Intelligent storage method based on psychological disease clinical treatment data

Abstract

The invention relates to the technical field of medical data storage, and discloses an intelligent storage method based on psychological disease clinical treatment data, which comprises the steps of data grading, access rule establishment, grading processing, grading storage and expired data cleaning. Data are divided into three security levels, so that differential protection of sensitive information is realized; the innovative weight access control model combines the double dimensions of roles and purposes, and dynamically adjusts the access authority through quantitative calculation, so that the special requirements of emergency diagnosis and treatment and the like are met, and the minimum authority principle is met; a hierarchical storage architecture is adopted to optimize the data management efficiency, meanwhile, data full-life-cycle management is achieved through timestamp marking and an intelligent cleaning mechanism, and important data are prevented from being deleted by mistake while storage space is released; the medical data security, the diagnosis and treatment efficiency and the scientific research value are comprehensively considered, the modular design is convenient to expand and apply, and a compliant, safe and efficient storage solution is provided for psychological disease data.

Description

Technical Field

[0001] This invention relates to the field of medical data storage technology, specifically to an intelligent storage method based on clinical medical data of psychosomatic diseases. Background Technology

[0002] With the rapid development of information technology and the continuous deepening of medical informatization, the digital storage and management of clinical data on psychosomatic diseases has become an important part of the modern medical system. As a special type of disease, psychosomatic diseases not only contain routine diagnostic and treatment information, but also involve sensitive information from multiple dimensions such as the patient's psychological state and social adaptability. This poses a serious challenge to data security and privacy protection.

[0003] Traditional healthcare information systems often employ uniform storage and access control strategies, failing to implement differentiated protection for data with varying levels of sensitivity. Highly sensitive information such as patient ID numbers and contact details are stored alongside routine medical records, posing a significant risk of leakage. While some systems utilize encryption technology, they lack fine-grained hierarchical protection mechanisms. Furthermore, existing systems are largely based on simple role-based access control, which is ill-suited to the complex access needs of healthcare scenarios. The data access requirements of different roles—medical staff, researchers, and patients—differ significantly, particularly in emergency care and research analysis scenarios, where existing systems cannot dynamically adjust access permissions.

[0004] Therefore, there is an urgent need for an intelligent storage method tailored to the characteristics of clinical data in psychosomatic diseases, which can achieve refined access control, optimized privacy protection, and intelligent lifecycle management while ensuring data security, thereby meeting multiple needs such as medical business, scientific research, and compliance. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent storage method based on clinical medical data of psychosomatic diseases, which solves the technical problems mentioned in the background art.

[0006] The objective of this invention can be achieved through the following technical solutions: A smart storage method based on clinical medical data of psychosomatic diseases includes the following steps: Step 1: Data Classification Clinical data on psychosomatic diseases are collected, including patient identity information and diagnosis and treatment records; the clinical data on psychosomatic diseases is then divided into primary data packages, secondary data packages and tertiary data packages. Step 2: Establishing Access Rules An access control model is established based on attribute-based access control technology. Multiple access roles and multiple access purposes are then set in the access control model, and corresponding preset weight values ​​are added to each access role and each access purpose. An access control calculation program is then constructed based on this. Step 3, Graded processing: For Level 1 data packets: The Level 1 information is encrypted using the AES-256 symmetric encryption algorithm, which generates ciphertext and a key, and a preset access condition is added. For secondary data packets: Partial masking technology is used to mask the secondary information, and a second preset access condition is added. For level 3 data packets: directly add the preset access condition 3; Step 4: Tiered storage: Based on the different levels of data packets, the clinical database is divided into different levels of storage areas, and data packets of different levels are then stored in the corresponding level of storage area. Step 5: Clean up expired data: Automatically clean up outdated clinical consultation data for psychosomatic diseases in the clinical database.

