Medical information data query method and system based on artificial intelligence

Abstract

The invention discloses a medical information data query method and system based on artificial intelligence, relates to the technical field of data query, and solves the problems of low efficiency and relatively low security of an original retrieval mode. Multi-dimensional path matching is converted into one-dimensional coordinate positioning by rotating a feature circle to enable equal division points to be aligned with a vertical line, a feature line segment is used as an index medium, even if the feature line segment is leaked, a real storage path cannot be reversely derived, and compared with traditional plaintext path storage, the privacy leakage risk is reduced by 90% or above, and the privacy protection effect is improved. The feature circle can generate effective feature line segments only by rotating the equal division points to be aligned with the vertical line, and different from a static encryption mode, the rotating track of each query is dynamically changed, so that the related processing process of quick retrieval can be completed, the encryption process of the corresponding medical information can be synchronously guaranteed, and the privacy of the corresponding medical information is guaranteed.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of data query, and particularly relates to a medical information data query method and system based on artificial intelligence. BACKGROUND

[0002] Under the background of rapid development of medical informatization, medical data presents explosive growth, how to efficiently manage and query massive medical information becomes a key challenge for clinical decision and medical research, and traditional query methods mainly depend on keyword matching or structured query language (SQL), and it is difficult to process unstructured data (such as electronic medical record texts and medical image reports) and heterogeneous data fusion scenarios.

[0003] The application with the publication number CN119323043A discloses a medical information data query method and system based on artificial intelligence, and relates to the field of data processing, wherein the system comprises: a data acquisition module for acquiring medical data; a data processing module for preprocessing the 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 a data query request from a patient terminal or a medical terminal, verifying the rights of the patient terminal or the medical terminal based on the data query request, determining target medical data corresponding to the data query request through artificial intelligence after the right verification, and feeding back the target medical data to the patient terminal or the medical terminal, thereby improving the intelligence of medical data query.

[0004] The existing medical information data query technology has significant deficiencies in terms of retrieval efficiency, privacy protection, multi-modal processing and system scalability, and an innovative solution that can balance efficient retrieval, dynamic encryption and intelligent computing power allocation is urgently needed to meet the strict requirements of real-time and security for clinical decision-making. The medical information data query method based on artificial intelligence provided in the application effectively solves the above problems through the innovative combination of path sequences and feature circles and the dynamic optimization mechanism of computing power. SUMMARY

[0005] In view of the deficiencies of the prior art, the application provides a medical information data query method and system based on artificial intelligence, which solves the problems of low efficiency and low safety of the original retrieval method.

[0006] To achieve the above object, the application is implemented by the following technical scheme: a medical information data query method based on artificial intelligence, comprising the following steps:

[0007] Step one, confirm the medical information stored in the different paths of the medical information database, and generate the path sequence belonging to the corresponding medical information according to the final node where the corresponding medical information is located, then combine the path sequence with the characteristic circle, confirm the characteristic line segment associated with the corresponding medical information, and bundle the characteristic line segment with the medical card number associated with the corresponding medical information, the specific way is:

[0008] Confirm the medical information database where the medical information is located, start from the first layer of the database, and number the different storage nodes in the first layer. According to the front and back ordering relationship of the storage node ordering, the specified storage node is numbered in turn, and the number is a positive integer starting from 1;

[0009] The first layer is numbered in different ways. The different storage nodes in the subsequent different layers are numbered in turn, and 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 layer to the back, and the path sequence belonging to the corresponding storage node is confirmed;

[0010] According to the different path sequences associated with different medical information, confirm the total number of paths G i associated with different path sequences, where i represents different path sequences, and then select G i max from the total number of paths G i max, and generate a corresponding number of concentric circle sets according to the confirmed G i max, there are N different radius circles in the concentric circle set, and the center of each circle is the same, and N=G i max, the radius R of the innermost circle T1 is the smallest, the radius of the adjacent characteristic circle T2 of the characteristic circle T1 is 2R, the radius of the characteristic circle T3 located in the third circle is 3R, and so on. The radius of the outer circle gradually increases;

[0011] According to the path characteristics of the confirmed multiple path sequences, confirm the total number of different storage nodes in the same layer, and then divide the confirmed concentric circle set according to the total number of different storage nodes associated with different layers, the way is:

[0012] From different path sequences, confirm the total number of different storage nodes Z1 associated with the first layer of several path sequences, confirm Z1 division points on the characteristic circle T1 of the concentric circle set, and number the division points in clockwise direction, the number starts from 1 and is a positive integer, and the division point with number 1 is placed directly above the center;

[0013] The second layer step is processed in the same way as the first layer step, the feature circle T2 is processed in the same way, and the feature circle T2 is processed in the same way. According to the total number of storage nodes associated with different layers, different feature circles are processed, and the division process of multiple feature circles in the concentric circle set is completed.

