An AI-based medical self-service terminal

Abstract

The present invention provides an AI-based medical self-service terminal, which relates to the field of data processing and includes multiple interactive nodes and multiple data processing nodes. The cloud platform is used to allocate corresponding data processing nodes to the interactive nodes based on allocation auxiliary information: the interactive node is used to receive registration instructions initiated by users, and the data processing node corresponding to the interactive node is used to generate registration recommendation information, and make an appointment for registration based on the user's feedback on the registration recommendation information; the interactive node is used to receive file query instructions initiated by users, and the data processing node corresponding to the interactive node is used to obtain target medical files from the cloud platform based on the file query instructions, and send the target medical files to the interactive node; the interactive node is used to receive information query instructions initiated by users, and the data processing node corresponding to the interactive node is used to obtain target medical information from the cloud platform, and send the target medical information to the interactive node, which has the advantage of improving the quality of medical self-service.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an AI-based medical self-service terminal. Background Art

[0002] With the rapid development of science and technology, artificial intelligence (AI) is gradually penetrating various industries, injecting new vitality into the innovation and development of enterprises. In the medical field, the application of AI technology is in full swing. From remote consultations to intelligent diagnosis, to the development of personalized treatment plans, AI is reshaping the medical landscape. Against this backdrop, AI-based medical self-service terminals have emerged, aiming to provide patients with more convenient and efficient medical services.

[0003] Existing medical self-service terminals lack natural language processing capabilities and are unable to accurately understand patients' verbal instructions, further reducing the interactive experience. Furthermore, despite the significant advantages of AI technology in data processing and analysis, existing medical self-service terminals still face bottlenecks in data processing capabilities. For one thing, the terminal's hardware configuration may not be sufficient to support the real-time processing of large amounts of data; for another, the terminal's software algorithms may not be efficient enough, resulting in slow data processing and an inability to meet patients' immediate needs. The intelligence level of existing medical self-service terminals still needs to be improved. Some terminals lack advanced AI algorithms and models, making them unable to provide precise medical services.

[0004] Therefore, it is necessary to provide an AI-based medical self-service terminal to improve the quality of medical self-service. Summary of the Invention

[0005] The present invention provides an AI-based medical self-service terminal, comprising multiple interactive nodes and multiple data processing nodes, wherein the interactive nodes are used to interact with users, and the multiple data processing nodes interact with data with a cloud platform, and the cloud platform is used to allocate corresponding data processing nodes to the interactive nodes based on allocation auxiliary information, wherein the allocation auxiliary information at least includes the number of registration instructions, the number of file query instructions, and the number of information query instructions of each interactive node in multiple current time periods: the interactive node is used to receive registration instructions initiated by users, and the data processing node corresponding to the interactive node is used to generate registration recommendation information based on the registration instruction, and make an appointment for registration based on the user's feedback on the registration recommendation information; the interactive node is used to receive file query instructions initiated by users, and the data processing node corresponding to the interactive node is used to obtain target medical files from the cloud platform based on the file query instructions, and send the target medical files to the interactive node; the interactive node is used to receive information query instructions initiated by users, and the data processing node corresponding to the interactive node is used to obtain target medical information from the cloud platform based on the information query instructions, and send the target medical information to the interactive node.

[0006] Furthermore, the cloud platform is used to allocate corresponding data processing nodes to interactive nodes based on allocation auxiliary information, including: obtaining the number of registration instructions, the number of file query instructions, and the number of information query instructions of the interactive nodes in multiple historical time periods; calculating the registration instruction number correlation coefficient, the file query instruction number correlation coefficient, and the information query instruction number correlation coefficient of any two interactive nodes based on the registration instruction number correlation coefficient, the file query instruction number correlation coefficient, and the information query instruction number correlation coefficient of any two interactive nodes; for each interactive node, determining the registration instruction number correlation coefficient, the file query instruction number correlation coefficient, and the information query instruction number correlation coefficient of any two interactive nodes based on the registration instruction number correlation coefficient, the file query instruction number correlation coefficient, and the information query instruction number correlation coefficient of any two interactive nodes. Positively associated interactive nodes and negatively associated interactive nodes of interactive nodes; obtaining the number of registration instructions, the number of file query instructions, and the number of information query instructions of the interactive nodes in multiple current time periods; based on the positively associated interactive nodes and negatively associated interactive nodes of each interactive node and the number of registration instructions, the number of file query instructions, and the number of information query instructions of each interactive node in multiple current time periods, predicting the number of registration instructions, the number of file query instructions, and the number of information query instructions of each interactive node in multiple future time periods; based on the number of registration instructions, the number of file query instructions, and the number of information query instructions of each interactive node in multiple future time periods, allocating corresponding data processing nodes to the interactive nodes.

