Data encryption method, device, equipment and storage medium

Abstract

The present disclosure relates to a data encryption method, device, equipment and storage medium, the method comprising: obtaining first geographic information of each sub-region in a target region, second geographic information of a plurality of data collection points in the target region, and monitoring data corresponding to the plurality of data collection points; for each data collection point, determining the sub-region to which the data collection point belongs based on the first geographic information of each sub-region and the second geographic information of the data collection point; and for each sub-region, encrypting the monitoring data corresponding to each data collection point included in the sub-region. By determining the division of the sub-region to which the data collection point belongs, the present disclosure can encrypt the monitoring data corresponding to each data collection point in the same sub-region, allocate different access permissions of sub-region data to different users, and further improve the security of the data.

Description

Technical Field

[0001] This disclosure relates to the field of data encryption technology, and in particular to a data encryption method, apparatus, device, and storage medium. Background Technology

[0002] With the development of information technology and the arrival of the data era, people often rely on large amounts of data collected in advance when making decisions. During data transmission and storage, it is crucial to ensure data security. Traditional methods often employ overall data encryption to improve security. However, in the data usage stage, even if a user only needs to access a portion of the data, they still need to obtain the complete encrypted dataset. This makes it impossible to set different access permissions for different users, significantly increasing the risk of data leakage and seriously threatening data security. Therefore, how to encrypt data to improve security is a technical problem that needs to be solved. Summary of the Invention

[0003] To address the aforementioned technical problems, this disclosure provides a data encryption method, apparatus, device, and storage medium.

[0004] A first aspect of this disclosure provides a data encryption method, the method comprising:

[0005] Acquire the first geographic information of each sub-region in the target area, the second geographic information of multiple data collection points in the target area, and the monitoring data corresponding to the multiple data collection points;

[0006] For each data collection point, the sub-region to which the data collection point belongs is determined based on the first geographic information of each sub-region and the second geographic information of the data collection point.

[0007] For each sub-region, the monitoring data corresponding to each data collection point contained in the sub-region is encrypted.

[0008] Optionally, determining the sub-region to which each data collection point belongs based on the first geographic information of each sub-region and the second geographic information of the plurality of data collection points includes:

[0009] For each data collection point, based on the first geographic information of each sub-region and the second geographic information of the data collection point, the target parameters corresponding to each sub-region are calculated, and the sub-region with the smallest target parameters is determined as the sub-region to which the data collection point belongs.

[0010] Optionally, the first geographic information includes at least one of the following: the first geographic coordinates of the center point, the first geographic feature vector, the data collection density, the regional correlation parameter, and the time change influence parameter; the second geographic information includes at least one of the following: the second geographic coordinates and the second geographic feature vector.

[0011] The calculation of target parameters corresponding to each sub-region based on the first geographic information of each sub-region and the second geographic information of the data collection point includes:

[0012] Based on the first geographic coordinates of the center point of each sub-region and the second geographic coordinates of the data collection point, calculate the first distance between each sub-region and the data collection point;

[0013] Based on the first geographic feature vector of each sub-region and the second geographic feature vector of the data collection point, the geographic feature similarity between each sub-region and the data collection point is calculated.

[0014] Based on the first distance between each sub-region and the data collection point, the geographical feature similarity between each sub-region and the data collection point, the data collection density, regional correlation parameter, and time change influence parameter of each sub-region, the target parameters corresponding to each sub-region are determined.

[0015] Optionally, the time-varying influence parameter is determined based on at least one of the following: the order of the Fourier series expansion, the period length, and the period variation frequency.

[0016] Optionally, the regional correlation parameter is determined based on at least one of the following: the second distance between the current sub-region and other sub-regions, the standard deviation of the second distance, the first geographic feature vector of the current sub-region, the first geographic feature vector of other sub-regions, the standard deviation of the geographic feature vectors, the timestamp of the current sub-region, the timestamp of other sub-regions, and the standard deviation of time.