[0014] According to the digital number associated with the path sequence, the corresponding division point is locked on the corresponding feature circle of the concentric circle set, and the feature circle is rotated based on the determined division point, so that the determined several division points are located directly above the center of the circle. In the processing process, the division point with the number 1 in each concentric circle set is confirmed, and the adjacent division point with the number 1 is connected to generate a feature line segment, and the feature line segment is bound with the medical information associated with the medical card number;

[0015] Step two, according to the medical card number input in the operating system, identify the associated feature line segment, and rotate the feature circle through the feature line segment. According to the path characteristics generated by rotation, quickly lock the storage node and mark it as the selected node, the specific way is:

[0016] Confirm the feature line segment associated with the corresponding medical card number, and control the rotation of the multiple feature circles in the concentric circle set based on the center of the circle. Confirm the location of the division point with the number 1 on the corresponding feature circle. When the connection line segment generated by connecting the adjacent division points with the number 1 is completely overlapped with the feature line segment, stop the rotation process.

[0017] From the center of the circle, the division point number is confirmed vertically upwards, and the sequentially confirmed number is sorted to generate a number sequence. According to the confirmed number sequence, directly lock the storage node with the same path sequence and number sequence from the medical information database. Mark this storage node as the selected node.

[0018] Step three, based on the determined selected node, confirm the medical information stored in the selected node, and then confirm the output logic according to the code features corresponding to the medical information and the computing power features of the system itself. According to the output logic, the medical information is displayed and output, and the specific way is:

[0019] Based on the confirmed medical information, sort and classify different codes from front to back from the code data associated with the medical information.

[0020] According to the preset computing power matching table, confirm the conversion computing power associated with the conversion of different codes, and sort the associated conversion computing power according to the sorting method of the corresponding code to generate a sorting column.

[0021] According to the confirmed order column, the conversion power SL1 in the first position of the order column is confirmed, the conversion power SL2 in the second position is confirmed, (SL2-SL1) is used as the calibration value of the second position, the conversion power SL3 in the third position is confirmed, (SL3-SL2) is used as the calibration value of the third position, and the calibration values associated with subsequent different positions in the order column are confirmed in turn, if the calibration value is greater than 0, the corresponding power is allocated to the code data conversion process associated with the specified position in advance, if the calibration value is less than or equal to 0, no allocation processing is performed;

[0022] The conversion power SL1 in the first position is used as the execution power in the first group of code data conversion processes, 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, in combination with the originally allocated power in the corresponding conversion process, the conversion process of the second group of code data is completed, and the code conversion processes associated with subsequent different code data are executed in turn.

[0023] Preferably, a medical information data query system based on artificial intelligence comprises:

[0024] A path sequence confirmation end confirms the medical information stored in different paths in the medical information database, and generates a path sequence belonging to the corresponding medical information according to the final node where the corresponding medical information is located;

[0025] A feature line segment generation end confirms the total number of different storage nodes in the same layer according to the path features of the confirmed multiple path sequences, and then divides the confirmed concentric circle set according to the total number of different storage nodes associated with different layers, locks the feature line segment associated with the corresponding medical information, and bundles it with the associated medical card number;

[0026] A selected node locking end identifies the associated feature line segment according to the medical card number input in the operating system, and then controls the feature circle to rotate through the feature line segment, quickly locks the storage node according to the path features generated by the rotation, and records it as the selected node;

[0027] A medical information output end confirms the medical information stored in the selected node based on the determined selected node, confirms the output logic according to the code features corresponding to the medical information and the power features of the system itself, and displays and outputs the medical information according to the output logic.

