Real-time city false report correction and privacy protection system and method based on spatial crowdsourcing

Abstract

The application discloses a kind of real-time city leak report correction and privacy protection system based on space crowd sourcing, including space crowd sourcing platform, early warning management system, information verification system and user terminal;Space crowd sourcing platform receives disaster feedback information from user terminal, the source attribute in each information is all carried out encryption processing, it will be encrypted information and stored in database;Early warning management system calls the encrypted information in database under the control of management personnel and carries out decryption, it will be decrypted information and sent to information verification system;Information verification system includes multiple verification subsystems;Each verification subsystem uses different verification method to verify the authenticity of information;Information verification system sends information verification result to early warning management system, and early warning management system generates early warning and rescue information accordingly and sends to space crowd sourcing platform.The application receives user terminal information in real time and protects the privacy of user terminal, improves the security of system.

Description

Technical Field

[0001] This invention relates to the field of spatial crowdsourcing technology, and in particular to a real-time urban underreporting correction and privacy protection system and method based on spatial crowdsourcing. Background Technology

[0002] Currently, city managers typically rely on resident reports to identify and address flood-damaged infrastructure. However, due to the subjectivity of individual participants and the uneven spatial distribution, underreporting is easily observed. Some studies have attempted to address this issue by quantifying reporting discrepancies; however, a challenge arises: distinguishing between unreported and non-occurring events, crucial for resource allocation. Subsequent research has utilized spatial correlation to differentiate between these two scenarios, inferring unreported events (i.e., underreporting) by observing surrounding areas reporting floods while central areas do not, and vice versa. While addressing these challenges, the accuracy of underreporting predictions is low, and real-time feedback is unavailable, relying solely on historical data. Furthermore, resource allocation in affected areas remains unresolved. Spatial crowdsourcing technology can effectively mobilize participation from all sectors of society in reporting flood events. Combined with spatiotemporal prediction models, it can effectively solve the problem of underreporting prediction accuracy. Spatial crowdsourcing can also leverage data analysis and intelligent algorithms for optimized resource allocation, thereby improving rescue efficiency. However, during the crowdsourcing process, a large amount of personal data and sensitive information is transmitted and stored on the crowdsourcing platform. Once leaked, it may be used by criminals for identity theft and fraud, causing great harm. Summary of the Invention

[0003] This invention provides a real-time urban underreporting correction and privacy protection system and method based on spatial crowdsourcing to solve the technical problems existing in the prior art.

[0004] The technical solution adopted by this invention to solve the technical problems existing in the prior art is as follows:

[0005] A real-time urban disaster reporting correction and privacy protection system based on spatial crowdsourcing includes a spatial crowdsourcing platform, an early warning management system, an information verification system, and user terminals. The public sends urban disaster feedback information to the spatial crowdsourcing platform through the user terminals. The user terminals encrypt the information before sending it. The spatial crowdsourcing platform receives and sends information from and to the user terminals; it has a database where it stores the received information. Under the control of administrators, the early warning management system retrieves and decrypts the encrypted information from the database and sends the decrypted urban disaster feedback information to the information verification system. The information verification system verifies the authenticity of the information collected by the spatial crowdsourcing platform and includes multiple verification subsystems. Each verification subsystem uses a different verification method to verify the same information. The information verification system sends the verification results to the early warning management system, which generates early warning and rescue information based on the verification results and sends it to the spatial crowdsourcing platform.

[0006] Furthermore, it also includes a volunteer matching system that matches volunteers based on demand; the user terminal includes a volunteer user terminal, through which volunteers send volunteer support request information to the space crowdsourcing platform; before sending the information, the volunteer user terminal encrypts the information and uses the plaintext distance between the person to be rescued and the volunteer as an identifier; the space crowdsourcing platform stores the received volunteer support request information with plaintext distance identifiers into a database; under the control of the administrator, the volunteer matching system calls the volunteer support request information in the database, compares the identifier distance with a threshold, retrieves the information of the person to be rescued who meets the conditions, and sends it to the space crowdsourcing platform, which then sends it to the corresponding volunteer user terminal.

[0007] Furthermore, the information verification system includes three verification subsystems, namely the first, second, and third verification subsystems. The first verification subsystem receives meteorological data released by the meteorological department and performs preliminary verification of urban disaster feedback information from the information source location. The second verification subsystem checks the probability of a disaster occurring in the information source location based on the prediction results of the Bayesian spatiotemporal model. The third verification subsystem searches the database storing feedback information to see if there are any other relevant feedbacks from the information source location.

