Medical data management method and system based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence computer systems, and particularly discloses a medical data management method and system based on artificial intelligence, and the method comprises the steps: collecting a medical data set, obtaining the attribute parameters of the medical data set, analyzing the data complexity index, considering the performance parameters of management equipment to which the medical data set belongs, and comprehensively evaluating the equipment performance. And performing encryption processing on the medical data set according to the performance evaluation factor by using an artificial intelligence technology, and marking the medical data set as an encrypted medical data set, thereby enhancing the security of the medical data, ensuring privacy protection of the data in transmission and storage processes, collecting encryption parameters of the encrypted medical data set, evaluating the storage complexity, and improving the security of the medical data. According to the method, whether the encrypted data set needs to be compressed and stored or not is intelligently judged, the data safety, privacy protection and storage efficiency are effectively balanced, risk abnormity possibly occurring in the storage process is reduced, the intelligent level and the safety level of medical data management are improved, and efficient and safe storage of medical data is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence computer systems, and specifically to a medical data management method and system based on artificial intelligence. Background Art

[0002] With the continuous deepening of medical informatization, medical data has shown explosive growth. These data are characterized by large data volume, complex data, and high data value. Intelligently and effectively managing and utilizing these data to improve the quality and efficiency of medical services has become an important issue faced by the current medical industry.

[0003] For example, the invention patent with the publication number CN117993500B discloses a medical teaching data management method and system based on artificial intelligence, which specifically includes the following steps: obtaining multi-dimensional medical data of patients; performing heterogeneous data fusion on the multi-dimensional medical data of patients to generate heterogeneous fusion medical feature data; performing variational coding on the heterogeneous fusion medical feature data to generate medical feature coding data; performing latent variable coding on the medical feature coding data to generate medical feature vector data; performing non-linear coding mapping on the medical feature vector data to generate latent variable space coding data; performing fine-grained segmentation on the multi-dimensional medical data of patients to generate medical fine-grained node data; performing vector similarity calculation on the latent variable space coding data to generate a latent variable vector similarity index.

[0004] For example, the invention patent with the publication number CN118428493B discloses an artificial intelligence data analysis method and system based on machine learning, which relates to the technical field of data analysis and includes collecting data in a data source and performing preprocessing, extracting features from the preprocessed data; constructing a multi-modal machine learning model for data prediction and predicting the probability of a prediction category; implementing data encryption, constructing an interactive data visualization interface, and storing the artificial intelligence data generated by collection and analysis.

[0005] However, in the process of implementing the embodiments of the present application, it is found that the above technologies have at least the following technical problems: When the prior art manages medical data, it mainly focuses on the processing and analysis process of medical data. The additional data volume in the analysis process may face threats to data integrity and availability, such as data corruption or slow data response, etc., which in turn hinders the continuous utilization and value mining of medical data and reduces the accuracy and efficiency of medical data storage. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a medical data management method and system based on artificial intelligence, which can effectively solve the problems involved in the above background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: In the first aspect of the present invention, a medical data management method based on artificial intelligence is provided, including: Step 1, collect a medical data set, obtain the attribute parameters of the medical data set, analyze the data complexity index of the medical data set, and at the same time obtain the performance parameters of the management device to which the medical data set belongs, and comprehensively analyze the performance evaluation factor of the management device to which the medical data set belongs based on the data complexity index of the medical data set; Step 2, according to the performance evaluation factor of the management device to which the medical data set belongs, use artificial intelligence technology to encrypt the medical data set, and mark the processed medical data set as an encrypted medical data set, collect the encryption parameters of the encrypted medical data set, and evaluate the storage complexity of the encrypted medical data set; Step 3, according to the storage complexity of the encrypted medical data set, determine whether to use artificial intelligence technology to compress and store the encrypted medical data set.

[0008] As a further method, the process of using artificial intelligence technology to encrypt the medical data set according to the performance evaluation factor of the management device to which the medical data set belongs is as follows: Compare the performance evaluation factor of the management device to which the medical data set belongs with the first performance evaluation factor range, the second performance evaluation factor range, and the third performance evaluation factor range stored in the medical database; If the performance evaluation factor of the management device to which the medical data set belongs belongs to the first performance evaluation factor range, then match the artificial intelligence first encryption strategy corresponding to the first performance evaluation factor range preset in the database; If the performance evaluation factor of the management device to which the medical data set belongs belongs to the second performance evaluation factor range, then match the artificial intelligence second encryption strategy corresponding to the second performance evaluation factor range preset in the database; If the performance evaluation factor of the management device to which the medical data set belongs belongs to the third performance evaluation factor range, then match the artificial intelligence third encryption strategy corresponding to the third performance evaluation factor range preset in the database.

[0009] As a further method, the specific encryption process of using artificial intelligence technology to encrypt the medical data set is as follows: If the artificial intelligence first encryption strategy is matched according to the performance evaluation factor of the management device to which the medical data set belongs, then encrypt the medical data set according to the artificial intelligence first encryption strategy, and obtain the encrypted medical data set after processing; If the artificial intelligence second encryption strategy is matched according to the performance evaluation factor of the management device to which the medical data set belongs, then encrypt the medical data set according to the artificial intelligence second encryption strategy, and obtain the encrypted medical data set after processing; If the artificial intelligence third encryption strategy is matched according to the performance evaluation factor of the management device to which the medical data set belongs, then encrypt the medical data set according to the artificial intelligence third encryption strategy, and obtain the encrypted medical data set after processing.

[0010] As a further method, the determination of whether to use artificial intelligence technology to compress and store the encrypted medical dataset is as follows: according to the performance evaluation factor of the management device to which the encrypted medical dataset belongs, match the storage complexity threshold from the detection database; compare the storage complexity of the encrypted medical dataset with the storage complexity threshold. If the storage complexity of the encrypted medical dataset is less than or equal to the storage complexity threshold, it is determined not to use artificial intelligence technology to compress and store the encrypted medical dataset, and the encrypted medical dataset is directly stored; if the storage complexity of the encrypted medical dataset is greater than the storage complexity threshold, it is determined to use artificial intelligence technology to compress and store the encrypted medical dataset.

[0011] As a further method, the process of using artificial intelligence technology to compress and store the encrypted medical dataset is as follows: perform a ratio process on the storage complexity of the encrypted medical dataset and the storage complexity threshold, and mark the processing result as the storage compression reference ratio of the encrypted medical dataset. The artificial intelligence technology compresses the data volume of the encrypted medical dataset according to the storage compression reference ratio of the encrypted medical dataset, and stores the compressed encrypted medical dataset.

