A privacy information encryption protection method and system of a service platform

Abstract

The application relates to the technical field of data encryption, in particular to a privacy information encryption protection method and system of a service platform, which comprises the following steps: obtaining the hash correction degree of each growth stage of crops according to the correlation of the crop parameter sequence of each growth stage of crops and the previous growth stage, screening out a target growth stage, determining the comparison fault tolerance of each target growth stage according to the hash correction degree and the environmental parameter deviation degree of each target growth stage, taking the comparison fault tolerance as the fault tolerance degree of the hash encryption result correction failure of the corresponding target growth stage, determining the final hash check value of each target growth stage, making the final hash check value suitable for environmental changes, thereby ensuring the accuracy of the hash check value, avoiding hash check failure, improving the privacy information encryption protection effect of the service platform, and having higher privacy and dynamic variability.

Description

Technical Field

[0001] This invention relates to the field of data encryption technology, specifically to a method and system for encrypting and protecting the privacy information of a service platform. Background Technology

[0002] Service platforms typically encompass multiple aspects such as finance, shopping, and daily life. For agricultural service platforms, such as the "Xingnong Service Platform," which covers plant and animal breeding projects, project results, and points systems, the privacy information involved primarily includes user personal data, project data, and points records. This privacy information needs to be encrypted to ensure that user privacy is not leaked. In agricultural service platforms, the main privacy data includes: user personal information, plant and animal breeding-related project data (project results, breeding records, and supply chain information), user behavior data, and points reward information. Encryption protection primarily employs methods such as data transmission encryption, database encryption, and encryption algorithm encryption. In agricultural service platforms, protecting the privacy of critical information such as crops is crucial, and it is necessary to ensure that crop information is effectively encrypted and protected at different stages.

[0003] Hash encryption technology, particularly in the tamper-proof verification of crop project data, demonstrates high feasibility and security. Hash encryption ensures data integrity and immutability, especially in the supply chain, guaranteeing the authenticity and consistency of crop data. For example, at different growth stages of crops, hash encryption is used to verify information such as variety, origin, and quality at multiple growth stages. However, in practical applications, due to the complexity of crops and their growing environment—such as temperature and humidity—crop parameters can change due to environmental variations. During the actual encryption protection of privacy information at different growth stages, environmental changes can render previously correct hash encryption results inapplicable, leading to inaccurate hash verification values ​​and ultimately affecting the privacy protection effectiveness of the service platform. Summary of the Invention

[0004] To address the technical problem of inaccurate hash verification values ​​caused by changes in external factors, the present invention aims to provide a method and system for encrypting and protecting the privacy information of a service platform. The specific technical solution adopted is as follows:

[0005] In a first aspect of the present invention, a method for encrypting and protecting privacy information on a service platform is provided, comprising:

[0006] Obtain crop parameter sequences at different growth stages;

[0007] The degree of hash verification for each growth stage is obtained based on the correlation between each growth stage and the crop parameter sequence of the previous growth stages.

[0008] Select the target growth stage from each growth stage that meets the target conditions for hash verification;

[0009] Determine the comparison tolerance for each target growth stage, wherein the comparison tolerance is related to the hash verification degree and the degree of environmental parameter deviation; the comparison tolerance characterizes the degree of fault tolerance for hash encryption result verification failure at the corresponding target growth stage; the degree of environmental parameter deviation characterizes the difference in environmental parameters between the target growth stage and its previous growth stages;

[0010] The final hash check value for each target growth stage is determined by the comparative fault tolerance of each target growth stage, and the final hash check value is used to indicate data encryption.

[0011] In an exemplary embodiment, the process of obtaining the hash verification degree includes:

[0012] The correlation between the reference growth stage and the crop parameter sequences of each preceding growth stage is determined, and then the average value of the correlation is calculated to obtain the hash verification degree of the reference growth stage; the reference growth stage can be any growth stage.

[0013] In one exemplary embodiment, the correlation is the Pearson correlation coefficient.

[0014] In one exemplary embodiment, the target condition is: the hash verification degree is less than a preset threshold.

