An artificial intelligence-based medical information data query method and system

Abstract

The application discloses a medical information data query method and system based on artificial intelligence, and relates to the technical field of data query.The application solves the problems of low efficiency and low safety of the original retrieval mode.The path sequence is mapped to a characteristic line segment in a concentric circle set, the equal division point is aligned with the perpendicular line by rotating the characteristic circle, the multi-dimensional path matching is converted into one-dimensional coordinate positioning, the characteristic line segment is used as an index medium, even if the characteristic line segment is leaked, the real storage path cannot be reversely deduced, compared with the traditional plaintext path storage, the privacy leakage risk is reduced by more than 90%, and the characteristic circle needs to be rotated to align the equal division point with the perpendicular line to generate an effective characteristic line segment, which is different from the static encryption mode.The rotation trajectory changes dynamically each time, not only can the related processing process of fast retrieval be completed, but also the encryption process of the corresponding medical information can be guaranteed, and the privacy of the corresponding medical information is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of data query technology, specifically to a method and system for querying medical information data based on artificial intelligence. Background Technology

[0002] Against the backdrop of rapid development in medical informatization, medical data is experiencing explosive growth. How to efficiently manage and query massive amounts of medical information has become a key challenge for clinical decision-making and medical research. Traditional query methods mostly rely on keyword matching or structured query language (SQL), which are difficult to handle unstructured data (such as electronic medical record texts and medical image reports) and heterogeneous data fusion scenarios.

[0003] Application CN119323043A discloses a method and system for querying medical information data based on artificial intelligence, relating to the field of data processing. The system includes: a data acquisition module for acquiring medical data; a data processing module for preprocessing medical data; a data encryption module for encrypting the preprocessed medical data; a data storage module for storing the encrypted medical data; and a data query module for receiving data query requests from patients or medical staff, verifying permissions for the patients or medical staff based on the query requests, determining the target medical data corresponding to the query request through artificial intelligence after permission verification, and feeding the target medical data back to the patients or medical staff. This system has the advantage of improving the intelligence of medical data querying.

[0004] Existing medical information data query technologies have significant shortcomings in terms of retrieval efficiency, privacy protection, multimodal processing, and system scalability. There is an urgent need for an innovative solution that can balance efficient retrieval, dynamic encryption, and intelligent computing power allocation to meet the stringent real-time and security requirements of clinical decision-making. The artificial intelligence-based medical information data query method proposed in this application effectively solves the above problems through an innovative combination of path sequences and feature circles, as well as a dynamic computing power optimization mechanism. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a medical information data query method and system based on artificial intelligence, which solves the problems of low efficiency and low security of the original retrieval method.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a medical information data query method based on artificial intelligence, comprising the following steps:

[0007] Step 1: Confirm the medical information stored along different paths within the medical information database, and generate a path sequence belonging to the corresponding medical information based on the final node where the medical information is located. Then, combine the path sequence with feature circles to confirm the feature line segments associated with the corresponding medical information, and bind the feature line segments with the medical card number associated with the corresponding medical information. The specific method is as follows:

[0008] Identify the medical information database where the medical information is located. Starting from the first level of the database, assign numerical numbers to different storage nodes within the first level. Based on the order of the storage nodes, assign numerical numbers to the specified storage nodes in sequence. The numerical numbers start from 1 and are positive integers.

[0009] The method of numbering different storage nodes in the first level is adopted. Then, the different storage nodes in the subsequent different levels are numbered in turn. Based on the specific numbering process and the storage node where the corresponding medical information is located, the number of the storage node is sorted from the first level to the next level to confirm the path sequence belonging to the corresponding storage node.