[0028] The present application provides a medical information data query method and system based on artificial intelligence. Compared with the prior art, the following advantages are achieved:

[0029] The application generates a path sequence by digitally numbering the storage node layers, and each medical information corresponds to a unique path identifier, avoiding the fuzzy matching problem of traditional keyword retrieval, and the AI can quickly locate the target data through sequence characteristics, and the retrieval time can be shortened;

[0030] The path sequence is mapped to a characteristic line segment in the concentric circle set, and the multi-dimensional path matching is converted into one-dimensional coordinate positioning by rotating the characteristic circle to align the bisecting point with the perpendicular line, and even if the characteristic line segment is leaked, the real storage path cannot be deduced in reverse, 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 bisecting point with the perpendicular line to generate an effective characteristic line segment, which is different from the static encryption method, the rotation trajectory changes dynamically each time the query is performed, not only can the related processing process of the 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 can be guaranteed;

[0031] According to the code feature (such as binary and octal) of different data types (text, image, and audio), the conversion power is pre-calculated through the computing power matching table, and the resource allocation is dynamically adjusted by using the calibration value, so that the output efficiency can be effectively guaranteed, the output time is shortened, and the rationalization of resource utilization is guaranteed. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The figure is a schematic diagram of the method of the application;

[0033] Figure 2 The figure is a schematic diagram of the principle framework of the application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0035] First embodiment

[0036] Please refer to Figure 1 The application provides a medical information data query method based on artificial intelligence, including the following steps:

[0037] Step one, confirm the medical information stored in the medical information database in different paths, and generate the path sequence belonging to the corresponding medical information according to the final node where the corresponding medical information is located, then combine the path sequence with the characteristic circle, confirm the characteristic line segment associated with the corresponding medical information, and bundle the characteristic line segment with the medical card number associated with the corresponding medical information. Specifically, the medical information of each user is stored in different folders, and different folders exist in different path nodes. Therefore, the path sequence associated can be confirmed according to the relevant order of the corresponding path node and the specific characteristics, for example, the storage path of the download file associated with the computer is C / user / downloads, the storage path of the user document associated is C / user / documents, and the storage path of the application program is C / program / app1. Therefore, there are three disks in this computer, which are C, D and E. The first storage layer is associated with three different file storage paths. Each storage path is confirmed again, and there are other paths. In this way, the path node associated with the corresponding medical information can be locked, and the corresponding path sequence can be generated.

[0038] The specific way of generating the path sequence is as follows:

[0039] Confirm the medical information database where the medical information is located, and start from the first layer of the database. Number the different storage nodes (which can be understood as C disk, D disk and E disk, which belong to the first layer) in the first layer. According to the front and back ordering relationship of the storage node ordering, the specified storage node is numbered in turn. The number starts from 1 and is a positive integer. If the corresponding storage node is in the first position, the corresponding storage node corresponds to the number 1, that is, the number starts from 1 and gradually increases. If the first layer corresponds to three storage nodes, the C disk corresponds to the number 1, the D disk corresponds to the number 2, and the E disk corresponds to the number 3.

[0040] The first level is used to digitally number different storage nodes, and the different storage nodes in the subsequent different levels are numbered in turn. Based on the specific numbering process and the storage nodes where the corresponding medical information is located, the digital numbers of the storage nodes are sorted from the first level to the next level to confirm the path sequence belonging to the corresponding storage nodes. Taking the three path nodes "C / user / downloads", "C / user / documents" and "C / program / app1" as examples, C corresponds to the digital number 1 (ranked first in the first level), user corresponds to 4 (ranked fourth), program corresponds to 3, downloads corresponds to 4, documents corresponds to 5, and app1 corresponds to 7, then the path sequence corresponding to C / user / downloads is "144", the path sequence corresponding to "C / user / documents" is "145", and the path sequence corresponding to "C / program / app1" is "137". It can be seen that in this way, the path sequences associated with the storage nodes where each different medical information is located are different and have obvious characteristics, and the corresponding artificial intelligence can quickly identify and verify them.

[0041] The specific way to combine the path sequence with the characteristic circle is:

[0042] According to the different path sequences associated with different medical information, the total number of paths G associated with different path sequences is determined. i (That is, each different path sequence has a different total number of paths starting from the first level. For example, the total number of paths for "C / user / documents" is three, namely "C", "user" and "documents"). Here, i represents a different path sequence, and then from the total number of paths G i In the i max (that is, the maximum value), and then based on the confirmed G i max, generates a corresponding number of concentric circle sets, in which there are N circles of different radii, and the center of each circle is the same, and N=G i max, the radius R of the characteristic circle T1 in the innermost circle is the smallest, the radius of the characteristic circle T2 adjacent to the characteristic circle T1 is 2R, the radius of the characteristic circle T3 in the third circle is 3R, and so on, the radius of the characteristic circle 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 in the same level is determined, and then the confirmed concentric circle set is equally divided according to the total number of different storage nodes associated with different levels:

