A cloud data security management method and device

Abstract

The application belongs to the technical field of data security, and discloses a cloud data security management method and device. The environment risk of the current cloud health record access is evaluated first, then the security parameters of the dynamic verification code are adaptively adjusted according to the environment risk evaluation value, and the dynamic verification code for granting the user the cloud health record access permission of the current session is generated according to the adjusted security parameters, so that the cloud personal health record data security can be effectively guaranteed, the user operation process is simplified, the hardware device cost is reduced, and the public service efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of data security technology, and more specifically, to a cloud-based data security management method and apparatus. Background Technology

[0002] In public service venues such as libraries, community centers, and citizen service stations, residents frequently need to use public computers to quickly handle various matters, including accessing personal cloud data, such as checking electronic health records or accessing online medical services. This health record data involves highly sensitive personal privacy, making data security paramount. Traditional account password authentication methods face serious security challenges in the open environment of public places. Passwords are easily spied on and maliciously recorded by criminals, and even users themselves are prone to forgetting their passwords, resulting in significant security risks and potential leaks. While multi-factor authentication technology can improve security to some extent, its operation process is relatively complex, incurring high learning and usage costs for elderly people unfamiliar with technology or temporary visitors, leading to a less user-friendly experience. Furthermore, frequent entry of account passwords or complex authentication operations contradicts the need for fast and convenient services in public service venues, impacting service efficiency. In addition, adding extra hardware for identity authentication in public service venues, such as fingerprint scanners or IC card readers, would undoubtedly significantly increase construction and maintenance costs, making it uneconomical and potentially creating new equipment management and maintenance problems, further burdening management.

[0003] Therefore, how to provide an efficient access control method that can significantly simplify user operation processes and effectively ensure the security of personal health record data in the cloud, based on making full use of existing public computer equipment and for specific application scenarios in public service venues, has become a key technical problem that urgently needs to be solved in the current public service field. It is directly related to the efficiency of public services and the level of user data security, and a more complete and efficient technical solution is urgently needed to fill the shortcomings of existing technologies.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide a cloud-based data security management method and device, which can effectively protect the security of personal health record data in the cloud, simplify user operation processes, reduce hardware costs, and improve the efficiency of public services.

[0006] Firstly, this application provides a cloud-based data security management method for securely managing the process of accessing cloud-based health records via a public computer. The method includes the following steps:

[0007] A1. In response to a user's request to access a cloud-based health record initiated via a public computer, collect risk-related information to calculate an environmental risk assessment value; the risk-related information includes network type, geographical location, device fingerprint, and access behavior information;

[0008] A2. Based on the environmental risk assessment value, adaptively adjust the security parameters of the dynamic verification code; the higher the environmental risk assessment value, the more stringent the security parameters; the security parameters include the verification code length, the verification code character set, the verification code validity period, and the number of retries for verification code errors.

[0009] A3. Generate a dynamic verification code based on the adjusted verification code length and character set, and send the generated dynamic verification code to the user's pre-bound mobile phone.

[0010] A4. Under the constraints of the adjusted validity period of the verification code and the number of retries for verification code errors, when a dynamic verification code is received from a user on a public computer, the correctness of the input dynamic verification code is verified based on the generated dynamic verification code.

[0011] A5. Once verification is successful, grant the user access to the cloud-based health record for this session.

[0012] This method adaptively adjusts the security parameters of the dynamic verification code based on environmental risk assessment values, and generates a dynamic verification code to grant users access to cloud-based health records for this session based on the adjusted security parameters. This effectively protects the security of cloud-based personal health record data, simplifies user operation processes, reduces hardware costs, and improves the efficiency of public services.

[0013] Preferably, step A1 includes:

[0014] A101. Obtain the device information of the public computer to calculate and generate a device fingerprint, and determine the device risk value based on the device fingerprint; the device information includes multiple of the following: operating system type, version number, browser type, list of installed plugins, screen resolution, CPU model, and hard disk serial number;

[0015] A102. Obtain the geographical location of the public computer to determine the location risk value;

[0016] A103. Obtain the network type of the public computer to determine the network risk value;

[0017] A104. Obtain the user's access behavior information to determine the behavior risk value; the access behavior information includes the time, frequency, and amount of data downloaded from the cloud health record.

[0018] A105. Calculate the environmental risk assessment value based on the equipment risk value, the location risk value, the network risk value, and the behavioral risk value.

[0019] Therefore, the system can comprehensively consider the device environment, geographical location, network environment, and user access behavior of public computers to achieve a comprehensive assessment of environmental risks. This provides accurate risk basis for subsequent adaptive adjustment of the security parameters of dynamic verification codes, thereby improving the security of public computers accessing cloud health records.

[0020] Preferably, step A101 includes:

[0021] Real-time acquisition of device information from the public computer, and calculation and generation of the current device fingerprint;

[0022] The SM3 cryptographic hash algorithm is used to perform a hash operation on the current device fingerprint to obtain the current device fingerprint hash value;

[0023] The current device fingerprint hash is compared with the reference device fingerprint hash to determine the device risk value; the reference device fingerprint hash is a device fingerprint hash value generated based on the original, tamper-proof device information of the public computer.

[0024] By following the steps above, it is possible to effectively identify whether information on public computer equipment has been tampered with, thereby more accurately assessing equipment risks and improving the effectiveness of subsequent security management.

[0025] Preferably, step A104 includes:

[0026] The system obtains the current time when the user accesses the cloud health record, and calculates the access time risk level based on the deviation between the current time and the user's reference access time; the reference access time is an access time reference value obtained by statistically analyzing the user's historical access records.

[0027] The access frequency of the user within a first preset time period is statistically analyzed, and the access frequency risk level is calculated based on the deviation between the access frequency within the first preset time period and the user's reference access frequency; the reference access frequency is the average access frequency obtained by statistically analyzing the user's historical access records.

[0028] Based on the user's data download volume and access frequency within a second preset time period, the download volume per access is calculated. Based on the deviation between the download volume per access and the user's reference download volume per access, the download volume risk level is calculated. The reference download volume per access is the average download volume per access obtained from the user's historical access records.

[0029] The behavioral risk value is calculated based on the access time risk level, the access frequency risk level, and the download volume risk level.

[0030] Preferably, step A2 includes:

[0031] A201. Calculate the safety coefficient of the current safety parameter, and denote it as the current safety coefficient; the current safety parameter is a preset safety parameter or a set of safety parameters randomly selected from the historical safety parameter set;

[0032] A202. Based on the environmental risk assessment value, query the preset safety parameter adjustment strategy table to determine the target safety coefficient corresponding to the environmental risk assessment value;

[0033] A203. If the target safety factor is higher than the current safety factor, then according to the first preset adjustment priority, each parameter in the current safety parameter is adjusted sequentially until the safety factor corresponding to the adjusted safety parameter reaches the target safety factor;

[0034] A204. If the target safety factor is less than the current safety factor, then according to the second preset adjustment priority, which is opposite to the first preset adjustment priority, the parameters in the current safety parameters are adjusted sequentially until the safety factor corresponding to the adjusted safety parameter reaches the target safety factor.