[0015] In one exemplary embodiment, the privacy information encryption protection method of the service platform further includes:

[0016] The similarity between the environmental parameter target vectors of the reference target growth stage and each of the previous growth stages is determined; the reference target growth stage can be any target growth stage.

[0017] The average value of the similarity is calculated to obtain the degree of deviation of the environmental parameters at the growth stage of the reference target; the degree of deviation of the environmental parameters is inversely correlated with the average value of the similarity.

[0018] In one exemplary embodiment, the privacy information encryption protection method of the service platform further includes:

[0019] Obtain a sequence of environmental parameters across multiple dimensions during the growth phase;

[0020] The environmental parameter sequence of multiple dimensions is reduced to one dimension to obtain the environmental parameter target vector.

[0021] In an exemplary embodiment, the process of obtaining the comparative fault tolerance includes:

[0022] The comparison tolerance is obtained from the hash verification degree and the environmental parameter deviation degree. The comparison tolerance is inversely correlated with the hash verification degree and positively correlated with the environmental parameter deviation degree.

[0023] In an exemplary embodiment, the process of obtaining the final hash verification value includes:

[0024] The hash check value correction coefficient for each target growth stage is obtained based on the comparison fault tolerance of each target growth stage; the hash check value correction coefficient is positively correlated with the comparison fault tolerance.

[0025] Based on the hash verification value correction coefficient for each target growth stage and the initial hash verification value, the final hash verification value for each target growth stage is obtained.

[0026] In an exemplary embodiment, the process of obtaining the crop parameter sequence includes:

[0027] Obtain various types of parameters for different growth stages of the crop, and quantify these parameters to form the crop parameter sequence.

[0028] In a second aspect of the present invention, a privacy information encryption protection system for a service platform is provided, comprising: a memory and a processor; the memory is connected to the processor; the memory is used to store program instructions; the processor is used to implement the above-described privacy information encryption protection method for the service platform when the program instructions are executed.

[0029] The present invention has the following beneficial effects: The present invention analyzes the crop parameter sequences at different growth stages of crops, and then performs relevant comparative fault-tolerant analysis based on the changes in the crop parameter sequences at each growth stage. Based on the fault-tolerant results, the final hash verification value of each growth stage that needs to be focused on is obtained, so that the final hash verification value is applicable to environmental changes. Compared with the traditional and relatively rigid hash encryption, the present invention is more targeted, ensures the accuracy of the hash verification value, thereby avoiding hash verification failure, improving the privacy information encryption protection effect of the service platform, and has higher privacy and dynamic variability. Attached Figure Description

[0030] Figure 1 This is a flowchart of a privacy information encryption protection method for a service platform provided in one embodiment of the present invention;

[0031] Figure 2 This is a flowchart illustrating the process of obtaining the degree of deviation of environmental parameters according to an embodiment of the present invention;

[0032] Figure 3 This is a flowchart illustrating the process of obtaining the final hash verification value according to an embodiment of the present invention. Detailed Implementation

[0033] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. All data and information collected in this application have been obtained with full consent.

[0035] like Figure 1 As shown, this embodiment provides a method for encrypting and protecting the privacy information of a service platform, including the following steps:

[0036] Step S1: Obtain crop parameter sequences at different growth stages;

[0037] Step S2: Based on the correlation between each growth stage and the crop parameter sequences of the previous growth stages, obtain the hash verification degree of each growth stage;

[0038] Step S3: Select the target growth stage from each growth stage whose hash verification degree meets the target condition;

[0039] Step S4: Determine the comparative tolerance for each target growth stage;

[0040] Step S5: Determine the final hash check value for each target growth stage based on the comparative fault tolerance of each target growth stage.

[0041] The following detailed explanation of each step, in conjunction with the accompanying drawings, is provided.

[0042] Step S1: Obtain crop parameter sequences at different growth stages of the crop.

[0043] The crop is divided into growth stages based on its total growth time. The rules for dividing the growth stages are set according to actual needs. Different rules may result in different numbers of growth stages and varying durations for each stage. In one exemplary embodiment, the total growth time is divided into several equal parts, each corresponding to a growth stage. For example, 5% of the total growth time is considered one growth stage, resulting in a total of 20 growth stages. For instance, if a crop has a maturity cycle of 60 days, meaning its total growth time is 60 days, then each growth stage lasts 3 days.