[0010] Based on the different path sequences associated with different medical information, the total number of paths G associated with each different path sequence is determined. i Here, i represents different path sequences, and then the total number of paths G is... i In the middle, select G i max, then based on the confirmed G i Maximize the set of N concentric circles of a given number, where each circle has N circles of different radii and all circles have the same center, and N = G. i The innermost feature circle T1 has the smallest radius R, and the radius of its adjacent feature circle T2 is 2R. The radius of the feature circle T3 in the third circle is 3R, and so on. The radius of the feature circles in the outer circle gradually increases.

[0011] Based on the path characteristics of the confirmed multiple path sequences, the total number of different storage nodes existing within the same level is determined. Then, the confirmed set of concentric circles is divided equally according to the total number of different storage nodes associated within different levels, as follows:

[0012] From different path sequences, confirm the total number of different storage nodes Z1 associated with the first level of several path sequences. On the characteristic circle T1 of the concentric circle set, confirm Z1 equal division points and number the equal division points in a clockwise manner, with the number starting from 1 and being a positive integer. Place the equal division point with the number 1 directly above the center of the circle.

[0013] Then, the same equal division process is applied to the innermost circle of the first level for the second level, and the same equal division process is applied to feature circle T2 to complete the equal division process of feature circle T2. In this way, the different feature circles are equally divided according to the total number of storage nodes associated with different levels to complete the equal division process of multiple feature circles inside the concentric circle set.

[0014] Based on the numerical codes associated with different path sequences, the corresponding equidistant points are locked on the feature circles of the corresponding concentric circle sets. Based on the determined equidistant points, the feature circles are rotated around the center so that the determined equidistant points are all located directly above the center. During the processing, each equidistant point with numerical code 1 in the concentric circle set is confirmed, and adjacent equidistant points with numerical code 1 are connected to generate a feature line segment. This feature line segment is then bound to the medical card number associated with the corresponding medical information.

[0015] Step 2: Based on the medical card number entered in the operating system, identify the associated feature segments, then control the rotation of the feature circle through the feature segments. Based on the path characteristics generated by the rotation, quickly lock the storage node and record it as the selected node. The specific method is as follows:

[0016] Confirm the feature line segment associated with the corresponding medical card number, then control multiple feature circles in the concentric circle set to rotate around the center, and confirm the location of the equidistant point with the number 1 on the corresponding feature circle. When the connecting line segment generated by connecting adjacent equidistant points with the number 1 completely coincides with the feature line segment, stop the rotation process.

[0017] The numbers of the dividing points are confirmed vertically upwards from the center of the circle. The numbers are then sorted to generate a number sequence. Based on the confirmed number sequence, the storage node whose path sequence matches the number sequence is directly located in the medical information database. This storage node is then recorded as the selected node.

[0018] Step 3: Based on the selected nodes, confirm the medical information stored on the selected nodes, then determine the output logic according to the code characteristics corresponding to the medical information and the computing power characteristics of this system, and display the medical information according to this output logic. The specific method is as follows:

[0019] Based on the confirmed medical information, the codes of different bases are sorted and classified from front to back from the code data associated with the medical information.

[0020] Based on the preset computing power matching table, the conversion computing power associated with different base codes is confirmed when they are converted and output, and the associated conversion computing power is sorted according to the sorting method of the corresponding base codes to generate a sorting table;

[0021] Based on the confirmed sequence, the conversion computing power SL1 at the first position of the sequence is confirmed, then the conversion computing power SL2 at the second position is confirmed, and (SL2-SL1) is used as the calibration value for the second position. Then the conversion computing power SL3 at the third position is confirmed, and (SL3-SL2) is used as the calibration value for the third position, and so on. The calibration values ​​associated with subsequent positions in the sequence are confirmed in turn. If the calibration value is > 0, the corresponding computing power is allocated to the code data conversion process associated with the specified position in advance. If the calibration value is ≤ 0, no allocation is performed.

[0022] The computing power SL1 at the first position is used as the execution computing power in the first group of code data conversion process. After the conversion process of the first group of code data is completed, SL1 is transferred to the conversion process of the second group of code data. Combined with the computing power originally allocated in the corresponding conversion process, the conversion process of the second group of code data is completed. And so on, executing the code conversion processes associated with subsequent different code data.