[0007] Furthermore, the cloud platform determines the positively associated interaction nodes and negatively associated interaction nodes of the interaction nodes based on the registration instruction quantity correlation coefficient, file query instruction quantity correlation coefficient and information query instruction quantity correlation coefficient of any two interaction nodes, including: taking a weighted sum of the registration instruction quantity correlation coefficient, file query instruction quantity correlation coefficient and information query instruction quantity correlation coefficient of any two interaction nodes to calculate the comprehensive correlation coefficient of any two interaction nodes; for each interaction node, judging whether there is an interaction node whose registration instruction quantity correlation coefficient, file query instruction quantity correlation coefficient or information query instruction quantity correlation coefficient is greater than a first correlation coefficient positive threshold, and whose registration instruction quantity correlation coefficient, file query instruction quantity correlation coefficient and information query instruction quantity correlation coefficient are all greater than a second correlation coefficient positive threshold; if so, as the positively associated interaction node of the interaction node, judging whether there is an interaction node whose comprehensive correlation coefficient is greater than the first comprehensive correlation coefficient threshold, If so, as a positively associated interactive node of the interactive node, wherein the first positive correlation coefficient threshold, the second positive correlation coefficient threshold and the first comprehensive correlation coefficient threshold are all greater than 0, and the first positive correlation coefficient threshold is greater than the second positive correlation coefficient threshold; for each interactive node, determine whether there is an interactive node whose at least one of the registration instruction quantity correlation coefficient, the file query instruction quantity correlation coefficient or the information query instruction quantity correlation coefficient is less than the first negative correlation coefficient threshold, and the registration instruction quantity correlation coefficient, the file query instruction quantity correlation coefficient and the information query instruction quantity correlation coefficient are all less than the second negative correlation coefficient threshold; if so, as a negatively associated interactive node of the interactive node, determine whether there is an interactive node whose comprehensive correlation coefficient is less than the second comprehensive correlation coefficient threshold; if so, as a negatively associated interactive node of the interactive node, wherein the first negative correlation coefficient threshold, the second negative correlation coefficient threshold and the second comprehensive correlation coefficient threshold are all less than 0, and the second negative correlation coefficient threshold is greater than the first negative correlation coefficient threshold.

[0008] Furthermore, the cloud platform predicts the number of registration instructions, file query instructions and information query instructions of each interaction node in multiple future time periods based on the positively associated interaction nodes and negatively associated interaction nodes of each interaction node and the number of registration instructions, file query instructions and information query instructions of each interaction node in multiple current time periods, including: for each interaction node, based on the positively associated interaction nodes and negatively associated interaction nodes of the interaction node, establishing a quantity prediction model and a quantity correction model corresponding to the interaction node; for each interaction node, through the quantity prediction model corresponding to the interaction node, based on the number of registration instructions, file query instructions and information query instructions of the interaction node in multiple current time periods and the positively associated interaction nodes of the interaction node The number of registration instructions, file query instructions and information query instructions of the interaction node and the negatively associated interaction node in multiple current time periods is used to predict the number of registration instructions, file query instructions and information query instructions of the interaction node in multiple future time periods; through the quantity correction model corresponding to each interaction node, based on the number of registration instructions, file query instructions and information query instructions of the positively associated interaction nodes and the negatively associated interaction nodes of each interaction node in multiple future time periods, the number of registration instructions, file query instructions and information query instructions of the interaction node in multiple future time periods is iteratively corrected to generate the corrected number of registration instructions, file query instructions and information query instructions of each interaction node in multiple future time periods.

[0009] Furthermore, the cloud platform allocates corresponding data processing nodes to interactive nodes based on the number of registration instructions, file query instructions and information query instructions of each interactive node in multiple future time periods, including: establishing a node scheduling objective function; generating multiple sample node scheduling schemes; calculating the node scheduling objective function value of the sample node scheduling scheme based on the node scheduling objective function, the number of registration instructions, file query instructions and information query instructions of each interactive node in multiple future time periods, and the registration instruction number correlation coefficient, file query instruction number correlation coefficient and information query instruction number correlation coefficient of any two interactive nodes; determining the target node scheduling scheme based on the node scheduling objective function value of each sample node scheduling scheme through a genetic algorithm; and allocating corresponding data processing nodes to the interactive nodes based on the target node scheduling scheme.

[0010] Furthermore, the data processing node corresponding to the interactive node generates registration recommendation information based on the registration instruction, including: reading the user's identity information based on the registration instruction; obtaining the user's electronic medical record from the cloud platform based on the user's identity information; obtaining the user's medical consultation information based on the disease feature knowledge graph and the user's electronic medical record through natural language processing; and generating registration recommendation information based on the user's medical consultation information.

[0011] Furthermore, the data processing node corresponding to the interactive node obtains the user's medical consultation information through natural language processing based on the disease characteristic knowledge graph and the user's electronic medical record, including: obtaining the electronic medical records of multiple historical users; determining the onset correlation parameters of any two diseases based on the electronic medical records of multiple historical users; obtaining the user's condition description information; sorting multiple diseases based on the user's condition description information, the disease characteristic knowledge graph and the onset correlation parameters of any two diseases, and generating disease sorting results; generating medical consultation questions based on the disease characteristic knowledge graph and the disease sorting results through natural language processing, and obtaining user feedback information on the medical consultation questions.

[0012] Furthermore, the data processing node corresponding to the interactive node is also used to: obtain the reaction time characteristics of multiple sample users to the basic interactive information; obtain the reaction time characteristics of the user to the basic interactive information; and adjust the display font size of the interactive node based on the reaction time characteristics of multiple sample users to the basic interactive information and the reaction time characteristics of the user to the basic interactive information.

[0013] Furthermore, the data processing node corresponding to the interactive node obtains the target medical file from the cloud platform based on the file query instruction, including: reading the user's identity information based on the file query instruction; obtaining keyword information of multiple medical files of the user from the cloud platform based on the user's identity information; generating file selection interaction information based on the keyword information of multiple medical files of the user; obtaining user feedback on the file selection interaction information through the interactive node; obtaining the encrypted target medical file from the cloud platform based on the user's feedback on the file selection interaction information; decrypting the encrypted target medical file to generate the target medical file.

[0014] Furthermore, the data processing node corresponding to the interactive node obtains target medical information from the cloud platform based on the information query instruction, including: determining information search keywords based on the information query instruction; and obtaining target medical information from the cloud platform based on the information search keywords.