[0017] Optionally, the encryption method for the monitoring data includes homomorphic encryption. After encrypting the monitoring data corresponding to each data collection point within each sub-region, the method further includes:

[0018] Perform privacy calculations on homomorphically encrypted monitoring data.

[0019] Optionally, the target area is a target river basin, and the monitoring data includes at least one of shipping data, ship data, meteorological data, hydrological data, and geological data.

[0020] A second aspect of this disclosure provides a data encryption apparatus, the apparatus comprising:

[0021] The acquisition module is used to acquire the first geographic information of each sub-region in the target area, the second geographic information of multiple data collection points in the target area, and the monitoring data corresponding to the multiple data collection points;

[0022] The region division module is used to determine the sub-region to which the data collection point belongs based on the first geographic information of each sub-region and the second geographic information of the data collection point for each data collection point.

[0023] The encryption module is used to encrypt the monitoring data corresponding to each data collection point contained in each sub-region.

[0024] A third aspect of this disclosure provides a computer device including a memory and a processor, and a computer program, wherein the memory stores the computer program, and when the computer program is executed by the processor, it implements the data encryption method of the first aspect described above.

[0025] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the data encryption method of the first aspect described above.

[0026] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0027] In the data encryption method, apparatus, device, and storage medium provided in this disclosure embodiment, by acquiring the first geographic information of each sub-region in the target area, the second geographic information of multiple data collection points in the target area, and the monitoring data corresponding to the multiple data collection points, for each data collection point, based on the first geographic information of each sub-region and the second geographic information of the data collection point, the sub-region to which the data collection point belongs is determined. For each sub-region, the monitoring data corresponding to each data collection point contained in the sub-region is encrypted. This enables the division of the sub-region to which the data collection point belongs and the encryption of the monitoring data corresponding to each data collection point within the same sub-region. Thus, when a user needs to access data, access permissions for different sub-region data are assigned to different users, further improving data security. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0029] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of a data encryption method provided in an embodiment of this disclosure;

[0031] Figure 2 This is a schematic diagram of the structure of a data encryption device provided in an embodiment of this disclosure;

[0032] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Detailed Implementation

[0033] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0034] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0035] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0036] Figure 1 This is a flowchart of a data encryption method provided in an embodiment of this disclosure. The method can be executed by a data encryption device, which can be implemented in software and / or hardware. The data encryption device can be configured in an electronic device, such as a server or terminal, where the terminal specifically includes a mobile phone, computer, or tablet computer. Figure 1 As shown, the data encryption method provided in this embodiment includes the following steps:

[0037] S101. Obtain the first geographic information of each sub-region in the target area, the second geographic information of multiple data collection points in the target area, and the monitoring data corresponding to the multiple data collection points.

[0038] In this embodiment of the disclosure, the target area can be understood as the geographical area where detection data needs to be acquired. The target area contains multiple data collection points, and the sub-areas can be understood as multiple sub-areas obtained by dividing the target area in advance according to preset rules (such as latitude and longitude, geographical features, natural boundaries, etc.). Optionally, the target area can be a target river system basin, and the monitoring data can include at least one of the following: shipping data, ship data, meteorological data, hydrological data, and geological data collected by various data collection points set up in the river system basin.

[0039] The first geographic information in this embodiment can be understood as the geographic information of a sub-region, which may specifically include the geographic coordinates and geographic features of the sub-region. The second geographic information can be understood as the geographic information of the data collection point, which may specifically include the geographic coordinates and geographic features of the data collection point, etc., and is not limited here.

[0040] In this embodiment of the disclosure, when it is necessary to encrypt the monitoring data corresponding to multiple data collection points, the data encryption device can obtain the first geographic information of each sub-region in the target area where the multiple data collection points are located, as well as the second geographic information of these multiple data collection points and the corresponding monitoring data.