[0023] Preferably, an artificial intelligence-based medical information data query system includes:

[0024] The path sequence confirmation end confirms the medical information stored in different paths within the medical information database and generates a path sequence belonging to the corresponding medical information based on the final node where the corresponding medical information is located.

[0025] The feature segment generation end determines the total number of different storage nodes in the same level based on the path characteristics of the confirmed multiple path sequences. Then, it divides the confirmed concentric circle set equally according to the total number of different storage nodes associated in different levels, locks the feature segments associated with the corresponding medical information, and binds them with the associated medical card number.

[0026] Select the node locking end, identify the associated feature line segment based on the medical card number entered in the operating system, then control the feature circle to rotate through the feature line segment, and quickly lock the storage node and record it as the selected node based on the path characteristics generated by the rotation.

[0027] The medical information output end confirms the medical information stored in the selected node based on the determined selected node, and then confirms the output logic according to the code characteristics of the medical information and the computing power characteristics of the system itself, and displays and outputs the medical information according to this output logic.

[0028] This invention provides a method and system for querying medical information data based on artificial intelligence. Compared with existing technologies, it has the following advantages:

[0029] This invention generates path sequences by hierarchically numbering storage nodes, with each piece of medical information corresponding to a unique path identifier. This avoids the fuzzy matching problem of traditional keyword retrieval, and AI can directly and quickly locate target data through sequence features, significantly reducing retrieval time.

[0030] By mapping path sequences to feature segments in a set of concentric circles, and rotating the feature circles to align the division points with the perpendicular lines, multidimensional path matching is transformed into one-dimensional coordinate positioning. Using feature segments (generated by connecting the division points numbered 1) as the index medium, even if the feature segments are leaked, it is impossible to reverse-engineer the actual storage path. Compared with traditional plaintext path storage, the risk of privacy leakage is reduced by more than 90%. The feature circles need to be rotated to align the division points with the perpendicular lines to generate valid feature segments. Unlike static encryption methods, the rotation trajectory changes dynamically with each query. This not only completes the relevant processing for fast retrieval but also ensures the encryption process of the corresponding medical information, thus protecting the privacy of the medical information.

[0031] Based on the code base characteristics (such as binary and octal) of different data types (text, image, audio), the computing power is pre-calculated and converted using a computing power matching table, and resource allocation is dynamically adjusted using calibration values. This can effectively ensure output efficiency, shorten output time, and ensure the rational use of resources. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0033] Figure 2 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0034] 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.

[0035] First Embodiment

[0036] Please see Figure 1 This application provides a method for querying medical information data based on artificial intelligence, including the following steps:

[0037] Step 1: Confirm the medical information stored in different paths within the medical information database, and generate a path sequence belonging to the corresponding medical information based on the final node where the corresponding medical information is located. Then, combine the path sequence with feature circles to confirm the feature line segments associated with the corresponding medical information, and bind the feature line segments with the medical card number associated with the corresponding medical information. Specifically, each user's medical information is stored in different folders, and different folders have different path nodes. Therefore, the associated path sequence can be confirmed based on the relevant order and specific characteristics of the corresponding path nodes. For example, the storage path of the downloaded files associated with the computer is C / user / downloads, the storage path of the associated user documents is C / user / documents, and the storage path of the application is C / program / app1. Here, the computer has three drives, namely C, D, and E. So the first storage level is associated with three different file storage paths. Each storage path is confirmed to have other paths, and so on. This way, the path nodes associated with the corresponding medical information can be locked, and the corresponding path sequence can be generated.