[0008] Furthermore, the user terminal includes an encryption module; the encryption module uses the AES-128 symmetric encryption algorithm to encrypt the information.

[0009] This invention also provides a method for real-time urban underreporting correction and privacy protection based on spatial crowdsourcing. The method includes a spatial crowdsourcing platform, an early warning management system, an information verification system, and user terminals. The public sends urban disaster feedback information to the spatial crowdsourcing platform through the user terminals. The user terminals encrypt the information before sending it. The spatial crowdsourcing platform has a database, enabling it to receive and send information from and to user terminals, and store the received information in the database. Under the control of administrators, the early warning management system retrieves and decrypts the encrypted information from the database, then sends the decrypted urban disaster feedback information to the information verification system. The information verification system verifies the authenticity of the information collected by the spatial crowdsourcing platform and includes multiple verification subsystems. Each verification subsystem uses a different verification method to verify the same information. The information verification system sends the verification results to the early warning management system, which then generates early warning and rescue information based on the verification results and sends it to the spatial crowdsourcing platform.

[0010] Furthermore, the information verification system is equipped with three verification subsystems, namely the first, second, and third verification subsystems. The first verification subsystem receives meteorological data released by the meteorological department and performs preliminary verification of urban disaster feedback information from the information source location. The second verification subsystem checks the probability of disasters occurring in the information source location based on the prediction results of the Bayesian spatiotemporal model. The third verification subsystem searches the database storing feedback information to see if there are any other relevant feedbacks from the information source location.

[0011] Furthermore, the user terminal is equipped with an encryption module; the encryption module uses the AES-128 symmetric encryption algorithm to encrypt the information.

[0012] Furthermore, a volunteer matching system is set up to match volunteers based on demand; a volunteer user terminal is set up, through which volunteers send volunteer support request information to the space crowdsourcing platform; before sending the information, the volunteer user terminal encrypts the information and uses the plaintext distance between the person to be rescued and the volunteer as an identifier; the space crowdsourcing platform stores the received volunteer support request information with plaintext distance identifiers into the database; under the control of the administrator, the volunteer matching system calls the volunteer support request information in the database, compares the identifier distance with a threshold, retrieves the information of the person to be rescued who meets the conditions, and sends it to the space crowdsourcing platform, which then sends it to the corresponding volunteer user terminal.

[0013] Furthermore, the method includes the following specific steps:

[0014] Step 1: The space crowdsourcing platform encrypts each disaster feedback message using the AES-128 symmetric encryption algorithm. Let the encrypted disaster feedback message be EM1, and store EM1 in the database.

[0015] Step 2: Under the control of the management personnel, the early warning management system calls EM1 in the database. The management personnel use their own key to decrypt EM1 and obtain the information source location attribute from the feedback information. Based on the information source location attribute, the information is verified in three ways: First verification: the first verification subsystem verifies the meteorological data of the information source location through meteorological information released by the meteorological department; second verification: the second verification subsystem checks the probability of a disaster occurring in the information source location based on the prediction results of the Bayesian spatiotemporal model; third verification: the third verification subsystem searches the database storing the feedback information to see if there are other related feedbacks from the information source location. If a piece of information can pass two of the three verifications, the information verification system determines that the feedback information is real.

[0016] Step 3: The second verification subsystem determines the locations of unreported disasters based on the Bayesian spatiotemporal model prediction results and sends these locations to the first verification subsystem for verification. When a location passes the first verification subsystem's verification, the early warning management system automatically generates a rescue request signal A. When the feedback information passes two of the three verification methods, the early warning management system automatically generates a rescue request signal B. The early warning management system uses the AES-128 symmetric encryption algorithm to encrypt both rescue request signals A and B. Let the encrypted rescue request signal A be EQ1 and the encrypted rescue request signal B be EQ2. The early warning management system sends EQ1 and EQ2 to the spatial crowdsourcing platform, which stores EQ1 and EQ2 in its database.

[0017] Step 4: The space crowdsourcing platform sends EQ1 and / or EQ2 to the volunteer user terminal. The volunteer uses their own key to decrypt EQ1 and / or EQ2 received by the volunteer user terminal; they check the location information of the person to be rescued and the disaster situation. If they request support, the volunteer user terminal merges the location of the person to be rescued with the volunteer's own information to generate an identity password, and encrypts the identity password using the AES-128 symmetric encryption algorithm. Let the encrypted identity password be EIT. The volunteer user terminal sends EIT along with a plaintext distance identifier to the space crowdsourcing platform. The space crowdsourcing platform sends EIT along with the plaintext distance identifier to the volunteer matching system. The volunteer matching system retrieves suitable rescue information that meets the criteria based on the comparison results of the distance of the identifier and the threshold, and sends it to the space crowdsourcing platform. The space crowdsourcing platform then sends the suitable rescue information to the corresponding volunteer user terminal.

