An online consultation intelligent interaction method and system

Abstract

The present invention provides an intelligent interaction method and system for online consultation, which relates to the technical field of online consultation interaction. It acquires consultation appointment data, determines consultation nodes, segments and stores consultation record information to obtain a consultation storage data packet, performs feature extraction and annotation of feature categories, and then classifies the consultation storage data packet to obtain a stage consultation report and a comprehensive consultation report. It conducts fusion comparison to obtain a report comparison result, performs node consultation data annotation on the current comprehensive consultation report, and then generates a consultation data trend chart; calculates a patient consultation weight factor and a patient consultation dynamic variable factor, and then calculates a patient consultation coefficient, and adjusts consultation parameters for the patient according to the patient consultation coefficient to obtain patient consultation adjustment data. The present invention realizes intelligent interaction and management of online consultation, not only improves the efficiency and quality of consultation, but also optimizes the allocation of medical resources and enhances the patient experience.

Description

Technical Field

[0001] The present invention proposes an online consultation intelligent interaction method and system, which relate to the field of intelligent interaction technology, and specifically to the field of online consultation intelligent interaction technology. Background Art

[0002] In today's medical process, the consultation method has gradually changed from offline to online. Although traditional online consultation is already possible, online consultation management still faces many technical problems. The traditional online consultation application and appointment process is cumbersome, and it is difficult to monitor and adjust changes in real time. At the same time, there is a lack of personalized consultation management based on data analysis. When the doctor withdraws from the consultation due to emergency or special circumstances during the consultation, the consultation records are difficult to retrieve, analyze and manage in a targeted manner. It is also difficult for traditional online consultations to intelligently adjust the patient's future consultation parameters based on the consultation situation. Summary of the invention

[0003] The present invention provides an online consultation intelligent interaction method and system to solve the above problems:

[0004] The present invention provides an online consultation intelligent interaction method and system, the method comprising:

[0005] S1. Obtain consultation application information, make an appointment for consultation, obtain real-time data information during the consultation process and dynamic variable information of the consultation appointment information, pre-process and record the real-time data information, and obtain consultation record information;

[0006] S2, obtaining consultation appointment data, determining consultation nodes, segmenting and packaging the consultation record information and storing it, obtaining consultation storage data packets, performing feature extraction and feature category labeling, and then classifying the consultation storage data packets to obtain feature category data groups;

[0007] S3. Acquire the phase consultation report and the comprehensive consultation report according to the consultation storage data packet, perform fusion comparison, obtain the report comparison result, annotate the node consultation data of the current comprehensive consultation report, and then generate a consultation data trend chart;

[0008] S4. Calculate the patient consultation weight factor and the patient consultation dynamic variable factor, and then calculate the patient consultation coefficient, adjust the patient consultation parameters according to the patient consultation coefficient, and obtain patient consultation adjustment data.

[0009] Further, the S1 includes:

[0010] Obtain consultation application information, determine the information of participants based on the consultation application information, and then determine the consultation appointment information;

[0011] Perform a consultation based on the consultation appointment information and obtain real-time data information during the consultation;

[0012] Obtain the consultation appointment status change information, and combine the real-time data information during the consultation through the consultation appointment status change information to obtain the dynamic variable information of the consultation appointment information;

[0013] Preprocess the real-time data information to obtain the preprocessed real-time data information;

[0014] Store the preprocessed real-time data information to obtain the consultation record information.

[0015] Further, the S2 includes:

[0016] Obtain the consultation appointment data and determine the consultation nodes according to the consultation appointment data;

[0017] Segment and package the consultation record information according to the consultation node information to obtain consultation data packets for multiple stages;

[0018] Store the consultation data for each stage of the consultation data packet to obtain the consultation storage data packet for each stage;

[0019] Obtain the preset consultation index types, and then obtain the characteristic keywords for each preset consultation index type;

[0020] Extract features from the consultation storage data packet for each stage according to the characteristic keywords to obtain the feature extraction data of the consultation storage data packet for each stage;

[0021] Label the feature categories of the consultation storage data packet for each stage according to the feature extraction data to obtain the annotation information of the consultation storage data packet for each stage;

[0022] Classify the consultation storage data packet for each stage according to the types of the annotation information to obtain the feature category data group.

[0023] Further, the S3 includes:

[0024] Obtain the consultation report data of the doctor for the consultation storage data packet for each stage to obtain the stage consultation report;

[0025] Sort each stage consultation report according to the stage order to generate a comprehensive consultation report;

[0026] Extract features from each stage consultation report according to the characteristic keywords to obtain the feature extraction data of the stage consultation report for each stage;

[0027] Integrate the feature extraction data for each stage to obtain the feature integration extraction data;

[0028] Obtain the comprehensive consultation report of the patient's history, fuse and compare the report data of the historical comprehensive consultation report with the report data of the current comprehensive consultation report to obtain a report comparison result;

[0029] Annotate the node consultation data of the current comprehensive consultation report according to the report comparison result; the node consultation data annotation includes the difference data between the current comprehensive consultation report data and the average data of the historical comprehensive consultation report data;

[0030] Obtain the node consultation data annotation of each historical comprehensive consultation report and generate a consultation data trend chart.

[0031] Further, the S4 includes:

[0032] Obtain the new information of the consultation data according to the consultation data trend chart, and calculate the patient consultation weight factor according to the new information of the consultation data;

[0033] Obtain the dynamic variable information of each historical comprehensive consultation report, and calculate the patient consultation dynamic variable factor according to the dynamic variable information;

[0034] Calculate the patient consultation coefficient according to the patient consultation weight factor combined with the patient consultation dynamic factor;

[0035] Adjust the duration or frequency of the patient's next consultation according to the patient consultation coefficient to obtain the patient consultation adjustment data.

[0036] Further, the interactive system includes:

[0037] The consultation appointment record module is used to obtain consultation application information, make an appointment for consultation, obtain the real-time data information during the consultation and the dynamic variable information of the consultation appointment information, preprocess and record the real-time data information to obtain consultation record information;

[0038] The consultation record packing and classification module is used to obtain consultation appointment data, determine consultation nodes, segment, pack and store the consultation record information to obtain a consultation storage data packet, perform feature extraction and feature category annotation, and then classify the consultation storage data packet to obtain a feature category data group;

[0039] The data fusion and comparison module is used to obtain the stage consultation report and the comprehensive consultation report according to the consultation storage data packet, perform fusion and comparison to obtain a report comparison result, annotate the node consultation data of the current comprehensive consultation report, and then generate a consultation data trend chart;

[0040] The consultation calculation and adjustment module is used to calculate the patient consultation weight factor and the patient consultation dynamic variable factor, and then calculate the patient consultation coefficient. According to the patient consultation coefficient, the consultation parameters of the patient are adjusted to obtain the patient consultation adjustment data.