[0015] Compared with the existing technology, the AI-based medical self-service terminal provided by the present invention has at least the following beneficial effects:

[0016] 1. Users can easily initiate registration instructions, file query instructions, and information query instructions through interactive nodes, eliminating the need to wait in line at the manual window, significantly saving time. Self-service terminals are open 24 hours a day, allowing users to operate them anytime and anywhere, meeting medical needs at different time periods. Data processing nodes can generate registration recommendation information based on users' registration instructions, helping users choose the department and doctor that suits them, improving the targetedness and efficiency of medical treatment. Self-service terminals can automatically process instructions such as registration, file query, and information query, reducing the workload of medical staff and allowing them to focus more on patient treatment and service. With the assistance of AI technology, data processing nodes can quickly and accurately process large amounts of medical data, improving the overall efficiency of medical services.

[0017] 2. Through historical data analysis and predictive models, the cloud platform can predict in advance the number of registration instructions, document query instructions, and information query instructions for each interactive node in the future time period. This predictive allocation mechanism ensures that data processing nodes are prepared in advance, scheduling and configuring resources based on predicted demand, avoiding idle or overloaded resources. Over time, the cloud platform continuously acquires new data and updates the predictive model, enabling dynamic adjustment of resource allocation. This dynamic adjustment mechanism ensures that data processing nodes are always in optimal working condition to meet evolving user needs. Through predictive allocation and dynamic adjustment, the cloud platform ensures that data processing nodes have sufficient resources during peak processing times, thereby reducing user wait times. Users can receive registration recommendations, medical documents, and information query results more quickly, improving medical efficiency and satisfaction.

[0018] 3. By weighting and summing the correlation coefficients of registration instructions, file query instructions, and information query instructions, the cloud platform can more comprehensively assess the correlation between two interaction nodes. This comprehensive consideration avoids the one-sidedness that can result from a single instruction number and improves the accuracy of correlation analysis. By setting a first positive correlation coefficient threshold and a first comprehensive correlation coefficient threshold to determine positively correlated interaction nodes, and a first negative correlation coefficient threshold and a second comprehensive correlation coefficient threshold to determine negatively correlated interaction nodes, the cloud platform can flexibly adjust the thresholds based on different business needs and scenarios. This flexibility helps ensure the accuracy and effectiveness of correlation analysis. It can comprehensively consider correlation coefficients across multiple dimensions to more accurately determine positively and negatively correlated interaction nodes.

[0019] 4. By analyzing the number of instructions for each interactive node over multiple future time periods, the cloud platform can predict resource needs in advance, enabling predictive scheduling. This approach prevents resource allocation from lagging behind actual demand and improves resource scheduling efficiency. When determining node scheduling plans, the cloud platform considers not only the number of instructions per interactive node but also the various correlation coefficients between any two interactive nodes. This comprehensive approach makes resource scheduling more accurate and better meets actual business needs. It also avoids overloading a particular interactive node due to a surge in instructions within a short period of time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0021] Figure 1 This is a module diagram of an AI-based medical self-service terminal according to some embodiments of this specification;

[0022] Figure 2 This is a flowchart of allocating corresponding data processing nodes to interactive nodes based on allocation auxiliary information according to some embodiments of this specification. DETAILED DESCRIPTION

[0023] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0024] Figure 1 This is a module diagram of an AI-based medical self-service terminal according to some embodiments of this specification, such as Figure 1 As shown, an AI-based medical self-service terminal includes multiple interactive nodes and multiple data processing nodes. The interactive nodes are used to interact with users, and the multiple data processing nodes interact with the cloud platform for data.

[0025] The cloud platform is configured to allocate corresponding data processing nodes to the interactive nodes based on allocation assistance information, wherein the allocation assistance information includes at least the number of registration instructions, file query instructions, and information query instructions for each interactive node in multiple current time periods. The current time period may be a time period in the current cycle (e.g., the current day), or a time period whose time distance from the current time is less than a preset time distance threshold.

[0026] Figure 2 is a flow chart of allocating corresponding data processing nodes to interactive nodes based on allocation auxiliary information according to some embodiments of this specification, such as Figure 2 As shown, in some embodiments, the cloud platform is used to allocate corresponding data processing nodes to interactive nodes based on allocation auxiliary information, including:

[0027] Obtain the number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple historical time periods;

[0028] Calculate the correlation coefficient of the number of registration instructions, the number of file query instructions, and the number of information query instructions of any two interactive nodes in multiple historical time periods;

[0029] For each interaction node, based on the correlation coefficient of the number of registration instructions, the correlation coefficient of the number of file query instructions, and the correlation coefficient of the number of information query instructions between any two interaction nodes, the positively associated interaction nodes and the negatively associated interaction nodes of the interaction node are determined;

[0030] Obtain the number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple current time periods;

[0031] Based on the positively associated interactive nodes and negatively associated interactive nodes of each interactive node and the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple current time periods, predict the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple future time periods;

[0032] Based on the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple future time periods, a corresponding data processing node is allocated to the interactive node.

[0033] Specifically, the number of registration instructions refers to the number of user-initiated registration instructions completed by an interactive node within a certain time period. The number of file query instructions refers to the number of user-initiated file query instructions completed by an interactive node within a certain time period. The number of information query instructions refers to the number of user-initiated information query instructions completed by an interactive node within a certain time period.

[0034] The correlation coefficient of the number of registered instructions between two interaction nodes can be calculated according to the following formula:

[0035]

[0036] Among them, r ((i,j),1) is the correlation coefficient of the number of registration instructions between the i-th interaction node and the j-th interaction node, M is the total number of sampled historical time periods, N (i,m) is the number of registration instructions of the i-th interaction node in the m-th historical time period of sampling, N (j,m) is the number of registered instructions of the jth interaction node in the mth historical time period of sampling.

[0037] The calculation method of the correlation coefficient of the number of file query instructions and the correlation coefficient of the number of information query instructions between two interactive nodes is the same as the calculation method of the correlation coefficient of the number of registration instructions, which will not be repeated here.