[0035] Preferably, step A2 further includes the step:

[0036] A205. Obtain the average speed at which the user inputs the dynamic verification code;

[0037] A206. Generate a duration correction coefficient based on the average speed to correct the adjusted validity period of the verification code.

[0038] Preferably, step A206 includes:

[0039] The verification code type is determined based on the adjusted character set of the verification code; the verification code type includes pure numeric verification code, pure alphanumeric verification code, alphanumeric mixed verification code, special symbol numeric mixed verification code, special symbol alphanumeric mixed verification code, and special symbol alphanumeric mixed verification code.

[0040] The type adjustment factor is determined based on the CAPTCHA type; the more complex the CAPTCHA type, the larger the type adjustment factor.

[0041] The duration correction coefficient is calculated based on the average speed and the type adjustment factor; wherein the duration correction coefficient is inversely proportional to the average speed and directly proportional to the type adjustment factor.

[0042] Preferably, step A3 includes:

[0043] A301. Based on the adjusted verification code length and character set, a dynamic verification code is generated using a random algorithm;

[0044] A302. Encrypt the generated dynamic verification code to obtain the encrypted dynamic verification code ciphertext;

[0045] A303. The encrypted dynamic verification code and the user's pre-bound mobile phone number are encapsulated into an SMS sending request. The SMS sending request is sent to the SMS center through the SMS gateway. The SMS center decrypts the encrypted dynamic verification code to obtain the original dynamic verification code and sends an SMS containing the dynamic verification code to the user's pre-bound mobile phone.

[0046] Preferably, step A302 includes:

[0047] Select the encryption algorithm type based on the adjusted CAPTCHA character set; when the CAPTCHA character set contains only numbers, select the XOR encryption algorithm; when the CAPTCHA character set contains letters but not special characters, select the AES encryption algorithm; when the CAPTCHA character set contains special characters, select the SM4 encryption algorithm.

[0048] Based on the selected encryption algorithm type, a corresponding encryption key is generated; if the XOR encryption algorithm is selected, a random number of the same length as the verification code is generated as the encryption key; if the AES or SM4 encryption algorithm is selected, a fixed-length encryption key that meets the algorithm requirements is generated.

[0049] The selected encryption algorithm and the encryption key are used to encrypt the dynamic verification code, resulting in encrypted dynamic verification code ciphertext.

[0050] Secondly, this application provides a cloud data security management device for security management of the process of accessing cloud health records via a public computer. The device includes:

[0051] The risk assessment module is used to respond to cloud-based health record access requests initiated by users through public computers, collect risk-related information, and calculate environmental risk assessment values; the risk-related information includes network type, geographical location, device fingerprint, and access behavior information;

[0052] The parameter adjustment module is used to adaptively adjust the security parameters of the dynamic verification code based on the environmental risk assessment value; the higher the environmental risk assessment value, the more stringent the security parameters; the security parameters include the verification code length, the verification code character set, the verification code validity period, and the number of retries for verification code errors;

[0053] The verification code generation and sending module is used to generate dynamic verification codes based on the adjusted verification code length and character set, and send the generated dynamic verification codes to the user's pre-bound mobile phone.

[0054] The verification code verification module is used to verify the correctness of the dynamic verification code entered by the user on a public computer when the dynamic verification code is received, under the constraints of the adjusted validity period of the verification code and the number of retries for verification code errors.

[0055] The access control module is used to grant users access to the cloud health profile for this session after successful verification.

[0056] Beneficial effects: The cloud data security management method and device provided in this application adaptively adjust the security parameters of the dynamic verification code by environmental risk assessment value, and generate a dynamic verification code for granting the user access to the cloud health record for this session based on the adjusted security parameters. This can effectively protect the security of cloud personal health record data, simplify user operation process, reduce hardware equipment costs, and improve public service efficiency. Attached Figure Description

[0057] Figure 1 A flowchart illustrating the cloud-based data security management method provided in this application embodiment.

[0058] Figure 2 This is a schematic diagram of the structure of a cloud-based data security management device provided in an embodiment of this application.

[0059] Labeling Explanation: 1. Risk Assessment Module; 2. Parameter Adjustment Module; 3. Verification Code Generation and Sending Module; 4. Verification Code Verification Module; 5. Access Control Module. Detailed Implementation

[0060] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0061] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0062] refer to Figure 1 This application proposes a cloud data security management method for the secure management of the process of accessing cloud-based health records via public computers. The method includes the following steps:

[0063] A1. In response to a user's request to access a cloud-based health record initiated through a public computer, collect risk-related information to calculate an environmental risk assessment value; the risk-related information includes network type, geographical location, device fingerprint, and access behavior information;

[0064] A2. Based on the environmental risk assessment value, the security parameters of the dynamic verification code are adaptively adjusted; the higher the environmental risk assessment value, the stricter the security parameters; the security parameters include the verification code length, the verification code character set, the verification code validity period, and the number of retries for verification code errors.

[0065] A3. Generate a dynamic verification code based on the adjusted verification code length and character set, and send the generated dynamic verification code to the user's pre-bound mobile phone.

[0066] A4. Under the constraints of the adjusted validity period of the verification code and the number of retries for verification code errors, when a dynamic verification code is received from a user on a public computer, the correctness of the input dynamic verification code is verified based on the generated dynamic verification code.

[0067] A5. Once verification is successful, grant the user access to the cloud-based health record for this session.

[0068] In step A1, risk-related information is collected. This information may include the type of network the public computer is connected to, such as whether it is public Wi-Fi or the organization's internal network; the geographical location of the public computer, which can be obtained through GPS or IP address positioning technology; device fingerprints, generated by extracting the hardware and software configuration information of the public computer to identify the uniqueness of the device; and user access behavior information, such as access time, frequency, and data download volume. The environmental risk assessment value can be calculated by weighting the collected risk-related information using a preset risk assessment model.

[0069] In step A2, the adaptive adjustment of security parameters is determined based on the magnitude of the environmental risk assessment value. A higher environmental risk assessment value indicates a higher security risk in the current access environment, requiring stricter adjustments to the dynamic verification code's security parameters; conversely, a lower value can be more lenient. Security parameter adjustments can be made using methods such as table lookup or function calculation. For example, a mapping table between environmental risk assessment values ​​and security parameters can be pre-defined, or a function can be designed to change the security parameters according to the environmental risk assessment value.

[0070] In step A3, the dynamic verification code is generated based on the adjusted verification code length and character set. For example, if the character set is adjusted to include special characters, the generated dynamic verification code will contain those special characters. After the dynamic verification code is generated, it is sent to the user's pre-bound mobile phone via an SMS gateway.

[0071] In step A4, when verifying the dynamic verification code entered by the user, the system checks whether the verification code was entered within the set valid time period and whether the number of times the user entered the wrong verification code exceeded the allowed number of error retries. Verification of the verification code's correctness involves comparing the user-entered verification code with the dynamic verification code generated in step A3.

[0072] In step A5, after the verification code is successfully verified, the system grants the user access to their cloud-based health record. This access permission is temporary and expires automatically when the user closes the browser or the session times out. This ensures that the user can securely access their cloud-based health record during the current session.