[0007] As a further aspect of the present invention: wherein: the identity information includes the patient's name, ID number, age, gender, contact information, and contact address; the diagnosis and treatment record includes the patient's consultation time, department, doctor ID, symptom description, examination results, and imaging report; Level 1 data packet: contains primary information corresponding to name, ID number, contact information, age, and gender; Level 2 data package: contact information, contact address, appointment time, department, and secondary information corresponding to the doctor's ID; Level 3 data package: Level 3 information corresponding to symptom description, examination results, treatment process, medication records, and imaging reports; As a further aspect of the present invention, the access control calculation procedure is as follows: pass: Calculate the access level D0; Among them, J i and M j Add corresponding weight values ​​to each access role and each access destination, where i represents the sequence number of the access role and j represents the sequence number of the access destination. In the formula, λ1 and λ2 are the corresponding preset weight coefficients.

[0008] As a further aspect of the present invention: the encrypted text is stored in a pre-built clinical database, and the key is managed by an HSM, which stands for Hardware Security Module.

[0009] As a further aspect of the present invention, the partial masking technique is as follows: Count the number of characters in the secondary information corresponding to contact information, contact address, consultation time, department, and doctor ID, and denote them as variable X. Subsequently passed: and

[0010] Calculate the number of masked characters (ZX) and the estimated number of characters (TX) corresponding to the contact information, contact address, consultation time, department, and doctor ID in the secondary data packet. In the formula, , These are the rounding up and rounding down symbols, respectively. Then, based on the calculated character count variable TX and the masked character count variable ZX, the characters at the corresponding positions in the secondary information are masked using "*".

[0011] As a further aspect of the present invention: wherein: The variable ZX, representing the number of characters to be masked, is used to define the "masking execution area," determining how many characters in the secondary information need to be replaced with "*". The character count variable TX is used to define the "plaintext retention area," determining how many characters need to be retained from the beginning of the secondary information. The masking cooperation logic between the calculated character count variable TX and the masked character count variable ZX is as follows: first, the first TX characters are retained, then the next ZX characters are masked, and the remaining characters are left as is.

[0012] As a further aspect of the present invention: access conditions one, two, and three are determined by preset different access level threshold ranges based on the access level obtained by the access control calculation program. Among them, access condition one > access condition two > access condition three; If the access level calculated by the access control calculation program based on the visitor's access role and access purpose is greater than access condition three, then the visitor is allowed to access level three data packets. If the access level calculated by the access control calculation program based on the visitor's access role and access purpose is greater than access condition two, then the visitor is allowed to access the level two data packet and is also allowed to access the level three data packet. If the access level calculated by the access control calculation program based on the visitor's access role and access purpose is greater than access condition one, then the visitor is allowed to access level one data packets, and is also allowed to access level two and level three data packets.

[0013] As a further aspect of the present invention: wherein the storage areas at different levels include: First storage layer: Used to store primary data packets of patients seeking medical treatment; Second storage layer: used to store secondary data packets of patients seeking medical treatment; The third storage layer is used to store the three-level data packets of patients seeking medical treatment.

[0014] As a further aspect of the present invention, the automatic cleaning steps are as follows: Step 1, Timeliness Marking: When each piece of clinical consultation data for psychosomatic diseases is written into the clinical database, a corresponding storage timestamp is added to each piece of clinical consultation data for psychosomatic diseases based on its writing time. Step 2, Expiration Analysis: The expiration time for each piece of clinical visit data for psychosomatic diseases is determined based on a pre-set lifespan T0: The expiration date is determined as follows: T g =T c +T 0; In the formula, T g For the expiration time of clinical consultation data for psychosomatic diseases, T c The timestamp for storing clinical consultation data for psychosomatic diseases; Step U3, Smart Cleaning: Regularly scan all clinical visit data for psychosomatic diseases in the clinical database; For each piece of clinical visit data for psychosomatic diseases, compare the current scan time with the expiration time: If the current scan time is less than the expiration time, the corresponding psychosomatic disease clinical data will continue to be stored and used normally without any processing. If the current scan time is greater than or equal to the expiration time, the responsible physician for the relevant psychosomatic disease clinical diagnosis data will be notified via internal system message push, informing them that the psychosomatic disease clinical diagnosis data is about to expire and requesting confirmation on whether it needs to be retained. If no confirmation response is received from the responsible physician within the pre-set time limit, or if the responsible physician confirms that the data will not be retained, the clinical consultation data for the psychosomatic disease will be erased. Once the responsible physician confirms the retention, the storage timestamp is updated based on the time the responsible physician confirms the retention, and normal storage continues.