[0041] Furthermore, the consultation appointment record module includes:

[0042] The consultation appointment module is used to obtain the consultation application information, determine the participant information according to the consultation application information, and then determine the consultation appointment information;

[0043] Conduct a consultation according to the consultation appointment information, and obtain the real-time data information during the consultation;

[0044] The variable acquisition module is used to obtain the consultation appointment status change information, and combine the consultation appointment status change information with the real-time data information during the consultation to obtain the dynamic variable information of the consultation appointment information;

[0045] Preprocess the real-time data information to obtain the preprocessed real-time data information;

[0046] The record storage module is used to store the preprocessed real-time data information to obtain the consultation record information.

[0047] Furthermore, the consultation record packing and classification module includes:

[0048] The node packing module is used to obtain the consultation appointment data and determine the consultation nodes according to the consultation appointment data;

[0049] Segment and pack the consultation record information according to the consultation node information to obtain consultation data packets at multiple stages;

[0050] The packing storage module is used to store the consultation data for each stage of the consultation data packet to obtain the consultation storage data packet for each stage;

[0051] The feature extraction module is used to obtain the preset consultation index types, and then obtain the feature keywords for each preset consultation index type;

[0052] Extract features from the consultation storage data packet for each stage according to the feature keywords to obtain the feature extraction data of the consultation storage data packet for each stage;

[0053] The feature classification module is used to label the feature categories of the consultation storage data packet for each stage according to the feature extraction data to obtain the annotation information of the consultation storage data packet for each stage;

[0054] Classify the consultation storage data packets for each stage according to the types of the annotation information to obtain the feature category data group.

[0055] Further, the data fusion and comparison module includes:

[0056] An integrated generation module, configured to obtain the consultation report data of the consultation storage data packet for each stage by a doctor, and obtain a stage consultation report;

[0057] Sort each stage consultation report according to the stage sequence to generate an integrated consultation report;

[0058] An integration and extraction module, configured to perform feature extraction on each stage consultation report according to feature keywords, and obtain feature extraction data of the stage consultation report for each stage;

[0059] Integrate the feature extraction data of each stage to obtain feature integration and extraction data;

[0060] A comparison and annotation module, configured to obtain a patient's historical integrated consultation report, fuse and compare the report data of the historical integrated consultation report with the report data of the current integrated consultation report, and obtain a report comparison result;

[0061] Perform node consultation data annotation on the current integrated consultation report according to the report comparison result; the node consultation data annotation includes difference data based on the average data of the current integrated consultation report data and the historical integrated consultation report data;

[0062] A trend chart generation module, configured to obtain the node consultation data annotation of each historical integrated consultation report and generate a consultation data trend chart.

[0063] Further, the consultation calculation and adjustment module includes:

[0064] A weight calculation module, configured to obtain new information of consultation data according to the consultation data trend chart, and calculate a patient consultation weight factor according to the new information of the consultation data;

[0065] A dynamic calculation module, configured to obtain dynamic variable information of each historical integrated consultation report, and calculate a patient consultation dynamic variable factor according to the dynamic variable information;

[0066] A consultation adjustment module, configured to calculate a patient consultation coefficient according to the patient consultation weight factor in combination with the patient consultation dynamic factor;

[0067] Adjust the duration or frequency of the patient's next consultation according to the patient consultation coefficient to obtain patient consultation adjustment data.

[0068] Beneficial effects of the present invention: Through online intelligent interaction, the time consumed in consultation is saved and the consultation efficiency is improved. The consultation resources are dynamically adjusted according to the consultation data to ensure the rational allocation and efficient use of medical resources. Real-time data recording and dynamic analysis improve the accuracy and comprehensiveness of the consultation process and the accuracy of diagnosis. Personalized consultation reports and trend charts are provided. The present invention solves the problem that the traditional online consultation application and appointment process is cumbersome and difficult to monitor and adjust changes in real time. At the same time, it also solves the lack of personalized consultation management based on data analysis. When the doctor withdraws from the consultation midway due to emergency or special circumstances during the consultation, the consultation records are difficult to retrieve, analyze and manage in a targeted manner. The technical effect of intelligently adjusting the patient's future consultation parameters according to the consultation situation is achieved.

[0069] Figure 1 A schematic diagram of an intelligent interactive method for online consultation;

[0070] Figure 2 This is a schematic diagram of the consultation data trend chart. DETAILED DESCRIPTION

[0071] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0072] In one embodiment of the present invention, an online consultation intelligent interaction method and system are proposed by the present invention, and the method includes:

[0073] S1. Obtain consultation application information, make an appointment for consultation, obtain real-time data information during the consultation process and dynamic variable information of the consultation appointment information, pre-process and record the real-time data information, and obtain consultation record information;

[0074] S2, obtaining consultation appointment data, determining consultation nodes, segmenting and packaging the consultation record information and storing it, obtaining consultation storage data packets, performing feature extraction and feature category labeling, and then classifying the consultation storage data packets to obtain feature category data groups;

[0075] S3. Acquire the phase consultation report and the comprehensive consultation report according to the consultation storage data packet, perform fusion comparison, obtain the report comparison result, annotate the node consultation data of the current comprehensive consultation report, and then generate a consultation data trend chart;

[0076] S4, calculating the patient consultation weight factor and the patient consultation dynamic variable factor, and then calculating the patient consultation coefficient, adjusting the patient consultation parameters according to the patient consultation coefficient, and obtaining the patient consultation adjustment data, such as Figure 1 shown.

