factors ,in, Represents the set of blinding factors. This represents the blinding factor. For all... ,sampling Obtained through homomorphic addition Then select those with polynomials The effective portion, extracting the effective blinding ,in Equivalent to task area Boundary vertex set ,extract , here of It represents the degree of the polynomial, divided into 8 slots. Based on the definition of a 3x3 grid, it means the center cell has 8 neighboring cells. The platform server will... Send to the encryption server, where, This represents the set of valid indexes that store the blinding factor.

[0129] Subsequently, for all The encryption server uses a private key. Decryption Obtain the polynomial And extract coefficients For all The encryption server uses a private key. Decryption Obtain the polynomial And extract coefficients (here) yes (index).

[0130] Furthermore, the encryption server... (Set of blinding distances within the region) Blinding distance set with neighboring regions The platform server calculates the parameters of the security comparison protocol (i.e., the first security comparison protocol parameters mentioned above) and then uses the blinding factor. The parameters of the security comparison protocol (i.e., the second security comparison protocol parameters mentioned above) are calculated and sent to the cryptographic server. The cryptographic server decrypts the comparison result, sends a portion of the result to the platform server, and then both simultaneously perform bubble sort on the comparison results until the previous result is selected. A set of task executors (i.e., a set of candidate task executors located in the target area).

[0131] In the implementation, the platform server and the encryption server interact to calculate the shortest path distance using the SecureDistanceScreening algorithm, and then select a group of suitable task executors. Specifically, the pseudocode for the SecureDistanceScreening algorithm is as follows: "Safe shortest distance filtering algorithm:" Input: Platform server (hereinafter referred to as PS) inputs task information. Task executor set Set of task executor locations { } and the number of candidates The encryption server (hereinafter referred to as CS) inputs the homomorphic encryption private key. Output: The closest object to the task. A collection of task executors The specific implementation details are as follows: 1. Filter location data by region in Photoshop: || Within the region, For the neighboring area; 2. PS performs homomorphic calculation of the encryption distance on the encrypted location data: || The set of distances within the encrypted region; || Encrypted cross-regional distance set; for in : || Use formula Calculate the distance within the area; for in : || Use formula Calculate cross-regional distance; 3. PS performs blinding processing on the encrypted distance set: || The set of distances within and across blinded areas; || The set of blinding factors within and across regions; || A set of valid indexes across regions; for in : || Randomly sampled integers are used as the blinding factor; || Homomorphic addition yields the blinding distance within the blinded region; ; || Storage blinding factor; for in : || A random set of integers is sampled as the blinding factor array; || The blinding distance across regions after blinding is obtained by homomorphic addition; ; || Extract effective blinding factors and indexes; || Store valid blinding factors; || Store valid indexes; 4. PS sends data to the encryption server: Send( ) to CS || Send the blinded data and index to the encryption server; Hold( ) || The platform server retains the blinding factor; 5. CS receives and decrypts data: || Receive data sent from the platform server; || Extract the set of valid blinded values ​​within and across regions after decryption; 6. CS decrypts and extracts valid distance values: for in : || Decrypt and extract the distances within the region: || Decryption yields the plaintext polynomial; || Extract effective blinding values; ; for in : || Decrypt and extract the distance across regions: || Decryption yields the plaintext polynomial; || According to the index Extract effective blinding values; ; 7. Security comparison between PS and CS interactions: || CS holds the decrypted blind value ( ); || PS holds blinding factor ( ); .".

[0132] Specifically, using the SecureMinimumSort algorithm to execute a bubble sort loop can output the algorithm that is closest to the task location. An index set of task executors Specifically, the pseudocode for the SecureMinimumSort algorithm is as follows:

[0133] The pseudocode for the SecureCompare algorithm is as follows:

[0134] 3) The platform server determines the target task executor based on a credit score threshold: When the platform server (hereinafter referred to as PS) performs the identity signature and credit signature verification steps for each task executor, it verifies the identity signature and credit signature of each task executor. PS has already cached the executor's identity locally. and its corresponding plaintext current credit score After screening using the safest shortest distance, the [relevant] criteria were determined. A set of candidate task executors Then, PS performs the credit screening process locally.