[0018] Furthermore, the volunteer matching system uses a simulated annealing algorithm to optimize routes to multiple suitable rescue locations received by volunteers, obtaining a shortest planned route. The volunteer matching system then sends the shortest planned route to the space crowdsourcing platform, which in turn sends it to the corresponding volunteer user terminal.

[0019] The advantages and positive effects of this invention are as follows: This invention provides a real-time urban flood underreporting correction and privacy protection method based on spatial crowdsourcing. It utilizes spatial crowdsourcing technology to coordinate flood information feedback from various sectors of society and efficiently allocate resources to flood-stricken areas. A three-stage verification mechanism is designed to verify the authenticity of the feedback information and improve the accuracy of urban flood underreporting prediction. This invention also achieves the goals of real-time reception of feedback information and optimization of rescue routes, while simultaneously protecting the privacy of crowdsourcing platform data and enhancing the security of the spatial crowdsourcing solution.

[0020] This invention uses spatial crowdsourcing to coordinate feedback from all sectors of society on urban flood events, achieving real-time information collection and broad coverage; the crowdsourcing platform uses simulated annealing algorithm to optimize the allocation of resources for relief, improving rescue efficiency; the Bayesian spatiotemporal model can make predictions using two-dimensional index data in time and space, improving the accuracy of missed reporting predictions.

[0021] The early warning management team conducted three verifications on the reported locations based on meteorological information, prediction models, and traversal results. Verification was deemed valid only if both criteria were met. This improved the accuracy and robustness of the judgments to some extent, reducing the false positive rate.

[0022] Searchable encryption technology is used to encrypt disaster feedback information, rescue requests, and volunteer identity passwords. The encrypted identity passwords and encrypted requests are matched to protect the location and identity information of volunteers and disaster victims, thus enhancing the security of the spatial crowdsourcing solution. At the same time, data updates are supported, enabling the crowdsourcing platform to receive real-time feedback information.

[0023] Security and simulation experiments demonstrate that the method in this invention is safe and feasible. Compared with existing solutions, it improves the accuracy of missed detection prediction and rescue efficiency, and provides stronger protection for encrypted data. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the structure of a real-time urban underreporting correction and privacy protection system based on spatial crowdsourcing, according to the present invention.

[0025] Figure 2 This is a flowchart of a real-time urban underreporting correction and privacy protection method based on spatial crowdsourcing according to the present invention.

[0026] Figure 3This is a flowchart of a rescue route planning method using a simulated annealing algorithm according to the present invention.

[0027] Figure 4 This is a schematic diagram of the principle of planning rescue routes using the simulated annealing algorithm of the present invention. Detailed Implementation

[0028] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0029] Please see Figures 1 to 4 A real-time urban disaster reporting correction and privacy protection system based on spatial crowdsourcing includes a spatial crowdsourcing platform, an early warning management system, an information verification system, and user terminals. The public sends urban disaster feedback information to the spatial crowdsourcing platform through the user terminals. The user terminals encrypt the information before sending it. The spatial crowdsourcing platform receives and sends information from and to the user terminals; it has a database where it stores the received information. Under the control of administrators, the early warning management system retrieves and decrypts the encrypted information from the database and sends the decrypted urban disaster feedback information to the information verification system. The information verification system verifies the authenticity of the information collected by the spatial crowdsourcing platform and includes multiple verification subsystems. Each verification subsystem uses a different verification method to verify the same information. The information verification system sends the verification results to the early warning management system, which generates early warning and rescue information based on the verification results and sends it to the spatial crowdsourcing platform.

[0030] Preferably, the system may also include a volunteer matching system that matches volunteers according to demand; the user terminal may include a volunteer user terminal, through which volunteers can send volunteer support request information to the space crowdsourcing platform; the volunteer user terminal encrypts the information before sending it and uses the plaintext distance between the person to be rescued and the volunteer as an identifier; the space crowdsourcing platform stores the received volunteer support request information with plaintext distance identifiers into a database; the volunteer matching system can call the volunteer support request information in the database under the control of the administrator, compare the identifier distance with a threshold, retrieve the information of the person to be rescued who meets the conditions, and send it to the space crowdsourcing platform, which then sends it to the corresponding volunteer user terminal.