[0077] The working principle of the above technical solution is as follows: by collecting the consultation application information of the patient, including basic information, medical history, current symptoms, etc. Make an appointment for consultation according to the application information, and capture the data information and dynamic changes of the appointment information during the consultation in real time (such as consultation time adjustment, doctor change, etc.). Preprocess the real-time data information (such as data cleaning and formatting) to ensure data quality and generate detailed consultation record information. The system segments and packages the consultation record information according to the progress and key nodes of the consultation to form a consultation storage data packet. Extract features from the data packet, classify the data packet according to the marked feature category, form a feature category data group, and use the classified data packet to generate a stage consultation report and a comprehensive consultation report. The two reports are fused and compared, and the changes and differences in the consultation process are analyzed to obtain the report comparison results. In the comprehensive consultation report, the consultation data of the current node is annotated, and the consultation data trend chart is generated through data visualization technology to facilitate the visualization of the data. The system calculates the patient consultation weight factor and dynamic variable factor based on the patient's consultation record, feature category data, and dynamic variables in the consultation process. Combining these factors, the patient consultation coefficient is calculated, which can reflect the patient's overall consultation data. According to the patient consultation coefficient, the system automatically adjusts the patient's consultation parameters.

[0078] The technical effects of the above technical solutions are: saving time consumed in consultation and improving consultation efficiency through online intelligent interaction. Dynamically adjusting consultation resources according to consultation data to ensure the rational allocation and efficient use of medical resources. Real-time data recording and dynamic analysis improve the accuracy and comprehensiveness of the consultation process and the accuracy of diagnosis. Provide personalized consultation reports and trend charts. The present invention solves the problem that the traditional online consultation application and appointment process is cumbersome and difficult to monitor and adjust changes in real time. At the same time, it also solves the lack of personalized consultation management based on data analysis. When the doctor withdraws from the consultation midway due to emergency or special circumstances during the consultation, the consultation records are difficult to retrieve, analyze and manage in a targeted manner. The technical effect of intelligently adjusting the patient's future consultation parameters according to the consultation situation is achieved.

[0079] In one embodiment of the present invention, the S1 includes:

[0080] Obtain consultation application information, determine the information of participants according to the consultation application information, and then determine the consultation appointment information; the consultation appointment information includes consultation time information, consultation personnel and consultation purpose, etc.;

[0081] A consultation is conducted according to the consultation appointment information, and real-time data information during the consultation process is obtained; the real-time data information includes consultation communication information and interactive file information, etc.

[0082] Obtain the information on the change of the consultation appointment status, and combine the information on the change of the consultation appointment status with the real-time data information during the consultation process to obtain the dynamic variable information of the consultation appointment information; the dynamic variable information includes changes in the consultation personnel, time, purpose, etc.

[0083] Preprocess the real-time data information to obtain the preprocessed real-time data information.

[0084] Store the preprocessed real-time data information to obtain the consultation record information.

[0085] The working principle of the above technical solution is as follows: The present invention collects the consultation application information of patients or medical institutions, and automatically or manually assists in determining the information of the participants, including the attending doctor, experts from relevant departments, and the appointment time, etc. After determining the participants, detailed consultation appointment information is generated, including the consultation time, location, list of participants and their respective roles, consultation purpose, etc. According to the consultation appointment information, online consultation invitations are sent to relevant personnel to conduct online consultations. During the consultation process, the system captures and records the consultation communication information in real time, such as text chats, voice calls, video calls, etc. During or before the consultation process, changes in the appointment status may occur, such as time adjustment, personnel replacement, purpose change, etc. By obtaining this information on the change of the appointment status and combining it with the real-time consultation data information, the dynamic variable information of the consultation appointment information is generated. The captured real-time data information is preprocessed, including data cleaning (removing useless or incorrect data), formatting (unifying the data format and standard), etc. The preprocessed real-time data information is stored as the consultation record information.

[0086] The technical effects of the above technical solution are as follows: Through the online process, the communication cost and time delay in traditional consultations are reduced, and the consultation efficiency is improved. The real-time data recording and storage ensure the traceability and transparency of the consultation process. The system can capture and process the change information of the appointment status in real time, making the consultation plan more flexible and adaptable to the actual situation. The preprocessed real-time data provides strong data support for medical decision-making, reducing the occurrence of errors or mistakes. According to the real-time data and dynamic variable information, the system can allocate medical resources more accurately, such as reasonably arranging the time of experts and optimizing the inspection process. Patients can participate in the consultation process more conveniently and understand the condition and treatment plan in real time.

[0087] In one embodiment of the present invention, S2 includes:

[0088] Obtain the consultation appointment data, and determine the consultation nodes according to the consultation appointment data; the consultation nodes can be the time nodes for personnel to enter and leave the meeting, communication information nodes, etc.

[0089] Segment and package the consultation record information according to the consultation node information to obtain consultation data packets for multiple stages;

[0090] Store the consultation data of each stage of the consultation data packet to obtain the consultation storage data packet for each stage;

[0091] Obtain the preset types of consultation indicators, and then obtain the characteristic keywords for each preset type of consultation indicator;

[0092] Extract features from the consultation storage data packet of each stage according to the characteristic keywords to obtain the feature extraction data of the consultation storage data packet of each stage; the feature extraction data includes the data of the consultation storage data packet with the characteristic keywords.

[0093] Label the feature categories of the consultation storage data packet of each stage according to the feature extraction data to obtain the annotation information of the consultation storage data packet of each stage; the annotation information includes the characteristic keywords of the consultation storage data packet.

[0094] Classify the consultation storage data packet of each stage according to the types of the annotation information to obtain the feature category data group. Each of the feature category data groups includes multiple data packets of the same feature category.

[0095] The working principle of the above technical solution is as follows: Obtain the consultation appointment data from the database. These data contain the detailed arrangements of the consultation, such as the participants, time, location, etc. According to the appointment data, the key time nodes and communication information nodes can be identified, such as the entry and exit times of personnel, important discussion time periods, etc. According to the identified consultation node information, the continuous consultation record information is segmented. Each segment corresponds to a specific node or time period. The segmented record information is packaged into consultation data packets for multiple stages. Each data packet contains all the relevant information within that stage. The consultation data packets of each stage are stored independently to ensure the integrity and accessibility of the data. During the storage process, security measures such as data compression and encryption may also be included to improve the storage efficiency and data security. The system presets multiple types of consultation indicators. For each type of consultation indicator, the system defines the corresponding characteristic keywords. By using these keywords to identify specific information content, the preset characteristic keywords are used to extract features from the consultation storage data packet of each stage. Identify the content in the data packet that conforms to the description of the characteristic keywords. Label the extracted feature data. The annotation information includes the characteristic keywords and their specific positions or context information in the data packet. According to the types of the annotation information, the system classifies the consultation storage data packet of each stage. The basis for classification is the types of characteristic keywords contained in the data packet. The classification results form multiple feature category data groups. Each data group contains all the data packets with the same characteristic keywords.