[0012] The second aspect of the present invention provides a medical data management system based on artificial intelligence, including: a dataset analysis module, configured to collect a medical dataset, obtain the attribute parameters of the medical dataset, analyze the data complexity index of the medical dataset, and at the same time obtain the performance parameters of the management device to which the medical dataset belongs, and comprehensively analyze the performance evaluation factor of the management device to which the medical dataset belongs according to the data complexity index of the medical dataset; a dataset encryption module, configured to use artificial intelligence technology to encrypt the medical dataset according to the performance evaluation factor of the management device to which the medical dataset belongs, mark the processed medical dataset as an encrypted medical dataset, collect the encryption parameters of the encrypted medical dataset, and evaluate the storage complexity of the encrypted medical dataset; a dataset compression module, configured to determine whether to use artificial intelligence technology to compress and store the encrypted medical dataset according to the storage complexity of the encrypted medical dataset.

[0013] Compared with the prior art, the embodiments of the present invention at least have the following advantages or beneficial effects:

[0014] (1) The present invention provides a medical data management method and system based on artificial intelligence. By collecting a medical data set and obtaining its attribute parameters, analyzing the data complexity index, and considering the performance parameters of the management device to which the medical data set belongs, the device performance is comprehensively evaluated. Using artificial intelligence technology, the medical data set is encrypted according to the performance evaluation factor and marked as an encrypted medical data set. This step not only enhances the security of medical data but also ensures privacy protection during data transmission and storage. Further, the encryption parameters of the encrypted medical data set are collected, the storage complexity is evaluated, and based on this, it is intelligently determined whether to compress and store the encrypted data set. This method effectively balances data security, privacy protection, and storage efficiency, reduces the risk anomalies that may occur during storage, improves the intelligent level and security level of medical data management, and realizes the efficient and secure storage of medical data.

[0015] (2) Based on the performance evaluation factor of the management device to which the medical data set belongs, the present invention specifically uses artificial intelligence technology to provide encryption protection for the medical data set. This approach effectively guarantees the privacy and security of medical data. At the same time, the encryption algorithm is flexibly adjusted according to the performance characteristics of the management device, optimizing the medical data encryption process. While ensuring data security, it also promotes the faster and more efficient processing and application of data on high-performance management devices.

[0016] (3) According to the storage complexity of the encrypted medical data set, the present invention flexibly decides whether to use artificial intelligence technology to implement a compression storage strategy. This can optimize the use of storage space, reduce storage overhead, and ensure the security and integrity of the data. In the context of the rapid increase in medical data and growing storage requirements, this method not only improves storage security and efficiency but also lays a solid foundation for the convenient access and efficient management of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the following drawings.

[0018] Figure 1 It is a schematic flow chart of the method steps of the present invention.

[0019] Figure 2 It is a schematic diagram of the connection of system modules of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Referring to Figure 1 As shown, the first aspect of the present invention provides a medical data management method based on artificial intelligence, including: Step 1, collecting a medical data set, obtaining the attribute parameters of the medical data set, analyzing the data complexity index of the medical data set, and at the same time obtaining the performance parameters of the management device to which the medical data set belongs, and comprehensively analyzing the performance evaluation factor of the management device to which the medical data set belongs according to the data complexity index of the medical data set.

[0022] The above-mentioned collection of the medical data set refers to the medical data to be updated or saved received by the management device after receiving an update or save instruction, and the data as a whole is marked as the medical data set, where the medical data set includes but is not limited to the physiological parameters of patients, diagnostic images, laboratory test data, etc.; the above-mentioned management device includes but is not limited to computer devices.

[0023] In a specific embodiment, based on the performance evaluation factor of the management device to which the medical data set belongs, the present invention uses artificial intelligence technology to provide encryption protection for the medical data set in a targeted manner. This approach effectively guarantees the privacy and security of the medical data, and at the same time flexibly adjusts the encryption algorithm according to the performance characteristics of the management device, optimizing the medical data encryption processing process. While ensuring data security, it also promotes the more rapid and efficient processing and application of data on high-performance management devices.

[0024] Specifically, the process of analyzing the performance evaluation factors of the management device to which the medical data set belongs is as follows: The performance parameters of the management device to which the medical data set belongs include the real-time cache hit rate of the management device to which the medical data set belongs within the monitoring period, the real-time throughput of the management device to which the medical data set belongs within the monitoring period, and the real-time CPU waiting queue length of the management device to which the medical data set belongs within the monitoring period. The above-mentioned monitoring period refers to the time period for detecting the performance of the management device, and its specific duration is formulated by the device administrator. It should also be noted that the time point for collecting the medical data set is the end time point of the monitoring period. The above-mentioned real-time cache hit rate represents the ratio between the number of times of successfully retrieving data from the management device cache and all data request times at each time point within the monitoring period, and can be monitored through a performance monitoring tool (such as Grafana). The above-mentioned real-time throughput represents the total amount of data successfully transmitted and processed by the management device at each time point within the monitoring period, and can be obtained through an application-level monitoring tool (such as Application Performance Management). The above-mentioned real-time CPU waiting queue length represents the number of tasks (or threads) waiting for the management device CPU to process at each time point within the monitoring period, and can be monitored through a performance monitoring tool (such as Grafana).

[0025] Perform data processing on the real-time cache hit rate of the management device to which the medical data set belongs within the monitoring period to obtain the real-time cache miss rate of the management device to which the medical data set belongs within the monitoring period. The specific data processing method is: subtract the cache hit rate of the management device to which the medical data set belongs at a certain time point within the monitoring period from 1 to obtain the cache miss rate at that time point. Through the above data processing method, the real-time cache miss rate of the management device to which the medical data set belongs within the monitoring period can be obtained based on the real-time cache hit rate of the management device to which the medical data set belongs within the monitoring period. The real-time cache miss rate represents the ratio between the number of times the cache system of the management device fails to obtain data from the cache and the real-time total data request times at each time point within the monitoring period.

[0026] Perform mean processing on the real-time throughput of the management device to which the medical data set belongs during the monitoring period to obtain the average throughput of the management device to which the medical data set belongs during the monitoring period. Locate the maximum and minimum values from the real-time throughput of the management device to which the medical data set belongs during the monitoring period, and perform difference processing. The result of the difference processing is ratio-processed with the average throughput of the management device to which the medical data set belongs during the monitoring period to obtain the throughput volatility of the management device to which the medical data set belongs during the monitoring period, and mark this value as the real-time throughput volatility of the management device to which the medical data set belongs during the monitoring period; the above average throughput represents the average level of the real-time throughput of the management device to which the medical data set belongs during the monitoring period; the throughput volatility of the management device to which the medical data set belongs during the monitoring period represents the ratio of the maximum change degree of the throughput of the management device to which the medical data set belongs during the monitoring period to the average throughput, reflecting the stability and consistency of the management device in data processing ability. The lower the throughput volatility, the more stable the processing ability of the management device and the smaller the fluctuation; it should be noted that the throughput volatility of the management device to which the medical data set belongs during the monitoring period is marked as the real-time throughput volatility of the management device to which the medical data set belongs during the monitoring period. Therefore, the real-time throughput volatility of the management device to which the medical data set belongs during the monitoring period is a fixed value. The marking in this embodiment lays a foundation for reasonably describing the throughput volatility in the following performance evaluation factors.