[0031] Preferably, the information verification system may include three verification subsystems, namely, a first verification subsystem, a second verification subsystem, and a third verification subsystem; the first verification subsystem may receive meteorological data released by the meteorological department and conduct preliminary verification of urban disaster feedback information from the information source location; the second verification subsystem may check the probability of a disaster occurring in the information source location based on the prediction results of the Bayesian spatiotemporal model; and the third verification subsystem may search the database storing feedback information to see if there are other relevant feedbacks from the information source location.

[0032] Preferably, the user terminal may include an encryption module; the encryption module may use the AES-128 symmetric encryption algorithm to encrypt the information.

[0033] This invention also provides a method for real-time urban underreporting correction and privacy protection based on spatial crowdsourcing. The method includes a spatial crowdsourcing platform, an early warning management system, an information verification system, and user terminals. The public sends urban disaster feedback information to the spatial crowdsourcing platform through the user terminals. The user terminals encrypt the information before sending it. The spatial crowdsourcing platform has a database, enabling it to receive and send information from and to user terminals, and store the received information in the database. Under the control of administrators, the early warning management system retrieves and decrypts the encrypted information from the database, then sends the decrypted urban disaster feedback information to the information verification system. The information verification system verifies the authenticity of the information collected by the spatial crowdsourcing platform and includes multiple verification subsystems. Each verification subsystem uses a different verification method to verify the same information. The information verification system sends the verification results to the early warning management system, which then generates early warning and rescue information based on the verification results and sends it to the spatial crowdsourcing platform.

[0034] Preferably, the information verification system can be configured with three verification subsystems, namely the first, second, and third verification subsystems; the first verification subsystem can receive meteorological data released by the meteorological department and conduct preliminary verification of urban disaster feedback information from the information source location; the second verification subsystem can check the probability of disasters occurring in the information source location based on the prediction results of the Bayesian spatiotemporal model; and the third verification subsystem can search the database storing feedback information to see if there are other relevant feedbacks from the information source location.

[0035] Preferably, the user terminal may be equipped with an encryption module; the encryption module may use the AES-128 symmetric encryption algorithm to encrypt information.

[0036] Preferably, a volunteer matching system can also be set up to match volunteers according to demand; the user terminal can be set up as a volunteer user terminal, through which volunteers can send volunteer support request information to the space crowdsourcing platform; the volunteer user terminal can encrypt the information before sending it and use the plaintext distance between the person to be rescued and the volunteer as the identifier; the space crowdsourcing platform can store the received volunteer support request information with plaintext distance identifier into the database; the volunteer matching system can call the volunteer support request information in the database under the control of the administrator, compare the identifier distance with the threshold, retrieve the information of the person to be rescued who meets the conditions and send it to the space crowdsourcing platform, which can then send it to the corresponding volunteer user terminal.

[0037] Preferably, the method may include the following specific steps:

[0038] Step 1: For each disaster feedback message, the space crowdsourcing platform can encrypt it using the AES-128 symmetric encryption algorithm. The encrypted disaster feedback message can be set as EM1 and stored in the database.

[0039] Step 2: Under the control of administrators, the early warning management system can access EM1 in the database. Administrators can decrypt EM1 using their own keys to obtain the information source location attribute in the feedback information. Based on the information source location attribute, three verification methods can be used: First verification: The first verification subsystem verifies the meteorological data of the information source location using meteorological information released by the meteorological department; if the meteorological data shows that severe weather has occurred in the information source location, the first verification passes. Second verification: The second verification subsystem checks the probability of a disaster occurring in the information source location based on the prediction results of the Bayesian spatiotemporal model; if the prediction results of the Bayesian spatiotemporal model show that the probability of severe weather occurring in the information source location is greater than a set threshold, the second verification passes. Third verification: The third verification subsystem searches the database storing feedback information to see if there are other related feedbacks from the information source location; if there are other feedbacks from the information source location related to this disaster feedback information in the database storing feedback information, the third verification passes.

[0040] If a message passes two of the three verification methods, the information verification system can determine that the feedback message is authentic.