[0096] The technical effects of the above technical solution are as follows: Through segmented packaging and feature extraction, the original and unstructured consultation record data is transformed into a structured data set that is conducive to querying and analysis. The formation of the feature category data group enables data analysts to locate the data set of interest more quickly, thereby improving the analysis efficiency. The preset types of consultation indicators and feature keywords help improve the recognition efficiency of specific fields and specific events. The security measures taken during data storage, such as compression and encryption, enhance data security and privacy protection.

[0097] In one embodiment of the present invention, S3 includes:

[0098] Obtain the consultation report data of the doctor for the consultation storage data packet of each stage to obtain the stage consultation report;

[0099] Sort each stage consultation report according to the stage order to generate a comprehensive consultation report;

[0100] Extract features from each stage consultation report according to the feature keywords to obtain the feature extraction data of the stage consultation report of each stage;

[0101] Integrate the feature extraction data of each stage to obtain the feature integrated extraction data; The above solution can flexibly integrate the consultation data of each feature, facilitating data analysis of each feature of the consultation.

[0102] Obtain the patient's historical comprehensive consultation report, and fuse and compare the report data of the historical comprehensive consultation report with the report data of the current comprehensive consultation report to obtain the report comparison result; The fusion comparison includes obtaining the different data of the current data of each type and the historical data of the same type for difference comparison.

[0103] Perform node consultation data annotation on the current comprehensive consultation report according to the report comparison result; The node consultation data annotation includes performing node consultation data annotation on the current comprehensive consultation report according to the difference data between the current comprehensive consultation report data and the average data of the historical comprehensive consultation report data.

[0104] Obtain the node consultation data annotation of each historical comprehensive consultation report and generate a consultation data trend chart, as Figure 2 shown.

[0105] The working principle of the above technical solution is as follows: Obtain the consultation report data written by doctors for each stage's consultation storage data packet to form a stage consultation report. Sort these stage consultation reports in chronological or logical order, and finally generate a complete comprehensive consultation report. Extract features from each stage consultation report, and identify key information points in the report based on preset feature keywords. Integrate the feature extraction data of all stages to form feature integration extraction data. Obtain the patient's historical comprehensive consultation reports, which record the patient's past consultation situations and doctors' suggestions. Fuse and compare the report data of the current comprehensive consultation report with the historical data, and identify the differences between the current data and the historical data through difference comparison. According to the report comparison result, the system performs node consultation data annotation on the current comprehensive consultation report. The basis for annotation is the degree of difference between the current data and the historical average data. Through annotation, doctors can intuitively see which nodes or time periods have the most significant data changes. Obtain the node consultation data annotation of each historical comprehensive consultation report, and combine it with the annotation information of the current report to generate a consultation data trend chart. This chart uses time as the horizontal axis and data change as the vertical axis, intuitively showing the long-term change trend and short-term fluctuations of the patient's consultation data.

[0106] The technical effects of the above technical solution are as follows: By generating a comprehensive consultation report and feature integration extraction data, doctors can more conveniently obtain comprehensive information and key features of the consultation, improving the readability and usability of the report. Fusing and comparing the current data with the historical data helps doctors understand the evolution process of the patient's condition and the change trend of the treatment effect, so as to make more scientific and reasonable decisions. Through node consultation data annotation and consultation data trend chart, doctors can identify time periods and patient groups that need to be focused on, optimizing the allocation and utilization efficiency of medical resources. Through detailed consultation reports and intuitive data trend charts, patients can more clearly understand their own conditions and treatment situations. The integration, comparison, and analysis results of consultation data can provide valuable data support for medical research.

[0107] In an embodiment of the present invention, S4 includes:

[0108] Obtain new consultation data information according to the consultation data trend chart, and calculate the patient's consultation weight factor according to the new consultation data information; the new information is the difference data of each node mentioned above, that is, the difference between the current data and the average value of the historical data.

[0109] The calculation formula of the patient's consultation weight factor is:

[0110]

[0111] Among them, Y qz is the patient's consultation weight factor, j is the total number of stages, ΔSd Add information to the consultation data for the current stage, ΔS d-1 Add information to the consultation data for the previous stage of the current stage, ΔS i Add information to the consultation data for the i-th stage, ΔS i-1 Add information to the consultation data for the (i - 1)-th stage, m is the total number of characteristic keyword corresponding to the i-th stage, E ik The preset enhancement factor for the k-th characteristic keyword in the i-th stage, E maxik The preset maximum possible value for the k-th characteristic keyword in the i-th stage, α ik The preset characteristic keyword weight parameter, C i The preset specific status correction factor;

[0112] By Assigning an enhancement factor to each characteristic keyword and comparing it with the maximum possible value can achieve the enhancement of the influence of these characteristic keywords in the evaluation. When E ik is close to or reaches E maxik the value of this part will increase significantly.

[0113] λij is used as a weight parameter to adjust the relative importance of different events or characteristics in the evaluation. By assigning different weight parameters to different events or characteristics, the model can more flexibly reflect the actual situation.

[0114] Obtain the dynamic variable information of each historical comprehensive consultation report, and calculate the patient consultation dynamic variable factor according to the dynamic variable information;

[0115] The calculation formula of the patient consultation dynamic variable factor is:

[0116]

[0117] where D bl is the patient consultation dynamic variable factor, z is the total number of comprehensive consultation reports, m is the number of types of reserved consultation indicators, △L ob is the data of the b-th type of reserved consultation indicator in the o-th comprehensive consultation report, △L o-1b is the data of the b-th type of reserved consultation indicator in the (o - 1)-th comprehensive consultation report;

[0118] Calculate the patient consultation coefficient according to the patient consultation weight factor combined with the patient consultation dynamic factor;

[0119] The calculation formula of the patient consultation coefficient is:

[0120]

[0121] where HZ is the patient consultation coefficient;

[0122] Compare the patient consultation coefficient with a preset coefficient threshold to obtain a coefficient comparison result. When the patient consultation coefficient is greater than the preset coefficient threshold, increase the adjustment. Otherwise, perform a decrease adjustment;

[0123] Adjust the duration or frequency of the patient's next consultation according to the patient consultation coefficient to obtain patient consultation adjustment data.