[0135] Specifically, the pseudocode for the credit screening and task assignment algorithm is as follows:

[0136] (6) The specific implementation process of credit update is as follows: During the credit update phase, the platform server will receive the feedback score from the task publisher and forward it to the encryption server. The encryption server will calculate the new credit score for the task executor, generate a new credit signature for the task executor, and send the new credit score and new credit signature to the task executor. The encryption server will then save the new credit signature.

[0137] The specific process by which the encryption server calculates the new credit score and generates the credit signature is as follows: The encryption server uses information from the platform server. The ratings received The encrypted server calculates the evaluation score. , A scaling factor will The value is taken to credit score The numerical range of the credit score (assuming a value of [0, 100]) is as follows: (e.g., [0,5]); then the encryption server calculates the new credit score. and using the signing private key Generate a new credit signature And send it to the task executor Finally, the encryption server stores the new credit signature. Overwrite old credit signatures .

[0138] It should be noted that this embodiment is only a brief illustrative description of the overall process of a crowdsourcing task allocation method. Detailed descriptions of each step can be found in the relevant content of the foregoing embodiments, and will not be repeated here. It is understood that the present invention does not limit this.

[0139] This application embodiment utilizes hypercube embedding technology to structurally process the original road network, transforming complex geospatial information into computable data in a high-dimensional vector space. This reduces the topological complexity of the road network and improves the efficiency and accuracy of subsequent location information matching and regional range calculation. Simultaneously, the embedded network more efficiently supports algorithmic analysis of spatial relationships, providing a technical foundation for the accurate delineation of target areas. By leveraging the efficient spatial computing capabilities of the embedded road network, target areas, including both internal and adjacent external regions, are accurately delineated. This ensures the spatial correlation between candidates and the task area while expanding the candidate range by incorporating neighboring areas, avoiding the problem of unaccepted tasks due to overly strict regional restrictions, thus improving the flexibility and success rate of task allocation. Furthermore, credit score threshold screening ensures that the credit level of the target executor meets the requirements of the task issuer, reducing credit risk during task execution. The optimal executor is accurately matched from the candidate set, achieving dual screening of spatial adaptation and creditworthiness, thereby improving the accuracy and efficiency of task allocation.

[0140] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of a crowdsourcing task allocation device provided in this application embodiment when applied to a platform server, as shown below. Figure 8 As shown in the figure, this application embodiment also provides a crowdsourcing task allocation device 800 applied to a platform server, which can implement the above-described method. The device includes the following modules: The embedded road network construction module 801 is used to map the original urban road network into an embedded road network using road network hypercube embedding technology. The first data receiving module 802 is used to receive the first encrypted information sent by the task publisher and the second encrypted information sent by the task executor, and to receive the signature public key broadcast by the encryption server. The first identity validity verification module 803 is used to verify the identity signature validity of the task publisher based on the first encrypted information and the signature public key. If the task publisher passes the identity signature verification, the first encrypted location information and credit score threshold are extracted from the first encrypted information. The second identity validity verification module 804 is used to verify the identity signature validity and credit signature validity of the task executor based on the second encrypted information and the signature public key. If the task executor passes the identity signature verification and credit signature verification, the second encrypted position information in the second encrypted information is extracted. The candidate task executor determination module 805 is used to determine a set of candidate task executors located in a target area based on the embedded road network and according to the first encrypted location information and the second encrypted location information; wherein, the target area includes the area inside the task area and the external area adjacent to the task area; The crowdsourcing task allocation module 806 is used to determine the target task executor from the candidate task executor set based on the credit score threshold and the current credit score corresponding to each candidate task executor in the candidate task executor set, and to allocate the crowdsourcing task information published by the task publisher to the target task executor so that the target task executor can perform the task in the crowdsourcing task information.