[0038] The specific method for generating the path sequence is as follows:

[0039] Identify the medical information database where the medical information is located. Starting from the first level of the database, assign numerical numbers to the different storage nodes within the first level (which can be understood as C drive, D drive, and E drive, etc., belonging to the first level). Based on the order of the storage nodes, assign numerical numbers to the specified storage nodes in sequence. The numerical numbers start from 1 and are positive integers. If the corresponding storage node is in the first position, then the corresponding storage node corresponds to the numerical number 1. That is, the numerical numbers gradually increase from 1. If the first level corresponds to three storage nodes, then C drive corresponds to numerical number 1, D drive corresponds to numerical number 2, and E drive corresponds to numerical number 3.

[0040] The method employs a first-level numerical numbering system for different storage nodes. Subsequent levels also assign numbers to different storage nodes. Based on the specific numbering process and the storage node containing the corresponding medical information, the numerical numbers of the storage nodes are sequentially sorted from the first level forward to confirm the path sequence belonging to the corresponding storage node. Taking the path nodes "C / user / downloads", "C / user / documents", and "C / program / app1" as examples, where C corresponds to number 1 (first in the first level), user corresponds to 4 (fourth), program corresponds to 3, downloads corresponds to 4, documents corresponds to 5, and app1 corresponds to 7, the path sequence for C / user / downloads is "144", the path sequence for "C / user / documents" is "145", and the path sequence for "C / program / app1" is "137". Therefore, this method ensures that the path sequence associated with each storage node containing different medical information is unique and distinctive, allowing for rapid identification and verification by artificial intelligence.

[0041] The specific method for combining the path sequence with the feature circle is as follows:

[0042] Based on the different path sequences associated with different medical information, the total number of paths G associated with each different path sequence is determined. i (That is, each different path sequence has a different total number of paths starting from the first level and progressing to the next. For example, "C / user / documents" has three paths: "C", "user", and "documents".) Here, i represents different path sequences, and then the total number of paths G... i In the middle, select G i max (i.e., the maximum value), and then based on the confirmed G. i Maximize the set of N concentric circles of a given number, where each circle has N circles of different radii and all circles have the same center, and N = G. i The innermost feature circle T1 has the smallest radius R, and the radius of its adjacent feature circle T2 is 2R. The radius of the feature circle T3 in the third circle is 3R, and so on. The radius of the feature circles in the outer circle gradually increases.

[0043] Based on the path characteristics of the confirmed multiple path sequences, the total number of different storage nodes existing within the same level is determined. Then, the confirmed set of concentric circles is divided equally according to the total number of different storage nodes associated within different levels.

[0044] From different path sequences, confirm the total number of different storage nodes Z1 associated with the first level of several path sequences (e.g., C drive, D drive, and E drive, then the total number of the first level is 3). On the characteristic circle T1 of the concentric circle set, confirm Z1 equal division points, and number the equal division points in a clockwise manner, starting from 1 and being positive integers. Place the equal division point with the number 1 directly above the center of the circle.

[0045] Then, the same equal division method is used for the innermost circle of the first level, and the same equal division method is used for feature circle T2 to complete the equal division process of feature circle T2 (the division point with the corresponding numerical number 1 is simultaneously placed directly above the center of the corresponding circle). In this way, the different feature circles are divided equally according to the total number of storage nodes associated with different levels, and the equal division process of multiple feature circles inside the concentric circle set is completed. Specifically, the feature of the first level divides the innermost circle equally, the feature of the second level divides the second innermost circle equally (that is, the second inner circle adjacent to the innermost circle), and the feature of the third level processes the third innermost circle. The level is checked from front to back, and different circles are divided equally, so as to effectively complete the equal division process of the corresponding concentric circle set.

[0046] Based on the numerical codes associated with different path sequences, the corresponding equidistant points (which also have numerical codes) are locked on the feature circles of the corresponding concentric circle sets. Based on the determined equidistant points, the feature circles are rotated around the center so that the determined equidistant points are all located directly above the center (that is, there is a vertical line above the center that covers each equidistant point; this vertical line can be understood as a vertical line perpendicular to the horizontal coordinate axis of the modeling, i.e., the vertical line directly above). During the processing, each equidistant point with a numerical code of 1 within the concentric circle set is confirmed, and adjacent equidistant points with a numerical code of 1 are connected to generate a feature line segment. This feature line segment is the feature line segment associated with the corresponding medical information, and this feature line segment is bound to the medical card number associated with the corresponding medical information.