[0073] Specifically, after a user initiates a request to access a cloud-based health record via a public computer, the system first executes step A1, collecting network type, geographic location, device fingerprint, and access behavior information. For example, the network type can be identified as public Wi-Fi, the geographic location can be located at a city public library, the device fingerprint is generated by collecting operating system, browser, and hardware information, and the access behavior information can be recorded as the user accessing the site outside of working hours and attempting to download large amounts of data. Subsequently, the system calculates an environmental risk assessment value based on this information, assuming the assessment value is high-risk. Next, in step A2, based on the high-risk assessment value, the system adaptively adjusts the security parameters of the dynamic verification code to a strict level. For example, the verification code length is increased to 8 characters, the character set is adjusted to include a combination of numbers, letters, and special characters, the validity period of the verification code is shortened to 30 seconds, and the number of retries is limited to 2. Then, in step A3, the system generates an 8-digit dynamic verification code containing numbers, letters, and special characters based on the adjusted parameters and sends it to the user's mobile phone via SMS. After a user enters the received dynamic verification code on a public computer, in step A4, the system verifies the correctness of the entered verification code within a 30-second validity period and two retries. If the verification passes, in step A5, the system grants the user access to the cloud-based health profile for this session. Conversely, if the environmental risk assessment value is low, such as when the user accesses the system from a public computer intended for internal use, the system can adaptively reduce the security parameters of the dynamic verification code, such as shortening the verification code length, simplifying the character set, extending the validity period, and increasing the number of retries, to improve the ease of use for the user. This achieves the technical effect of dynamically adjusting security policies under different risk environments, balancing security and usability.

[0074] In some implementations, step A1 includes:

[0075] A101. Obtain device information from public computers to calculate and generate device fingerprints, and determine device risk values ​​based on device fingerprints; device information includes multiple items such as operating system type, version number, browser type, list of installed plugins, screen resolution, CPU model, and hard drive serial number;

[0076] A102. Obtain the geographic location of public computers to determine location risk values;

[0077] A103. Obtain the network type of the public computer to determine the network risk value;

[0078] A104. Obtain user access behavior information to determine behavioral risk values; access behavior information includes the time, frequency, and amount of data downloaded from accessing cloud-based health records;

[0079] A105. Calculate the environmental risk assessment value based on the equipment risk value, location risk value, network risk value, and behavioral risk value.

[0080] In step A101, device information can be obtained in various ways, such as through JavaScript in a browser or through client software at the operating system level. Device fingerprint generation can be achieved by combining multiple pieces of device information and performing hash operations, such as using hash algorithms like MD5, SHA256, or SM3. Determining the device risk value involves pre-setting risk levels (e.g., high, medium, and low) and corresponding device risk values, and assigning different risk levels to different device fingerprints. For example, if a high-risk plugin is detected installed on a public computer, the device risk value can be set to the high-risk level.

[0081] In step A102, obtaining the geographical location can be achieved using IP address positioning technology, GPS positioning technology, or Wi-Fi positioning technology. The location risk value can be determined based on the degree of anomaly in the geographical location. For example, if a user's geographical location changes drastically within a short period (e.g., the distance between the public computer used in this visit and the public computer used in the previous visit exceeds a preset distance threshold, and the time interval between the current time and the time of the last access to the cloud health record is less than a preset interval threshold, then it is determined that the user's geographical location has changed drastically within a short period), or if the geographical location is far from the user's permanent residence (which can be the user's pre-entered residential or work location), then the location risk value can be set to a higher level. The location risk value can be set by setting a threshold based on the degree of geographical location deviation or anomaly.

[0082] In step A103, obtaining the network type can be achieved by detecting the network environment of the public computer connection, such as determining whether the current connection is to a Wi-Fi network, mobile network, or wired network. The network risk value can be determined based on an assessment of the network type's security. For example, public Wi-Fi networks, due to their openness, may pose a risk of data leakage; therefore, the network risk value can be set to a higher level. For relatively secure network types such as home networks or corporate leased lines, the network risk value can be set to a lower level.

[0083] In step A104, the access behavior information includes the time, frequency, and data download volume of accessing the cloud-based health records. This access behavior information can be obtained through server-side log recording, client-side event tracking, etc. The determination of the behavior risk value can be based on the degree of abnormality in the user's access behavior. For example, if a user accesses the cloud-based health records during unusual time periods, or with a significantly higher-than-average access frequency, or downloads a large amount of data in a short period, the behavior risk value can be set to a higher level. The behavior risk value can be set by setting thresholds based on deviations in access time, access frequency, and download volume.

[0084] In step A105, equipment risk value, location risk value, network risk value, and behavioral risk value are comprehensively considered to calculate the environmental risk assessment value. The environmental risk assessment value can be calculated using methods such as weighted summation, fuzzy evaluation, or neural networks. In the weighted summation method, different weight coefficients can be set according to the importance of each risk factor; for example, the weight of equipment risk value is higher than that of location risk value and network risk value, and the weight of behavioral risk value is higher than that of equipment risk value. By comprehensively considering multiple risk factors, the environmental risks of users accessing cloud-based health records can be assessed more comprehensively and accurately.

[0085] Specifically, this application's solution, when a user accesses a cloud-based health record via a public computer, first comprehensively collects risk-related information from four dimensions—device, location, network, and behavior—through steps A101 to A104. Step A101 generates device fingerprints and determines device risk values ​​by acquiring various device information from the public computer, thereby identifying potential risks in the device environment. Steps A102 and A103 assess environmental risks from the dimensions of geographical location and network type, respectively, to determine whether the user is in an abnormal geographical location or an insecure network environment. Step A104 analyzes user access behavior characteristics such as access time, frequency, and data download volume to identify whether the user's access behavior is abnormal. Subsequently, step A105 integrates the risk values ​​from the above four dimensions to calculate the final environmental risk assessment value. This multi-dimensional and multi-angle risk assessment method can more refined and accurately identify the environmental risks of users accessing cloud-based health records, overcoming the problem of insufficient detail and comprehensiveness in environmental risk assessment in traditional technologies, and providing a more reliable basis for subsequent adaptive adjustment of the security parameters of dynamic CAPTCHAs. Compared to solutions that rely on a single or few factors for risk assessment, the proposed solution can more effectively identify potential security risks, thereby improving the security of accessing cloud-based health records in open environments such as public computers.

[0086] Through the above technical solution, this application can more accurately and comprehensively assess the environmental risks when users access cloud-based health records through public computers, thereby providing a more accurate basis for subsequently adaptively adjusting the security parameters of dynamic verification codes, thus improving the security of accessing cloud-based health records in public places such as public computers, and effectively reducing the risk of health record data leakage.

[0087] In some preferred embodiments, step A101 includes:

[0088] Real-time acquisition of device information from public computers, and calculation and generation of the current device fingerprint;

[0089] The SM3 cryptographic hash algorithm is used to perform a hash operation on the current device fingerprint to obtain the current device fingerprint hash value;

[0090] The current device fingerprint hash is compared with the reference device fingerprint hash to determine the device risk value; the reference device fingerprint hash is a device fingerprint hash value generated based on the original, tamper-proof device information of a public computer.