[0041] Step 3: The second verification subsystem can determine the locations of unreported disasters in the feedback information based on the prediction results of the Bayesian spatiotemporal model, and send the locations to the first verification subsystem for verification. When the unreported disaster location passes the verification of the first verification subsystem, i.e., the meteorological data shows that the unreported disaster location has severe weather, the early warning management system can automatically generate a rescue request signal A. When the feedback information passes two of the three verifications, the early warning management system can automatically generate a rescue request signal B. The early warning management system can use the AES-128 symmetric encryption algorithm to encrypt the rescue request signal A and the rescue request signal B. The encrypted rescue request signal A can be set as EQ1, and the encrypted rescue request signal B can be set as EQ2. The early warning management system can send EQ1 and EQ2 to the spatial crowdsourcing platform, and the spatial crowdsourcing platform stores EQ1 and EQ2 in its database.

[0042] Step 4: The space crowdsourcing platform sends EQ1 and / or EQ2 to the volunteer user terminal. Volunteers can use their own keys to decrypt EQ1 and / or EQ2 received by the volunteer user terminal. Volunteers view the location information of those to be rescued and the disaster situation. If a volunteer requests support, the volunteer user terminal merges the location of those to be rescued with the volunteer's own information to generate an identity password, which can be encrypted using the AES-128 symmetric encryption algorithm. The encrypted identity password can be set to EIT. The volunteer user terminal can send EIT along with a plaintext distance identifier to the space crowdsourcing platform. The space crowdsourcing platform can send EIT along with the plaintext distance identifier to the volunteer matching system. Based on the comparison results of the distance of the identifier and the threshold, the volunteer matching system, under the control of the administrator, retrieves suitable rescue information that meets the conditions from the database and sends it to the space crowdsourcing platform, which then sends the suitable rescue information to the corresponding volunteer user terminal.

[0043] Preferably, the volunteer matching system can use simulated annealing algorithm to optimize the routes of multiple suitable rescue locations received by volunteers to obtain a shortest planned route. The volunteer matching system can send the shortest planned route to the space crowdsourcing platform, which will then send the shortest planned route to the corresponding volunteer user terminal.

[0044] The following preferred embodiment of a method for real-time urban underreporting correction and privacy protection based on spatial crowdsourcing will further illustrate the workflow and working principle of the present invention.

[0045] A method for real-time urban underreporting correction and privacy protection based on spatial crowdsourcing includes the following steps:

[0046] The system comprises a spatial crowdsourcing platform, an early warning management system, an information verification system, and user terminals. The public sends urban disaster feedback information to the spatial crowdsourcing platform via user terminals. Information is encrypted before being sent by user terminals. The spatial crowdsourcing platform has a database, enabling it to receive and send information from user terminals, and store the received information in the database. Under the control of administrators, the early warning management system retrieves and decrypts the encrypted information from the database, then sends the decrypted urban disaster feedback information to the information verification system. The information verification system verifies the authenticity of information collected by the spatial crowdsourcing platform and consists of multiple verification subsystems. Each verification subsystem uses different verification methods to verify the same information.

[0047] The information verification system is set up with three verification subsystems: the first verification subsystem receives meteorological data released by the meteorological department and performs preliminary verification of urban disaster feedback information from the information source area; the second verification subsystem checks the probability of disasters occurring in the information source area based on the prediction results of the Bayesian spatiotemporal model; and the third verification subsystem searches the database storing feedback information to see if there are any other relevant feedbacks from the information source area.

[0048] The information verification system sends the verification results to the early warning management system, which then generates early warning and rescue information based on the verification results and sends it to the space crowdsourcing platform.

[0049] The following tasks were accomplished using the aforementioned space crowdsourcing platform, early warning management system, information verification system, and user terminal:

[0050] 1. Generate encrypted feedback: Enable the space crowdsourcing platform to receive urban disaster feedback information from user terminals; for each feedback message M i ∈{M1,M2,M3,…M n The user terminal uses the AES-128 symmetric encryption algorithm to encrypt it. Let the encrypted disaster feedback information be EM1, and store EM1 in the database.

[0051] 2. Analysis and Verification of Feedback Information: Under the control of administrators, the early warning management system can retrieve and decrypt encrypted information from the database. Administrators can use their own keys to decrypt EM1 and obtain feedback information M. i ∈{M1,M2,M3,…M n}, from which the feedback location P is obtained. i .

[0052] Regarding feedback location P iThree types of verification are performed. The first verification subsystem verifies the meteorological data of the information source location using meteorological information released by the meteorological department. The second verification subsystem checks the probability of a disaster occurring in the information source location based on the prediction results of the Bayesian spatiotemporal model. The third verification subsystem searches the feedback information database to see if there are any related feedbacks from the information source location. If a piece of information can pass two of the three verifications, the information verification system determines that the feedback information is genuine.