[0041] In one exemplary embodiment of this disclosure, the data encryption device can obtain sub-region division information, first geographic information of each sub-region in the target region, and second geographic information of multiple data collection points from a preset geographic information related file. Then, it can obtain monitoring data corresponding to multiple data collection points through a preset monitoring data aggregation interface and associate the monitoring data with the data collection points.

[0042] S102. For each data collection point, based on the first geographic information of each sub-region and the second geographic information of the data collection point, determine the sub-region to which the data collection point belongs.

[0043] In this embodiment of the disclosure, the data encryption device can obtain the first geographic information of each sub-region and the second geographic information of multiple data collection points. For each data collection point, according to the preset regional division rules, it uses the first geographic information of each sub-region and the second geographic information of the current data collection point to determine the sub-region to which the current data collection point belongs, until all data collection points are divided into regions.

[0044] In another exemplary embodiment of the present disclosure, the data encryption device can determine the sub-region where the current data collection point is located based on the geographical location of the current data collection point and the boundary location of each sub-region.

[0045] In another exemplary embodiment of the present disclosure, the data encryption device can perform feature matching on the first geographic information of each sub-region and the second geographic information of the current data collection point, and determine the sub-region with the highest matching degree with the current data collection point as the sub-region to which the current data collection point belongs.

[0046] S103. For each sub-region, encrypt the monitoring data corresponding to each data collection point contained in the sub-region.

[0047] In this embodiment of the disclosure, after determining the sub-region to which each data collection point belongs, the data encryption device can, for each sub-region, determine the data collection points contained in the current sub-region, and encrypt the monitoring data corresponding to each data collection point contained in the current sub-region as a whole, until the monitoring data corresponding to each data collection point contained in each sub-region is encrypted.

[0048] In one exemplary embodiment of this disclosure, the data encryption device can perform homomorphic encryption on the monitoring data corresponding to each data collection point in the current sub-region, and after encryption, directly perform privacy calculations on the homomorphically encrypted monitoring data, so that the data does not need to be decrypted first, and the calculation result remains encrypted. Only users with the decryption key can see the true value of the calculation result.

[0049] This embodiment of the disclosure acquires first geographic information of each sub-region in the target area, second geographic information of multiple data collection points in the target area, and monitoring data corresponding to the multiple data collection points. For each data collection point, based on the first geographic information of each sub-region and the second geographic information of the data collection point, the sub-region to which the data collection point belongs is determined. For each sub-region, the monitoring data corresponding to each data collection point contained in the sub-region is encrypted. This allows for the division of the sub-regions to which the data collection point belongs and the encryption of the monitoring data corresponding to each data collection point within the same sub-region. As a result, when a user needs to access the data, different access permissions for different sub-region data are assigned to different users, further improving data security.

[0050] In some embodiments, when executing S102, the data encryption device may calculate the target parameters corresponding to each sub-region for each data collection point based on the first geographic information of each sub-region and the second geographic information of the data collection point, and determine the sub-region to which the data collection point belongs as the sub-region with the smallest target parameters.

[0051] The target parameter is used to measure the regional division result of the data collection point. The smaller the value of the target parameter, the greater the probability that the data collection point belongs to the current sub-region.

[0052] The first geographic information includes at least one of the following: the first geographic coordinates of the center point, the first geographic feature vector, the data collection density, the regional correlation parameter, and the time change influence parameter; the second geographic information includes at least one of the following: the second geographic coordinates and the second geographic feature vector.

[0053] The data encryption device, when calculating the target parameters corresponding to each sub-region, can calculate a first distance between each sub-region and the data collection point based on the first geographic coordinates of the center point of each sub-region and the second geographic coordinates of the data collection point; and calculate the geographic feature similarity between each sub-region and the data collection point based on the first geographic feature vector of each sub-region and the second geographic feature vector of the data collection point. Based on the first distance between each sub-region and the data collection point, the geographic feature similarity between each sub-region and the data collection point, as well as the data collection density, regional correlation parameter, and time change influence parameter of each sub-region, the target parameters corresponding to each sub-region are determined.