[0091] Real-time acquisition of device information means that the system re-collects the device information of the public computer each time a user attempts to access the cloud-based health record through the public computer, ensuring the timeliness of the device fingerprint. Device information can include data reflecting the device's status, such as operating system type, version number, browser type, and a list of installed plugins. Calculating and generating the current device fingerprint involves combining multiple pieces of collected device information and using a preset algorithm to calculate a fingerprint representing the current device's characteristics. The SM3 cryptographic hash algorithm is used to hash the current device fingerprint, converting it into a fixed-length hash value. The one-way hashing property of the SM3 algorithm ensures that the original device fingerprint cannot be deduced from the hash value, thus guaranteeing the security of the device fingerprint. The current device fingerprint hash is compared with a reference device fingerprint hash. The reference device fingerprint hash is pre-calculated and stored, representing the hash value of the public computer's device fingerprint in its original, tamper-free state. By comparing these two hash values, it can be determined whether the public computer's device information has been tampered with. The device risk value is determined based on the hash value comparison results. For example, if the current device fingerprint hash is consistent with the reference device fingerprint hash, the device risk value can be set to a lower level, indicating that the risk of the device information not being tampered with is low; conversely, if the two are inconsistent, the device risk value can be set to a higher level, indicating that the risk of the device information being tampered with is high.

[0092] Specifically, when a user initiates a request to access a cloud-based health record on a public computer, the security management device first collects the computer's device information in real time, such as the operating system version, browser type, and list of installed plugins. Then, the device combines this device information and calculates a fingerprint for the current device using a fingerprint generation algorithm. To ensure the security of the device fingerprint, the device uses the SM3 cryptographic hash algorithm to hash the current device fingerprint, obtaining a hash value. The device pre-stores a reference device fingerprint hash value, which is calculated using the same method when the public computer is initially configured and in a secure state. The device compares the current device fingerprint hash value with the reference device fingerprint hash value. If they match, it indicates that the public computer's device information has not been tampered with, and the device risk value is low. If they do not match, it indicates that the public computer's device information may have been tampered with, and the device risk value is high. The device risk value will be used as part of the environmental risk assessment value for adaptive adjustment of subsequent dynamic CAPTCHA security parameters. Through these steps, it is possible to effectively identify whether the public computer's device information has been tampered with, thereby more accurately assessing device risk and improving the effectiveness of subsequent security management.

[0093] By employing the aforementioned technical methods, the problem of easily tampered information on public computer equipment, leading to inaccurate equipment risk assessments, can be effectively solved. Utilizing the SM3 cryptographic hash algorithm to perform hash operations on device fingerprints ensures their security. By comparing the hash values ​​of the current and reference device fingerprints, it is possible to accurately determine whether device information has been tampered with, thus providing a reliable basis for equipment risk assessment for the effective implementation of subsequent security management measures.

[0094] In some preferred embodiments, step A104 includes:

[0095] Obtain the current time when the user accesses the cloud health record, and calculate the access time risk level based on the deviation between the current time and the user's reference access time; the reference access time is an access time reference value obtained from the user's historical access records.

[0096] The frequency of user access within a first preset time period is statistically analyzed. The access frequency risk level is calculated based on the deviation between the access frequency within the first preset time period and the user's reference access frequency. The reference access frequency is the average access frequency obtained from the user's historical access records.

[0097] The download volume per access is calculated based on the user's data download volume and access frequency within the second preset time period. The download volume risk level is calculated based on the deviation between the download volume per access and the user's reference download volume per access. The reference download volume per access is the average download volume per access obtained from the user's historical access records.

[0098] Calculate the behavioral risk value based on the risk level of access time, access frequency, and download volume.

[0099] This technical solution focuses on calculating behavioral risk values, specifically considering three dimensions: access time, access frequency, and data download volume. Access time risk is quantified by comparing the deviation between the current access time and a reference access time (where access time refers to a time within a 24-hour period, such as 2 PM). For example, if a user typically accesses the site during the day, and the current access occurs at night, the access time risk increases. The reference access time can be determined from the average access time of the user's historical access records or a frequently used access time range. The access time risk value can be calculated using a pre-defined calculation model or obtained by looking up a table based on the deviation between the current time and the user's reference access time.

[0100] The access frequency risk level is assessed by examining the deviation between the access frequency within a first preset time period and a reference access frequency. For example, if a user accesses the site once a week on average, frequent access within a short period increases the access frequency risk level. The reference access frequency can be calculated from the average access frequency of the user's historical access records, and the first preset time period can be set to days, weeks, or months, etc. The access frequency risk level can be calculated using a preset calculation model or obtained by looking up a table based on the deviation between the access frequency within the first preset time period and the user's reference access frequency.

[0101] The download risk level is measured by analyzing the difference between the download volume of a single visit within a second preset time period and a reference download volume for a single visit. For example, if a user typically downloads only a small amount of data, but attempts to download a large amount of data in this visit, the download risk level increases. The reference download volume can be obtained by statistically analyzing the average download volume of a single visit from the user's historical access records. The second preset time period can be set according to actual needs. The download risk level can be calculated using a preset calculation model or obtained by looking up a table based on the deviation between the single visit download volume and the user's reference download volume.

[0102] Ultimately, the risk levels of access time, access frequency, and download volume are combined to calculate the behavioral risk value, thereby achieving a comprehensive risk assessment of user access behavior.

[0103] Specifically, the behavioral risk value calculation method aims to more precisely assess the security risks of user access behavior. The system first learns and records users' historical access habits, establishing benchmarks such as reference access time, reference access frequency, and reference single download volume. When a user initiates a cloud-based health record access request on a public computer, the system obtains the user's current access time, access frequency, and data download volume in real time, comparing this information with historical benchmarks. If the current access behavior deviates significantly from the user's historical habits—for example, abnormal access time, excessively high access frequency, or an abnormal increase in data download volume—the system will determine that the access behavior is of high risk and assign a higher behavioral risk value. This method can effectively identify user account theft or abnormal access behavior because the behavior patterns of thieves or malicious visitors usually differ significantly from users' historical habits. By comprehensively considering the risk levels of access time, access frequency, and download volume, the system can more comprehensively and accurately assess behavioral risks, providing a more reliable basis for subsequent environmental risk assessments and security parameter adjustments.

[0104] In some specific implementations, a weighted average method can be used to calculate the behavioral risk value. For example, different weighting coefficients can be pre-set for access time risk, access frequency risk, and download volume risk. The weighting coefficients can be determined based on the degree of influence of each risk factor on security. Then, each risk level is multiplied by its corresponding weighting coefficient, and the products are summed to obtain the final behavioral risk value. As a preferred implementation, the reference access time can be set to the most frequently occurring time period in the user's historical access records, the reference access frequency can be set to the average access frequency in the user's historical access records, and the reference single download volume can be set to the average single download volume in the user's historical access records. The first and second preset time periods can be flexibly adjusted according to the actual application scenario. For example, for users with low access frequency, the time period can be set to be longer to more accurately count the access frequency.