[0053] The first verification requires reviewing meteorological data from the meteorological department. The lowest level of rainstorm warning is a blue alert, indicating that rainfall will reach or has already reached 50 mm within 12 hours and that the rainfall is likely to continue. Considering factors such as drainage systems and terrain conditions, the threshold for determining severe weather is set at 50 mm or more of rainfall within 24 hours. If meteorological data shows that rainfall in the area where the information is sourced will reach 50 mm or more within 24 hours, then the first verification passes.

[0054] When conducting the second type of verification, if the probability of severe weather occurring in the source area of ​​the Bayesian spatiotemporal model's predicted information is greater than or equal to 50%, then the second type of verification is passed.

[0055] When performing the third type of verification, if two or more relevant or similar feedback messages for the location are obtained by traversing the feedback information database, the third type of verification is passed.

[0056] 3. Generating Encrypted Requests: To compensate for the inability of some regions to provide self-feedback due to various reasons, the second verification subsystem, based on the prediction results of the Bayesian spatiotemporal model, identifies the locations of unreported disasters and sends these locations to the first verification subsystem for verification. When a location is verified by the first verification subsystem, the early warning management system automatically generates a rescue request signal A; let A = Q. 1i ∈{Q 11 Q 12 Q 13 ,…Q 1n The rescue request signal A is encrypted using the AES-128 symmetric encryption algorithm to generate signal EQ1. When the feedback information passes two of the three verification methods, the early warning management system automatically generates rescue request signal B, let B = Q. 2i ∈{Q 21 Q 22 Q 23 ,…Q 2n The rescue request signal B is encrypted using the AES-128 symmetric encryption algorithm to generate signal EQ2; the early warning management system sends EQ1 and EQ2 to the space crowdsourcing platform, and the space crowdsourcing platform stores EQ1 and EQ2 in its database.

[0057] 4. Volunteer Matching: The space crowdsourcing platform sends EQ1 and / or EQ2 to the volunteer user's terminal. Volunteers use their own keys to decrypt the EQ1 and / or EQ2 received on their user terminals. By viewing the location information of those awaiting rescue and the disaster situation, volunteers intending to participate in the rescue combine the location of those awaiting rescue with their own information to generate an identity password. i ∈{IT1,IT2,IT3,…IT n The identity password is encrypted using the AES-128 symmetric encryption algorithm, generating the encrypted identity password EIT. Let IT be... i After encryption, it becomes EIT i EIT i ∈{EIT1,EIT2,EIT3,…EIT n}; and the distance d between the rescuers and volunteers. i Each encrypted identity password EIT is in plaintext. i The process involves identifying volunteers by sending the EIT (Electronic Information Request) along with a plaintext distance identifier to the space crowdsourcing platform. The space crowdsourcing platform then stores the EIT along with the plaintext distance identifier in its database. Under the control of administrators, the volunteer matching system retrieves volunteer support request information from the database, compares the identified distance with a threshold, identifies eligible individuals in need of rescue, and sends the information to the space crowdsourcing platform, which then forwards it to the corresponding volunteer user terminals.

[0058] 5. Rescue Route Optimization: For successfully matched volunteers, the volunteer matching system uses a simulated annealing algorithm to iterate through multiple iterations from route R... i ∈{R1,R2,R3,…R n Find the shortest path R to the disaster-stricken area in the given information. v The volunteer matching system will plan the shortest route R. v The data is sent to a space crowdsourcing platform, which then plans the shortest route R. v Send to the corresponding volunteer user terminal.

[0059] Each volunteer can match multiple requests, and each request can be accepted by multiple volunteers. The distance between a volunteer's current position and the position of an accepted request is d. i It uploads encrypted identity passwords in plaintext to the space crowdsourcing platform. Because the location information of the two endpoints is encrypted, even if the database is leaked, the distance d is not considered. i Furthermore, it is also impossible to obtain the user's location and identity information.

[0060] The simulated annealing algorithm optimizes the routes to multiple locations requested by volunteers, resulting in the shortest route. Volunteers then use this route to travel to multiple locations to deliver supplies and carry out rescue operations.

[0061] This invention introduces spatial crowdsourcing technology to design a feedback and resource allocation mechanism for missed flood reporting in urban areas. It uses a Bayesian spatiotemporal model to improve the accuracy of missed reporting location prediction, employs simulated annealing algorithms to optimize rescue routes, and finally introduces searchable encryption technology to protect data privacy within the spatial crowdsourcing platform. This effectively solves the problem of distinguishing between missed reports and events that have not occurred, rationally allocates resource needs in disaster-stricken areas, supports updates for real-time feedback, and further enhances the security of the spatial crowdsourcing solution.