[0141] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a crowdsourcing task allocation device provided in this application embodiment when applied to a task publisher, such as... Figure 9 As shown, this application embodiment also provides a crowdsourcing task allocation device 900 applied to a task publisher, which can implement the above-described method. The device includes the following modules: The second data receiving module 901 is used to receive the homomorphic encryption public key broadcast by the encryption server, and to receive the identity credential information generated by the task publisher during identity registration sent by the encryption server; wherein, the identity credential information includes identity identification information, identity identification signature information, credit score information, and credit score signature information; The task location region determination module 902 is used to determine the task location and task region of the task publisher based on the embedded road network built by the platform server; wherein, the task location includes the region boundary vertex and the vertex position distance; The shortest boundary vertex distance calculation module 903 is used to calculate the shortest boundary vertex distance between the task location and the regional boundary vertex of the task area based on the road network information of the embedded road network. The target label acquisition module 904 is used to determine the initial label corresponding to the boundary vertex of the region based on the embedded road network, and to package the initial label according to the number theory transformation technique to obtain the target label; The boundary relative distance acquisition module 905 is used to package the vertex position distance and the shortest boundary vertex distance according to the number theory transformation technique to obtain the boundary relative distance. The first encrypted location information construction module 906 is used to encrypt the target tag and the relative distance of the boundary according to the homomorphic encryption public key, respectively, to obtain the encrypted tag and the relative distance of the encrypted boundary, and to construct the first encrypted location information according to the encrypted tag, the relative distance of the encrypted boundary and the task area; The first encrypted information construction module 907 is used to generate a credit score threshold, construct first encrypted information based on the identity information, the identity signature information, the first encrypted location information and the credit score threshold, and send the first encrypted information to the platform server. The embedded road network is based on Figure 8 The crowdsourcing task allocation device is determined.

[0142] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0143] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0144] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0145] Please see Figure 10 , Figure 10 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1002 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 using the methods described in the embodiments of this application. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0146] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0147] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0148] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0149] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0150] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0151] This application provides a crowdsourcing task allocation method, apparatus, electronic device, storage medium, and program product. It utilizes hypercube embedding technology to structurally process the original road network, transforming complex geospatial information into computable data in a high-dimensional vector space. This reduces the topological complexity of the road network and improves the efficiency and accuracy of subsequent location information matching and regional range calculation. Simultaneously, the embedded network more efficiently supports algorithmic analysis of spatial relationships, providing a technical foundation for the accurate delineation of target areas. By leveraging the efficient spatial computing capabilities of the embedded road network, it accurately delineates target areas, including both internal and adjacent external regions. This ensures the spatial correlation between candidates and task areas while expanding the candidate range by incorporating neighboring areas, avoiding the problem of unaccepted tasks due to overly strict regional restrictions, thus improving the flexibility and success rate of task allocation. Through credit score threshold screening, it ensures that the credit level of the target executor meets the requirements of the task publisher, reducing credit risk during task execution. It accurately matches the optimal executor from the candidate set, achieving dual screening of spatial adaptation and creditworthiness, thereby improving the accuracy and efficiency of task allocation.

[0152] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A crowdsourcing task allocation method, characterized in that, Applied to a platform server, the method includes the following steps: The original urban road network is mapped into an embedded road network using road network hypercube embedding technology. Receive the first encrypted message sent by the task publisher and the second encrypted message sent by the task executor, and receive the signature public key broadcast by the encryption server; The identity signature validity of the task publisher is verified based on the first encrypted information and the signature public key. If the task publisher passes the identity signature verification, the first encrypted location information and credit score threshold are extracted from the first encrypted information. The identity signature validity and credit signature validity of the task executor are verified according to the second encrypted information and the signature public key. If the task executor passes the identity signature verification and credit signature verification, the second encrypted location information in the second encrypted information is extracted. Based on the embedded road network, a set of candidate task executors located in the target area is determined according to the first encrypted location information and the second encrypted location information; wherein, the target area includes the area inside the task area and the external area adjacent to the task area; Based on the credit score threshold and the current credit score corresponding to each candidate task executor in the candidate task executor set, a target task executor is determined from the candidate task executor set, and the crowdsourcing task information published by the task publisher is assigned to the target task executor so that the target task executor can perform the task in the crowdsourcing task information.