[0044] This system acquires various parameters for each stage of crop growth. These parameters are specifically related to crop growth, and the types and number of parameters, as well as the specific implementation of each type, are set according to the actual situation. For example, parameter types include: crop variety (e.g., wheat, corn, rice), crop quantity, planting date, planting location information, seed weight, and crop quality grade (e.g., premium and common). For instance: Variety: Wheat; Quantity: 1000 plants; Planting date: 2025-06-01; Planting location information: XX province; Seed weight: 500 kg; Quality grade: Premium.

[0045] For any growth stage, various types of parameters are quantified. Numerical parameters, such as crop quantity and seed weight, are directly used as the required values. Non-numerical parameters, such as variety, planting location information, and quality grade, are converted into corresponding numerical values ​​using one-hot encoding. For example, if the variety is wheat, the corresponding parameter value is [1,0,0]; if the planting location is XX province, the corresponding parameter value is [1,0,0]; and if the quality grade is excellent, the corresponding parameter value is [1,0].

[0046] After quantifying various types of parameters, the values ​​of each type of parameter are arranged according to a preset order, and integrated into a unified sequence as the crop parameter sequence. Taking the following example: Variety: Wheat; Quantity: 1000 plants; Planting date: 2025-06-01; Planting location: XX province; Seed weight: 500 kg; Quality grade: Excellent, the crop parameter sequence is: [1,0,0,1000,2025,6,1,1,0,0,500,1,0]. Thus, the crop parameter sequences for each growth stage are obtained.

[0047] The crop parameter sequences at each growth stage can undergo further hash encryption or other encryption processing (next step) to ensure that the data is immutable on the platform and to guarantee the security and consistency of information. Since the crop parameter sequences at each growth stage need to be protected in a timely manner, hash encryption is performed on them, and the hash-encrypted sequence is then displayed on the service platform, forming the initial encryption result.

[0048] The crop parameter sequence at each growth stage represents the information results of each growth stage. Due to changes in the environment and other factors, some data in the crop parameter sequence may change, leading to changes in the encryption results at different growth stages. For example, due to the growth characteristics of crops, after several growth stages, some seedlings may die due to survival rate issues. Furthermore, due to changes in environmental factors, the corresponding quality may increase or decrease. In this case, the crop parameter sequence at the growth stage might become: [1,0,0,989,2025,6,1,1,0,0,500,0,0], where the number of plants decreases to 989, and the quality changes from excellent to ordinary, with the ordinary value being [0,0], indicating a lower quality due to climate change and unstable temperature. At this point, the hash encryption result objectively needs to be adjusted in a timely manner. However, when comparing the new encryption result with the encryption result from the previous stage, a clear problem of verification failure will occur.

[0049] During the verification process, due to the growth of the crop itself, dynamic hashing occurs at each growth stage, meaning the hash check value begins to change, which can cause problems in verifying the hash encryption result. This embodiment ultimately adjusts the hash check value to solve this problem.

[0050] Step S2: Based on the correlation between each growth stage and the crop parameter sequence of the previous growth stage, obtain the hash verification degree of each growth stage.

[0051] The crop parameter sequences at each growth stage are analyzed, and the changes in crop parameters at each growth stage are determined based on the correlation between each growth stage and the crop parameter sequences of the preceding growth stages. This determines the hash verification degree for each growth stage. The hash verification degree characterizes the probability of successful hash verification. The stronger the correlation between each growth stage and the crop parameter sequences of the preceding growth stages, the more similar the crop parameter sequences are to each growth stage, the lower the degree of change in crop properties, the higher the hash verification degree for each growth stage, the higher the probability of successful hash verification, the lower the possibility of hash value verification failure, and ultimately, the smaller the adjustment to the hash verification value.

[0052] For ease of explanation, we set the reference growth stage as any growth stage. Since each growth stage is determined chronologically, we then determine the growth stages preceding the reference growth stage. If the reference growth stage is the 4th growth stage, we obtain the 1st to 3rd growth stages. This yields the crop parameter sequence for the reference growth stage, as well as the crop parameter sequences for each growth stage preceding the reference growth stage.