[0062] The aforementioned spatial crowdsourcing platform, early warning management system, information verification system, user terminal, volunteer matching system, volunteer user terminal, first verification subsystem, second verification subsystem, third verification subsystem, database, Bayesian spatiotemporal model, simulated annealing algorithm, and other systems, functional modules, and software can all adopt existing systems, functional modules, and software, or adopt existing systems, functional modules, and software and construct them using conventional technical means.

[0063] The embodiments described above are only used to illustrate the technical ideas and features of the present invention. Their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The patent scope of the present invention should not be limited by these embodiments. That is, any equivalent changes or modifications made in accordance with the spirit disclosed in the present invention still fall within the patent scope of the present invention.

Claims

1. A real-time city false report correction and privacy protection system based on spatial crowdsourcing, characterized in that, The application relates to a spatial crowdsourcing platform, an early warning management system, an information verification system and a user terminal. The public sends city disaster feedback information to the spatial crowdsourcing platform through the user terminal; the user terminal encrypts the information before sending; the spatial crowdsourcing platform receives the information from the user terminal and sends information to the user terminal, and is provided with a database which stores the received information; the early warning management system calls the encrypted information in the database and decrypts it under the control of management personnel, and sends the decrypted city disaster feedback information to the information verification system; the information verification system is used for verifying the authenticity of the information collected by the spatial crowdsourcing platform, and comprises multiple verification subsystems; each verification subsystem uses different verification methods to verify the same information; the information verification system sends the information verification result to the early warning management system, and the early warning management system generates early warning and rescue information according to the information verification result and sends the information to the spatial crowdsourcing platform.

2. The real-time city false report correction and privacy protection system based on spatial crowdsourcing according to claim 1, wherein, A volunteer matching system is further included for matching volunteers according to requirements; the user terminal comprises a volunteer user terminal, and volunteers send volunteer support request information to the spatial crowdsourcing platform through the volunteer user terminal; the volunteer user terminal encrypts the information before sending, and uses the plaintext distance between the person to be rescued and the volunteer to identify; the spatial crowdsourcing platform stores the received volunteer support request information with the plaintext distance identification in the database; the volunteer matching system calls the volunteer support request information in the database under the control of management personnel, compares the identification distance with a threshold value, retrieves the information of the person to be rescued meeting the condition and sends the information to the spatial crowdsourcing platform, and the spatial crowdsourcing platform sends the information to the corresponding volunteer user terminal.

3. The real-time city false report correction and privacy protection system based on spatial crowd-sourcing of claim 1, wherein, The information verification system comprises three verification subsystems, namely a first, a second and a third verification subsystem; the first verification subsystem receives meteorological data published by a meteorological department and preliminarily verifies the city disaster feedback information from the information source; The second verification subsystem checks the possibility of disaster occurrence in the information source according to the prediction result of a Bayesian space-time model; The third verification subsystem checks whether there is other related feedback of the information source in the database storing the city disaster feedback information.

4. The real-time city false report correction and privacy protection system based on spatial crowd-sourcing of claim 1, wherein, The user terminal comprises an encryption module; the encryption module uses an AES-128 symmetric encryption algorithm to encrypt the information.

5. A real-time city false report correction and privacy protection method based on spatial crowdsourcing, characterized in that, The spatial crowdsourcing platform, the early warning management system, the information verification system and the user terminal are arranged; the public sends city disaster feedback information to the spatial crowdsourcing platform through the user terminal; the information is encrypted before being sent by the user terminal; the spatial crowdsourcing platform is provided with a database, the spatial crowdsourcing platform receives the information from the user terminal and sends information to the user terminal, and stores the received information in the database; The early warning management system calls and decrypts the encrypted information in the database under the control of the manager, and then sends the decrypted city disaster feedback information to the information verification system; the information verification system is used to verify the authenticity of the information collected by the spatial crowdsourcing platform, and is provided with a plurality of verification subsystems; each verification subsystem uses different verification methods to verify the same information; the information verification system sends the information verification result to the early warning management system, so that the early warning management system generates early warning and rescue information according to the information verification result and sends it to the spatial crowdsourcing platform.

6. The real-time city false negative correction and privacy protection method based on spatial crowdsourcing according to claim 5, characterized in that, The information verification system is provided with three verification subsystems, namely first, second and third verification subsystems; the first verification subsystem receives meteorological data published by the meteorological department and preliminarily verifies the city disaster feedback information from the information source; the second verification subsystem checks the possibility of disaster occurrence in the information source according to the Bayesian spatio-temporal model prediction result; and the third verification subsystem searches the database storing the city disaster feedback information to check whether there is other related feedback of the information source.