[0124] The working principle of the above technical solution is as follows: Identify new information points or trend changes from the consultation data trend chart. These information may represent new progress in the patient's condition or new situations in the treatment response. According to this new information, calculate the weight factor of the patient consultation. The size of the weight factor reflects the importance and influence degree of the new information on the overall patient consultation situation. Obtain each historical comprehensive consultation report and extract the dynamic variable information therein, such as changes in the consultation personnel, time, purpose, etc. Analyze the dynamic variable information and calculate the dynamic variable factor of the patient consultation. The dynamic variable factor reflects the change situation and influence degree of the unstable factors in the patient consultation process. Combine the patient consultation weight factor with the patient consultation dynamic variable factor and calculate to obtain the patient consultation coefficient. The patient consultation coefficient is a comprehensive index that takes into account both the importance of the new information and the influence of the historical dynamic variables, and is used to evaluate the comprehensive complexity and urgency of the patient's current consultation situation. According to the size of the patient consultation coefficient, the system automatically adjusts the duration or frequency of the patient's next consultation. If the patient consultation coefficient is high, it indicates that the patient's condition is complex or changing rapidly, and more frequent consultations and longer consultation times are required; otherwise, the number of consultations can be appropriately reduced or the consultation time can be shortened.

[0125] The technical effect of the above technical solution is as follows: By calculating the patient consultation coefficient and adjusting the consultation duration or frequency accordingly, personalized management of patient consultations is achieved, which better meets the actual needs of patients and improves the pertinence and effectiveness of consultations. A reasonable arrangement of consultation duration and frequency helps to optimize the utilization of medical resources. For patients who need frequent consultations, it can ensure that they receive timely and effective treatment; for patients with relatively stable conditions, unnecessary consultation times can be reduced to avoid waste of medical resources. Personalized consultation management can better meet the needs of patients. Through in-depth analysis and utilization of consultation data, medical institutions can continuously improve and optimize the consultation process and service quality.

[0126] In an embodiment of the present invention, the interaction system includes:

[0127] A consultation appointment record module, configured to obtain consultation application information, make an appointment for a consultation, obtain real-time data information during the consultation and dynamic variable information of the consultation appointment information, preprocess and record the real-time data information to obtain consultation record information;

[0128] The consultation record packaging and classification module is used to obtain consultation appointment data, determine consultation nodes, segment, package and store the consultation record information, obtain a consultation storage data packet, perform feature extraction and feature category annotation, and then classify the consultation storage data packet to obtain a feature category data group;

[0129] The data fusion and comparison module is used to obtain a stage consultation report and a comprehensive consultation report according to the consultation storage data packet, perform fusion and comparison, obtain a report comparison result, perform node consultation data annotation on the current comprehensive consultation report, and then generate a consultation data trend chart;

[0130] The consultation calculation and adjustment module is used to calculate the patient's consultation weight factor and the patient's consultation dynamic variable factor, and then calculate the patient's consultation coefficient. According to the patient's consultation coefficient, the patient's consultation parameters are adjusted to obtain the patient's consultation adjustment data.

[0131] The working principle of the above technical solution is as follows: By collecting the patient's consultation application information, including basic information, medical history, current symptoms, etc. Make an appointment for consultation according to the application information, and at the same time, capture the dynamic changes of the data information and appointment information during the consultation process in real time (such as consultation time adjustment, doctor change, etc.). Preprocess the real-time data information (such as data cleaning, formatting) to ensure data quality and generate detailed consultation record information. The system segments and packages the consultation record information according to the progress and key nodes of the consultation to form a consultation storage data packet. Perform feature extraction on the data packet, classify the data packet according to the marked feature category to form a feature category data group, and use the classified data packet to generate a stage consultation report and a comprehensive consultation report. Perform fusion and comparison on these two reports, analyze the changes and differences during the consultation process, and obtain a report comparison result. In the comprehensive consultation report, perform consultation data annotation on the current node, and generate a consultation data trend chart through data visualization technology to facilitate visual display of the data. The system calculates the patient's consultation weight factor and dynamic variable factor according to the patient's consultation record, feature category data and dynamic variables during the consultation process. Combining these factors, calculate the patient's consultation coefficient, which can reflect the overall consultation situation data of the patient. According to the patient's consultation coefficient, the system automatically adjusts the patient's consultation parameters.

[0132] The technical effects of the above technical solutions are: saving time consumed in consultation and improving consultation efficiency through online intelligent interaction. Dynamically adjusting consultation resources according to consultation data to ensure the rational allocation and efficient use of medical resources. Real-time data recording and dynamic analysis improve the accuracy and comprehensiveness of the consultation process and the accuracy of diagnosis. Provide personalized consultation reports and trend charts. The present invention solves the problem that the traditional online consultation application and appointment process is cumbersome and difficult to monitor and adjust changes in real time. At the same time, it also solves the lack of personalized consultation management based on data analysis. When the doctor withdraws from the consultation midway due to emergency or special circumstances during the consultation, the consultation records are difficult to retrieve, analyze and manage in a targeted manner. The technical effect of intelligently adjusting the patient's future consultation parameters according to the consultation situation is achieved.

[0133] In one embodiment of the present invention, the consultation appointment recording module includes:

[0134] The consultation appointment module is used to obtain consultation application information, determine the information of the participants according to the consultation application information, and then determine the consultation appointment information; the consultation appointment information includes consultation time information, consultation personnel and consultation purpose, etc.;

[0135] A consultation is conducted according to the consultation appointment information, and real-time data information during the consultation process is obtained; the real-time data information includes consultation communication information and interactive file information, etc.

[0136] The variable acquisition module is used to obtain the consultation appointment status change information, and obtain the dynamic variable information of the consultation appointment information by combining the consultation appointment status change information with the real-time data information during the consultation process; the dynamic variable information includes the change of the consultation personnel, the change of time and the change of purpose, etc.;

[0137] Preprocessing the real-time data information to obtain preprocessed real-time data information;

[0138] The record storage module is used to store the pre-processed real-time data information to obtain consultation record information.