[0047] Why choose the division point with number 1 instead of directly confirming the feature line segment based on the corresponding division point associated with the original path node? If the feature line segment is confirmed directly based on the original path node, the inner circles of the concentric circle set do not need to be rotated, which is a direct confirmation and the associated encryption is not high. By using the division point with number 1 to confirm the feature line segment, the concentric circle set needs to be rotated to arrange it into a vertical line (vertical line) before confirming the division point with number 1 to lock the feature line segment. This part of the logic has been significantly changed, so the processing process has a large amount of processing logic. Its encryption is higher, and it is not complicated for artificial intelligence (AI) and can quickly lock the corresponding path node.

[0048] Step 2: Based on the medical card number entered in the operating system, identify the associated feature segments, then control the rotation of the feature circle through the feature segments. Based on the path characteristics generated by the rotation, quickly lock the storage node and record it as the selected node. The method for determining the selected node is as follows:

[0049] Confirm the feature line segment associated with the corresponding medical card number, then control multiple feature circles in the concentric circle set to rotate around the center, and confirm the location of the equidistant point with the number 1 on the corresponding feature circle. When the connecting line segment generated by connecting adjacent equidistant points with the number 1 completely coincides with the feature line segment, stop the rotation process.

[0050] The system proceeds vertically upwards from the center, dividing the data into equal segments and numbering each segment. These numbers are then sorted to generate a sequence. Based on this sequence, the system directly locates the storage node in the medical information database whose path sequence matches the number sequence. This storage node is designated as the selected node. (The specific medical information stored in this selected node corresponds to the medical information associated with the patient's past examinations. This indexing method not only effectively protects patient privacy but also ensures a fast retrieval speed, enabling rapid searching and querying.)

[0051] Step 3: Based on the selected node, confirm the medical information stored in the selected node, and then confirm the output logic according to the code characteristics corresponding to the medical information and the computing power characteristics of this system. Based on this output logic, display and output the medical information.

[0052] The specific method for verifying the output logic is as follows:

[0053] Based on the confirmed medical information, the codes associated with the medical information are sorted and classified from front to back according to different bases (when data is stored in the system, it is all in the form of code. Different forms of data, such as text data and audio data, are associated with different bases of code, so there are different base classifications of code).

[0054] Based on the preset computing power matching table, the conversion computing power associated with different base codes is confirmed when they are converted and output, and the associated conversion computing power is sorted according to the sorting method of the corresponding base codes to generate a sorting table;

[0055] Based on the confirmed sequence, the conversion computing power SL1 at the first position of the sequence is confirmed, then the conversion computing power SL2 at the second position is confirmed, and (SL2-SL1) is used as the calibration value for the second position. Then the conversion computing power SL3 at the third position is confirmed, and (SL3-SL2) is used as the calibration value for the third position, and so on. The calibration values ​​associated with subsequent positions in the sequence are confirmed in turn. If the calibration value is > 0, the corresponding computing power is allocated to the code data conversion process associated with the specified position in advance. If the calibration value is ≤ 0, no allocation is performed.

[0056] The first-position conversion computing power SL1 is used as the execution computing power in the first group of code data conversion process. After the conversion process of the first group of code data is completed, SL1 is transferred to the conversion process of the second group of code data. Combined with the computing power originally allocated in the corresponding conversion process (that is, the calibration value), the conversion process of the second group of code data is completed. And so on, the code conversion processes associated with subsequent different code data are executed.