[0105] In some implementations, step A2 includes:

[0106] A201. Calculate the safety factor of the current safety parameter, and denot it as the current safety factor; the current safety parameter is a preset safety parameter or a set of safety parameters randomly selected from the historical safety parameter set;

[0107] A202. Based on the environmental risk assessment value, consult the preset safety parameter adjustment strategy table to determine the target safety coefficient corresponding to the environmental risk assessment value;

[0108] A203. If the target safety factor is higher than the current safety factor, then according to the first preset adjustment priority, adjust each parameter in the current safety parameters in turn until the safety factor corresponding to the adjusted safety parameter reaches the target safety factor.

[0109] A204. If the target safety factor is less than the current safety factor, then according to the second preset adjustment priority, which is opposite to the first preset adjustment priority, the parameters in the current safety parameters are adjusted in sequence until the safety factor corresponding to the adjusted safety parameter reaches the target safety factor.

[0110] In step A201, the current safety factor is calculated. The safety factor is a quantitative indicator of the safety level. The default safety parameter can be pre-set or randomly selected from a historical safety parameter set, providing a benchmark for subsequent adjustments. The historical safety parameter set can be a collection of all safety parameters generated within a preset time window, or a collection of N recently generated safety parameters, where N is a preset positive integer. The historical safety parameter set can also record the historical environmental risk assessment values ​​corresponding to each set of safety parameters. Several sets of safety parameters whose historical environmental risk assessment values ​​are closest to the current environmental risk assessment value can be extracted from the historical safety parameter set as candidate safety parameters. Then, a set of safety parameters is randomly selected from the candidate safety parameters as the current safety parameter. This reduces the adjustment workload in subsequent steps A203 and A204, allowing the safety parameter to reach the target safety factor more quickly.

[0111] The security factor can be calculated using a pre-defined security factor calculation model. This model comprehensively considers factors such as CAPTCHA length, CAPTCHA character set complexity, validity period, and number of error retries, assigning different weights to each to calculate a comprehensive security factor value. The CAPTCHA character set complexity can be obtained by weighting the character types and their corresponding number of characters in the CAPTCHA character set. For example, if the CAPTCHA character set includes A numbers, B letters, and C special symbols, then the CAPTCHA character set complexity is... , where w1, w2, and w3 are the weight values ​​for numbers, letters, and special symbols, respectively.

[0112] In step A202, a preset safety parameter adjustment strategy table is introduced. The system queries this table based on the environmental risk assessment value, and the target safety factor is determined, matching the target safety factor with the current risk level. The strategy table establishes a mapping relationship between risk level and safety factor, enabling the adjustment of safety parameters to be based on the risk assessment results.

[0113] Steps A203 and A204 describe specific adjustment strategies. When the target security coefficient is higher than the current value, it indicates an increased risk. The system adjusts the security parameters step by step according to a first preset adjustment priority (e.g., the first preset adjustment priority is: CAPTCHA length > CAPTCHA character set > CAPTCHA validity period > CAPTCHA error retries, i.e., first adjust the CAPTCHA length, then the CAPTCHA character set, then the CAPTCHA validity period, and finally the CAPTCHA error retries). This adjustment process ensures that the security enhancement is hierarchical and targeted. Conversely, when the target security coefficient is lower than the current value, it indicates a decreased risk. The system then lowers the security parameters according to a second preset adjustment priority (e.g., the first preset adjustment priority is: CAPTCHA length < CAPTCHA character set < CAPTCHA validity period < CAPTCHA error retries, i.e., first adjust the CAPTCHA error retries, then the CAPTCHA validity period, then the CAPTCHA character set, and finally the CAPTCHA length). This optimizes the user experience while maintaining security. Therefore, by introducing a security factor, a policy table, and a priority adjustment mechanism, a structured and refined dynamic CAPTCHA security parameter adaptive adjustment scheme is provided. This scheme can not only flexibly adjust the security strength according to environmental risk assessment values, but also take into account efficiency and user experience during the security strength adjustment process.

[0114] Specifically, this solution provides an effective mechanism for adaptively adjusting the security parameters of dynamic CAPTCHAs through steps A201 to A204. First, step A201 calculates the security coefficient of the current security parameters, providing a quantitative benchmark for adjustment. The introduction of the security coefficient makes security level assessment and comparison possible, laying the foundation for subsequent adaptive adjustment. Second, step A202 introduces a preset security parameter adjustment strategy table, which establishes a mapping relationship between environmental risk assessment values ​​and target security coefficients. By querying this strategy table, the system can quickly determine a suitable target security coefficient based on the current environmental risk assessment value, ensuring that the adjustment of security parameters matches the risk level. Then, steps A203 and A204 propose a specific implementation method based on priority adjustment. When the target security coefficient is higher than the current security coefficient, the system adjusts according to a first preset priority; for example, it can prioritize increasing the CAPTCHA length, then increase the character set complexity, gradually enhancing the security strength. This hierarchical adjustment method avoids excessive adjustment of security parameters, minimizing the impact on user experience while meeting security requirements. Conversely, when the risk decreases, the security parameters are reduced according to the opposite priority, achieving a balance between security strength and user experience. Therefore, this solution can adaptively adjust the security parameters of the dynamic CAPTCHA based on the environmental risk assessment value, providing a refined, effective, and user-friendly security parameter adjustment mechanism that overcomes the shortcomings of the background technology.

[0115] Furthermore, step A2 may also include the following steps:

[0116] A205. Obtain the average speed at which users input dynamic verification codes;

[0117] A206. Generate a duration correction coefficient based on the average speed to correct the validity period of the CAPTCHA.

[0118] In step A205, the average speed of the user's input of the dynamic verification code can be obtained by the system recording the total duration and total number of times the user inputs the dynamic verification code, and then calculating the average speed by dividing the total duration by the total number of times. Alternatively, the average speed can be obtained through a weighted average, assigning higher weights to recorded data points that are closer to the current input speed. This allows the average speed to more accurately reflect the user's recent input habits.

[0119] In step A206, the generation of the duration correction coefficient can take into account the correlation between the user's average speed and the CAPTCHA type. The more complex the CAPTCHA type, the longer the user's input time typically is. Therefore, a type adjustment factor can be introduced; the higher the complexity of the CAPTCHA type, the larger the value of the type adjustment factor. The duration correction coefficient can be designed to be inversely proportional to the average speed and directly proportional to the type adjustment factor. For example, the duration correction coefficient can be calculated as the type adjustment factor divided by the average speed. Through the duration correction coefficient, the adjusted effective duration of the CAPTCHA can better match the user's actual input speed and the complexity of the CAPTCHA itself.