[0027] Comprehensively analyze the data complexity index of the medical data set, the real-time cache miss rate of the management device to which the medical data set belongs during the monitoring period, the real-time CPU wait queue length of the management device to which the medical data set belongs during the monitoring period, and the real-time throughput volatility of the management device to which the medical data set belongs during the monitoring period to obtain the performance evaluation factor of the management device to which the medical data set belongs. Among them, the performance evaluation factor of the management device to which the medical data set belongs represents a quantitative evaluation of the performance of the management device to which the medical data set belongs. The larger the performance evaluation factor of the management device to which the medical data set belongs, the better the management device to which the medical data set belongs can perform in data storage and processing.

[0028] The specific analysis method for the performance evaluation factor of the management device to which the medical data set belongs is as follows:

[0029]

[0030] Among them, PTD(ts) is specifically:

[0031]

[0032] Wherein, PEF is the performance evaluation factor of the management device to which the medical data set belongs, CWQ(YDC, YDM, YNC) is the data complexity index of the medical data set, W is the weight factor corresponding to the preset data complexity index in the medical database, ts 2 is the end time point of the monitoring period, ts 1 is the start time point of the monitoring period, ts is any time point within the monitoring period, ts ∈ [ts 1 , ts 2 , PTD(ts) is the performance evaluation function of the management device to which the medical data set belongs within the monitoring period, RC(ts) is the cache miss rate of the management device to which the medical data set belongs at the time point ts within the monitoring period, MD(ts) is the CPU wait queue length of the management device to which the medical data set belongs at the time point ts within the monitoring period, FR(ts) is the throughput volatility of the management device to which the medical data set belongs at the time point ts within the monitoring period, e is the natural constant, fs 1 is the performance evaluation proportion factor corresponding to the preset cache miss rate unit value in the medical database, fs 2 is the performance evaluation proportion factor corresponding to the preset CPU wait queue length unit value in the medical database, fs 3 is the performance evaluation proportion factor corresponding to the preset throughput volatility unit value in the medical database.

[0033] The above PTD(ts) is the performance evaluation function of the management device to which the medical data set belongs within the monitoring period, indicating the change relationship of the performance of the management device to which the medical data set belongs with the time variable ts.

[0034] The weight factor corresponding to the above data complexity index represents the proportion of the data complexity index of the medical data set in the performance evaluation factor of the management device to which the medical data set belongs. The medical database stores the corresponding relationship between the data complexity index and its corresponding weight factor. For example, when the data complexity index of the medical data set is input into the medical database, the medical database can match the weight factor corresponding to the data complexity index, and the value range is between 0 and 1. The performance evaluation proportion factor corresponding to the unit value of the above cache miss rate represents the value of the influence degree of the unit value of the cache miss rate on the performance evaluation factor of the management device to which the medical data set belongs. The performance evaluation proportion factor corresponding to the unit value of the above CPU waiting queue length represents the value of the influence degree of the unit value of the CPU waiting queue length on the performance evaluation factor of the management device to which the medical data set belongs. The performance evaluation proportion factor corresponding to the unit value of the above throughput volatility represents the value of the influence degree of the unit value of the throughput volatility on the performance evaluation factor of the management device to which the medical data set belongs. The medical database stores the corresponding relationship between the cache miss rate, the CPU waiting queue length, and the throughput volatility and their corresponding performance evaluation proportion factors. For example, when the real-time cache miss rate of the management device to which the medical data set belongs during the monitoring period, the real-time CPU waiting queue length of the management device to which the medical data set belongs during the monitoring period, and the real-time throughput volatility of the management device to which the medical data set belongs during the monitoring period are input into the medical database, the medical database can respectively match the performance evaluation proportion factor corresponding to the unit value of the cache miss rate, the performance evaluation proportion factor corresponding to the unit value of the CPU waiting queue length, and the performance evaluation proportion factor corresponding to the unit value of the throughput volatility, and the value range of all is between 0 and 1.

[0035] It should be noted that when the cache miss rate increases, it directly reflects that a large number of data accesses in the management device to which the medical data set belongs fail to effectively utilize the cache. This forces the management device to frequently access its underlying storage system. Such frequent storage access not only increases the burden on the storage device but also significantly prolongs the time for the CPU to wait for the required data. Since the CPU has to wait for the underlying storage to return the data before it can continue processing, the length of the CPU waiting queue increases accordingly. This directly reflects the exacerbation of the latency when the management device processes data requests. The increase in the length of the CPU waiting queue not only means that the management device responds to requests more slowly but also reduces its overall efficiency in data processing and storage. This decline in efficiency further affects the throughput of the management device, resulting in an increase in throughput volatility. Specifically, due to the increase in data access latency and CPU waiting time, the stability of the management device when processing consecutive data requests is affected, and its processing ability becomes inconsistent, manifested as unstable fluctuations in throughput. Therefore, the increase in the cache miss rate ultimately leads to an increase in the throughput volatility of the management device by exacerbating the CPU waiting time, prolonging the storage access time, and increasing the length of the CPU waiting queue. This series of chain reactions not only affects the efficiency of the management device in processing data but also has a significant adverse impact on its overall performance, especially the performance of storing data.

[0036] Meanwhile, in this embodiment, the data complexity index of the medical data set is incorporated to comprehensively evaluate the performance evaluation factors of the management device to which the medical data set belongs. Medical data sets often contain a large amount of information of various types such as images, videos, texts, and physiological parameters, and there are often complex correlations and hierarchies among these data. Therefore, the level of the data complexity index directly reflects the requirements of the medical data set for the processing ability and storage performance of the management device. Secondly, incorporating the data complexity index into the performance evaluation factors of the management device can more comprehensively evaluate the overall performance of the management device when processing medical data sets. Traditional performance evaluation methods often only focus on single indicators such as the throughput and response time of the management device, while ignoring the impact of the complexity of the medical data set itself on the performance of the management device. By introducing the data complexity index, it can more accurately reflect the performance of the device when processing medical data sets of different complexities, thus providing more reliable device performance evaluation results.