[0053] The correlation between a reference growth stage and the crop parameter sequences of each preceding growth stage is obtained. In an exemplary embodiment, the correlation is specifically the Pearson correlation coefficient, i.e., the Pearson correlation coefficient is calculated for each reference growth stage and the crop parameter sequences of each preceding growth stage. For the Pearson correlation coefficient, the closer the Pearson correlation coefficient is to 1, the more similar the two sequences are, meaning the crop parameters at the two growth stages are more similar, including crop quantity, quality, etc. Then, the average of the Pearson correlation coefficients of the reference growth stage and the crop parameter sequences of each preceding growth stage is calculated, and this average is normalized. The result is the hash collation degree of the reference growth stage. Therefore, the closer this average is to 1, the higher the hash collation degree of the reference growth stage; conversely, the lower the hash collation degree of the reference growth stage, the more it proves that the crop parameters may have changed significantly at different growth stages due to the influence of environmental factors. It should be understood that since the Pearson correlation coefficient ranges from -1 to 1, the average Pearson correlation coefficient also ranges from -1 to 1. Therefore, in this embodiment, the normalization method for the average Pearson correlation coefficient is: (average Pearson correlation coefficient + 1) / 2. The formula for calculating the hash verification degree is as follows:

[0054]

[0055] Among them, A t M represents the hash verification level at the t-th growth stage; t M represents the sequence of crop parameters at the t-th growth stage; t-i p(M) represents the crop parameter sequence for the ti-th growth stage; t M t-i ) represents M t and M t-i The Pearson correlation coefficient. norm represents the normalization function.

[0056] The hash verification degree of each growth stage is obtained through the above method. Since there are no other growth stages before the first growth stage, the hash verification degree of the first growth stage is no longer obtained, nor is it used for the selection of subsequent target growth stages.

[0057] Step S3: Select the target growth stage from each growth stage that meets the target conditions for hash verification.

[0058] The lower the hash verification level, the greater the change in crop parameters, and the more necessary it is to analyze environmental variations. This is because environmental issues can easily lead to hash encryption verification failures, resulting in false positives of platform data tampering. Therefore, it is necessary to select growth stages with lower hash verification levels based on the hash verification level at each growth stage. In an exemplary embodiment, the target condition is that the hash verification level is less than a preset threshold. The purpose of the preset threshold is to determine whether the hash verification level is low. The preset threshold ranges from 0 to 1, and the specific value is set according to the judgment requirements. This embodiment uses 0.7 as an example.

[0059] Compare the hash verification degree of each growth stage with the preset threshold, obtain the growth stage corresponding to the hash verification degree that is less than the preset threshold, and set the growth stage corresponding to the hash verification degree that is less than the preset threshold as the target growth stage.

[0060] Crops are affected by environmental factors during their growth, such as temperature, humidity, airflow, and light. These factors can lead to several changes: crop mortality: excessively high or low temperatures, humidity levels, and oxygen deficiency can cause crop death, especially in crops that are not tolerant of environmental changes, resulting in alterations in crop parameter sequences; quality changes: changes in temperature and humidity can cause crops to mature prematurely, rot, and lose their original flavor or texture, thus affecting their market value; appearance damage: improper lighting and other factors can cause surface damage, discoloration, or deformation of crops, affecting their appearance. Therefore, it is necessary to obtain environmental parameters for each target growth stage, including but not limited to temperature and humidity.

[0061] In an exemplary embodiment, environmental parameter sensors are set up to collect environmental parameters of crop growth during a reference growth stage at a preset sampling frequency. These parameters are then arranged chronologically to obtain a sequence of environmental parameters for the reference growth stage. Since the environmental parameters involved can be of various types, such as temperature, humidity, airflow, and light, each type of environmental parameter can be considered as a single dimension, thus generating multiple sequences of environmental parameters for different dimensions of the reference growth stage. For example, taking temperature and humidity as examples, temperature and humidity sensors are set up to collect environmental temperature and humidity data for crop growth during the reference growth stage at preset sampling frequencies. These data are then arranged chronologically to obtain two-dimensional environmental parameter sequences: a temperature sequence and a humidity sequence. It should be understood that the above only uses temperature and humidity as examples; other types of environmental parameters can be added to the monitoring of temperature and humidity. Furthermore, environmental parameters of each dimension are collected at the same sampling frequency and synchronously to ensure a one-to-one correspondence between the data in each dimension's environmental parameter sequence.