[0139] The working principle of the above technical solution is as follows: The present invention collects consultation application information of patients or medical institutions, and automatically or manually determines the information of participants, including attending doctors, experts from relevant departments, and appointment time, etc. After determining the participants, detailed consultation appointment information is generated, including consultation time, location, list of participants and their respective roles, consultation purpose, etc. According to the consultation appointment information, online consultation invitation information is sent to relevant personnel to conduct an online consultation. During the consultation process, the system captures and records the consultation communication information in real time, such as text chat, voice call, video call, etc. During or before the consultation process, changes in the appointment status may occur, such as time adjustment, personnel replacement, purpose change, etc. By obtaining these appointment status change information and combining with the real-time consultation data information, dynamic variable information of the consultation appointment information is generated. The captured real-time data information is preprocessed, including data cleaning (removing useless or incorrect data), formatting (unifying data formats and standards), etc. The preprocessed real-time data information is stored as consultation record information.

[0140] The technical effects of the above technical solution are as follows: Through the online process, the communication cost and time delay in traditional consultations are reduced, and the consultation efficiency is improved. Real-time data recording and storage ensure the traceability and transparency of the consultation process. The system can capture and process the change information of the appointment status in real time, making the consultation plan more flexible and adaptable to the actual situation. The preprocessed real-time data provides strong data support for medical decision-making, reducing the occurrence of errors or mistakes. According to the real-time data and dynamic variable information, the system can allocate medical resources more accurately, such as reasonably arranging experts' time and optimizing the examination process. Patients can participate in the consultation process more conveniently and understand the condition and treatment plan in real time.

[0141] In an embodiment of the present invention, the consultation record packaging and classification module includes:

[0142] The node packaging module is used to obtain consultation appointment data and determine consultation nodes according to the consultation appointment data; the consultation nodes can be time nodes for personnel to enter and leave the meeting, communication information nodes, etc.;

[0143] Segment and package the consultation record information according to the consultation node information to obtain consultation data packets at multiple stages;

[0144] The packaging and storage module is used to store the consultation data of each stage of the consultation data packet to obtain the consultation storage data packet of each stage;

[0145] The feature extraction module is used to obtain the preset consultation index types, and then obtain the feature keywords of each preset consultation index type;

[0146] Feature extraction is performed on the consultation storage data packets for each stage according to the described feature keywords to obtain the feature extraction data of the consultation storage data packets for each stage; the feature extraction data includes the data of the consultation storage data packets with feature keywords.

[0147] A feature classification module is used to label the feature categories of the consultation storage data packets for each stage according to the feature extraction data to obtain the annotation information of the consultation storage data packets for each stage; the annotation information includes the feature keywords of the consultation storage data packets.

[0148] Classify the consultation storage data packets for each stage according to the types of the annotation information to obtain a feature category data group. Each of the feature category data groups includes multiple data packets of the same feature category.

[0149] The working principle of the above technical solution is as follows: Obtain consultation reservation data from the database. These data contain the detailed arrangements of the consultation, such as participants, time, location, etc. According to the reservation data, key time nodes and communication information nodes can be identified, such as the time when personnel enter and leave the meeting, important discussion time periods, etc. According to the identified consultation node information, the continuous consultation record information is segmented, and each segment corresponds to a specific node or time period. The segmented record information is packaged into consultation data packets for multiple stages, and each data packet contains all relevant information within that stage. The consultation data packets for each stage are stored independently to ensure the integrity and accessibility of the data. During the storage process, security measures such as data compression and encryption may also be included to improve storage efficiency and data security. The system presets multiple types of consultation metrics. For each type of consultation metric, the system defines corresponding feature keywords to identify specific information content. Using the preset feature keywords, feature extraction is performed on the consultation storage data packets for each stage. Identify the content in the data packet that conforms to the description of the feature keywords. Label the extracted feature data, and the annotation information includes the feature keywords and their specific positions or context information in the data packet. According to the types of the annotation information, the system classifies the consultation storage data packets for each stage. The basis for classification is the types of feature keywords contained in the data packet. The classification results form multiple feature category data groups, and each data group contains all data packets with the same feature keywords.

[0150] The technical effects of the above technical solution are as follows: By segmenting and packing and feature extraction, the original unstructured consultation record data is transformed into a structured data set that is conducive to querying and analysis. The formation of the feature category data group enables data analysts to locate the data set of interest more quickly, thereby improving the analysis efficiency. The preset types of consultation indicators and feature keywords help to improve the recognition efficiency of specific fields and specific events. The security measures taken during data storage, such as compression and encryption, enhance data security and privacy protection.

[0151] In one embodiment of the present invention, the data fusion and comparison module includes:

[0152] A comprehensive generation module, configured to obtain the consultation report data of the doctor for the consultation storage data packet at each stage, and obtain a stage consultation report;

[0153] Sort each stage consultation report according to the stage order to generate a comprehensive consultation report;

[0154] An integration and extraction module, configured to perform feature extraction on each stage consultation report according to the feature keywords to obtain the feature extraction data of the stage consultation report at each stage;

[0155] Integrate the feature extraction data at each stage to obtain the feature integration and extraction data; The above solution can flexibly integrate the consultation data of each feature, facilitating data analysis of each feature of the consultation.

[0156] A comparison and annotation module, configured to obtain the patient's historical comprehensive consultation report, fuse and compare the report data of the historical comprehensive consultation report with the report data of the current comprehensive consultation report to obtain a report comparison result; The fusion comparison includes obtaining the different data of the current data of each type and the historical data of the same type for difference comparison.

[0157] Perform node consultation data annotation on the current comprehensive consultation report according to the report comparison result; The node consultation data annotation includes performing node consultation data annotation on the current comprehensive consultation report according to the difference data between the current comprehensive consultation report data and the average data of the historical comprehensive consultation report data.

[0158] A trend chart generation module, configured to obtain the node consultation data annotation of each historical comprehensive consultation report and generate a consultation data trend chart.

[0159] The working principle of the above technical solution is as follows: Obtain the consultation report data written by the doctor for the consultation storage data packet at each stage to form a stage consultation report. Sort these stage consultation reports in chronological or logical order, and finally generate a complete comprehensive consultation report. Extract features from each stage consultation report, and identify key information points in the report based on the preset feature keywords. Integrate the feature extraction data of all stages to form feature integration extraction data. Obtain the patient's historical comprehensive consultation reports, which record the patient's past consultation situations and the doctor's suggestions. Fuse and compare the report data of the current comprehensive consultation report with the historical data, and identify the differences between the current data and the historical data through difference comparison. According to the report comparison results, the system performs node consultation data annotation on the current comprehensive consultation report. The basis for annotation is the degree of difference between the current data and the historical average data. Through annotation, doctors can intuitively see which nodes or time periods have the most significant data changes. Obtain the node consultation data annotation of each historical comprehensive consultation report, and generate a consultation data trend chart in combination with the annotation information of the current report. This chart uses time as the horizontal axis and data change as the vertical axis, and intuitively shows the long-term change trend and short-term fluctuation of the patient's consultation data.