[0037] Furthermore, the data complexity index of the analyzed medical data set is specifically analyzed as follows: The attribute parameters of the medical data set include the data volume of the medical data set, the data dimension of the medical data set, the ratio of vacant fields in the medical data set, and the ratio of redundant fields in the medical data set. The data volume of the above-mentioned medical data set represents the size of the data volume contained in the medical data set. The data dimension of the above-mentioned medical data set represents the number of data attributes in the medical data set. For example, the data attributes in the medical data set include age, gender, disease type, and ID number. Age, gender, disease type, and ID number are the four data attributes in the medical data set, so the data dimension of the medical data set is marked as 4. The vacant fields in the above-mentioned medical data set represent the ratio of the vacant fields in the medical data set to the total number of fields in the medical data set. The ratio of redundant fields in the above-mentioned medical data set represents the ratio of the number of duplicate fields in the medical data set to the total number of fields. The medical data set can be deduplicated by data analysis software (such as Statistical Analysis System) to obtain the number of deduplicated fields, and the ratio is processed with the total number of fields in the medical data set before deduplication to obtain the ratio of redundant fields in the medical data set. Among them, the data volume, data dimension, and ratio of vacant fields of the medical data set can all be extracted from the data processing log of the management device.

[0038] Add the result of multiplying the ratio of vacant fields in the medical data set by the vacant field ratio factor to the result of multiplying the ratio of redundant fields in the medical data set by the redundant field ratio factor to finally obtain the data noise factor of the medical data set. The above-mentioned vacant field ratio factor is extracted from the medical database, and its value range is 0 to 1, which represents the value of the influence degree of the unit value of the ratio of vacant fields in the medical data set on the data noise factor of the medical data set. The above-mentioned redundant field ratio factor is extracted from the medical database, and its value range is 0 to 1, which represents the value of the influence degree of the unit value of the ratio of redundant fields in the medical data set on the data noise factor of the medical data set. The data noise factor of the above-mentioned medical data set represents the level of data impurities introduced by vacant fields and redundant fields in the medical data set. A high data noise factor not only reveals the defects of the data set itself but also indicates that during the storage process, the management device needs to invest more performance resources to handle these complexities, thus having a greater negative impact on the overall data processing efficiency and performance of the management device.

[0039] By comprehensively analyzing the data volume, data dimension, and data noise factor of a medical dataset, the data complexity index of the medical dataset is obtained. Specifically, the data complexity index of the medical dataset is obtained by substituting the data volume, data dimension, and data noise factor of the medical dataset into the data complexity index function, resulting in the data complexity index CWQ(YDC, YDM, YNC) of the medical dataset. It should be noted that the above data complexity index of the medical dataset is a numerical value representing the complexity of the medical dataset.

[0040] In the formula, CWQ(YDC, YDM, YNC) is the data complexity index of the medical dataset, YDC is the data volume of the medical dataset, YDM is the data dimension of the medical dataset, and YNC is the data noise factor of the medical dataset. Among them, the data complexity index function is CWQ(DC, DM, NC), and it is specifically expressed as follows:

[0041]

[0042] CWQ(DC, DM, NC) is the data complexity index function, DC is the data volume, DM is the data dimension, NC is the data noise factor, gt 1 is the data complexity index proportion factor corresponding to the preset data volume unit value in the medical database, gt 2 is the data complexity index proportion factor corresponding to the preset data dimension unit value in the medical database, gt 3 is the data complexity index proportion factor corresponding to the preset data noise factor unit value in the medical database, and e is the natural constant.

[0043] The above data complexity index function is a mathematical expression that describes the dynamic change of the data complexity index with the changes in the data volume, data dimension, and data noise factor.

[0044] The data complexity index proportion factor corresponding to the above data volume unit value represents the degree of influence of the data volume unit value on the data complexity index. In this embodiment, the correspondence between the data volume stored in the medical database and its corresponding data complexity index proportion factor is such that, for example, when the data volume of a medical data set is input into the medical database, the medical database can match the data complexity index proportion factor corresponding to the data volume unit value, and its value range is between 0 and 1. The data complexity index proportion factor corresponding to the above data dimension unit value represents the degree of influence of the data dimension unit value on the data complexity index. In this embodiment, the medical database stores the correspondence between the data dimension and its corresponding data complexity index proportion factor. For example, when the data dimension of a medical data set is input into the medical database, the medical database can match the data complexity index proportion factor corresponding to the data dimension unit value, and its value range is between 0 and 1. The data complexity index proportion factor corresponding to the above data noise factor unit value represents the degree of influence of the data noise factor unit value on the data complexity index. In this embodiment, the medical database stores the correspondence between the data noise factor and its corresponding data complexity index proportion factor. For example, when the data noise factor of a medical data set is input into the medical database, the medical database can match the data complexity index proportion factor corresponding to the data noise factor unit value, and its value range is between 0 and 1.

[0045] It should be noted that an increase in the data dimension means that each data point will contain more feature information. During the data processing and storage process, since the space and content occupied by different dimensions may vary, this will naturally lead to an increase in the data volume. The increase in the data volume not only means that more physical storage space is required, but also means that more data points need to be processed during the data processing and analysis, thus increasing the computational complexity and time cost. As the data volume increases, the probability of abnormal situations occurring in the data set will also increase accordingly. These abnormalities may appear in the form of missing fields, redundant fields, etc., which are all manifestations of data noise. An increase in the data noise factor means that there are more uncertainties and interference factors in the data set. These factors will obscure the true data signal, making it more difficult to identify and extract the patterns and relationships in the data. The expansion of the data dimension not only increases the data volume, but also significantly enhances the complexity of the data. In a high-dimensional space, the relationships between data points become more intricate. This increase in complexity not only increases the difficulty of data processing, but may also lead to problems such as the curse of dimensionality, severely affecting the accuracy and reliability of data analysis. In summary, there is a close and complex interaction relationship among the data dimension, data volume, and data noise factor. An increase in the data dimension will lead to an increase in the data volume, and the increase in the data volume may in turn trigger more data abnormalities and data noise, jointly exacerbating the data complexity and making it more complex for the management device to analyze the data.

[0046] Step 2: According to the performance evaluation factors of the management device to which the medical data set belongs, use artificial intelligence technology to encrypt the medical data set, mark the processed medical data set as an encrypted medical data set, collect the encryption parameters of the encrypted medical data set, and evaluate the storage complexity of the encrypted medical data set.

[0047] Specifically, the process of evaluating the storage complexity of the encrypted medical data set is as follows:

[0048] The encryption parameters of the encrypted medical data set include the data volume of the encrypted medical data set, the encryption complexity level of the encryption algorithm to which the encrypted medical data set belongs, and the encryption speed of the encrypted medical data set; the data volume of the above-mentioned encrypted medical data set represents the total amount of data contained in the encrypted data set and can be obtained by extracting from the data processing log of the management device; the encryption complexity level of the encryption algorithm to which the encrypted medical data set belongs is an index used to quantify the computing resources and time complexity required by the encryption algorithm when processing the encrypted medical data set. The encryption complexity levels corresponding to each encryption algorithm are stored in the medical database. The specific quantification rules of the encryption complexity level are formulated by the data analysis administrator based on the time complexity and space complexity of the encryption algorithm and can be directly queried from the medical database to obtain the encryption complexity level of the encryption algorithm to which the medical data set belongs; the encryption speed of the above-mentioned encrypted medical data set refers to the amount of data that can be encrypted per unit time during the encryption process of the management device for the encrypted medical data set. Extract the encryption duration of the encrypted medical data set from the data processing log of the management device, and perform a ratio process on the data volume of the encrypted medical data set and the encryption duration of the encrypted medical data set to obtain the encryption speed of the encrypted medical data set.