[0120] Specifically, in the adaptive adjustment of dynamic CAPTCHA security parameters, in addition to considering the environmental risk assessment value, the average speed at which users input the dynamic CAPTCHA is also taken into account. The system first obtains the average speed at which users input the dynamic CAPTCHA, reflecting their input habits and speed capabilities. Then, based on this average speed, a duration correction coefficient is generated. This coefficient is subsequently used to adjust the CAPTCHA validity duration, which has already been adjusted based on the environmental risk assessment value. Thus, the adjustment of the CAPTCHA validity duration no longer relies solely on the environmental risk assessment value but also incorporates the user's actual input speed, achieving a more refined dynamic adjustment. This adjustment mechanism makes the setting of the CAPTCHA validity duration more reasonable and user-friendly. For users with slower input speeds, the system can provide more sufficient input time, avoiding verification failures due to insufficient time, thereby improving user-friendliness. Simultaneously, for users with fast input speeds, the system can avoid setting excessively long CAPTCHA validity durations, reducing potential security risks.

[0121] In some specific implementations, after a user initiates a cloud-based health record access request on a public computer, the risk assessment module first collects risk-related information such as network type, geographical location, device fingerprint, and access behavior information, and calculates an environmental risk assessment value. Based on the environmental risk assessment value, the parameter adjustment module queries a preset security parameter adjustment strategy table to initially determine the security parameters of the dynamic verification code, including the verification code's validity period. Before sending the verification code, the module further obtains the user's historical average speed for dynamic verification code input, for example, by dividing the total duration of the user's most recent ten verification code inputs by ten. Then, a type adjustment factor is determined based on the character set type of the verification code; for example, the type adjustment factor for a purely numeric verification code is set to 1, and the type adjustment factor for a mixed alphanumeric verification code is set to 2, etc. The duration correction coefficient is calculated by dividing the type adjustment factor by the average speed, and then multiplied by the initially determined verification code validity period (i.e., the adjusted verification code validity period obtained in step A203 or step A204). Assuming the initial validity period of the verification code is determined to be 60 seconds, and the calculated duration correction factor is 0.8, then the final adjusted validity period of the verification code will be 60 seconds. 0.8 = 48 seconds. The adjusted verification code validity period is used in subsequent verification steps to ensure that users complete the verification code input within the adjusted validity period. Therefore, the verification code validity period can be dynamically adjusted according to the user's input speed, achieving a security control that better matches the user's input efficiency and improving the user experience.

[0122] In some possible implementations, step A206 includes:

[0123] The verification code type is determined based on the adjusted character set; the verification code types include pure numeric verification code, pure alphanumeric verification code, alphanumeric mixed verification code, special symbol numeric mixed verification code, special symbol alphanumeric mixed verification code, and special symbol alphanumeric mixed verification code.

[0124] The type adjustment factor is determined based on the type of the verification code; the more complex the verification code type, the larger the type adjustment factor.

[0125] The duration correction factor is calculated based on the average speed and the type adjustment factor; the duration correction factor is inversely proportional to the average speed and directly proportional to the type adjustment factor.

[0126] The CAPTCHA type is identified based on the adjusted CAPTCHA character set. For example, if the character set contains only numbers, the CAPTCHA type is determined to be a numeric CAPTCHA.

[0127] The type adjustment factor is set according to the complexity of the CAPTCHA type. More complex CAPTCHA types correspond to larger type adjustment factors. Sorted from largest to smallest complexity, the types are: pure numeric CAPTCHA, pure alphanumeric CAPTCHA, alphanumeric mixed CAPTCHA, special symbol numeric mixed CAPTCHA, special symbol alphanumeric mixed CAPTCHA, and special symbol alphanumeric mixed CAPTCHA. For example, the type adjustment factor for a pure numeric CAPTCHA can be set to 1.0, while the type adjustment factor for a special symbol alphanumeric mixed CAPTCHA can be set to 1.5.

[0128] The duration correction factor is calculated based on the average speed and the type adjustment factor. The duration correction factor is inversely proportional to the average speed; for example, the faster the average speed, the smaller the duration correction factor. The duration correction factor is directly proportional to the type adjustment factor; for example, the larger the type adjustment factor, the larger the duration correction factor. Therefore, the duration correction factor can be expressed as: Duration Correction Factor = Type Adjustment Factor / Average Speed.

[0129] Specifically, in the process of adjusting the validity period of the verification code, firstly, the system analyzes the character set of the verification code determined after adjustment in step A2 to determine the type of the current dynamic verification code. Verification code types are divided into multiple levels, including but not limited to pure numeric, pure alphanumeric, alphanumeric, special symbol numeric, special symbol alphanumeric, and special symbol alphanumeric verification codes. Each verification code type is pre-assigned a type adjustment factor, which quantifies the input complexity of that type of verification code. For example, a pure numeric verification code, due to its relatively simple input, is assigned a lower type adjustment factor, such as 1.0. For alphanumeric verification codes containing special symbols, due to their more complex and time-consuming input, a higher type adjustment factor is assigned, such as 1.5. Then, the system combines the average input speed of the user's dynamic verification code obtained in step A204 with the type adjustment factor corresponding to the current verification code type to calculate the duration correction coefficient. The calculation method is to divide the type adjustment factor by the average speed to obtain the duration correction coefficient. The duration correction coefficient is then used to correct the validity period of the verification code after adjustment in step A2 to generate the final validity period of the verification code. In this way, the adjustment of the validity period of the verification code not only takes into account the average input speed of users, but also takes into account the complexity of the verification code itself, making the setting of the validity period of the verification code more reasonable.

[0130] In some implementations, step A3 includes:

[0131] A301. Based on the adjusted verification code length and character set, a dynamic verification code is generated using a random algorithm;

[0132] A302. Encrypt the generated dynamic verification code to obtain the encrypted dynamic verification code ciphertext;

[0133] A303. Encapsulate the encrypted dynamic verification code and the user's pre-bound mobile phone number into an SMS sending request, send the SMS sending request to the SMS center through the SMS gateway, and have the SMS center decrypt the encrypted dynamic verification code to obtain the original dynamic verification code, and send the SMS containing the dynamic verification code to the user's pre-bound mobile phone.

[0134] In step A301, the dynamic verification code is generated based on the adjusted verification code length and character set from the previous steps. Specifically, if the adjusted verification code length is 6 digits and the character set consists of numbers and letters, the generated dynamic verification code will be a 6-digit string composed of random combinations of numbers and letters. A mature pseudo-random number generator can be used for the randomization algorithm; for example, Java's `Random` class or Python's `random` module can be used to implement the random generation of the dynamic verification code.

[0135] Step A302 involves encrypting the dynamic verification code generated in step A301. The choice of encryption algorithm requires a trade-off between actual security and performance requirements. For example, symmetric encryption algorithms such as AES or SM4 can be used, as well as lightweight XOR encryption algorithms. Key management is also crucial; keys must be stored and transmitted securely. For instance, a key management system can be used to centrally manage encryption keys.

[0136] Step A303 describes the process of sending the encrypted dynamic verification code. The encrypted dynamic verification code and the user's mobile phone number are encapsulated into an SMS sending request and sent to the SMS center through the SMS gateway. Importantly, after receiving the SMS sending request, the SMS center needs to perform a decryption operation to restore the encrypted dynamic verification code to the original dynamic verification code before sending the SMS containing the original dynamic verification code to the user's mobile phone. This encryption-then-decryption design ensures that the dynamic verification code exists in encrypted form during the SMS transmission link, so even if the SMS is illegally intercepted, the original dynamic verification code cannot be directly obtained.