[0062] The environmental parameter sequences of each dimension during the reference growth stage are fitted using the least squares method to obtain fitting curves for each dimension. Then, the dimensionality of the environmental parameter sequences of the reference growth stage is reduced to one dimension, and the resulting one-dimensional result is the environmental parameter target vector for the reference growth stage. In an exemplary embodiment, the dimensionality reduction of the multi-dimensional environmental parameter sequences is performed using the PCA (principal components analysis) algorithm, and the resulting one-dimensional vector serves as the environmental parameter target vector for the reference growth stage. That is, under the reference growth stage, there exists an environmental parameter target vector that contains the combined results of environmental parameters for the crop under the reference growth stage. The purpose of dimensionality reduction of the multi-dimensional environmental parameter sequences is to extract the environmental parameters that have the greatest impact on the crop, and to use these parameters in the calculation of environmental variability, thereby obtaining the comparative tolerance discussed later.

[0063] The degree of deviation of environmental parameters at each target growth stage is determined, wherein the degree of environmental parameter deviation characterizes the difference in environmental parameters between the target growth stage and its previous growth stages. In an exemplary embodiment, such as Figure 2 As shown, the privacy information encryption protection method for a service platform provided in this embodiment also includes a specific process for obtaining the degree of deviation of the following environmental parameters:

[0064] Step S31: Determine the similarity between the reference target growth stage and the environmental parameter target vectors of each previous growth stage.

[0065] For ease of explanation, we define the reference target growth stage as any target growth stage and obtain all growth stages preceding it. It should be understood that the growth stages preceding the reference target growth stage may include other target growth stages, or they may all be non-target growth stages. Furthermore, the required number of growth stages preceding the reference target growth stage is set according to actual needs; it can be a fixed number or all growth stages preceding the reference target growth stage. Moreover, if a fixed number of growth stages preceding the reference target growth stage is selected, and the actual number of growth stages preceding the reference target growth stage is less than this fixed number, then all growth stages preceding it are included in the calculation.

[0066] The similarity between the environmental parameter target vectors of the reference target at each growth stage and each previous growth stage is obtained. Similarity can be calculated using Pearson correlation coefficient, cosine similarity, etc. This embodiment uses Pearson correlation coefficient as an example. The Pearson correlation coefficients of the environmental parameter target vectors of the reference target at each growth stage and each previous growth stage are then obtained.

[0067] Step S32: Calculate the average similarity to obtain the degree of deviation of environmental parameters during the growth stage of the reference target.

[0068] The average Pearson correlation coefficients of the environmental parameter target vectors of the reference target growth stage and its previous growth stages are calculated. A larger average value indicates a greater similarity between the environmental parameters of the reference target growth stage and its previous growth stages, a lower environmental variation value (i.e., smaller environmental parameter deviation), and a lower degree of environmental parameter deviation in the reference target growth stage. Therefore, the degree of environmental parameter deviation is inversely correlated with this average value. In an exemplary embodiment, a specific quantification method for the degree of environmental parameter deviation is given below:

[0069]

[0070] Among them, En r Y represents the degree of deviation of environmental parameters in the r-th target growth stage. r Y represents the environmental parameter target vector for the r-th target growth stage. j Let Y represent the environmental parameter target vector for the j-th growth stage preceding the r-th target growth stage, and J represent the total number of growth stages preceding the r-th target growth stage. r ,Y j ) represents Y r and Y j The Pearson correlation coefficient.

[0071] Step S4: Determine the comparative tolerance for each target growth stage.