7. The real-time city false report correction and privacy protection method based on spatial crowd-sourcing according to claim 5, wherein, The user terminal is provided with an encryption module; the encryption module uses the AES-128 symmetric encryption algorithm to encrypt the information.

8. The real-time city false negative correction and privacy protection method based on spatial crowd-sourcing according to claim 5, characterized in that, A volunteer matching system is further provided according to the demand; the user terminal is provided with a volunteer user terminal, and the volunteer sends a volunteer support request information to the spatial crowdsourcing platform through the volunteer user terminal; the volunteer user terminal encrypts the information before sending it and uses the plaintext distance between the person to be rescued and the volunteer for identification; the spatial crowdsourcing platform stores the received volunteer support request information with the plaintext distance identification in the database; the volunteer matching system calls the volunteer support request information in the database under the control of the manager, compares the identification distance with the threshold value, searches for the information of the person to be rescued meeting the conditions and sends it to the spatial crowdsourcing platform, which sends it to the corresponding volunteer user terminal.

9. The real-time city false positive correction and privacy protection method based on spatial crowd-sourcing of claim 8, wherein, The method comprises the following specific steps: Step 1: the spatial crowdsourcing platform receives the city disaster feedback information from the user terminal; for each city disaster feedback information, the user terminal uses the AES-128 symmetric encryption algorithm to encrypt it, and sets the encrypted city disaster feedback information as EM1, and stores EM1 in the database; Step 2: the early warning management system calls EM1 in the database under the control of the manager; the manager uses its own secret key to decrypt EM1 and obtains the information source attribute in the city disaster feedback information; based on the information source attribute, the information is verified in three ways, the first verification: the first verification subsystem verifies the meteorological data of the information source through the meteorological information published by the meteorological department; the second verification: the second verification subsystem checks the possibility of disaster occurrence in the information source according to the Bayesian spatio-temporal model prediction result; the third verification: the third verification subsystem searches the database storing the city disaster feedback information to check whether there is other related feedback of the information source; if a piece of information can pass two of the three verifications, the information verification system judges that the city disaster feedback information is real information; Step 3, the second verification subsystem determines the disaster missing report location of the city disaster feedback information missing report according to the prediction result of the Bayesian space-time model, and sends the disaster missing report location to the first verification subsystem for verification, when the disaster missing report location passes the verification of the first verification subsystem, the early warning management system automatically generates a rescue request signal A; when the city disaster feedback information passes two of the three verifications, the early warning management system automatically generates a rescue request signal B, the early warning management system uses AES-128 symmetric encryption algorithm to encrypt the rescue request signal A and the rescue request signal B, and sets the encrypted rescue request signal A as EQ1 and the encrypted rescue request signal B as EQ2; the early warning management system sends EQ1 and EQ2 to the spatial crowdsourcing platform, and the spatial crowdsourcing platform stores EQ1 and EQ2 in the database; Step 4, the spatial crowdsourcing platform sends EQ1 and / or EQ2 to the volunteer user terminal, the volunteer uses its own secret key to decrypt EQ1 and / or EQ2 received by the volunteer user terminal; check the location information of the person to be rescued and the disaster situation, if the application support is needed, the volunteer user terminal combines the location of the person to be rescued with the information of the volunteer to generate an identity password, and encrypts the identity password by using the AES-128 symmetric encryption algorithm, and sets the encrypted identity password as EIT; the volunteer user terminal sends EIT together with the plaintext distance identifier to the spatial crowdsourcing platform; the spatial crowdsourcing platform sends EIT together with the plaintext distance identifier to the volunteer matching system; the volunteer matching system retrieves the suitable rescue information meeting the conditions according to the comparison result of the distance identifier and the threshold value, and sends the suitable rescue information to the spatial crowdsourcing platform, and the spatial crowdsourcing platform sends the suitable rescue information to the corresponding volunteer user terminal.

10. The real-time city false positive correction and privacy protection method based on spatial crowd-sourcing according to claim 9, wherein, The volunteer matching system uses the simulated annealing algorithm to optimize the route of multiple suitable rescue locations received by the volunteer, and obtains a shortest planning route, and the volunteer matching system sends the shortest planning route to the spatial crowdsourcing platform, and the spatial crowdsourcing platform sends the shortest planning route to the corresponding volunteer user terminal.

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