[0015] As a further aspect of the present invention, the erasure operation specifically involves: first, filling the data block with random numbers to overwrite the original data content, and then deleting the storage pointer so that it cannot be accessed or retrieved in the storage system.

[0016] The beneficial effects of this invention are: Data security is tiered and protected: Clinical medical data for psychosomatic diseases is divided into Level 1, Level 2, and Level 3 data packets, each using AES-256 encryption, partial masking technology, and access control to ensure differentiated protection for data at different sensitivity levels. Level 1 data is stored with high-strength encryption, Level 2 data undergoes intelligent masking, and Level 3 data has access restrictions, effectively preventing the leakage of sensitive information and complying with medical data security compliance requirements.

[0017] Fine-grained dynamic access control: An attribute-based access control model combines role and purpose weights to quantify access levels, enabling flexible and precise permission management. Users with different permissions can only access data at their corresponding levels, ensuring data security while meeting the diverse needs of medical, research, and patient self-checking applications.

[0018] Intelligent data masking and storage optimization: Secondary data partial masking technology balances information availability and privacy by dynamically calculating the number of masked characters and the number of retained characters. A tiered storage mechanism optimizes the database structure, improves query efficiency, and reduces storage redundancy.

[0019] Automated lifecycle management: By using timestamps and preset lifecycles, expired data is scanned and cleaned up periodically. A confirmation notification is sent to the responsible physician before cleaning to avoid accidental deletion of important data; during erasure, random number overwriting + pointer deletion technology is used to ensure that the data is unrecoverable, thus freeing up storage space and complying with the principle of minimizing data retention.

[0020] End-to-end compliance and scalability: Integrating encryption, access control, masking, and cleanup functions, it covers the entire data lifecycle from entry to destruction, meeting relevant regulatory requirements. Modular design facilitates expansion and can be adapted to different medical scenarios or new data types, providing a standardized solution for the long-term secure management and efficient utilization of psychosomatic disease clinical data.

[0021] In summary, this invention significantly improves the usability, management efficiency, and compliance of medical data while ensuring patient privacy and data security, and has high clinical practical value and social benefits. Attached Figure Description

[0022] The invention will now be further described with reference to the accompanying drawings.

[0023] Figure 1 This is a flowchart illustrating an intelligent storage method for clinical medical data of psychosomatic diseases according to the present invention.

[0024] Figure 2 This is a schematic diagram of the process for cleaning up expired data in an intelligent storage method based on clinical medical data of psychosomatic diseases according to the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1