[0038] In some embodiments, the cloud platform determines positively associated interaction nodes and negatively associated interaction nodes of an interaction node based on the correlation coefficient of the number of registration instructions, the correlation coefficient of the number of file query instructions, and the correlation coefficient of the number of information query instructions of any two interaction nodes, including:

[0039] The weighted sum of the correlation coefficients of the number of registration instructions, the number of file query instructions, and the number of information query instructions of any two interaction nodes is performed to calculate the comprehensive correlation coefficient of any two interaction nodes;

[0040] For each interactive node, determine whether there is an interactive node whose registration instruction quantity correlation coefficient, file query instruction quantity correlation coefficient, or information query instruction quantity correlation coefficient is greater than the first correlation coefficient positive threshold, and whose registration instruction quantity correlation coefficient, file query instruction quantity correlation coefficient, and information query instruction quantity correlation coefficient are all greater than the second correlation coefficient positive threshold; if so, use it as a positively associated interactive node for the interactive node; determine whether there is an interactive node whose comprehensive correlation coefficient is greater than the first comprehensive correlation coefficient threshold; if so, use it as a positively associated interactive node for the interactive node, wherein the first correlation coefficient positive threshold, the second correlation coefficient positive threshold, and the first comprehensive correlation coefficient threshold are all greater than 0, and the first correlation coefficient positive threshold is greater than the second correlation coefficient positive threshold;

[0041] For each interactive node, determine whether there is an interactive node whose registration instruction quantity correlation coefficient, file query instruction quantity correlation coefficient or information query instruction quantity correlation coefficient is less than the first correlation coefficient negative threshold, and whose registration instruction quantity correlation coefficient, file query instruction quantity correlation coefficient and information query instruction quantity correlation coefficient are all less than the second correlation coefficient negative threshold. If so, act as a negatively associated interactive node of the interactive node. Determine whether there is an interactive node whose comprehensive correlation coefficient is less than the second comprehensive correlation coefficient threshold. If so, act as a negatively associated interactive node of the interactive node, wherein the first correlation coefficient negative threshold, the second correlation coefficient negative threshold and the second comprehensive correlation coefficient threshold are all less than 0, and the second correlation coefficient negative threshold is greater than the first correlation coefficient negative threshold.

[0042] Specifically, the weights of the correlation coefficient of the number of registration instructions, the correlation coefficient of the number of file query instructions, and the correlation coefficient of the number of information query instructions may be determined based on the following process:

[0043]

[0044] Where w1 is the weight of the correlation coefficient for the number of registration instructions, w2 is the weight of the correlation coefficient for the number of file query instructions, w3 is the weight of the correlation coefficient for the number of information query instructions, γ1 is the fluctuation parameter for the correlation coefficient for the number of registration instructions, γ2 is the fluctuation parameter for the correlation coefficient for the number of file query instructions, γ3 is the fluctuation parameter for the correlation coefficient for the number of information query instructions, and K is the total number of interactive nodes. The calculation method for the fluctuation parameter for the correlation coefficient for the number of file query instructions and the number of information query instructions is the same as that for the correlation coefficient for the number of registration instructions and is not further described here.

[0045] As an example only, the weight of the correlation coefficient for the number of registration instructions is 0.5, the weight of the correlation coefficient for the number of file query instructions is 0.3, and the weight of the correlation coefficient for the number of information query instructions is 0.2. For any two interactive nodes A and B, if their correlation coefficient for the number of registration instructions is 0.8, the correlation coefficient for the number of file query instructions is 0.6, and the correlation coefficient for the number of information query instructions is 0.4, then the comprehensive correlation coefficient is 0.5×0.8+0.3×0.6+0.2×0.4=0.66. For any two interactive nodes A and B, the cloud platform has calculated the correlation coefficient for the number of registration instructions, the correlation coefficient for the number of file query instructions, and the correlation coefficient for the number of information query instructions between them, denoted as R_AB (registration), F_AB (file query), and I_AB (information query), respectively. Check whether there is at least one correlation coefficient (any one of R_AB, F_AB, and I_AB) greater than the first positive correlation coefficient threshold (for example, 0.5). If so, it is considered that B is a positively correlated interactive node of A, and A is also a positively correlated interactive node of B. If there are two interaction nodes A and C, the correlation coefficient of the number of registration instructions, the correlation coefficient of the number of file query instructions, or the correlation coefficient of the number of information query instructions are all less than or equal to the first positive correlation coefficient threshold, and the comprehensive correlation coefficient of the interaction nodes A and C is greater than the first comprehensive correlation coefficient threshold, then C is considered to be a positively associated interaction node of A, and A is also a positively associated interaction node of C.

[0046] In some embodiments, the cloud platform predicts the number of registration instructions, file query instructions, and information query instructions for each interactive node in multiple future time periods based on the positively associated interactive nodes and negatively associated interactive nodes of each interactive node and the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple current time periods, including:

[0047] For each interaction node, based on the positively associated interaction nodes and negatively associated interaction nodes of the interaction node, a quantity prediction model and a quantity correction model corresponding to the interaction node are established, wherein the quantity prediction model and the quantity correction model can be a multiple linear regression model;

[0048] For each interactive node, using the quantity prediction model corresponding to the interactive node, based on the number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple current time periods, as well as the number of registration instructions, file query instructions, and information query instructions of the positively associated interactive nodes and negatively associated interactive nodes of the interactive node in multiple current time periods, predict the number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple future time periods;

[0049] Through the quantity correction model corresponding to each interaction node, based on the number of registration instructions, file query instructions and information query instructions of the positively associated interaction nodes and negatively associated interaction nodes of each interaction node in multiple future time periods, the number of registration instructions, file query instructions and information query instructions of the interaction node in multiple future time periods are iteratively corrected to generate the corrected number of registration instructions, file query instructions and information query instructions of each interaction node in multiple future time periods.