[0160] The technical effects of the above technical solution are as follows: By generating a comprehensive consultation report and feature integration extraction data, doctors can more conveniently obtain the comprehensive information and key features of the consultation, improving the readability and usability of the report. Fusing and comparing the current data with the historical data helps doctors understand the evolution process of the patient's condition and the change trend of the treatment effect, so as to make more scientific and reasonable decisions. Through node consultation data annotation and consultation data trend chart, doctors can identify the time periods and patient groups that need to be focused on, optimizing the allocation and utilization efficiency of medical resources. Through detailed consultation reports and intuitive data trend charts, patients can more clearly understand their own conditions and treatment situations. The integration, comparison, and analysis results of consultation data can provide valuable data support for medical research.

[0161] In an embodiment of the present invention, the consultation calculation and adjustment module includes:

[0162] A weight calculation module, which is used to obtain the newly added information of the consultation data according to the consultation data trend chart, and calculate the patient consultation weight factor according to the newly added information of the consultation data;

[0163] The calculation formula of the patient consultation weight factor is:

[0164]

[0165] Among them, Y qz is the patient consultation weight factor, j is the total number of stages, ΔS dAdd information to the consultation data for the current stage, ΔS d-1 Add information to the consultation data for the previous stage of the current stage, ΔS i Add information to the consultation data for the i-th stage, ΔS i-1 Add information to the consultation data for the (i - 1)-th stage. m is the total number of characteristic keyword for the i-th stage, E ik The preset enhancement factor for the k-th characteristic keyword in the i-th stage, E maxik The preset maximum possible value for the k-th characteristic keyword in the i-th stage, α ik The preset characteristic keyword weight parameter, C i The preset specific status correction factor;

[0166] A dynamic calculation module for obtaining dynamic variable information of each historical comprehensive consultation report and calculating a patient consultation dynamic variable factor according to the dynamic variable information;

[0167] The calculation formula for the patient consultation dynamic variable factor is:

[0168]

[0169] where, D bl is the patient consultation dynamic variable factor, z is the total number of comprehensive consultation reports, m is the number of types of reserved consultation indicators, △L ob is the data of the b-th type of reserved consultation indicator in the o-th comprehensive consultation report, △L o-1b is the data of the b-th type of reserved consultation indicator in the (o - 1)-th comprehensive consultation report;

[0170] A consultation adjustment module for calculating a patient consultation coefficient according to a patient consultation weight factor and a patient consultation dynamic factor;

[0171] The calculation formula for the patient consultation coefficient is:

[0172]

[0173] where, HZ is the patient consultation coefficient;

[0174] Adjust the next consultation duration or frequency of the patient according to the patient consultation coefficient to obtain patient consultation adjustment data.

[0175] The working principle of the above technical solution is as follows: New information points or trend changes are identified from the consultation data trend chart. This information may represent new developments in the patient's condition or new situations in the treatment response. Based on this new information, the weight factor of the patient's consultation is calculated. The magnitude of the weight factor reflects the importance and impact degree of the new information on the overall consultation situation of the patient. Each historical comprehensive consultation report is obtained, and the dynamic variable information therein is extracted, such as changes in the consultation personnel, time, purpose, etc. The dynamic variable information is analyzed, and the dynamic variable factor of the patient's consultation is calculated. The dynamic variable factor reflects the change situation and impact degree of the unstable factors during the patient's consultation process. The patient consultation weight factor and the patient consultation dynamic variable factor are combined to calculate the patient consultation coefficient. The patient consultation coefficient is a comprehensive index that takes into account both the importance of the new information and the impact of the historical dynamic variables, and is used to evaluate the comprehensive complexity and urgency of the patient's current consultation situation. According to the magnitude of the patient consultation coefficient, the system automatically adjusts the duration or frequency of the patient's next consultation. If the patient consultation coefficient is relatively high, it indicates that the patient's condition is complex or changing rapidly, and more frequent consultations and longer consultation times are required; conversely, the number of consultations can be appropriately reduced or the consultation time can be shortened.

[0176] The technical effect of the above technical solution is as follows: By calculating the patient consultation coefficient and adjusting the consultation duration or frequency accordingly, personalized management of the patient's consultation is achieved, which better meets the actual needs of the patient and improves the pertinence and effectiveness of the consultation. A reasonable arrangement of the consultation duration and frequency helps to optimize the utilization of medical resources. For patients who need frequent consultations, it can ensure that they receive timely and effective treatment; while for patients with relatively stable conditions, unnecessary consultation times can be reduced to avoid waste of medical resources. Personalized consultation management can better meet the needs of patients. Through in-depth analysis and utilization of consultation data, medical institutions can continuously improve and optimize the consultation process and service quality.

[0177] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. An online consultation intelligent interaction method, characterized in that: The method comprises: S1. Obtain consultation application information, make an appointment for consultation, obtain real-time data information during the consultation process and dynamic variable information of the consultation appointment information, pre-process and record the real-time data information, and obtain consultation record information; S2, obtaining consultation appointment data, determining consultation nodes, segmenting and packaging the consultation record information and storing it, obtaining consultation storage data packets, performing feature extraction and feature category labeling, and then classifying the consultation storage data packets to obtain feature category data groups; S3, according to the consultation storage data packet, obtain the phase consultation report and the comprehensive consultation report, perform fusion comparison, obtain the report comparison result, annotate the node consultation data of the current comprehensive consultation report, and then generate a consultation data trend chart; S4, calculating the patient consultation weight factor and the patient consultation dynamic variable factor, and then calculating the patient consultation coefficient, adjusting the patient consultation parameters according to the patient consultation coefficient, and obtaining the patient consultation adjustment data; Wherein, the S4 includes: Acquire new information of consultation data according to the consultation data trend chart, and calculate the patient consultation weight factor according to the new information of consultation data; Acquire dynamic variable information of each historical comprehensive consultation report, and calculate the patient consultation dynamic variable factor according to the dynamic variable information; The patient consultation coefficient is calculated based on the patient consultation weight factor combined with the patient consultation dynamic factor; The duration or frequency of the patient's next consultation is adjusted according to the patient consultation coefficient to obtain patient consultation adjustment data.