[0027] Please see Figure 1 As shown, this invention is an intelligent storage method based on clinical medical data of psychosomatic diseases, comprising the following steps: Step 1: Data Classification Collect clinical consultation data for psychosomatic diseases, including patient identity information and diagnosis and treatment records; in: The identity information includes the patient's name, ID number, age, gender, contact information, and contact address; Diagnostic and treatment records include the patient's visit time, department, doctor's ID, symptom description, examination results, and imaging reports; The clinical data on psychosomatic illnesses was then divided into three different levels of data packages, as follows: Level 1 data packet: contains primary information corresponding to name, ID number, contact information, age, and gender; Level 2 data package: contact information, contact address, appointment time, department, and secondary information corresponding to the doctor's ID; Level 3 data package: Level 3 information corresponding to symptom description, examination results, treatment process, medication records, and imaging reports; Step 2: Establishing Access Rules An access control model is established based on attribute-based access control technology. Multiple access roles and multiple access purposes are then set in the access control model, and corresponding preset weight values ​​are added to each access role and each access purpose. An access control calculation program is then constructed based on this. For example: Assuming multiple access roles, such as doctor, researcher, nurse, and patient, their corresponding weight values ​​can be set to 4, 3, 3, and 2 respectively; Assuming multiple access purposes such as emergency medical treatment, routine clinical treatment, scientific research analysis, and patient self-query, their corresponding weight values ​​can be set to 5, 4, 2, and 1 respectively; The access control calculation procedure is as follows: pass: Calculate the access level D0; Among them, J i and M jAdd corresponding weight values ​​to each access role and each access destination, where i represents the sequence number of the access role and j represents the sequence number of the access destination. In the formula, λ1 and λ2 are the corresponding preset weight coefficients. In this embodiment, λ1 is 0.6 and λ2 is 0.4. In this embodiment, the access control model is used to control access to resources such as systems, networks, and data, ensuring that only authorized users, devices, or applications can access specific resources; the access control model is prior art, so it will not be described in detail here. Step 3, Graded processing: For Level 1 data packets: The AES-256 symmetric encryption algorithm is used to encrypt the first-level information, thereby generating ciphertext and key, and a preset access condition is added at the same time. In this embodiment, the preprocessing is to encrypt the primary data using the AES-256 symmetric encryption algorithm. The AES-256 symmetric encryption algorithm is existing technology, so it will not be described in detail. The encrypted text is stored in a pre-built clinical database, and the key is managed by an HSM, which stands for Hardware Security Module. For secondary data packets: Partial masking technology is used to mask the secondary information, and a preset access condition is added at the same time; The specific details of partial masking technology are as follows: Count the number of characters in the secondary information corresponding to contact information, contact address, consultation time, department, and doctor ID, and denote them as variable X. Subsequently passed: and

[0028] Calculate the number of masked characters (ZX) and the estimated number of characters (TX) corresponding to the contact information, contact address, consultation time, department, and doctor ID in the secondary data packet. In the formula, , These are the rounding up and rounding down symbols, respectively. Then, based on the calculated character count variable TX and the masked character count variable ZX, the characters at the corresponding positions in the secondary information are masked using "*". in: The variable ZX, representing the number of characters to be masked, is used to define the "masking execution area," determining how many characters in the secondary information need to be replaced with "*". The character count variable TX is used to define the "plaintext retention area," determining how many characters need to be retained from the beginning of the secondary information. The masking cooperation logic between the calculated character count variable TX and the masked character count variable ZX is as follows: first, keep the first TX characters, then mask the next ZX characters, and leave the remaining characters as they are. For example: Taking a contact address as an example: Suppose the patient's contact address is "Room 1001, Building 10, XXX Community, XXX District, XXX City, XXX Province", which has 23 characters. The formula calculates that the estimated number of characters TX is 7 and the number of masked characters ZX is 8. Then, based on the calculated character count variable TX=7, the first 7 characters are obtained sequentially from the patient's contact address. Then, based on the masked character count variable ZX=8, the next 8 characters are obtained sequentially from the patient's contact address after the first 7 characters, and these 8 characters are replaced with "*". The patient's contact address, after being concealed, was finally obtained as "Room 1001, Building 10, [City Name], [Province Name]". For Level 3 data packets: Add the third preset access condition directly; Among them, access conditions one, two, and three are determined by preset different access level threshold ranges based on the access level obtained by the access control calculation program. Among them, access condition one > access condition two > access condition three; If the access level calculated by the access control calculation program based on the visitor's access role and access purpose is greater than access condition three, then the visitor is allowed to access level three data packets. If the access level calculated by the access control calculation program based on the visitor's access role and access purpose is greater than access condition two, then the visitor is allowed to access the level two data packet and is also allowed to access the level three data packet. If the access level calculated by the access control calculation program based on the visitor's access role and access purpose is greater than access condition one, then the visitor is allowed to access level one data packets, and is also allowed to access level two and level three data packets. For example: Access condition 1 uses [4, ∞) as the access level threshold range; Access condition two uses [3,4) as the access level threshold range; Access condition three uses [0, 3) as the access level threshold range; When the visitor's role is a doctor and the purpose of the visit is emergency medical treatment, then their access level D0 = 0.6 × 4 + 0.4 × 5 = 4.4 ≥ 4, which means that they can access all levels of data packets; When the visitor's role is that of a patient and the purpose of the visit is for self-query, then their access level D0 = 0.6 × 1 + 0.4 × 2 = 1.4 < 3, which means that the visitor can only view access level 3 data packets related to their own business. Step 4: Tiered storage: Based on the different levels of data packets, the clinical database is divided into different levels of storage areas, and data packets of different levels are then stored in the corresponding level of storage area. In this embodiment, the storage areas at different levels include: First storage layer: Used to store primary data packets of patients seeking medical treatment; Second storage layer: used to store secondary data packets of patients seeking medical treatment; The third storage layer is used to store the three-level data packets of patients seeking medical treatment.