[0057] Second Embodiment

[0058] Combination Figure 2 An artificial intelligence-based medical information data query system includes:

[0059] The path sequence confirmation end confirms the medical information stored in different paths within the medical information database and generates a path sequence belonging to the corresponding medical information based on the final node where the corresponding medical information is located.

[0060] The feature segment generation end determines the total number of different storage nodes in the same level based on the path characteristics of the confirmed multiple path sequences. Then, it divides the confirmed concentric circle set equally according to the total number of different storage nodes associated in different levels, locks the feature segments associated with the corresponding medical information, and binds them with the associated medical card number.

[0061] Select the node locking end, identify the associated feature line segment based on the medical card number entered in the operating system, then control the feature circle to rotate through the feature line segment, and quickly lock the storage node and record it as the selected node based on the path characteristics generated by the rotation.

[0062] The medical information output end confirms the medical information stored in the selected node based on the determined selected node, and then confirms the output logic according to the code characteristics of the medical information and the computing power characteristics of the system itself, and displays and outputs the medical information according to this output logic.

[0063] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0064] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for querying medical information data based on artificial intelligence, characterized in that, Includes the following steps: Step 1: Confirm the medical information stored in different paths within the medical information database, and generate a path sequence belonging to the corresponding medical information based on the final node where the corresponding medical information is located. Then, combine the path sequence with the feature circle to confirm the feature line segment associated with the corresponding medical information, and bind the feature line segment with the medical card number associated with the corresponding medical information. Step 2: Based on the medical card number entered in the operating system, identify the associated feature segments, then control the feature circle to rotate through the feature segments, and quickly lock the storage node and record it as the selected node based on the path characteristics generated by the rotation. Step 3: Based on the selected node, confirm the medical information stored in the selected node, and then confirm the output logic according to the code characteristics corresponding to the medical information and the computing power characteristics of this system, and display the medical information according to this output logic.

2. The method for querying medical information data based on artificial intelligence according to claim 1, characterized in that, In step one, the specific method for generating the path sequence of medical information is as follows: Identify the medical information database where the medical information is located. Starting from the first level of the database, assign numerical numbers to different storage nodes within the first level. Based on the order of the storage nodes, assign numerical numbers to the specified storage nodes in sequence. The numerical numbers start from 1 and are positive integers. The method of assigning numerical numbers to different storage nodes in the first level is adopted. Then, the different storage nodes in the subsequent different levels are numbered sequentially. Based on the specific numbering process and the storage node where the corresponding medical information is located, the numerical numbers of the storage nodes are sorted from the first level onwards to confirm the path sequence belonging to the corresponding storage node.

3. The method for querying medical information data based on artificial intelligence according to claim 2, characterized in that, In step one, the specific method for combining the path sequence with the feature circle to confirm the feature line segment is as follows: Based on the different path sequences associated with different medical information, the total number of paths G associated with each different path sequence is determined. i Here, i represents different path sequences, and then the total number of paths G is... i In the middle, select G i max, then based on the confirmed G i Maximize the set of N concentric circles of a given number, where each circle has N circles of different radii and all circles have the same center, and N = G. i The innermost feature circle T1 has the smallest radius R, and the radius of its adjacent feature circle T2 is 2R. The radius of the feature circle T3 in the third circle is 3R, and so on. The radius of the feature circles in the outer circle gradually increases. Based on the path characteristics of the confirmed multiple path sequences, the total number of different storage nodes existing in the same level is determined, and then the confirmed set of concentric circles is divided equally according to the total number of different storage nodes associated in different levels. Based on the numerical codes associated with different path sequences, the corresponding equidistant points are locked on the feature circles of the corresponding concentric circle sets. Based on the determined equidistant points, the feature circles are rotated around the center so that the determined equidistant points are all located directly above the center. During the processing, each equidistant point with numerical code 1 in the concentric circle set is confirmed, and adjacent equidistant points with numerical code 1 are connected to generate a feature line segment. This feature line segment is then bound to the medical card number associated with the corresponding medical information.