[0054] Specifically, the i-th timestamp t i The second geographic coordinate (x) of the i-th data collection point i ,y i Subregion R to which ) belongs j This can be expressed using the following formula:

[0055]

[0056] Where n is the number of subregions, x i c is the x-coordinate of the second geographic coordinate of the i-th data collection point. k Let y be the x-coordinate of the first geographic coordinate of the center point of the k-th sub-region. i Let d be the ordinate of the second geographic coordinate of the i-th data collection point. k Let y be the ordinate of the first geographic coordinate of the center point of the k-th sub-region. This represents the first distance between the k-th sub-region and the i-th data collection point, where α is the geographic feature weight, and v k w is the first geographic feature vector of the k-th sub-region. i Let v be the second geographic feature vector of the i-th data collection point. k ·w i Then, it represents the geographical feature similarity between the k-th sub-region and the i-th data collection point, β is the data collection density weight, and ρ k Let A be the data collection density of the k-th sub-region, γ be the correlation weight, and A be the data collection density of the k-th sub-region. kl Let δ be the correlation parameter between the k-th sub-region and the l-th sub-region, and let ψ(t) be the weight of time influence. iThe parameter representing the time-varying influence is used to characterize the impact of time factors on the regional division of data points. The target parameter of the k-th sub-region can be obtained through... express.

[0057] Optionally, the time-varying influence parameter is used to characterize the impact of time factors on the sub-region to which the data points belong. The time-varying influence parameter is determined based on at least one of the order of the Fourier series expansion, the period length, and the frequency of period variation, and can be expressed by the following formula:

[0058]

[0059] Wherein, ψ(t) i ) represents the parameter affected by time variation, α′ k′ Let β′ be the weight of the sine term at the k′-th order, K be the order of the Fourier series expansion, T be the period length, and β′ be the weight of the sine term at the k′-th order. k′ is the weight of the cosine term at the k′ order, and k″ is the periodic frequency.

[0060] Optionally, the regional correlation parameter is determined based on at least one of the following: the second distance between the current sub-region and other sub-regions, the standard deviation of the second distance, the first geographic feature vector of the current sub-region, the first geographic feature vector of other sub-regions, the standard deviation of the geographic feature vectors, the timestamp of the current sub-region, the timestamp of other sub-regions, and the standard deviation of time. Specifically, the regional correlation parameter A between the k-th sub-region and the l-th sub-region... kl This can be expressed using the following formula:

[0061]

[0062] Where, d kl σ is the second distance between the k-th sub-region and the l-th sub-region. d σ is the standard deviation of the second distance. f σ represents the standard deviation of the geographic feature vector. t v is the standard deviation of time. k Let v be the first geographic feature vector of the k-th sub-region. l Let t be the first geographic feature vector of the l-th sub-region. k Let t be the timestamp of the k-th sub-region. l This is the timestamp of the l-th sub-region.

[0063] Figure 2 This is a schematic diagram of the structure of a data encryption device provided in an embodiment of this disclosure. Figure 2As shown, the data encryption device 200 includes: an acquisition module 210, a region division module 220, and an encryption module 230. The acquisition module 210 is used to acquire first geographic information of each sub-region within a target region, second geographic information of multiple data collection points within the target region, and monitoring data corresponding to the multiple data collection points. The region division module 220 is used to determine the sub-region to which each data collection point belongs, based on the first geographic information of each sub-region and the second geographic information of the data collection point. The encryption module 230 is used to encrypt the monitoring data corresponding to each data collection point within each sub-region.

[0064] Optionally, the region division module 220 is specifically used to calculate the target parameters corresponding to each sub-region based on the first geographic information of each sub-region and the second geographic information of the data collection point for each data collection point, and determine the sub-region with the smallest target parameters as the sub-region to which the data collection point belongs.