[0029] This embodiment divides clinical consultation data for psychosomatic diseases into three levels (Level 1, Level 2, and Level 3 data packets) and employs differentiated encryption, masking, or access control strategies (such as AES-256 encryption and partial masking technology) for different levels of data. Combined with an attribute-based access control model, it achieves hierarchical secure storage and fine-grained access control of data. By assigning weight values ​​to access roles and access purposes and calculating access levels, it dynamically controls user access permissions to different levels of data (e.g., doctors can access all data for emergency treatment, while patients can only query Level 3 data). This effectively balances data security and medical business needs, protecting sensitive patient information (such as ID numbers and contact information) while ensuring the efficient conduct of diagnosis, treatment, and research.

[0030] Example 2

[0031] Please see Figure 2 As shown, as a second embodiment of the present invention, in specific implementation, the technical solution of this embodiment differs from that of embodiment one only in that, in this embodiment... Also includes: expired data cleanup steps: This step is used to automatically clean up outdated clinical consultation data for psychosomatic diseases in the clinical database; The specific steps are as follows: Step 1, Timeliness Marking: When each piece of clinical consultation data for psychosomatic diseases is written into the clinical database, a corresponding storage timestamp is added to each piece of clinical consultation data for psychosomatic diseases based on its writing time. Step 2, Expiration Analysis: The expiration time for each piece of clinical visit data for psychosomatic diseases is determined based on a pre-set lifespan T0: The expiration date is determined as follows: Tg =T c +T 0; In the formula, T g For the expiration time of clinical consultation data for psychosomatic diseases, T c The timestamp for storing clinical consultation data for psychosomatic diseases; For example, a clinical visit record for a psychosomatic disease was entered into the clinical database on January 1, 2020, i.e., T c =2020−01−01; If the pre-set lifespan T0 = 10 years; The expiration time T of this psychosomatic disease clinical visit data is then... g =2030−01−01; Step U3, Smart Cleaning: Regularly scan all clinical visit data for psychosomatic diseases in the clinical database; For each piece of clinical visit data for psychosomatic diseases, compare the current scan time with the expiration time: If the current scan time is less than the expiration time, the corresponding psychosomatic disease clinical data will continue to be stored and used normally without any processing. If the current scan time is greater than or equal to the expiration time, the responsible physician for the relevant psychosomatic disease clinical diagnosis data will be notified via internal system message push, informing them that the psychosomatic disease clinical diagnosis data is about to expire and requesting confirmation on whether it needs to be retained. If no confirmation response is received from the responsible physician within the pre-set time limit, or if the responsible physician confirms that the data will not be retained, the clinical consultation data for the psychosomatic disease will be erased. The erasure operation specifically involves: first, filling the data block with random numbers to overwrite the original data content; then, deleting the storage pointer so that it cannot be accessed or retrieved in the storage system, thereby achieving the purpose of data cleanup. If the attending physician confirms the retention, the storage timestamp will be updated based on the time the attending physician confirms the retention, and normal storage will continue. This embodiment adds an expired data cleanup function to the existing embodiment. Through timestamp marking and lifecycle management (e.g., setting a 10-year validity period), it automatically identifies and cleans up expired data. A dual mechanism of "notification confirmation + erasure operation" (e.g., filling in random numbers to overwrite the original data, deleting storage pointers) is employed to ensure secure data cleanup while preventing the accidental deletion of important information (e.g., the responsible physician can manually extend the retention period). This solution optimizes storage resource utilization, meets medical data compliance requirements, and reduces manual intervention through a systematic process, thereby improving the automation level of data management.