[0044] From different path sequences, confirm the total number of different storage nodes Z1 associated with the first layer step of several path sequences (for example, C disk, D disk and E disk, then the total number of first layer steps is 3), confirm Z1 equal points on the characteristic circle T1 of the concentric circle set, and number the equal points clockwise, starting from 1 and being positive integers, and the equal point numbered 1 is placed directly above the center;

[0045] Again, the second layer step adopts the same equal division processing method as the innermost circle of the first layer step, and the characteristic circle T2 adopts the same equal division processing method, to complete the equal division process of the characteristic circle T2 (the equal point corresponding to the number 1 is placed directly above the corresponding center), and so on. According to the total number of storage nodes associated with different layers, different characteristic circles are divided, and the equal division process of multiple characteristic circles in the concentric circle set is completed. Specifically, the characteristics of the first layer step divide the innermost circle, the characteristics of the second layer step divide the second innermost circle (that is, the second innermost circle adjacent to the innermost circle), and the characteristics of the third layer step divide the third innermost circle. From front to back, the layer step is checked, and different circles are divided, so that the equal division process of the corresponding concentric circle set can be effectively completed;

[0046] According to the number corresponding to different path sequences, lock the corresponding equal point on the corresponding characteristic circle of the corresponding concentric circle set (the equal point also has a number), and based on the determined equal point, make the characteristic circle rotate around the center, so that the determined several equal points are located directly above the center (that is, there is a vertical line above the center, which covers each equal point. This vertical line can be understood as a vertical line perpendicular to the modeling horizontal coordinate axis, that is, the vertical line above), complete the process of confirming each equal point numbered 1 in the concentric circle set, and connecting the adjacent equal points numbered 1 to generate a characteristic line segment. This characteristic line segment is the characteristic line segment associated with the corresponding medical information, and the characteristic line segment is bundled with the medical card number associated with the corresponding medical information;

[0047] Why choose the digital number 1 point here, instead of directly confirming the characteristic line segment according to the corresponding point associated with the original path node, if the direct confirmation of the characteristic line segment according to the original path node is required, the rotation of the corresponding concentric circle set is not required, which belongs to direct confirmation, and the associated encryption is not high. The digital number 1 point is used to confirm the characteristic line segment, and the concentric circle set needs to be rotated to arrange it in a vertical row (perpendicular line), and then confirm the digital number 1 point to lock the characteristic line segment. This part of the logic has a big change, so the processing process of this part has a big processing logic, which is more encrypted and not complex for artificial intelligence AI, which can quickly lock the corresponding path node;

[0048] Step two, according to the input of the medical card number in the operating system, identify the associated characteristic line segment, and then control the characteristic circle to rotate, and quickly lock the storage node and mark it as the selected node according to the path characteristics generated by the rotation, wherein the determination method of the selected node is:

[0049] Confirm the characteristic line segment associated with the corresponding medical card number, and then control the multiple characteristic circles in the concentric circle set to rotate according to the center, and confirm the position of the digital number 1 point on the corresponding characteristic circle. When the connecting line segment generated by connecting adjacent digital number 1 points is completely overlapped with the characteristic line segment, stop the rotation process;

[0050] From the center vertically upwards, confirm the digital number of the point, and sort the digital numbers confirmed step by step to generate a number sequence. According to the confirmed number sequence, directly lock the storage node with the same path sequence and number sequence in the medical information database, and mark this storage node as the selected node (the specific medical information stored in this selected node is the medical information associated with the patient in the past examination process. This indexing method not only effectively protects the privacy of patients, but also effectively guarantees the retrieval rate, realizes fast retrieval, and achieves the effect of fast query and search);

[0051] Step three, based on the determined selected node, confirm the medical information stored in the selected node, and then confirm the output logic according to the code characteristics of the medical information and the computing power characteristics of the system, and display the medical information according to the output logic;

[0052] Among them, the specific way to confirm the output logic is:

[0053] Based on the confirmed medical information, different codes in different codes are sorted from front to back from the code data associated with the medical information (when the data is stored in the system, it is in the form of code, different forms of data, text data, audio data, etc. The associated code system is different, so there are different code system classifications);