[0065] Optionally, the first geographic information includes at least one of the following: first geographic coordinates of the center point, first geographic feature vector, data collection density, regional correlation parameter, and time change influence parameter; the second geographic information includes at least one of the following: second geographic coordinates and second geographic feature vector; the region division module 220 includes: a first calculation unit, used to calculate a first distance between each sub-region and the data collection point based on the first geographic coordinates of the center point of each sub-region and the second geographic coordinates of the data collection point; a second calculation unit, used to calculate the geographic feature similarity between each sub-region and the data collection point based on the first geographic feature vector of each sub-region and the second geographic feature vector of the data collection point; and a parameter determination unit, used to determine the target parameters corresponding to each sub-region based on the first distance between each sub-region and the data collection point, the geographic feature similarity between each sub-region and the data collection point, and the data collection density, regional correlation parameter, and time change influence parameter of each sub-region.

[0066] Optionally, the time-varying influence parameter is determined based on at least one of the following: the order of the Fourier series expansion, the period length, and the periodic variation frequency.

[0067] Optionally, the regional correlation parameter is determined based on at least one of the following: the second distance between the current sub-region and other sub-regions, the standard deviation of the second distance, the first geographic feature vector of the current sub-region, the first geographic feature vector of other sub-regions, the standard deviation of the geographic feature vector, the timestamp of the current sub-region, the timestamp of other sub-regions, and the standard deviation of time.

[0068] Optionally, the encryption method of the monitoring data includes homomorphic encryption, and the data encryption device 200 further includes: a calculation module for performing privacy calculations on the homomorphically encrypted monitoring data.

[0069] Optionally, the target area is a target river basin, and the monitoring data includes at least one of shipping data, ship data, meteorological data, hydrological data, and geological data.

[0070] The data encryption device provided in this embodiment can execute the method described in any of the above embodiments, and its execution method and beneficial effects are similar, so they will not be described again here.

[0071] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure.

[0072] like Figure 3 As shown, the computer device may include a processor 310 and a memory 320 storing computer program instructions.

[0073] Specifically, the processor 310 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0074] Memory 320 may include a mass storage device for information or instructions. For example, and not limitingly, memory 320 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 320 may include removable or non-removable (or fixed) media. Where appropriate, memory 320 may be internal or external to the integrated gateway device. In a particular embodiment, memory 320 is a non-volatile solid-state memory. In a particular embodiment, memory 320 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0075] The processor 310 reads and executes computer program instructions stored in the memory 320 to perform the steps of the data encryption method provided in the embodiments of this disclosure.

[0076] In one example, the computer device may also include a transceiver 330 and a bus 340. Wherein, as... Figure 3 As shown, the processor 310, memory 320 and transceiver 330 are connected via bus 340 and communicate with each other.

[0077] Bus 340 may include hardware, software, or both. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 340 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0078] This disclosure also provides a computer-readable storage medium that can store a computer program that, when executed by a processor, enables the processor to implement the data encryption method provided in this disclosure.

[0079] The aforementioned storage medium may, for example, include a memory 320 containing computer program instructions, which can be executed by the processor 310 of the data encryption device to complete the data encryption method provided in the embodiments of this disclosure. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc ROM (CD-ROM), magnetic tape, floppy disk, and optical data storage device. The aforementioned computer program may be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code may be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0080] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0081] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data encryption method, characterized in that, The method includes: Acquire the first geographic information of each sub-region in the target area, the second geographic information of multiple data collection points in the target area, and the monitoring data corresponding to the multiple data collection points; For each data collection point, the sub-region to which the data collection point belongs is determined based on the first geographic information of each sub-region and the second geographic information of the data collection point; For each sub-region, the monitoring data corresponding to each data collection point contained in the sub-region is encrypted; For each data collection point, determining the sub-region to which the data collection point belongs, based on the first geographic information of each sub-region and the second geographic information of the data collection point, includes: For each data collection point, based on the first geographic information of each sub-region and the second geographic information of the data collection point, the target parameter corresponding to each sub-region is calculated, and the sub-region with the smallest target parameter is determined as the sub-region to which the data collection point belongs. The target parameter is used to measure the regional division result of the data collection point, and the value of the target parameter is inversely proportional to the probability that the data collection point belongs to the sub-region corresponding to the target parameter. The first geographic information includes at least one of the following: the first geographic coordinates of the center point, the first geographic feature vector, the data collection density, the regional correlation parameter, and the time change influence parameter; the second geographic information includes at least one of the following: the second geographic coordinates and the second geographic feature vector. The calculation of target parameters corresponding to each sub-region based on the first geographic information of each sub-region and the second geographic information of the data collection point includes: Based on the first geographic coordinates of the center point of each sub-region and the second geographic coordinates of the data collection point, calculate the first distance between each sub-region and the data collection point; Based on the first geographic feature vector of each sub-region and the second geographic feature vector of the data collection point, the geographic feature similarity between each sub-region and the data collection point is calculated. Based on the first distance between each sub-region and the data collection point, the geographical feature similarity between each sub-region and the data collection point, the data collection density, regional correlation parameter, and time change influence parameter of each sub-region, the target parameters corresponding to each sub-region are determined.