[0072] Step S3 obtains the degree of environmental parameter deviation for each target growth stage. When the degree of environmental parameter deviation is low, it can be considered that the possibility of changes in crop parameters due to external factors is low. In this case, the comparison tolerance for the target growth stage is low because it is highly likely that the hash encryption verification failure is caused by data tampering, rather than actual environmental influences. Conversely, the higher the degree of environmental parameter deviation, the higher the tolerance for hash encryption result verification failure. Since the hash encryption result change is not caused by tampering, a certain degree of fault tolerance is required to ensure the credibility of the verification result. Therefore, the comparison tolerance for the target growth stage is related to the degree of environmental parameter deviation, and the comparison tolerance is positively correlated with the degree of environmental parameter deviation.

[0073] The higher the hash verification level at the target growth stage, the closer other factors of the crop within that stage, such as quantity and quality, are. Therefore, the tolerance for hash verification failures is lower, as the failure is not due to the crop itself, increasing the likelihood of platform data tampering. Thus, the comparison fault tolerance at the target growth stage is related to the hash verification level, and the two are inversely related. The comparison fault tolerance at the target growth stage characterizes the tolerance for hash encryption result verification failures at that stage.

[0074] Based on the degree of deviation of environmental parameters at each target growth stage, and combined with the hash verification degree of each target growth stage obtained in step S2, the comparison fault tolerance of each target growth stage is obtained. The comparison fault tolerance is derived from the hash verification degree and the degree of deviation of environmental parameters. In an exemplary embodiment, a specific quantification method for the comparison fault tolerance is given below:

[0075] D r =En r ×(1-A r );

[0076] Among them, D r A represents the contrast tolerance of the r-th target growth stage. r This indicates the hash verification level of the r-th target growth stage.

[0077] Step S5: Determine the final hash check value for each target growth stage based on the comparative fault tolerance of each target growth stage.

[0078] Step S4 yields the comparison tolerance for each target growth stage. Based on the comparison tolerance for each target growth stage, the final hash verification value for each target growth stage is determined. The final hash verification value is used to indicate subsequent data encryption.

[0079] In one exemplary embodiment, such as Figure 3The following describes one process for obtaining the final hash verification value:

[0080] Step S51: Obtain the hash verification value correction coefficient for each target growth stage based on the comparative fault tolerance of each target growth stage.

[0081] Taking the reference target growth stage as an example, the higher the comparison fault tolerance of the reference target growth stage, the larger the hash check value correction coefficient. This leads to a greater increase in the hash check value, reducing the probability of misjudgment due to failed hash checks. Therefore, the hash check value correction coefficient is positively correlated with the comparison fault tolerance. In an exemplary embodiment, the hash check value correction coefficient for the r-th target growth stage is: 1 + D r .

[0082] Step S52: Based on the hash verification value correction coefficient of each target growth stage and the initial hash verification value, obtain the final hash verification value of each target growth stage.

[0083] Determining the initial hash check value for each target growth stage should be understood as follows: the initial hash check value is the initially set hash check value, and the initial hash check value is the same for all growth stages. In an exemplary embodiment, the initial hash check value for the r-th target growth stage is k. r The percentage (%) ranges from 0 to 100%, and a pass rate of 100% is considered valid. The final hash verification value is calculated as follows:

[0084] K r =k r %*(1+D r );

[0085] Among them, K r This represents the final hash check value for the r-th target growth stage. The final hash check value can also be called the enhanced hash value. If K r If the value exceeds 100%, it is limited to 100%. This means that even if the hash verification fails, it is considered that the failure is due to actual environmental factors, as analyzed above. Therefore, it is necessary to increase its fault tolerance and reduce the probability of misjudgment caused by the failure of hash verification.

[0086] It should be understood that for each growth stage that is not the target growth stage, its hash check value is no longer adjusted and the initial hash check value is still used. Therefore, it can be understood that the final hash check value of each growth stage that is not the target growth stage is its initial hash check value.