[0050] Specifically, iterative correction can be performed according to the following process:

[0051] S11. For each interactive node, using the quantity correction model corresponding to the interactive node, based on the number of registration instructions, file query instructions, and information query instructions of the positively associated interactive nodes and the negatively associated interactive nodes of the interactive node corresponding to the t-1th iteration in multiple future time periods, the number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple future time periods are corrected to generate the corrected number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple future time periods corresponding to the t-1th iteration, wherein the number of registration instructions, file query instructions, and information query instructions of the positively associated interactive nodes and the negatively associated interactive nodes of the interactive node corresponding to the t-1th iteration used in the first iteration are the number of registration instructions, file query instructions, and information query instructions output by the quantity prediction model;

[0052] S12. Calculate the global difference corresponding to the tth iteration based on the revised number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple future time periods corresponding to the tth iteration and the revised number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple future time periods corresponding to the t-1th iteration;

[0053] S13, determine whether t is greater than 3, if so, execute S14, if not, set t = t + 1, execute S11;

[0054] S14. Determine whether the global difference corresponding to the t-th iteration, the global difference corresponding to the t-1-th iteration, and the global difference corresponding to the t-2-th iteration are all less than the global difference threshold. If so, complete the correction; if not, set t=t+1 and execute S11.

[0055] The global difference can be calculated according to the following formula:

[0056]

[0057] Among them, △ tis the global difference corresponding to the tth iteration, N (k,1,t,e) N is the number of registration instructions of the kth interaction node in the eth future time period corresponding to the tth iteration, (k,1,t-1,e) is the number of registration instructions of the kth interaction node in the eth future time period corresponding to the t-1th iteration, N (k,2,t,e) N is the number of file query instructions of the kth interactive node in the eth future time period corresponding to the tth iteration, (k,2,t-1,e) N is the number of file query instructions of the kth interactive node in the eth future time period corresponding to the t-1th iteration, (k,3,t,e) N is the number of information query instructions of the kth interactive node corresponding to the tth iteration in the eth future time period, (k,3,t-1,e) is the number of information query instructions of the kth interactive node in the eth future time period corresponding to the t-1th iteration, K is the total number of interactive nodes, and E is the total number of future time periods.

[0058] In some embodiments, the cloud platform allocates corresponding data processing nodes to the interactive nodes based on the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple future time periods, including:

[0059] Establish node scheduling objective function;

[0060] Generate multiple sample node scheduling schemes;

[0061] Calculate the node scheduling objective function value of the sample node scheduling solution based on the node scheduling objective function, the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple future time periods, and the correlation coefficient of the number of registration instructions, the correlation coefficient of the number of file query instructions, and the correlation coefficient of the number of information query instructions between any two interactive nodes;

[0062] Determine the target node scheduling scheme based on the node scheduling objective function value of each sample node scheduling scheme through genetic algorithm;

[0063] Based on the target node scheduling scheme, corresponding data processing nodes are allocated to the interactive nodes.

[0064] Specifically, the node scheduling objective function is a mathematical model that evaluates the performance of different node scheduling solutions. It calculates the total value, or score, of each scheduling solution based on multiple factors. The node scheduling objective function considers the number of instructions (registrations, file queries, information queries) from interacting nodes and the correlation between these instructions (represented by the correlation coefficient).

[0065] As an example only, the node scheduling objective function can be:

[0066]

[0067] Among them, F is the node scheduling objective function, w4, w5 and w6 are preset weights, w4, w5 and w6 are greater than 0, w4+w5+w6=1, P1, P2 and P3 are normalized parameters, P1, P2 and P3 are greater than 0, N1 is the number of interactive nodes whose corresponding data processing nodes change, L (g,e) is the computational load of the gth data processing node in the eth future time period, E is the total number of future time periods, G is the total number of data processing nodes, r (i,j) is the comprehensive correlation coefficient between the i-th interaction node and the j-th interaction node corresponding to the g-th data processing node, Q g is the total number of interaction nodes corresponding to the g-th data processing node.

[0068] A genetic algorithm is an optimization algorithm that simulates natural selection and heredity. It gradually approaches the optimal solution by iteratively selecting, crossover, and mutating sample scheduling solutions. The genetic algorithm selects and evolves the sample scheduling solutions based on the node scheduling objective function values, ultimately determining the optimal target node scheduling solution.

[0069] The interactive node is used to receive registration instructions initiated by users, and the data processing node corresponding to the interactive node is used to generate registration recommendation information based on the registration instructions, and make an appointment for registration based on the user's feedback on the registration recommendation information.

[0070] Specifically include:

[0071] Based on the registration instructions, read the user's identity information, such as medical insurance card number, ID number, etc.

[0072] Based on the user's identity information, the user's electronic medical record is obtained from the cloud platform. Among them, EMR (Electronic Medical Record), also known as computerized medical record system or computer-based patient record (CPR), refers to the medical records generated by medical personnel in the process of medical activities using information systems, including text, symbols, charts, graphics, numbers, images and other digital information, and can be stored, managed, transmitted and reproduced. It is a form of medical record, including outpatient (emergency) medical records and inpatient medical records;

[0073] Through natural language processing, based on the disease characteristic knowledge graph and the user's electronic medical record, the user's medical consultation information is obtained. The construction of the disease characteristic knowledge graph is based on a graph data structure, which consists of nodes (representing entities) and edges (representing relationships). In the disease characteristic knowledge graph, nodes can represent diseases and symptoms. The disease characteristic knowledge graph can systematically record a variety of diseases and their corresponding symptoms. For example, for allergic rhinitis, the graph can record its typical symptoms such as paroxysmal sneezing, watery nasal discharge, nasal congestion and nasal itching, as well as symptoms such as hyposmia that may occur in some patients. Similarly, for hyperthyroidism, the graph can list in detail its facial features (such as shocked face, widened palpebral fissures, bulging eyeballs, etc.) and psychological symptoms such as excitement, restlessness and irritability;

[0074] Generate registration recommendation information based on the user's medical consultation information.