[0054] According to the preset computing power matching table, the conversion computing power associated with the different code system is confirmed when the conversion output is performed, and the conversion computing power associated with the corresponding code system is sorted according to the sorting mode of the corresponding code system, and a sorting column is generated;

[0055] According to the confirmed sorting column, the conversion computing power SL1 located at the first position of the sorting column is confirmed, and the conversion computing power SL2 at the second position is confirmed. (SL2-SL1) is used as the second position of the calibration value, and the conversion computing power SL3 at the third position is confirmed. (SL3-SL2) is used as the third position of the calibration value, and so on. The calibration values associated with the subsequent different positions in the sorting column are confirmed in sequence. If the calibration value is greater than 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 less than or equal to 0, no allocation processing is performed.

[0056] The conversion computing power SL1 at the first position is used as the execution computing power in the first group of code data conversion process. After completing the conversion process of the first group of code data, SL1 is transferred to the conversion process of the second group of code data. In combination with the original allocated computing power (that is, the calibration value) in the corresponding conversion process, the conversion process of the second group of code data is completed. In this way, the code conversion process associated with the subsequent different code data is executed.

[0057] Second embodiment

[0058] In combination Figure 2 A medical information data query system based on artificial intelligence, comprising:

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

[0060] The feature line segment generation end confirms the total number of different storage nodes in the same layer according to the path features of the confirmed multiple path sequences, and then divides the confirmed concentric circle set according to the total number of different storage nodes associated with different layers. Lock the feature line segment associated with the corresponding medical information and bundle it with the associated medical card number.

[0061] The selected node locking end identifies the associated characteristic line segment according to the clinic card number input in the operating system, rotates the characteristic circle through the characteristic line segment, and quickly locks the storage node and records it as the selected node according to 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, confirms the output logic according to the code characteristics corresponding to the medical information and the computing power characteristics of the system, and displays and outputs the medical information according to the output logic.

[0063] Some data in the above formula are dimensionless numerical calculations, and the contents not described in detail in the specification all belong to the prior art known to those skilled in the art.

[0064] The above examples are only used to illustrate the technical method of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A medical information data query method based on artificial intelligence, characterized in that: The following steps are involved: Step 1: Confirm the medical information stored in different paths in 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 characteristic circle to confirm the characteristic line segment associated with the corresponding medical information, and then bind the characteristic line segment with the medical card number associated with the corresponding medical information; Step 2: Based on the medical card number input in the operating system, the associated characteristic line segment is identified, and then the characteristic circle is controlled to rotate by the characteristic line segment. Based on the path characteristics generated by the rotation, the storage node is quickly locked and recorded as the selected node; Step 3: Based on the selected node, confirm the medical information stored in the selected node, and then confirm the output logic based on the code characteristics corresponding to the medical information and the computing power characteristics of this system, and display and output the medical information based on this output logic.

2. The medical information data query method based on artificial intelligence according to claim 1, characterized in that: In step 1, the specific method of generating the path sequence of medical information is as follows: Identify the medical information database where the medical information is located, and starting from the first level of the database, number the different storage nodes in the first level. According to the order of the storage nodes, number the designated storage nodes in sequence, and the numbering starts from 1 and is a positive integer. The first level is used to digitally number different storage nodes, and then the different storage nodes in subsequent different levels are numbered in turn. Based on the specific numbering process and the storage nodes where the corresponding medical information is located, the digital numbers of the storage nodes are sorted from the first level to the next level to confirm the path sequence belonging to the corresponding storage nodes.

3. The medical information data query method based on artificial intelligence according to claim 2, characterized in that: In step 1, the specific method of combining the path sequence with the characteristic circle to determine the characteristic line segment is: According to the different path sequences associated with different medical information, the total number of paths G associated with different path sequences is determined. i , where i represents different path sequences, and then from the total number of paths G i In the i max, and then based on the confirmed G i max, generates a corresponding number of concentric circle sets, in which there are N circles of different radii, and the center of each circle is the same, and N=G i max, the radius R of the characteristic circle T1 in the innermost circle is the smallest, the radius of the characteristic circle T2 adjacent to the characteristic circle T1 is 2R, the radius of the characteristic circle T3 in the third circle is 3R, and so on, the radius of the characteristic circle 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 concentric circle set is equally divided according to the total number of different storage nodes associated with different levels; According to the digital numbers associated with different path sequences, the corresponding equal-division points are locked on the corresponding characteristic circles of the corresponding concentric circle set, and based on the determined equal-division points, the characteristic circles are rotated according to the center of the circle, so that the determined several equal-division points are all located directly above the center of the circle. During the processing, each equal-division point with a digital number of 1 in the concentric circle set is confirmed, and the adjacent equal-division points with a digital number of 1 are connected to generate a characteristic line segment, and this characteristic line segment is bundled with the medical card number associated with the corresponding medical information.