[0032] Example 3

[0033] Please see Figure 1 and Figure 2 As shown, as a third embodiment of the present invention, in specific implementation, compared with embodiments one and two, the technical solution of this embodiment is to combine the solutions of embodiments one and two.

[0034] This embodiment integrates the hierarchical storage and access control mechanism of Embodiment 1 with the intelligent cleanup function of Embodiment 2, forming a complete lifecycle management solution for clinical data of psychosomatic diseases. It features hierarchical data security protection (encryption / masking), dynamic access control (based on role / purpose weights), and automated expiration handling capabilities, comprehensively covering all stages from data entry and storage to cleanup. Its synergistic effect significantly improves the overall performance of the system: it meets the high security and flexible access requirements of medical scenarios, reduces storage costs through regular cleanup of redundant data, and ensures compliance with laws and regulations, providing a standardized solution for the long-term and efficient management of clinical data of psychosomatic diseases.

[0035] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0036] It should be stated that all medical data collected in this application was collected with the user's consent and authorization, and the use of the medical data is legal and compliant, and the use and processing of the medical data comply with the relevant laws, regulations and standards of the relevant regions.

[0037] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent storage method based on clinical medical data of psychosomatic diseases, characterized in that, Includes the following steps: Data classification: Collect clinical consultation data on psychosomatic diseases and then classify them into primary data packages, secondary data packages and tertiary data packages; Access rule establishment: An access control model is established based on attribute-based access control technology. Multiple access roles and multiple access purposes are set in the access control model, and corresponding preset weight values ​​are added to each access role and each access purpose. Based on this, an access control calculation program is constructed. Tiered processing: Level 1 data packets are encrypted using the AES-256 symmetric encryption algorithm, generating ciphertext and a key, while adding a preset access condition one; Level 2 data packets are partially masked using partial masking technology, while adding a preset access condition two; for Level 3 data packets: a preset access condition three is directly added. Hierarchical storage: Based on the different levels of data packets, the clinical database is divided into different levels of storage areas, and data packets of different levels are stored in the corresponding level of storage area. Expired data cleanup: Automatically cleans up expired clinical consultation data for psychosomatic diseases in the clinical database.

2. The intelligent storage method based on clinical medical data of psychosomatic diseases according to claim 1, characterized in that, Clinical consultation data for psychosomatic diseases includes patient identity information and diagnosis and treatment records; identity information includes patient name, ID number, age, gender, contact information, and contact address; diagnosis and treatment records include patient consultation time, department, doctor ID, symptom description, examination results, and imaging reports; Level 1 data packet: contains primary information corresponding to name, ID number, contact information, age, and gender; Level 2 data package: contact information, contact address, appointment time, department, and secondary information corresponding to the doctor's ID; Level 3 data package: Level 3 information corresponding to symptom description, examination results, treatment process, medication records, and imaging reports.

3. The intelligent storage method based on clinical medical data of psychosomatic diseases according to claim 2, characterized in that, The access control calculation procedure is as follows: pass: Calculate the access level D0; Among them, J i and M j Add corresponding weight values ​​to each access role and each access destination, where i represents the sequence number variable of the access role and j represents the sequence number variable of the access destination; In the formula, λ1 and λ2 are the corresponding preset weight coefficients.

4. The intelligent storage method based on clinical medical data of psychosomatic diseases according to claim 1, characterized in that, in, The encrypted data is stored in a pre-built clinical database, and the key is hosted via an HSM, which stands for Hardware Security Module.