[0075] In some embodiments, the data processing node corresponding to the interaction node obtains the user's medical information through natural language processing based on the disease feature knowledge graph and the user's electronic medical record, including:

[0076] Access electronic medical records of multiple historical users;

[0077] Determine the association parameters between any two diseases based on the electronic medical records of multiple historical users. This can be achieved through statistical methods, machine learning algorithms, or deep learning models. For example, analysis can reveal that the presence of a symptom or disease A often indicates an increased likelihood of another disease B, thereby calculating the association parameter between A and B.

[0078] Obtain the user's condition description information. Specifically, the user can enter their condition description through the interactive node, which usually includes symptoms, duration, severity, etc. This information is an important basis for the subsequent generation of medical questions and disease ranking;

[0079] Based on the user's condition description information, disease feature knowledge graph and the incidence correlation parameters of any two diseases, multiple diseases are ranked and disease ranking results are generated;

[0080] Through natural language processing, based on the disease feature knowledge graph and disease ranking results, we generate diagnostic questions and obtain user feedback on these questions. These questions are intended to further clarify the user's condition or verify previous hypotheses about the disease. Questions may cover details of symptoms, lifestyle habits, family medical history, and other aspects. User feedback on these questions will be used to further refine the disease ranking or adjust the diagnostic direction.

[0081] Specifically, a disease identification model can be used to determine whether a user may have a disease based on the user's condition description information and the disease feature knowledge graph. The disease identification model can be a convolutional neural network model. Based on the onset correlation parameters of any two diseases, the user's associated diseases are determined and ranked based on the onset correlation parameters.

[0082] In the process of generating medical questions, questions are asked based on the symptoms of the diseases that the user may have as recorded in the disease feature knowledge graph, and then questions are asked based on the sorting results.

[0083] The interactive node is used to receive file query instructions initiated by the user, and the data processing node corresponding to the interactive node is used to obtain the target medical file from the cloud platform based on the file query instruction and send the target medical file to the interactive node.

[0084] Specifically include:

[0085] Read the user's identity information based on the file query instruction;

[0086] Based on the user's identity information, keyword information of multiple medical files of the user (e.g., blood test, urine analysis, imaging examination, etc.) is obtained from the cloud platform;

[0087] Generate document selection interaction information based on keyword information of multiple medical documents of the user;

[0088] Obtain user feedback on file selection interaction information through interactive nodes;

[0089] Based on the user's feedback on the file selection interaction information, the encrypted target medical file is obtained from the cloud platform;

[0090] The encrypted target medical file is decrypted to generate the target medical file.

[0091] As an example, a cloud platform is a server cluster that stores a large amount of medical data, including multiple medical files belonging to a user. Based on the user's identity information, keyword information for all medical files related to the user is retrieved from the cloud platform. This keyword information may include the file name, type, creation date, or content summary, helping the user quickly identify the files they need. Based on the keyword information obtained from the cloud platform, a file selection interactive message is generated. This interactive message is typically presented to the user in a list, grid, or tree structure, allowing them to intuitively see the medical files available. Each file may also include additional information such as file size, creator, or modification date to aid in selection. The user reviews the file selection interactive message and selects one or more medical files based on their needs. Their selection is reflected through interactive nodes (such as a touchscreen, keyboard, mouse, or voice input device). This feedback directly reflects the user's intent and can be used to determine which medical files the user would like to view. Based on the user's feedback, the corresponding medical files are retrieved from the cloud platform. However, due to the sensitive nature of medical files, these files are typically stored encrypted on the cloud platform. Therefore, the data processing node corresponding to the interaction node receives the encrypted target medical file. This step ensures that even if the data is intercepted during file transfer, it cannot be read by unauthorized individuals. Using the appropriate decryption algorithm and key, the encrypted target medical file is decrypted. The decryption process converts the encrypted data back to its original form. Once decryption is successful, the data processing node corresponding to the interaction node generates a readable version of the target medical file and displays it to the user or stores it on the user's device.

[0092] The interactive node is used to receive information query instructions initiated by the user, and the data processing node corresponding to the interactive node is used to obtain target medical information from the cloud platform based on the information query instructions and send the target medical information to the interactive node.

[0093] Specifically include:

[0094] Determine information search keywords based on information query instructions;

[0095] Based on information search keywords, target medical information is obtained from the cloud platform.

[0096] As an example, a user enters an information query through an interactive node, such as "Search for the causes and treatments of diabetes." Upon receiving the query, the data processing node first parses it and extracts key information, known as search keywords. In this example, the keywords might be "diabetes," "cause," and "treatment." Based on the extracted keywords, the data processing node constructs a query request to the cloud platform. This request typically includes the keywords, the query scope (e.g., medical field), and sorting requirements. The data processing node sends the query request to the cloud platform. The cloud platform searches its vast medical information database for relevant information based on the keywords and scope in the request. The search results may include various types of medical information, such as medical papers, clinical guidelines, and expert recommendations. After the cloud platform returns the search results, the data processing node filters and organizes them. It sorts and selects the information based on factors such as authority, relevance, and timeliness to ensure that users receive the most accurate and useful information. The filtered and organized target medical information is then sent back to the interactive node. The interactive node displays this information in a user-friendly format, such as a list, paragraph, or chart. Users view the retrieved target medical information through the interactive node. If users have questions about certain information or need further explanation, they can initiate query instructions again through the interactive node to interact.