[0049] Perform a difference process on the data volume of the encrypted medical data set and the data volume of the medical data set, and perform a ratio process on the processing result and the data volume of the medical data set to finally obtain the data expansion rate of the encrypted medical data set, indicating the expansion degree of the data volume of the encrypted medical data set compared to the data volume before encryption.

[0050] Obtain the allowable storage capacity of the management device to which the encrypted medical data set belongs, and perform a difference process with the data volume of the encrypted medical data set to obtain the storage reserve capacity of the management device to which the encrypted medical data set belongs; the allowable storage capacity of the management device to which the above-mentioned encrypted medical data set belongs represents the remaining capacity of the management device to which the encrypted medical data set belongs before storing the encrypted medical data set and can be obtained by extracting from the storage management log of the management device; the storage reserve capacity of the management device to which the above-mentioned encrypted medical data set belongs represents the capacity that the management device can still use to store other data after storing the encrypted medical data set.

[0051] It should be noted that the encrypted medical dataset is the name after encrypting the medical dataset, and the management device to which the encrypted medical dataset belongs is the same as the management device to which the medical dataset belongs.

[0052] Obtain the performance evaluation factor of the management device to which the encrypted medical dataset belongs, and comprehensively evaluate the performance evaluation factor of the management device to which the encrypted medical dataset belongs, the storage reserve capacity of the management device to which the encrypted medical dataset belongs, the data inflation rate of the encrypted medical dataset, the encryption complexity level of the encryption algorithm to which the medical dataset belongs, and the encryption speed of the encrypted medical dataset to obtain the storage complexity of the encrypted medical dataset; the performance evaluation factor of the management device to which the encrypted medical dataset belongs is the performance evaluation factor of the management device to which the medical dataset belongs. In this embodiment, to keep the performance evaluation factor in the same naming dimension as the following storage complexity, the performance evaluation factor of the management device to which the medical dataset belongs is marked here as the performance evaluation factor of the management device to which the encrypted medical dataset belongs; the storage complexity of the encrypted medical dataset represents the complexity during the storage process of the encrypted medical dataset.

[0053] Specifically, the method for specifically evaluating the storage complexity of the encrypted medical dataset is as follows:

[0054]

[0055] In the formula, SMS is the storage complexity of the encrypted medical dataset, PEFD is the performance evaluation factor of the management device to which the encrypted medical dataset belongs, LR is the storage reserve capacity of the management device to which the encrypted medical dataset belongs, LP is the data inflation rate of the encrypted medical dataset, OT is the encryption complexity level of the encryption algorithm to which the medical dataset belongs, VM is the encryption speed of the encrypted medical dataset, YU is the storage complexity weight factor corresponding to the performance evaluation factor preset in the medical database, ow 1 is the storage complexity weight factor corresponding to the unit value of the storage reserve capacity preset in the medical database, ow 2 is the storage complexity weight factor corresponding to the unit value of the data inflation rate preset in the medical database, ow 3 is the storage complexity weight factor corresponding to the unit value of the encryption complexity level preset in the medical database, ow 4 The storage complexity weight factor corresponding to the unit value of the encryption speed preset in the medical database, and e is the natural constant.

[0056] The storage complexity weight factor corresponding to the above performance evaluation factor represents the proportion of the performance evaluation factor in the storage complexity of the encrypted medical dataset. The correspondence between the performance evaluation factor stored in the medical database and its corresponding storage complexity weight factor. For example, if the performance evaluation factor of the management device to which the encrypted medical dataset belongs is input into the medical database, the medical database can match the storage complexity weight factor corresponding to the performance evaluation factor, and the value range is between 0 and 1. The storage complexity weight factor corresponding to the above storage reserve capacity unit value represents the value of the influence degree of the storage reserve capacity unit value on the storage complexity of the encrypted medical dataset. The correspondence between the storage reserve capacity stored in the medical database and its corresponding storage complexity weight factor. For example, if the storage reserve capacity of the management device to which the encrypted medical dataset belongs is input into the medical database, the medical database can match the storage complexity weight factor corresponding to the storage reserve capacity unit value, and the value range is between 0 and 1. The storage complexity weight factor corresponding to the above data inflation rate unit value represents the value of the influence degree of the data inflation rate unit value on the storage complexity of the encrypted medical dataset. The correspondence between the data inflation rate stored in the medical database and its corresponding storage complexity weight factor. For example, if the data inflation rate of the encrypted medical dataset is input into the medical database, the medical database can match the storage complexity weight factor corresponding to the data inflation rate unit value, and the value range is between 0 and 1. The storage complexity weight factor corresponding to the above encryption complexity level unit value represents the value of the influence degree of the encryption complexity level unit value on the storage complexity of the encrypted medical dataset. The correspondence between the encryption complexity level stored in the medical database and its corresponding storage complexity weight factor. For example, if the encryption complexity level of the encryption algorithm to which the medical dataset belongs is input into the medical database, the medical database can match the storage complexity weight factor corresponding to the encryption complexity level unit value, and the value range is between 0 and 1. The storage complexity weight factor corresponding to the above encryption speed unit value represents the value of the influence degree of the encryption speed unit value on the storage complexity of the encrypted medical dataset. The correspondence between the encryption speed stored in the medical database and its corresponding storage complexity weight factor. For example, if the encryption speed of the encrypted medical dataset is input into the medical database, the medical database can match the storage complexity weight factor corresponding to the encryption speed unit value, and the value range is between 0 and 1.

[0057] It should be noted that the performance of the management device not only affects the ability of the management device to handle encryption and decryption operations, but also affects the encryption speed. A high-performance device can execute encryption algorithms faster, thus shortening the time required for the encryption process, which is crucial for improving the overall storage efficiency. The storage reserve capacity is the size of the space that the management device can accommodate the encrypted medical data set. When the medical data set is encrypted, it may occupy more storage space due to the characteristics of the encryption algorithm (such as the data expansion rate), increasing the data expansion rate of the encrypted data set. A high data expansion rate will exacerbate the consumption of the storage space of the management device. Therefore, when selecting an encryption algorithm, it is necessary to balance its security and data expansion rate. At the same time, the encryption complexity level of the encryption algorithm to which the encrypted medical data set belongs not only affects the encryption speed, but also indirectly affects the storage complexity. Although the encryption algorithm with a high complexity level provides higher security, it usually requires more computing resources and time to complete the encryption process, which may lead to delays and performance degradation during the storage process of the encrypted data set. At the same time, complex encryption algorithms may also increase the data expansion rate of the encrypted medical data set, further increasing the storage pressure of the encrypted medical data set under the limitation of the management device performance, resulting in a relatively high storage complexity of the encrypted medical data set and prone to packet loss phenomena.