5. The intelligent storage method based on clinical medical data of psychosomatic diseases according to claim 1, characterized in that, The specific details of partial masking technology are as follows: Count the number of characters in the secondary information corresponding to contact information, contact address, consultation time, department, and doctor ID, and denote them as variable X. Subsequently passed: and ; Calculate the number of masked characters (ZX) and the estimated number of characters (TX) corresponding to the contact information, contact address, consultation time, department, and doctor ID in the secondary data packet. In the formula, , These are the rounding up and rounding down symbols, respectively. Subsequently, based on the calculated character count variable TX and the masked character count variable ZX, the characters at the corresponding positions in the secondary information are masked using "*".

6. The intelligent storage method based on clinical medical data of psychosomatic diseases according to claim 5, characterized in that, in: The variable ZX, representing the number of characters to be masked, is used to define the "masking execution area," determining how many characters in the secondary information need to be replaced with "*". The character count variable TX is used to define the "plaintext retention area," determining how many characters need to be retained from the beginning of the secondary information. The masking cooperation logic between the calculated character count variable TX and the masked character count variable ZX is as follows: first, the first TX characters are retained, then the next ZX characters are masked, and the remaining characters are left as is.

7. The intelligent storage method based on clinical medical data of psychosomatic diseases according to claim 1, characterized in that, in, Access conditions one, two, and three are determined by preset different access level threshold ranges based on the access level obtained by the access control calculation program. Among them, access condition one > access condition two > access condition three; If the access level calculated by the access control calculation program based on the visitor's access role and access purpose is greater than access condition three, then the visitor is allowed to access level three data packets. If the access level calculated by the access control calculation program based on the visitor's access role and access purpose is greater than access condition two, then the visitor is allowed to access the level two data packet and is also allowed to access the level three data packet. If the access level calculated by the access control calculation program based on the visitor's access role and access purpose is greater than access condition one, then the visitor is allowed to access level one data packets, and is also allowed to access level two and level three data packets.

8. The intelligent storage method based on clinical medical data of psychosomatic diseases according to claim 7, characterized in that, in, Different levels of storage areas include: First storage layer: Used to store primary data packets of patients seeking medical treatment; Second storage layer: used to store secondary data packets of patients seeking medical treatment; The third storage layer is used to store the three-level data packets of patients seeking medical treatment.

9. The intelligent storage method based on clinical medical data of psychosomatic diseases according to claim 1, characterized in that, The automatic cleaning steps are as follows: Step 1, Timeliness Marking: When each piece of clinical consultation data for psychosomatic diseases is written into the clinical database, a corresponding storage timestamp is added to each piece of clinical consultation data for psychosomatic diseases based on its writing time. Step 2, Expiration Analysis: The expiration time for each piece of clinical visit data for psychosomatic diseases is determined based on a pre-set lifespan T0: The expiration date is determined as follows: T g =T c +T 0; In the formula, T g For the expiration time of clinical consultation data for psychosomatic diseases, T c The timestamp for storing clinical consultation data for psychosomatic diseases; Step U3, Smart Cleaning: Regularly scan all clinical visit data for psychosomatic diseases in the clinical database; For each piece of clinical visit data for psychosomatic diseases, compare the current scan time with the expiration time: If the current scan time is less than the expiration time, the corresponding psychosomatic disease clinical data will continue to be stored and used normally without any processing. If the current scan time is greater than or equal to the expiration time, the responsible physician for the relevant psychosomatic disease clinical diagnosis data will be notified via internal system message push, informing them that the psychosomatic disease clinical diagnosis data is about to expire and requesting confirmation on whether it needs to be retained. If no confirmation response is received from the responsible physician within the pre-set time limit, or if the responsible physician confirms that the data will not be retained, the clinical consultation data for the psychosomatic disease will be erased. Once the responsible physician confirms the retention, the storage timestamp is updated based on the time the responsible physician confirms the retention, and normal storage continues.

10. The intelligent storage method based on clinical medical data of psychosomatic diseases according to claim 9, characterized in that, The erasure operation is as follows: first, the data block is filled with random numbers to overwrite the original data content, and then the storage pointer is deleted so that it cannot be accessed or retrieved in the storage system.

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