[0097] In some embodiments, the data processing node corresponding to the interaction node is further configured to:

[0098] Obtaining the reaction time characteristics of multiple sample users to basic interaction information;

[0099] Obtain the user's reaction time characteristics to basic interaction information;

[0100] Based on the reaction time characteristics of multiple sample users to the basic interaction information and the reaction time characteristics of the users to the basic interaction information, the display font size of the interaction node is adjusted.

[0101] Specifically, the design of interactive nodes focuses not only on accurate information delivery but also on enhancing the user experience, ensuring that information is presented to users in the most efficient and comfortable manner. To achieve this goal, the data processing nodes corresponding to interactive nodes utilize a dynamic adjustment mechanism based on user reaction time characteristics to optimize the interactive node's display settings, such as adjusting the display font size. A certain number of sample users (who may represent a wide range of age, gender, and vision conditions) are selected and presented with a series of basic interactive information. This basic interactive information may include simple text prompts, button labels, menu options, and so on. The reaction time of each sample user to this information is recorded—the time from information presentation to the user's response (e.g., click, completion of reading, etc.). Statistical analysis can be used to obtain characteristics such as the sample user's average reaction time and reaction time distribution to this basic interactive information. When a user begins using the system, the interactive node also records the user's reaction time characteristics to this basic interactive information. These characteristics are compared with previously collected sample user data to assess whether the target user's reaction speed is within the normal range or whether there are any specific needs or limitations. With these two sets of data, the data processing node corresponding to the interactive node can begin adjusting the interactive node's display settings. Specifically, if the target user's reaction time is significantly longer than the average level of sample users, this may mean that the user has difficulty reading or identifying information. To improve the user experience, the data processing node corresponding to the interactive node can automatically increase the display font size in the interactive node, making the information clearer and easier to read. Conversely, if the target user's reaction time is shorter, the data processing node corresponding to the interactive node can consider reducing the font size to save screen space and provide a more compact information layout.

[0102] An AI-based medical self-service terminal can also be used to perform other functions. For example, an AI-based medical self-service terminal supports multiple payment methods, including cash, bank cards, and mobile payments (such as WeChat and Alipay). Patients can complete the payment of various fees, such as registration fees, examination fees, and medication fees, on the terminal. During the payment process, patients can view detailed fee information to ensure transparent consumption. Another example is an AI-based medical self-service terminal that provides self-service card issuance for first-time patients. Patients simply enter basic personal information such as name and ID number, and the device automatically issues a medical card. Another example is an AI-based medical self-service terminal that can measure basic vital signs such as blood pressure, blood sugar, temperature, height, and weight. The test results are automatically uploaded to the hospital information system for doctors' reference. Another example is that patients can use the terminal to conduct medical insurance inquiries, medical insurance reimbursement, and medical insurance payment services, among other procedures. Multiple authentication methods are supported, including medical insurance cards and ID cards.

[0103] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. An AI-based medical self-service system, characterized in that: The system comprises a plurality of interactive nodes and a plurality of data processing nodes, wherein the interactive nodes are used to interact with users, and the plurality of data processing nodes interact with data with a cloud platform, and the cloud platform is used to allocate corresponding data processing nodes to the interactive nodes based on allocation auxiliary information, wherein the allocation auxiliary information includes at least the number of registration instructions, the number of file query instructions, and the number of information query instructions for each interactive node in multiple current time periods: The interactive node is used to receive a registration instruction initiated by a user, and the data processing node corresponding to the interactive node is used to generate registration recommendation information based on the registration instruction, and make an appointment for registration based on the user's feedback on the registration recommendation information; The interactive node is used to receive a file query instruction initiated by a user, and the data processing node corresponding to the interactive node is used to obtain a target medical file from the cloud platform based on the file query instruction and send the target medical file to the interactive node; The interactive node is used to receive an information query instruction initiated by a user, and the data processing node corresponding to the interactive node is used to obtain target medical information from the cloud platform based on the information query instruction and send the target medical information to the interactive node; The cloud platform is used to allocate corresponding data processing nodes to interactive nodes based on allocation auxiliary information, including: Obtain the number of registration instructions, file query instructions, and information query instructions for the interactive node in multiple historical time periods; Calculate the correlation coefficient of the number of registration instructions, the number of file query instructions, and the number of information query instructions of any two interactive nodes in multiple historical time periods; For each interaction node, based on the correlation coefficient of the number of registration instructions, the correlation coefficient of the number of file query instructions, and the correlation coefficient of the number of information query instructions between any two interaction nodes, the positively associated interaction nodes and the negatively associated interaction nodes of the interaction node are determined; Obtain the number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple current time periods; Based on the positively associated interactive nodes and negatively associated interactive nodes of each interactive node and the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple current time periods, predict the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple future time periods; Based on the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple future time periods, a corresponding data processing node is allocated to the interactive node.