[0058] Specifically, the encryption process of the medical data set by using artificial intelligence technology according to the performance evaluation factors of the management device to which the medical data set belongs is as follows: comparing the performance evaluation factors of the management device to which the medical data set belongs with the first performance evaluation factor interval, the second performance evaluation factor interval, and the third performance evaluation factor interval stored in the medical database; if the performance evaluation factors of the management device to which the medical data set belongs belong to the first performance evaluation factor interval, then matching the artificial intelligence first encryption strategy corresponding to the first performance evaluation factor interval preset in the database; if the performance evaluation factors of the management device to which the medical data set belongs belong to the second performance evaluation factor interval, then matching the artificial intelligence second encryption strategy corresponding to the second performance evaluation factor interval preset in the database; if the performance evaluation factors of the management device to which the medical data set belongs belong to the third performance evaluation factor interval, then matching the artificial intelligence third encryption strategy corresponding to the third performance evaluation factor interval preset in the database.

[0059] Further, the encryption process of the medical data set using artificial intelligence technology is as follows: If the first artificial intelligence encryption strategy is matched according to the performance evaluation factor of the management device to which the medical data set belongs, the medical data set is encrypted according to the first artificial intelligence encryption strategy, and the encrypted medical data set is obtained after the processing is completed; if the second artificial intelligence encryption strategy is matched according to the performance evaluation factor of the management device to which the medical data set belongs, the medical data set is encrypted according to the second artificial intelligence encryption strategy, and the encrypted medical data set is obtained after the processing is completed; if the third artificial intelligence encryption strategy is matched according to the performance evaluation factor of the management device to which the medical data set belongs, the medical data set is encrypted according to the third artificial intelligence encryption strategy, and the encrypted medical data set is obtained after the processing is completed.

[0060] In an exemplary embodiment, assume that the performance evaluation factor of the management device to which the medical data set belongs is G, the first performance evaluation factor range is [G - 220%, G - 120%], the second performance evaluation factor range is (G - 120%, G - 20%], and the third performance evaluation factor range is (G - 20%, G + 80%]. If the performance evaluation factor of the management device to which the medical data set belongs falls within the third performance evaluation factor range, the third artificial intelligence encryption strategy is matched to encrypt the medical data set. In this exemplary embodiment, the third artificial intelligence encryption strategy includes the Advanced Encryption Standard - 256-bit encryption algorithm; it should be noted that in this exemplary embodiment, the management device corresponding to the first performance evaluation factor range is a low-performance device, and the first artificial intelligence encryption strategy has simplicity, that is, in the case of limited management device performance, relatively simple encryption algorithms and technologies are adopted to ensure basic data security; the management device corresponding to the second performance evaluation factor range is a medium-performance device, and the second artificial intelligence encryption strategy has balance, that is, while ensuring a certain level of security, the encryption speed and resource consumption are taken into account to achieve the balance of performance and security; the management device corresponding to the third performance evaluation factor range is a high-performance device, and the third artificial intelligence encryption strategy has high efficiency, that is, due to the excellent performance of the management device, this strategy can make full use of the computing power of the device to achieve fast encryption and decryption, and also has high-level security, that is, advanced encryption algorithms and technologies are adopted to provide the highest level of protection for the medical data set to prevent data leakage and unauthorized access.

[0061] Step 3: Determine whether to use artificial intelligence technology to compress and store the encrypted medical data set according to the storage complexity of the encrypted medical data set.

[0062] In a specific embodiment, the present invention flexibly determines whether to implement a compression storage strategy using artificial intelligence technology based on the storage complexity of the encrypted medical data set. This can optimize the use of storage space, reduce storage overhead, and ensure the security and integrity of the data. Against the background of the rapid growth of medical data and the increasing storage requirements, this method not only improves the security and efficiency of storage but also lays a solid foundation for the convenient access and efficient management of the data.

[0063] Specifically, the method for determining whether to use artificial intelligence technology to compress and store the encrypted medical data set is as follows: According to the performance evaluation factor of the management device to which the encrypted medical data set belongs, the storage complexity threshold is matched from the detection database; the above storage complexity threshold represents the maximum value of the reasonable range of the storage complexity of the encrypted medical data set; among them, the specific matching process of the storage complexity threshold is: the storage complexity thresholds corresponding to each performance evaluation factor interval are stored in the medical database, and the performance evaluation factor interval to which the storage complexity of the encrypted medical data set belongs is queried in the medical database. The storage complexity threshold corresponding to this performance evaluation factor interval is the storage complexity threshold matched by the performance evaluation factor of the management device to which the encrypted medical data set belongs.

[0064] Compare the storage complexity of the encrypted medical data set with the storage complexity threshold. If the storage complexity of the encrypted medical data set is less than or equal to the storage complexity threshold, it is determined not to use artificial intelligence technology to compress and store the encrypted medical data set, and the encrypted medical data set is directly stored; if the storage complexity of the encrypted medical data set is greater than the storage complexity threshold, it is determined to use artificial intelligence technology to compress and store the encrypted medical data set; the above situation where the storage complexity of the encrypted medical data set is less than or equal to the storage complexity threshold indicates that the storage requirement of the encrypted medical data set is within the performance range of the management device, and the management device has sufficient resources and capabilities to directly store the encrypted medical data set without additional compression processing. The above situation where the storage complexity of the encrypted medical data set is greater than the storage complexity threshold indicates that the storage requirement of the encrypted medical data set has exceeded the direct storage capacity of the management device. If directly stored, it may lead to problems such as low storage efficiency, resource tension, and data loss. In order to make full use of the storage resources of the management device and ensure the integrity and security of the encrypted medical data set, it is necessary to use artificial intelligence technology for compression storage. Such a decision-making process takes into account both storage efficiency and data security as well as the performance limitations of the management device.

[0065] Further, the use of artificial intelligence technology to compress and store the encrypted medical dataset is as follows. The specific compression process is: taking the ratio of the storage complexity of the encrypted medical dataset to the storage complexity threshold, and marking the processing result as the storage compression reference ratio of the encrypted medical dataset. The artificial intelligence technology compresses the data capacity of the encrypted medical dataset according to the storage compression reference ratio of the encrypted medical dataset, and stores the compressed encrypted medical dataset; the above-mentioned storage compression reference ratio of the encrypted medical dataset represents the ratio between the storage complexity of the encrypted medical dataset and the storage complexity threshold. In an exemplary embodiment, assuming that the storage compression reference ratio of the encrypted medical dataset is 80%, the artificial intelligence technology compresses the data capacity of the encrypted medical dataset according to the storage compression reference ratio of the encrypted medical dataset, that is, compresses the data capacity of the encrypted medical dataset to 80% of the current data capacity. The compression process of the artificial intelligence technology is: using the JPEG2000 image compression algorithm to compress the images in the encrypted medical dataset, setting the compression ratio to 80%, using the Gzip data compression algorithm to compress the text in the encrypted medical dataset, setting a higher compression level to achieve an 80% compression ratio, using the H.265 compression algorithm to compress the videos in the encrypted medical dataset, setting appropriate encoding parameters to achieve an 80% compression ratio, while maintaining the smoothness and clarity of the videos.