[0137] Specifically, addressing security concerns during the dynamic verification code transmission process, this solution effectively mitigates the potential security risk of verification code leakage during transmission by encrypting the code before transmission. In scenarios where users access cloud-based health records using public computers in public service locations, after a user initiates an access request, the system adaptively adjusts the security parameters of the dynamic verification code, including its length and character set, based on the public computer's environmental risk assessment. These adjusted parameters are then used to generate the dynamic verification code. To prevent interception during SMS transmission, the generated dynamic verification code is encrypted before sending the SMS. The encrypted ciphertext, along with the user's mobile phone number, is encapsulated into an SMS sending request and sent to the SMS center via the SMS gateway. Upon receiving the request, the SMS center first decrypts the ciphertext to recover the original dynamic verification code, and then sends an SMS containing the original dynamic verification code to the user's mobile phone. The user then enters the received dynamic verification code on the public computer, and the system verifies it. Only after successful verification can the user gain access to the cloud-based health records. Therefore, even if a text message is illegally intercepted by a third party during transmission, the attacker cannot directly obtain valid verification information because what is intercepted is encrypted ciphertext rather than the original dynamic verification code. This ensures the security of the dynamic verification code during transmission and improves the overall security protection capability of the system.

[0138] Preferably, step A302 may include:

[0139] Select the encryption algorithm type based on the adjusted CAPTCHA character set; when the CAPTCHA character set contains only numbers, select the XOR encryption algorithm; when the CAPTCHA character set contains letters but not special characters, select the AES encryption algorithm; when the CAPTCHA character set contains special characters, select the SM4 encryption algorithm.

[0140] Based on the selected encryption algorithm type, a corresponding encryption key is generated; if the XOR encryption algorithm is selected, a random number of the same length as the verification code is generated as the encryption key; if the AES or SM4 encryption algorithm is selected, a fixed-length encryption key that meets the algorithm requirements is generated.

[0141] The selected encryption algorithm and encryption key are used to encrypt the dynamic verification code, resulting in the encrypted dynamic verification code ciphertext.

[0142] Regarding the selection of encryption algorithm type, the system first analyzes the adjusted CAPTCHA character set. If the character set only contains numbers, the faster XOR encryption algorithm is selected to ensure encryption efficiency. When the character set expands to include letters but not special characters, the security requirements increase, and the system upgrades to the more secure AES encryption algorithm. If the character set further includes special characters, the more secure SM4 encryption algorithm is selected to meet the requirements of higher security levels. The generation of the encryption key is determined based on the selected encryption algorithm type. Specifically, if the XOR encryption algorithm is selected, a random number of the same length as the CAPTCHA is generated as the encryption key, ensuring that the key and plaintext lengths are consistent and meet the requirements of XOR encryption. If the AES or SM4 encryption algorithm is selected, a fixed-length encryption key conforming to the requirements of the corresponding algorithm is generated, such as the commonly used 128-bit, 192-bit, or 256-bit keys for the AES algorithm. After selecting and generating the encryption algorithm and key, the system will use the selected encryption algorithm and the generated encryption key to encrypt the dynamic verification code, ultimately obtaining the encrypted dynamic verification code ciphertext, which will be used for subsequent SMS sending and verification processes to ensure the security of the dynamic verification code during transmission.

[0143] Specifically, this solution aims to address the issue that fixed encryption algorithms may not simultaneously achieve both efficiency and security during dynamic CAPTCHA encryption. By analyzing the dynamic CAPTCHA character set and adaptively selecting a suitable encryption algorithm, a dynamic match between the encryption algorithm and the complexity of the CAPTCHA character set is achieved. When the CAPTCHA character set contains only numbers, the computationally fast XOR encryption algorithm is used to ensure encryption efficiency. When the character set expands to include letters but no special characters, the more secure AES encryption algorithm is upgraded. When the character set further includes special characters, the even more secure SM4 encryption algorithm is selected. Thus, a balance between efficiency and security is achieved under different security requirements, more effectively ensuring the security of dynamic CAPTCHAs during transmission. The XOR encryption algorithm, with its high efficiency, is suitable for pure numeric CAPTCHA scenarios with relatively low security requirements, while advanced encryption algorithms such as AES and SM4 provide stronger security guarantees for CAPTCHAs containing more complex character sets. This differentiated approach allows the encryption process to meet the needs of different security levels while avoiding efficiency reduction due to over-encryption, thus improving the overall performance and security of the system.

[0144] refer to Figure 2 This application provides a cloud data security management device for security management of the process of accessing cloud health records via a public computer. The device includes:

[0145] Risk assessment module 1 is used to respond to cloud health record access requests initiated by users through public computers, collect risk-related information, and calculate environmental risk assessment values. Risk-related information includes network type, geographical location, device fingerprint, and access behavior information (refer to step A1 above for details).

[0146] Parameter adjustment module 2 is used to adaptively adjust the security parameters of the dynamic verification code based on the environmental risk assessment value; the higher the environmental risk assessment value, the more stringent the security parameters; the security parameters include the verification code length, the verification code character set, the verification code validity period, and the number of retries for verification code errors (refer to step A2 above for details).

[0147] The verification code generation and sending module 3 is used to generate a dynamic verification code based on the adjusted verification code length and verification code character set, and send the generated dynamic verification code to the user's pre-bound mobile phone (for details, refer to step A3 above).

[0148] The verification code verification module 4 is used to verify the correctness of the dynamic verification code entered by the user on a public computer when the dynamic verification code is received, under the constraints of the adjusted validity period of the verification code and the number of retries for verification code errors (for details, please refer to step A4 above).

[0149] Access control module 5 is used to grant users access to the cloud health profile for this session after successful verification (refer to step A5 above for details).

[0150] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0151] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0152] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0153] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0154] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A cloud data security management method for security management of a process of accessing a cloud health record through a public computer, characterized by, The method comprises the following steps: A1. In response to a user initiating a cloud health record access request through a public computer, collect risk-related information for calculating an environmental risk assessment value; the risk-related information includes network type, geographic location, device fingerprint, and access behavior information; A2. According to the environmental risk assessment value, adaptively adjust the security parameters of the dynamic verification code; the greater the environmental risk assessment value, the stricter the security parameters; the security parameters include the length of the verification code, the character set of the verification code, the validity period of the verification code, and the number of error retries of the verification code; A3. Generate a dynamic verification code according to the adjusted length of the verification code and the character set of the verification code, and send the generated dynamic verification code to the user's pre-bound mobile phone; A4. Under the constraints of the adjusted validity period of the verification code and the number of error retries of the verification code, when receiving the dynamic verification code input by the user on the public computer, verify the correctness of the input dynamic verification code according to the generated dynamic verification code; A5. When the verification is passed, grant the user access to the cloud health record for this session; Step A2 comprises: A201. Calculate the security coefficient of the current security parameter, denoted as the current security coefficient; the current security parameter is a preset security parameter or a set of security parameters randomly selected from a historical security parameter set; A202. According to the environmental risk assessment value, query the preset security parameter adjustment strategy table to determine the target security coefficient corresponding to the environmental risk assessment value; A203. If the target security coefficient is higher than the current security coefficient, adjust each parameter in the current security parameter in turn according to a first preset adjustment priority until the security coefficient corresponding to the adjusted security parameter reaches the target security coefficient; A204. If the target security coefficient is less than the current security coefficient, adjust each parameter in the current security parameter in turn according to a second preset adjustment priority opposite to the first preset adjustment priority until the security coefficient corresponding to the adjusted security parameter reaches the target security coefficient; Step A2 further comprises the following steps: A205. Obtain the average speed of the user inputting the dynamic verification code; A206. Generate a time correction coefficient according to the average speed for correcting the adjusted validity period of the verification code; Step A206 comprises: According to the adjusted character set of the verification code, determine the type of the verification code; the verification code type includes pure digital verification code, pure letter verification code, letter and number mixed verification code, special symbol and number mixed verification code, special symbol and letter mixed verification code, and special symbol, letter and number mixed verification code; Determine a type adjustment factor according to the type of the verification code; the more complex the verification code type, the larger the type adjustment factor; According to the average speed and the type adjustment factor, calculate the time correction coefficient; wherein the time correction coefficient is inversely proportional to the average speed and proportional to the type adjustment factor.