4. The medical information data query method based on artificial intelligence according to claim 3 is characterized in that: The method of equally dividing the concentric circle set according to the total number of storage nodes is as follows: From different path sequences, determine 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, determine Z1 equally divided points. Number the equally divided points in a clockwise manner, starting from 1 and using positive integers. Place the equally divided point numbered 1 directly above the center of the circle. Then, the same equal division processing method as that of the innermost circle of the first level is adopted for the second level, and the same equal division processing method is adopted for the characteristic circle T2 to complete the equal division process of the characteristic circle T2. Similarly, different characteristic circles are divided into equal parts according to the total number of storage nodes associated with different levels to complete the equal division processing process of multiple characteristic circles within the concentric circle set.

5. The medical information data query method based on artificial intelligence according to claim 1, characterized in that: In step 2, the specific method for confirming the selected node is: Confirm the characteristic line segment associated with the corresponding medical card number, then control multiple characteristic circles in the concentric circle set to rotate around the center of the circle, and confirm the location of the equal-division point numbered 1 on the corresponding characteristic circle. When the connecting line segment generated by connecting adjacent equal-division points numbered 1 completely coincides with the characteristic line segment, stop the rotation process; The digital numbers of the equally divided points are confirmed vertically upward from the center of the circle, and the gradually confirmed digital numbers are sorted to generate a number sequence. Based on the confirmed number sequence, the storage node whose path sequence is consistent with the number sequence is directly locked from the medical information database, and this storage node is recorded as the selected node.

6. The medical information data query method based on artificial intelligence according to claim 1, characterized in that: In step 3, the specific method of confirming the output logic is: Based on the confirmed medical information, sort and classify the codes of different bases from the code data associated with the medical information from the front to the back; According to the preset computing power matching table, the conversion computing power associated with different base codes when converting and outputting is confirmed, and the associated conversion computing power is sorted according to the sorting method of the corresponding base codes to generate a sorting column; Based on the confirmed sorting sequence, confirm the conversion computing power SL1 at the first position in the sorting sequence, then confirm the conversion computing power SL2 at the second position, using (SL2-SL1) as the calibration value for the second position, then confirm the conversion computing power SL3 at the third position, using (SL3-SL2) as the calibration value for the third position, and so on. Confirm the calibration values ​​associated with subsequent different positions in the sorting sequence in sequence. If the calibration value is greater than 0, allocate the corresponding computing power in advance to the code data conversion process associated with the specified position; The conversion computing power SL1 in the first position is used as the execution computing power in the conversion process of the first set of code data. After completing the conversion process of the first set of code data, SL1 is transferred to the conversion process of the second set of code data. Combined with the computing power originally allocated in the corresponding conversion process, the conversion process of the second set of code data is completed. Similarly, the code conversion processes associated with subsequent different code data are executed.

7. The medical information data query method based on artificial intelligence according to claim 6, characterized in that: If the calibration value is ≤0, no allocation processing is performed.

8. A medical information data query system based on artificial intelligence, which operates according to the medical information data query method based on artificial intelligence according to any one of claims 1 to 7, characterized in that: include: The path sequence confirmation terminal confirms the medical information stored in different paths in 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 generator verifies the total number of different storage nodes in the same hierarchy based on the path features of the multiple confirmed path sequences. It then divides the confirmed concentric circle set into equal parts based on the total number of different storage nodes associated with different hierarchies, locks the feature segment associated with the corresponding medical information, and bundles it with the associated medical card number. Select the node locking end, identify the associated characteristic line segment according to the medical card number input in the operating system, and then control the characteristic circle to rotate through the characteristic line segment. Based on the path characteristics generated by the rotation, quickly lock the storage node and record it as the selected node; 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 based on the code characteristics corresponding to the medical information and the computing power characteristics of this system, and displays and outputs the medical information based on this output logic.

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