[0066] In a specific embodiment, the present invention provides a medical data management method based on artificial intelligence. By collecting a medical dataset and obtaining its attribute parameters, analyzing the data complexity index, and considering the performance parameters of the management device to which the medical dataset belongs, comprehensively evaluating the device performance, using artificial intelligence technology, encrypting the medical dataset according to the performance evaluation factor, and marking it as an encrypted medical dataset. This step not only enhances the security of medical data but also ensures privacy protection during data transmission and storage. Further, collecting the encryption parameters of the encrypted medical dataset, evaluating the storage complexity, and accordingly intelligently determining whether to compress and store the encrypted dataset. This method effectively balances data security, privacy protection, and storage efficiency, reduces the risk anomalies that may occur during storage, and improves the intelligent level and security level of medical data management, achieving the efficient and secure storage of medical data.

[0067] Refer to Figure 2 As shown, the second aspect of the present invention provides a medical data management system based on artificial intelligence, including: a dataset analysis module, a dataset encryption module, and a dataset compression module.

[0068] In the second aspect of the present invention, a medical data management system based on artificial intelligence is provided, further including a medical database, which is used to store the data complexity index proportion factors corresponding to the data capacity unit values, the data complexity index proportion factors corresponding to the data dimension unit values, the data complexity index proportion factors corresponding to the data noise factor unit values, the weight factors corresponding to the data complexity index, the performance evaluation proportion factors corresponding to the cache miss rate unit values, the performance evaluation proportion factors corresponding to the CPU waiting queue length unit values, the performance evaluation proportion factors corresponding to the throughput volatility unit values, the first performance evaluation factor interval, the second performance evaluation factor interval, the third performance evaluation factor interval, the artificial intelligence first encryption strategy, the artificial intelligence second encryption strategy, the artificial intelligence third encryption strategy, the storage complexity weight factors corresponding to the performance evaluation factors, the storage complexity weight factors corresponding to the storage reserve capacity unit values, the storage complexity weight factors corresponding to the data inflation rate unit values, the storage complexity weight factors corresponding to the encryption complexity level unit values, the storage complexity weight factors corresponding to the encryption speed unit values, the encryption complexity level, the storage complexity threshold, the redundant field ratio factor, and the vacant field ratio factor.

[0069] The dataset analysis module is connected to the dataset encryption module, and the dataset encryption module is connected to the dataset compression module. The dataset analysis module, the dataset encryption module, and the dataset compression module are all connected to the information feedback center.

[0070] The dataset analysis module is used to collect medical datasets, obtain the attribute parameters of the medical datasets, analyze the data complexity index of the medical datasets, and at the same time obtain the performance parameters of the management devices to which the medical datasets belong, and comprehensively analyze the performance evaluation factors of the management devices to which the medical datasets belong based on the data complexity index of the medical datasets.

[0071] The dataset encryption module is used to encrypt the medical datasets by using artificial intelligence technology according to the performance evaluation factors of the management devices to which the medical datasets belong, mark the processed medical datasets as encrypted medical datasets, collect the encryption parameters of the encrypted medical datasets, and evaluate the storage complexity of the encrypted medical datasets.

[0072] The dataset compression module is used to determine whether to compress and store the encrypted medical datasets by using artificial intelligence technology according to the storage complexity of the encrypted medical datasets.

[0073] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A medical data management method based on artificial intelligence, characterized in that: include: Step 1: Collect medical data sets, obtain attribute parameters of the medical data sets, and analyze the data complexity index of the medical data sets. Meanwhile, obtain performance parameters of the management equipment to which the medical data sets belong, and analyze the performance evaluation factor of the management equipment to which the medical data sets belong based on the data complexity index of the medical data sets. Step 2: Based on the performance evaluation factor of the management device to which the medical data set belongs, the medical data set is encrypted using artificial intelligence technology, and the processed medical data set is marked as an encrypted medical data set, encryption parameters of the encrypted medical data set are collected, and the storage complexity of the encrypted medical data set is evaluated; Step 3: Based on the storage complexity of the encrypted medical data set, determine whether to use artificial intelligence technology to compress and store the encrypted medical data set.

2. The medical data management method based on artificial intelligence according to claim 1, characterized in that: The data complexity index of the medical data set is analyzed, and the specific analysis process is as follows: The attribute parameters of the medical data set include the data capacity of the medical data set, the data dimension of the medical data set, the ratio of vacant fields of the medical data set, and the ratio of redundant fields of the medical data set; The result of multiplying the missing field ratio of the medical data set by the missing field ratio factor is added to the result of multiplying the redundant field ratio of the medical data set by the redundant field ratio factor, and finally obtaining a data noise factor of the medical data set; The data capacity of the medical data set, the data dimension of the medical data set and the data noise factor of the medical data set are comprehensively analyzed to obtain the data complexity index of the medical data set. The data complexity index of the medical data set is specifically obtained by bringing the data capacity of the medical data set, the data dimension of the medical data set and the data noise factor of the medical data set into the data complexity index function to obtain the data complexity index of the medical data set.

3. The medical data management method based on artificial intelligence according to claim 1, characterized in that: The performance evaluation factor of the management device to which the medical data set belongs is analyzed, and the specific analysis process is as follows: The performance parameters of the management device to which the medical data set belongs include a real-time cache hit rate of the management device to which the medical data set belongs within a monitoring period, a real-time throughput of the management device to which the medical data set belongs within a monitoring period, and a real-time CPU waiting queue length of the management device to which the medical data set belongs within a monitoring period; Processing the real-time cache hit rate of the management device to which the medical data set belongs within the monitoring period to obtain the real-time cache miss rate of the management device to which the medical data set belongs within the monitoring period; Performing mean processing on the real-time throughput of the management device to which the medical data set belongs within the monitoring period to obtain the average throughput of the management device to which the medical data set belongs within the monitoring period, locating the maximum value and the minimum value from the real-time throughput of the management device to which the medical data set belongs within the monitoring period, and performing difference processing, performing ratio processing on the difference processing result and the average throughput of the management device to which the medical data set belongs within the monitoring period to obtain the throughput fluctuation rate of the management device to which the medical data set belongs within the monitoring period, and marking the value as the real-time throughput fluctuation rate of the management device to which the medical data set belongs within the monitoring period; The performance evaluation factor of the management device to which the medical dataset belongs is obtained by comprehensively analyzing the data complexity index of the medical dataset, the real-time cache failure rate of the management device to which the medical dataset belongs during the monitoring period, the real-time CPU waiting queue length of the management device to which the medical dataset belongs during the monitoring period, and the real-time throughput fluctuation rate of the management device to which the medical dataset belongs during the monitoring period.