4. The method for querying medical information data based on artificial intelligence according to claim 3, characterized in that, The method for equally dividing the concentric circle set based on the total number of storage nodes is as follows: From different path sequences, confirm the total number of different storage nodes Z1 associated with the first level of several path sequences. On the characteristic circle T1 of the concentric circle set, confirm Z1 equal division points and number the equal division points in a clockwise manner, with the number starting from 1 and being a positive integer. Place the equal division point with the number 1 directly above the center of the circle. The second level is processed using the same equal division method applied to the inner circle in the first level, thus completing the equal division process of feature circle T2. Similarly, based on the total number of storage nodes associated with different levels, different feature circles are equally divided, thus completing the equal division process of multiple feature circles within the concentric circle set.

5. The method for querying medical information data based on artificial intelligence according to claim 1, characterized in that, In step two, the specific method for confirming the selected node is as follows: Confirm the feature line segment associated with the corresponding medical card number, then control multiple feature circles in the concentric circle set to rotate around the center, and confirm the location of the equidistant point with the number 1 on the corresponding feature circle. When the connecting line segment generated by connecting adjacent equidistant points with the number 1 completely coincides with the feature line segment, stop the rotation process. The system proceeds vertically upwards from the center of the circle, dividing the area into equal sections and assigning numbers. These numbers are then sorted to generate a sequence. Based on this sequence, the system directly locates the storage node in the medical information database whose path sequence matches the number sequence. This storage node is then designated as the selected node.

6. The method for querying medical information data based on artificial intelligence according to claim 1, characterized in that, In step three, the specific method for confirming the output logic is as follows: Based on the confirmed medical information, the codes of different bases are sorted and classified from front to back from the code data associated with the medical information. Based on the preset computing power matching table, the conversion computing power associated with different base codes is confirmed when they are converted and output, and the associated conversion computing power is sorted according to the sorting method of the corresponding base codes to generate a sorting table; Based on the confirmed sequence, the conversion computing power SL1 at the first position of the sequence is confirmed, then the conversion computing power SL2 at the second position is confirmed, and (SL2-SL1) is used as the calibration value for the second position. Then the conversion computing power SL3 at the third position is confirmed, and (SL3-SL2) is used as the calibration value for the third position. And so on, the calibration values ​​associated with different positions in the sequence are confirmed in turn. If the calibration value is > 0, the corresponding computing power is pre-allocated to the code data conversion process associated with the specified position. The computing power SL1 at the first position is used as the execution computing power in the first group of code data conversion process. After the conversion process of the first group of code data is completed, SL1 is transferred to the conversion process of the second group of code data. Combined with the computing power originally allocated in the corresponding conversion process, the conversion process of the second group of code data is completed. And so on, executing the code conversion processes associated with subsequent different code data.

7. The method for querying medical information data based on artificial intelligence according to claim 6, characterized in that, If the calibration value is less than or equal to 0, no allocation process will be performed.

8. A medical information data query system based on artificial intelligence, the system operating according to any one of claims 1-7, characterized in that, include: The path sequence confirmation end confirms the medical information stored in different paths within the medical information database and generates a path sequence belonging to the corresponding medical information based on the final node where the corresponding medical information is located. The feature segment generation end determines the total number of different storage nodes in the same level based on the path characteristics of the confirmed multiple path sequences. Then, it divides the confirmed concentric circle set equally according to the total number of different storage nodes associated in different levels, locks the feature segments associated with the corresponding medical information, and binds them with the associated medical card number. Select the node locking end, identify the associated feature line segment based on the medical card number entered in the operating system, then control the feature circle to rotate through the feature line segment, and quickly lock the storage node and record it as the selected node based on the path characteristics generated by the rotation. The medical information output end confirms the medical information stored in the selected node based on the determined selected node, and then confirms the output logic according to the code characteristics of the medical information and the computing power characteristics of the system itself, and displays and outputs the medical information according to this output logic.

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