2. The method according to claim 1, characterized in that, The time-varying influence parameter is determined based on at least one of the following: the order of the Fourier series expansion, the period length, and the period variation frequency.

3. The method according to claim 1, characterized in that, The regional correlation parameter is determined based on at least one of the following: the second distance between the current sub-region and other sub-regions, the standard deviation of the second distance, the first geographic feature vector of the current sub-region, the first geographic feature vector of other sub-regions, the standard deviation of the geographic feature vector, the timestamp of the current sub-region, the timestamp of other sub-regions, and the standard deviation of time.

4. The method according to claim 1, characterized in that, The encryption method for the monitoring data includes homomorphic encryption. After encrypting the monitoring data corresponding to each data collection point within each sub-region, the method further includes: Perform privacy calculations on homomorphically encrypted monitoring data.

5. The method according to claim 1, characterized in that, The target area is the target river basin, and the monitoring data includes at least one of the following: shipping data, ship data, meteorological data, hydrological data, and geological data.

6. A data encryption device, characterized in that, The device includes: The acquisition module is used to acquire the first geographic information of each sub-region in the target area, the second geographic information of multiple data collection points in the target area, and the monitoring data corresponding to the multiple data collection points; The region division module is used to determine the sub-region to which the data collection point belongs based on the first geographic information of each sub-region and the second geographic information of the data collection point for each data collection point. An encryption module is used to encrypt the monitoring data corresponding to each data collection point contained in each sub-region. The region division module is specifically used to calculate the target parameters corresponding to each sub-region for each data collection point based on the first geographic information of each sub-region and the second geographic information of the data collection point, and to determine the sub-region to which the data collection point belongs as the sub-region with the smallest target parameter. The target parameter is used to measure the region division result of the data collection point, and the value of the target parameter is inversely proportional to the probability that the data collection point belongs to the sub-region corresponding to the target parameter. The first geographic information includes at least one of the following: first geographic coordinates of the center point, first geographic feature vector, data collection density, regional correlation parameter, and time change influence parameter; the second geographic information includes at least one of second geographic coordinates and second geographic feature vector; the region division module includes: The first calculation unit is used to calculate the first distance between each sub-region and the data collection point based on the first geographic coordinates of the center point of each sub-region and the second geographic coordinates of the data collection point. The second calculation unit is used to calculate the geographical feature similarity between each sub-region and the data collection point based on the first geographical feature vector of each sub-region and the second geographical feature vector of the data collection point. The parameter determination unit is used to determine the target parameters corresponding to each sub-region based on the first distance between each sub-region and the data collection point, the geographical feature similarity between each sub-region and the data collection point, the data collection density of each sub-region, the regional correlation parameter, and the time change influence parameter.

7. A computer device, characterized in that, include: Memory; processor; And a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the data encryption method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the data encryption method as described in any one of claims 1-5.

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