2. The cloud data security management method of claim 1, wherein, Step A1 comprises: A101. Obtain the device information of the public computer to calculate the device fingerprint and determine the device risk value according to the device fingerprint; the device information includes multiple of operating system type, version number, browser type, installed plug-in list, screen resolution, CPU model and hard disk serial number; A102. Obtain the geographic location of the public computer to determine the location risk value; A103. Obtain the network type of the public computer to determine the network risk value; A104. Obtain the access behavior information of the user to determine the behavior risk value; the access behavior information includes access time, access frequency and data download volume of the cloud health record; A105. Calculate the environment risk assessment value according to the device risk value, the location risk value, the network risk value and the behavior risk value.

3. The cloud data security management method of claim 2, wherein, Step A101 includes: Real-time acquisition of device information of the public computer to calculate the current device fingerprint; Hash operation on the current device fingerprint by using SM3 cryptographic hash algorithm to obtain the current device fingerprint hash value; Compare the current device fingerprint hash with the reference device fingerprint hash to determine the device risk value; the reference device fingerprint hash is the device fingerprint hash value generated according to the original tamper-free device information of the public computer.

4. The cloud data security management method of claim 2, wherein, Step A104 includes: Obtain the current time of the user accessing the cloud health record, and calculate the access time risk degree according to the deviation of the current time from the reference access time of the user; the reference access time is the access time reference value obtained according to the historical access records of the user; Statistical the access frequency of the user within a first preset time period, and calculate the access frequency risk degree according to the deviation of the access frequency within the first preset time period from the reference access frequency of the user; the reference access frequency is the average access frequency obtained according to the historical access records of the user; Calculate the single access download volume according to the data download volume and the access times of the user within a second preset time period, and calculate the download volume risk degree according to the deviation of the single access download volume from the reference single access download volume of the user; the reference single download volume is the average single access download volume obtained according to the historical access records of the user; Calculate the behavior risk value according to the access time risk degree, the access frequency risk degree and the download volume risk degree.

5. The cloud data security management method of claim 1, wherein, Step A3 includes: A301. Generate a dynamic verification code by using a random algorithm according to the adjusted verification code length and verification code character set; A302. Encrypt the generated dynamic verification code to obtain the encrypted dynamic verification code ciphertext; A303. Package the dynamic verification code ciphertext and the user's pre-bound mobile phone number into a short message sending request, send the short message sending request to the short message center through the short message gateway, make the short message center decrypt the dynamic verification code ciphertext to obtain the original dynamic verification code, and send the short message containing the dynamic verification code to the user's pre-bound mobile phone.

6. The cloud data security management method of claim 5, wherein, Step A302 includes: According to the adjusted verification code character set, a type of encryption algorithm is selected; when the verification code character set only contains numbers, an XOR encryption algorithm is selected, when the verification code character set contains letters but does not contain special characters, an AES encryption algorithm is selected, and when the verification code character set contains special characters, an SM4 encryption algorithm is selected; According to the selected type of encryption algorithm, a corresponding encryption key is generated; if the XOR encryption algorithm is selected, a random number equal in length to the verification code is generated as the encryption key, and if the AES or SM4 encryption algorithm is selected, a fixed-length encryption key meeting the algorithm requirements is generated; The selected encryption algorithm and the encryption key are used to encrypt the dynamic verification code, to obtain the encrypted dynamic verification code ciphertext.

7. A cloud data security management device for security management of a process of accessing a cloud health record through a public computer, characterized by, The device comprises: A risk assessment module configured to collect risk-related information in response to a user initiating a cloud health record access request through a public computer, and to calculate an environmental risk assessment value based on the risk-related information; the risk-related information includes network type, geographic location, device fingerprint, and access behavior information; A parameter adjustment module configured to adjust security parameters of a dynamic verification code based on the environmental risk assessment value; the greater the environmental risk assessment value, the more stringent the security parameters; the security parameters include verification code length, verification code character set, verification code validity period, and verification code error retry count; A verification code generation and sending module configured to generate a dynamic verification code based on the adjusted verification code length and verification code character set, and to send the generated dynamic verification code to a mobile phone previously bound to the user; A verification code verification module configured to verify the correctness of the dynamic verification code input by the user on the public computer based on the generated dynamic verification code, within the constraints of the adjusted verification code validity period and verification code error retry count; A permission control module configured to grant the user access to the cloud health record for the current session after verification; The parameter adjustment module performs the following when adjusting the security parameters of the dynamic verification code based on the environmental risk assessment value: A201. Calculate a security coefficient of a current security parameter, denoted as a current security coefficient; the current security parameter is a preset security parameter or a set of security parameters randomly selected from a historical security parameter set; A202. Query a preset security parameter adjustment strategy table based on the environmental risk assessment value to determine a target security coefficient corresponding to the environmental risk assessment value; A203. If the target security coefficient is higher than the current security coefficient, adjust each parameter in the current security parameter in turn according to a first preset adjustment priority until the security coefficient of the adjusted security parameter reaches the target security coefficient; A204. If the target security coefficient is lower than the current security coefficient, adjust each parameter in the current security parameter in turn according to a second preset adjustment priority opposite to the first preset adjustment priority until the security coefficient of the adjusted security parameter reaches the target security coefficient; A205. Obtain an average speed of the dynamic verification code input by the user; A206. generating a time length correction coefficient according to the average speed, to correct the adjusted valid time length of the verification code; Step A206 includes: determining a verification code type according to the adjusted verification code character set; the verification code type includes a pure digital verification code, a pure letter verification code, a letter-number mixed verification code, a special symbol-number mixed verification code, a special symbol-letter mixed verification code, and a special symbol-letter-number mixed verification code; determining a type adjustment factor according to the verification code type; the more complex the verification code type, the greater the type adjustment factor; calculating the time length correction coefficient according to the average speed and the type adjustment factor; wherein the time length correction coefficient is inversely proportional to the average speed and proportional to the type adjustment factor.

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