2. According to claim 1, an online consultation intelligent interaction method is characterized in that: The S1 includes: Obtain consultation application information, determine the information of participants based on the consultation application information, and then determine the consultation appointment information; Conduct a consultation according to the consultation appointment information and obtain real-time data information during the consultation process; Acquire consultation appointment status change information, and acquire dynamic variable information of the consultation appointment information by combining the consultation appointment status change information with real-time data information during the consultation process; Preprocessing the real-time data information to obtain preprocessed real-time data information; The pre-processed real-time data information is stored to obtain consultation record information.

3. According to claim 1, an online consultation intelligent interaction method is characterized in that: The S2 includes: Obtain consultation appointment data, and determine a consultation node according to the consultation appointment data; The consultation record information is segmented and packaged according to the consultation node information to obtain consultation data packets of multiple stages; The consultation data packets of each stage are stored to obtain the consultation storage data packets of each stage; Obtaining preset consultation indicator types, and then obtaining characteristic keywords for each preset consultation indicator type; Extracting features of the consultation storage data packets at each stage according to the feature keywords to obtain feature extraction data of the consultation storage data packets at each stage; Marking the consultation storage data packets of each stage with feature categories according to the feature extraction data to obtain the marking information of the consultation storage data packets of each stage; The consultation storage data packets of each stage are classified according to the types of annotation information to obtain feature category data groups.

4. According to claim 1, an online consultation intelligent interaction method is characterized in that: The S3 includes: Obtain the doctor's consultation report data of each stage of the consultation storage data package to obtain the stage consultation report; Sort the consultation reports of each stage according to the order of the stages and generate a comprehensive consultation report; Extract features from the consultation report of each stage according to the feature keywords, and obtain feature extraction data of the consultation report of each stage; Integrate the feature extraction data of each stage to obtain feature integration extraction data; Obtain the patient's historical comprehensive consultation report, merge and compare the report data of the historical comprehensive consultation report with the report data of the current comprehensive consultation report, and obtain a report comparison result; Mark the node consultation data of the current comprehensive consultation report according to the report comparison results; Obtain the node consultation data annotation of each historical comprehensive consultation report and generate a consultation data trend chart.

5. An online consultation intelligent interactive system, characterized in that: The interactive system comprises: The consultation reservation record module is used to obtain consultation application information, make an appointment for consultation, obtain real-time data information during the consultation process and dynamic variable information of the consultation reservation information, pre-process and record the real-time data information, and obtain consultation record information; The consultation record packaging and classification module is used to obtain consultation appointment data, determine the consultation node, segment and package the consultation record information and store it, obtain consultation storage data packets, perform feature extraction and feature category labeling, and then classify the consultation storage data packets to obtain feature category data groups; The data fusion comparison module is used to obtain the phase consultation report and the comprehensive consultation report according to the consultation storage data packet, perform fusion comparison, obtain the report comparison result, annotate the node consultation data of the current comprehensive consultation report, and then generate a consultation data trend chart; A consultation calculation and adjustment module is used to calculate a patient consultation weight factor and a patient consultation dynamic variable factor, and then calculate a patient consultation coefficient, and adjust the patient consultation parameters according to the patient consultation coefficient to obtain patient consultation adjustment data; Wherein, the consultation calculation and adjustment module includes: A weight calculation module, used to obtain new information of consultation data according to the consultation data trend chart, and calculate the patient consultation weight factor according to the new information of consultation data; A dynamic calculation module, used to obtain dynamic variable information of each historical comprehensive consultation report, and calculate the patient consultation dynamic variable factor according to the dynamic variable information; A consultation adjustment module, used to calculate a patient consultation coefficient based on a patient consultation weight factor combined with a patient consultation dynamic factor; The duration or frequency of the patient's next consultation is adjusted according to the patient consultation coefficient to obtain patient consultation adjustment data.

6. According to claim 5, an online consultation intelligent interactive system is characterized in that: The consultation appointment record module includes: The consultation appointment module is used to obtain consultation application information, determine the information of participants according to the consultation application information, and then determine the consultation appointment information; Conduct a consultation according to the consultation appointment information and obtain real-time data information during the consultation process; A variable acquisition module is used to acquire consultation appointment status change information, and acquire dynamic variable information of the consultation appointment information by combining the consultation appointment status change information with real-time data information during the consultation process; Preprocessing the real-time data information to obtain preprocessed real-time data information; The record storage module is used to store the pre-processed real-time data information to obtain consultation record information.

7. The online consultation intelligent interactive system according to claim 5, characterized in that: The consultation record packaging and classification module includes: A node packaging module, used to obtain consultation appointment data and determine the consultation node according to the consultation appointment data; The consultation record information is segmented and packaged according to the consultation node information to obtain consultation data packets of multiple stages; A packaging storage module is used to store the consultation data packets of each stage and obtain the consultation storage data packets of each stage; A feature extraction module is used to obtain the preset consultation indicator types, and then obtain the characteristic keywords of each preset consultation indicator type; Extracting features of the consultation storage data packets at each stage according to the feature keywords to obtain feature extraction data of the consultation storage data packets at each stage; A feature classification module is used to mark the consultation storage data packets of each stage with feature categories according to the feature extraction data, and obtain the marking information of the consultation storage data packets of each stage; The consultation storage data packets of each stage are classified according to the types of annotation information to obtain feature category data groups.

8. The online consultation intelligent interactive system according to claim 5, characterized in that: The data fusion comparison module includes: A comprehensive generation module is used to obtain the consultation report data of the doctor's consultation storage data package for each stage and obtain the stage consultation report; Sort the consultation reports of each stage according to the order of the stages and generate a comprehensive consultation report; An integrated extraction module is used to extract features from the consultation report of each stage according to feature keywords, and obtain feature extraction data of the consultation report of each stage; Integrate the feature extraction data of each stage to obtain feature integration extraction data; A comparison and annotation module is used to obtain the patient's historical comprehensive consultation report, merge and compare the report data of the historical comprehensive consultation report with the report data of the current comprehensive consultation report, and obtain a report comparison result; Perform node consultation data annotation on the current comprehensive consultation report according to the report comparison result; the node consultation data annotation includes difference data between the current comprehensive consultation report data and the average data of the historical comprehensive consultation report data; The trend chart generation module is used to obtain the node consultation data annotations of each historical comprehensive consultation report and generate a consultation data trend chart.

Images