4. The medical data management method based on artificial intelligence according to claim 1, characterized in that: According to the performance evaluation factor of the management device to which the medical data set belongs, the medical data set is encrypted using artificial intelligence technology. The specific process is as follows: comparing the performance evaluation factor of the management device to which the medical data set belongs with the first performance evaluation factor interval, the second performance evaluation factor interval, and the third performance evaluation factor interval stored in the medical database; If the performance evaluation factor of the management device to which the medical data set belongs belongs to the first performance evaluation factor interval, then the artificial intelligence first encryption strategy corresponding to the first performance evaluation factor interval preset in the database is matched; If the performance evaluation factor of the management device to which the medical data set belongs belongs to the second performance evaluation factor interval, then the artificial intelligence second encryption strategy corresponding to the second performance evaluation factor interval preset in the database is matched; If the performance evaluation factor of the management device to which the medical data set belongs belongs to the third performance evaluation factor interval, the artificial intelligence third encryption strategy corresponding to the third performance evaluation factor interval preset in the database is matched.

5. The medical data management method based on artificial intelligence according to claim 4, characterized in that: The medical data set is encrypted using artificial intelligence technology, and the specific encryption process is as follows: If the first artificial intelligence encryption strategy is matched according to the performance evaluation factor of the management device to which the medical data set belongs, the medical data set is encrypted according to the first artificial intelligence encryption strategy, and the encrypted medical data set is obtained after the processing is completed; If the second artificial intelligence encryption strategy is matched according to the performance evaluation factor of the management device to which the medical data set belongs, the medical data set is encrypted according to the second artificial intelligence encryption strategy, and the encrypted medical data set is obtained after the processing is completed; If the third artificial intelligence encryption strategy is matched according to the performance evaluation factor of the management device to which the medical data set belongs, the medical data set is encrypted according to the third artificial intelligence encryption strategy, and the encrypted medical data set is obtained after the processing is completed.

6. The medical data management method based on artificial intelligence according to claim 1, characterized in that: The storage complexity of the encrypted medical data set is evaluated, and the specific evaluation process is as follows: The encryption parameters of the encrypted medical data set include the data capacity of the encrypted medical data set, the encryption complexity level of the encryption algorithm to which the encrypted medical data set belongs, and the encryption speed of the encrypted medical data set; Performing difference processing on the data capacity of the encrypted medical data set and the data capacity of the medical data set, and performing ratio processing on the processing result and the data capacity of the medical data set, and finally obtaining the data expansion rate of the encrypted medical data set; Obtaining the allowed storage capacity of the management device to which the encrypted medical dataset belongs, and performing subtraction processing with the data capacity of the encrypted medical dataset to obtain the storage reserve capacity of the management device to which the encrypted medical dataset belongs; The performance evaluation factor of the management device to which the encrypted medical dataset belongs is obtained, and the performance evaluation factor of the management device to which the encrypted medical dataset belongs, the storage reserve capacity of the management device to which the encrypted medical dataset belongs, the data expansion rate of the encrypted medical dataset, the encryption complexity level of the encryption algorithm to which the medical dataset belongs, and the encryption speed of the encrypted medical dataset are comprehensively evaluated to obtain the storage complexity of the encrypted medical dataset.

7. The medical data management method based on artificial intelligence according to claim 6, characterized in that: The storage complexity of the encrypted medical dataset is evaluated by: Wherein, SMS is the storage complexity of the encrypted medical dataset, PEFD is the performance evaluation factor of the management device to which the encrypted medical dataset belongs, LR is the storage reserve capacity of the management device to which the encrypted medical dataset belongs, LP is the data expansion rate of the encrypted medical dataset, OT is the encryption complexity level of the encryption algorithm to which the medical dataset belongs, VM is the encryption speed of the encrypted medical dataset, YU is the storage complexity weight factor corresponding to the performance evaluation factor preset in the medical database, ow1 is the storage complexity weight factor corresponding to the unit value of the storage reserve capacity preset in the medical database, ow2 is the storage complexity weight factor corresponding to the unit value of the data expansion rate preset in the medical database, ow3 is the storage complexity weight factor corresponding to the unit value of the encryption complexity level preset in the medical database, ow4 is the storage complexity weight factor corresponding to the unit value of the encryption speed preset in the medical database, and e is a natural constant.

8. The medical data management method based on artificial intelligence according to claim 1, characterized in that: The specific determination method for determining whether to compress and store the encrypted medical data set using artificial intelligence technology is as follows: According to the performance evaluation factor of the management device to which the encrypted medical data set belongs, a storage complexity threshold is matched from the detection database; Comparing the storage complexity of the encrypted medical data set with the storage complexity threshold, if the storage complexity of the encrypted medical data set is less than or equal to the storage complexity threshold, determining not to compress and store the encrypted medical data set using artificial intelligence technology, and directly storing the encrypted medical data set; If the storage complexity of the encrypted medical data set is greater than the storage complexity threshold, it is determined to compress and store the encrypted medical data set using artificial intelligence technology.

9. The medical data management method based on artificial intelligence according to claim 8, characterized in that: The encrypted medical data set is compressed and stored using artificial intelligence technology. The specific compression process is as follows: The storage complexity of the encrypted medical dataset is ratio-processed with the storage complexity threshold, and the processing result is marked as the storage compression reference ratio of the encrypted medical dataset. Artificial intelligence technology compresses the data capacity of the encrypted medical dataset based on the storage compression reference ratio of the encrypted medical dataset, and stores the compressed encrypted medical dataset.

10. A system using the artificial intelligence-based medical data management method according to any one of claims 1 to 9, characterized in that: include: The data set analysis module is used to collect medical data sets, obtain attribute parameters of medical data sets, and analyze the data complexity index of medical data sets, and at the same time obtain the performance parameters of the management equipment to which the medical data sets belong, and analyze the performance evaluation factor of the management equipment to which the medical data sets belong based on the data complexity index of the medical data sets; A data set encryption module is used to encrypt the medical data set using artificial intelligence technology according to the performance evaluation factor of the management device to which the medical data set belongs, mark the processed medical data set as an encrypted medical data set, collect encryption parameters of the encrypted medical data set, and evaluate the storage complexity of the encrypted medical data set; The data set compression module is used to determine whether to use artificial intelligence technology to compress and store the encrypted medical data set according to the storage complexity of the encrypted medical data set.

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