2. The method according to claim 1, characterized in that, After determining the target task executor from the candidate task executor set based on the credit score threshold and the current credit score corresponding to each candidate task executor in the candidate task executor set, and allocating the crowdsourcing task information published by the task publisher to the target task executor, the method further includes: Receive task feedback data sent by the task publisher; wherein, the task feedback data includes the identity information of the task publisher, the identity information of the target task executor, and the task evaluation score given by the task publisher to the target task executor after the task is performed; The validity of the task publisher's identity is verified based on the task publisher's identity information, and the validity of the target task executor's identity is verified based on the task executor's identity information. If both the task publisher and the target task executor pass identity verification, the task feedback data is sent to the encryption server. The encryption server then calculates the target task executor's new credit score and new credit signature based on the task evaluation score in the task feedback data, and sends the new credit score and new credit signature to the target task executor. This allows the target task executor to update their current credit score based on the new credit score and their current credit signature based on the new credit signature.

3. The method according to claim 1, characterized in that, The process of determining a set of candidate task executors located in the target area based on the embedded road network, according to the first encrypted location information and the second encrypted location information, includes: Based on the embedded road network, the internal road distance between the task executor and the task location within the task area is calculated according to the first encrypted location information and the second encrypted location information. Based on the embedded road network, the external road distance between the task executor and the task location in the external area adjacent to the task area is calculated according to the first encrypted location information and the second encrypted location information. Based on the internal road distance and the external road distance, the set of candidate task executors located in the target area is determined.

4. The method according to claim 3, characterized in that, The step of determining the set of candidate task executors located in the target area based on the internal road distance and the external road distance includes: The internal road distance and the external road distance are blinded to obtain blinded internal road distance and blinded external road distance, and the blinded internal road distance and the blinded external road distance are sent to the encryption server; so that the encryption server can decrypt the blinded internal road distance and the blinded external road distance using the homomorphic encryption private key, and calculate the first security comparison protocol parameters based on the blinded internal road distance and the blinded external road distance; The second security comparison protocol parameter is calculated based on the blinding factor, and the second security comparison protocol parameter is sent to the encryption server; so that the encryption server compares the first security comparison protocol parameter and the second security comparison protocol parameter to obtain the decryption comparison result, and sends the decryption comparison result to the platform server; Based on the decryption comparison results, the set of candidate task executors located in the target region is determined.

5. The method according to claim 1, characterized in that, The step of determining the target task executor from the candidate task executor set based on the credit score threshold and the current credit score corresponding to each candidate task executor in the candidate task executor set, and allocating the crowdsourcing task information published by the task publisher to the target task executor, includes: When performing the step of verifying the credit signature validity of the task executor based on the second encryption information and the signature public key, the current credit score corresponding to the task executor is cached; wherein, if the task executor is determined as the candidate task executor in the credit signature validity verification stage, the current credit score corresponding to the candidate task executor is the current credit score corresponding to the task executor in the credit signature validity verification stage; Traverse the set of candidate task executors, and filter out target candidates that meet the credit score threshold based on the current credit score of each candidate task executor in the set; wherein the number of target candidates is one or more. The target task executor is determined from the target candidates, and the crowdsourcing task information published by the task publisher is assigned to the target task executor.