[0087] Subsequent staged verifications and privacy encryption of crop parameters are performed based on the final hash verification value. It should be understood that the verification and encryption process based on the final hash verification value is existing technology. This embodiment is not limited to a specific encryption process; a specific example is given below: Based on the final hash verification value, multi-stage hash encryption processing is performed on the crop parameters of the service platform to encrypt the service platform's privacy information. The multi-stage hash encryption process involves progressively encrypting the crop parameters of the service platform multiple times. First, a hash algorithm is used to initially encrypt the original data (the crop parameters to be encrypted). Then, different algorithms and keys are applied in multiple encryption stages for further encryption, ensuring that the data is irreversible and highly secure during storage and transmission. Each stage of encryption increases the difficulty of cracking, thereby effectively protecting privacy information. The final hash verification value, as the "fingerprint" of the data, can be verified and used while ensuring privacy.

[0088] This embodiment also provides a privacy information encryption protection system for a service platform, including: a memory and a processor; the memory is connected to the processor, and the memory is used to store program instructions; the processor is used to implement the steps in the above-described privacy information encryption protection method embodiment for a service platform when the program instructions are executed.

[0089] In one exemplary embodiment, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described embodiment of the privacy information encryption protection method for the service platform.

[0090] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0091] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for encrypting and protecting the privacy information of a service platform, characterized in that, include: Obtain crop parameter sequences at different growth stages; The degree of hash verification for each growth stage is obtained based on the correlation between each growth stage and the crop parameter sequence of the previous growth stages. Select the target growth stage from each growth stage that meets the target conditions for hash verification; Determine the comparison tolerance for each target growth stage, wherein the comparison tolerance is related to the hash verification degree and the degree of environmental parameter deviation; the comparison tolerance characterizes the degree of fault tolerance for hash encryption result verification failure at the corresponding target growth stage; the degree of environmental parameter deviation characterizes the difference in environmental parameters between the target growth stage and its previous growth stages; The final hash check value of each target growth stage is determined by the comparative fault tolerance of each target growth stage, and the final hash check value is used to indicate data encryption. The process of obtaining the hash verification degree includes: determining the correlation between the reference growth stage and the crop parameter sequences of each previous growth stage, and then calculating the average value of the correlation to obtain the hash verification degree of the reference growth stage; the reference growth stage can be any growth stage. The target condition is: the hash verification degree is less than a preset threshold; The process of obtaining the comparison tolerance includes: the comparison tolerance is obtained from the hash verification degree and the environmental parameter deviation degree, the comparison tolerance is inversely correlated with the hash verification degree and positively correlated with the environmental parameter deviation degree; The process of obtaining the final hash verification value includes: obtaining the hash verification value correction coefficient for each target growth stage based on the comparison fault tolerance of each target growth stage; the hash verification value correction coefficient is positively correlated with the comparison fault tolerance. Based on the hash verification value correction coefficient for each target growth stage and the initial hash verification value, the final hash verification value for each target growth stage is obtained; the initial hash verification value is the initially set hash verification value, and the initial hash verification value is the same for each growth stage.

2. The privacy information encryption protection method for a service platform as described in claim 1, characterized in that, The correlation is the Pearson correlation coefficient.

3. The privacy information encryption and protection method for a service platform as described in claim 1, characterized in that, The privacy information encryption protection method of the service platform also includes: The similarity between the environmental parameter target vectors of the reference target growth stage and each of the previous growth stages is determined; the reference target growth stage can be any target growth stage. The average value of the similarity is calculated to obtain the degree of deviation of the environmental parameters at the growth stage of the reference target; the degree of deviation of the environmental parameters is inversely correlated with the average value of the similarity.

4. The privacy information encryption and protection method for a service platform as described in claim 3, characterized in that, The privacy information encryption protection method of the service platform also includes: Obtain a sequence of environmental parameters across multiple dimensions during the growth phase; The environmental parameter sequence of multiple dimensions is reduced to one dimension to obtain the environmental parameter target vector.

5. The privacy information encryption and protection method for a service platform as described in claim 1, characterized in that, The process of obtaining the crop parameter sequence includes: Obtain various types of parameters for different growth stages of the crop, and quantify these parameters to form the crop parameter sequence.

6. A privacy information encryption protection system for a service platform, characterized in that, include: Memory and processor; The memory is connected to the processor; The memory is used to store program instructions; The processor is configured to implement the privacy information encryption protection method of the service platform according to any one of claims 1-5 when the program instructions are executed.

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