2. The AI-based medical self-service system according to claim 1, characterized in that: The cloud platform determines positively associated interaction nodes and negatively associated interaction nodes of interaction nodes based on the registration instruction quantity correlation coefficient, the file query instruction quantity correlation coefficient, and the information query instruction quantity correlation coefficient of any two interaction nodes, including: The weighted sum of the correlation coefficients of the number of registration instructions, the number of file query instructions, and the number of information query instructions of any two interaction nodes is performed to calculate the comprehensive correlation coefficient of any two interaction nodes; For each interactive node, determine whether there is an interactive node whose registration instruction quantity correlation coefficient, file query instruction quantity correlation coefficient, or information query instruction quantity correlation coefficient is greater than the first correlation coefficient positive threshold, and whose registration instruction quantity correlation coefficient, file query instruction quantity correlation coefficient, and information query instruction quantity correlation coefficient are all greater than the second correlation coefficient positive threshold; if so, use it as a positively associated interactive node for the interactive node; determine whether there is an interactive node whose comprehensive correlation coefficient is greater than the first comprehensive correlation coefficient threshold; if so, use it as a positively associated interactive node for the interactive node, wherein the first correlation coefficient positive threshold, the second correlation coefficient positive threshold, and the first comprehensive correlation coefficient threshold are all greater than 0, and the first correlation coefficient positive threshold is greater than the second correlation coefficient positive threshold; For each interactive node, determine whether there is an interactive node whose at least one of the registration instruction quantity correlation coefficient, the file query instruction quantity correlation coefficient or the information query instruction quantity correlation coefficient is less than the first correlation coefficient negative threshold, and whose registration instruction quantity correlation coefficient, the file query instruction quantity correlation coefficient and the information query instruction quantity correlation coefficient are all less than the second correlation coefficient negative threshold. If so, act as a negatively associated interactive node of the interactive node. Determine whether there is an interactive node whose comprehensive correlation coefficient is less than the second comprehensive correlation coefficient threshold. If so, act as a negatively associated interactive node of the interactive node, wherein the first correlation coefficient negative threshold, the second correlation coefficient negative threshold and the second comprehensive correlation coefficient threshold are all less than 0, and the second correlation coefficient negative threshold is greater than the first correlation coefficient negative threshold.

3. The AI-based medical self-service system according to claim 2, characterized in that: The cloud platform predicts the number of registration instructions, file query instructions, and information query instructions for each interactive node in multiple future time periods based on the positively associated interactive nodes and negatively associated interactive nodes of each interactive node and the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple current time periods, including: For each interaction node, based on the positively associated interaction nodes and negatively associated interaction nodes of the interaction node, a quantity prediction model and a quantity correction model corresponding to the interaction node are established; For each interactive node, using the quantity prediction model corresponding to the interactive node, based on the number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple current time periods, as well as the number of registration instructions, file query instructions, and information query instructions of the positively associated interactive nodes and negatively associated interactive nodes of the interactive node in multiple current time periods, predict the number of registration instructions, file query instructions, and information query instructions of the interactive node in multiple future time periods; Through the quantity correction model corresponding to each interaction node, based on the number of registration instructions, file query instructions and information query instructions of the positively associated interaction nodes and negatively associated interaction nodes of each interaction node in multiple future time periods, the number of registration instructions, file query instructions and information query instructions of the interaction node in multiple future time periods are iteratively corrected to generate the corrected number of registration instructions, file query instructions and information query instructions of each interaction node in multiple future time periods.

4. The AI-based medical self-service system according to claim 3, characterized in that: The cloud platform allocates corresponding data processing nodes to the interactive nodes based on the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple future time periods, including: Establish node scheduling objective function; Generate multiple sample node scheduling schemes; Calculate the node scheduling objective function value of the sample node scheduling solution based on the node scheduling objective function, the number of registration instructions, file query instructions, and information query instructions of each interactive node in multiple future time periods, and the correlation coefficient of the number of registration instructions, the correlation coefficient of the number of file query instructions, and the correlation coefficient of the number of information query instructions between any two interactive nodes; Determine the target node scheduling scheme based on the node scheduling objective function value of each sample node scheduling scheme through genetic algorithm; Based on the target node scheduling scheme, corresponding data processing nodes are allocated to the interactive nodes.

5. The AI-based medical self-service system according to any one of claims 1 to 4, characterized in that: The data processing node corresponding to the interactive node generates registration recommendation information based on the registration instruction, including: Read the user's identity information based on the registration instruction; Based on the user's identity information, obtain the user's electronic medical records from the cloud platform; Through natural language processing, based on the disease feature knowledge graph and the user's electronic medical record, the user's medical information is obtained; Generate registration recommendation information based on the user's medical consultation information.

6. The AI-based medical self-service system according to claim 5, characterized in that: The data processing node corresponding to the interaction node obtains the user's medical information through natural language processing based on the disease feature knowledge graph and the user's electronic medical record, including: Access electronic medical records of multiple historical users; Determine the incidence correlation parameters of any two diseases based on the electronic medical records of multiple historical users; Get the user's disease description information; Based on the user's disease description information, disease feature knowledge graph and the incidence correlation parameters of any two diseases, multiple diseases are ranked and disease ranking results are generated; Through natural language processing, based on the disease feature knowledge graph and disease ranking results, medical questions are generated to obtain user feedback on the medical questions.

7. The AI-based medical self-service system according to claim 5, characterized in that: The data processing node corresponding to the interaction node is further used for: Obtaining the reaction time characteristics of multiple sample users to basic interaction information; Obtain the user's reaction time characteristics to basic interaction information; Based on the reaction time characteristics of multiple sample users to the basic interaction information and the reaction time characteristics of the users to the basic interaction information, the display font size of the interaction node is adjusted.

8. The AI-based medical self-service system according to any one of claims 1 to 4, characterized in that: The data processing node corresponding to the interactive node obtains the target medical file from the cloud platform based on the file query instruction, including: Read the user's identity information based on the file query instruction; Based on the user's identity information, obtain keyword information of multiple medical files of the user from the cloud platform; Generate document selection interaction information based on keyword information of multiple medical documents of the user; Obtain user feedback on file selection interaction information through interactive nodes; Based on the user's feedback on the file selection interaction information, the encrypted target medical file is obtained from the cloud platform; The encrypted target medical file is decrypted to generate the target medical file.

9. The AI-based medical self-service system according to any one of claims 1 to 4, characterized in that: The data processing node corresponding to the interactive node obtains target medical information from the cloud platform based on the information query instruction, including: Determine information search keywords based on information query instructions; Based on information search keywords, target medical information is obtained from the cloud platform.

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