6. A crowdsourcing task allocation method, characterized in that, Applied to task publishers, the method includes the following steps: The system receives a homomorphic encryption public key broadcast by an encryption server, and also receives identity credential information generated by the task publisher during identity registration sent by the encryption server; wherein the identity credential information includes identity identification information, identity identification signature information, credit score information, and credit score signature information. An embedded road network built on a platform server is used to determine the task location and task area of ​​the task publisher; wherein, the task location includes the area boundary vertex and the distance to the vertex; Based on the road network information of the embedded road network, calculate the shortest boundary vertex distance between the task location and the region boundary vertex of the task area; Based on the embedded road network, the initial labels corresponding to the boundary vertices of the region are determined, and the initial labels are packaged using number theory transformation techniques to obtain the target labels; The vertex position distance and the shortest boundary vertex distance are packaged using number theory transformation techniques to obtain the relative boundary distance; Based on the homomorphic encryption public key, the target label and the relative distance of the boundary are encrypted respectively to obtain the encrypted label and the relative distance of the encrypted boundary. Based on the encrypted label, the relative distance of the encrypted boundary and the task area, the first encrypted location information is constructed. A credit score threshold is generated. Based on the identity information, the identity signature information, the first encrypted location information, and the credit score threshold, first encrypted information is constructed and sent to the platform server. The embedded road network is determined by the crowdsourcing task allocation method according to any one of claims 1-5.

7. A crowdsourcing task allocation device, characterized in that, Applied to a platform server, the device includes the following modules: An embedded road network building module is used to map the original urban road network into an embedded road network using road network hypercube embedding technology; The first data receiving module is used to receive the first encrypted information sent by the task publisher and the second encrypted information sent by the task executor, and to receive the signature public key broadcast by the encryption server. The first identity validity verification module is used to verify the identity signature validity of the task publisher based on the first encrypted information and the signature public key. If the task publisher passes the identity signature verification, the first encrypted location information and credit score threshold are extracted from the first encrypted information. The second identity validity verification module is used to verify the identity signature validity and credit signature validity of the task executor based on the second encrypted information and the signature public key. If the task executor passes the identity signature verification and credit signature verification, the second encrypted location information in the second encrypted information is extracted. A candidate task executor determination module is used to determine a set of candidate task executors located in a target area based on the embedded road network and according to the first encrypted location information and the second encrypted location information; wherein, the target area includes an area inside the task area and an external area adjacent to the task area; The crowdsourcing task allocation module is used to determine the target task executor from the candidate task executor set based on the credit score threshold and the current credit score corresponding to each candidate task executor in the candidate task executor set, and to allocate the crowdsourcing task information published by the task publisher to the target task executor so that the target task executor can perform the task in the crowdsourcing task information.

8. A crowdsourcing task allocation device, characterized in that, For use by task publishers, the device includes the following modules: The second data receiving module is used to receive the homomorphic encryption public key broadcast by the encryption server, and to receive the identity credential information generated by the task publisher during identity registration sent by the encryption server; wherein, the identity credential information includes identity identification information, identity identification signature information, credit score information, and credit score signature information; The task location region determination module is used to determine the task location and task region of the task publisher based on the embedded road network built by the platform server; wherein, the task location includes the region boundary vertex and the vertex location distance; The shortest boundary vertex distance calculation module is used to calculate the shortest boundary vertex distance between the task location and the regional boundary vertex of the task area based on the road network information of the embedded road network. The target label acquisition module is used to determine the initial label corresponding to the boundary vertex of the region based on the embedded road network, and to package the initial label according to the number theory transformation technique to obtain the target label; The boundary relative distance acquisition module is used to package the vertex position distance and the shortest boundary vertex distance according to the number theory transformation technique to obtain the boundary relative distance; The first encrypted location information construction module is used to encrypt the target tag and the relative distance of the boundary according to the homomorphic encryption public key to obtain the encrypted tag and the relative distance of the encrypted boundary, and construct the first encrypted location information according to the encrypted tag, the relative distance of the encrypted boundary and the task area; The first encrypted information construction module is used to generate a credit score threshold, construct first encrypted information based on the identity information, the identity signature information, the first encrypted location information and the credit score threshold, and send the first encrypted information to the platform server. The embedded road network is determined by the crowdsourcing task allocation device according to claim 7.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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