Postoperative patient handheld intelligent propaganda and education system

Abstract

The invention discloses a postoperative patient handheld intelligent propaganda and education system, and relates to the technical field of intelligent propaganda and education. Through an identity recognition module, after a patient starts a propaganda and education machine, identity recognition is performed on the patient, and an identity recognition result is established; the reading module is used for reading patient medical record information based on the identity recognition result and establishing a medical record reading result and a nursing evaluation result; the attention establishing module is used for establishing time sequence propaganda and education attention according to the medical record and the nursing evaluation result, and configuring attention propaganda and education content through the time sequence propaganda and education attention; the basic display module is used for classifying the attention propaganda and education contents and then displaying the contents on a display screen; the attention matching module is used for acquiring the retrieval interaction of the patient, performing attention matching according to the retrieval interaction and the time sequence propaganda and education attention, and establishing an attention matching result; and the matching display module is used for displaying the concerned matching result through a display screen. Therefore, the technical effects of online real-time comprehensive monitoring, accurate positioning of patient demands and personalized guidance providing are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent education, and particularly to a palm-top intelligent education system for postoperative patients. Background Art

[0002] With the continuous progress of medical technology, the concept of "enhanced recovery after surgery" has been gradually promoted in the surgical departments. By optimizing the perioperative management strategy, it can promote the rapid recovery of patients, shorten the hospital stay, and improve the bed turnover rate. Standardized nutritional support and scientific exercise have been proven to significantly improve the postoperative recovery effect of patients. However, how to effectively provide personalized rehabilitation education for postoperative patients and their families to ensure that they master relevant knowledge such as postoperative diet and exercise is still an important challenge to be faced.

[0003] Currently, the rehabilitation education for postoperative patients is mainly carried out by medical staff through immediate oral explanations after the patients return to the ward, which has technical problems such as insufficient timeliness of education, lack of personalization and interactivity, and lack of feedback mechanism. Summary of the Invention

[0004] The present invention provides a palm-top intelligent education system for postoperative patients to solve the technical problems of insufficient timeliness of education, lack of personalization and interactivity, and lack of feedback mechanism in the prior art, and achieve the technical effects of improving the immediacy, interactivity and personalization level of education and providing a feedback channel.

[0005] The palm-top intelligent education system for postoperative patients provided by the present invention includes:

[0006] An identity recognition module, configured to recognize the identity of a patient after the patient activates the education machine and establish an identity recognition result.

[0007] A reading module, configured to read the medical record information of the patient based on the identity recognition result, establish a medical record reading result, and synchronously obtain a nursing assessment result.

[0008] A concern establishment module, configured to establish a sequential education concern according to the medical record reading result and the nursing assessment result, and configure the concerned education content through the sequential education concern.

[0009] A basic display module, configured to classify and organize the concerned education content and display it on a display screen.

[0010] A concern matching module, configured to obtain the retrieval interaction of the patient, perform concern matching according to the retrieval interaction and the sequential education concern, and establish a concern matching result.

[0011] A matching display module, configured to display the concern matching result on the display screen.

[0012] In a feasible implementation manner, the attention establishment module includes:

[0013] A timing information sorting module, configured to extract timing data from the medical record reading result and the nursing assessment result, and establish a timing data set, where the timing data set includes time point data and time period data.

[0014] A feature extraction module, configured to perform multimodal feature extraction on the timing data set and establish a multimodal feature extraction result.

[0015] A feature aggregation module, configured to perform feature clustering on the multimodal feature extraction result based on the timing data set, establish a feature clustering result, and establish a timing education attention by using the feature clustering result.

[0016] In a feasible implementation manner, the system further includes:

[0017] A record recognition module, configured to determine whether the attention matching result is an unsuccessful matching result. If the attention matching result is an unsuccessful matching result, a retrieval interaction record is generated.

[0018] A reminder module, configured to send a medical staff trigger set to the patient after the retrieval interaction record is generated, establish a medical staff association according to the feedback of the patient, and generate a reminder instruction when the education machine detects that the corresponding medical staff is less than a preset distance.

[0019] In a feasible implementation manner, the identity recognition module further includes:

[0020] An image acquisition module, configured to perform patient image acquisition and establish a first recognition result.

[0021] An interaction input module, configured to obtain input data of the patient, establish a second recognition result according to the input data, perform identity recognition on the patient according to the first recognition result and the second recognition result, and establish an identity recognition result.

[0022] In a feasible implementation manner, the basic display module further includes:

[0023] A patient reading feature extraction module, configured to perform reading comprehension information adaptation on the patient and establish an adaptation feature set.

[0024] A display specialization module, configured to perform personalized statement optimization on the attention education content based on the adaptation feature set, establish an optimization result, and display the optimization result on a display screen after classification and sorting.

[0025] In a feasible implementation manner, the display specialization module further includes:

[0026] The first optimization module is used to obtain the patient's educational background feature in the adaptation feature set, perform personalized statement optimization of the concerned education content according to the educational background feature, and establish a first optimization result.

[0027] The second optimization module is used to obtain the language feature set in the adaptation feature set, perform personalized statement optimization of the first optimization result according to the language feature set, and establish a second optimization result. The language feature set includes dialect features and speech rate features.

[0028] The third optimization module is used to obtain the patient's defect feature in the adaptation feature set, perform personalized statement optimization of the second optimization result according to the patient's defect feature, establish a third optimization result, and establish an optimization result according to the third optimization result.

[0029] In a feasible implementation manner, the system further includes:

[0030] The linkage warning module is used to establish a physical sign trigger warning threshold based on the sequential education concern, perform trigger analysis of the physical sign trigger warning threshold by reading the real-time physical sign data of the patient, establish a linkage warning according to the trigger analysis result, and give out a warning.

[0031] In a feasible implementation manner, the system further includes:

[0032] The message module is used to record the patient's message and feedback the message to the mobile terminal of the medical staff for message interaction between the patient and the medical staff.

[0033] The present invention discloses a postoperative patient's palm-top intelligent education system, including: an identity recognition module for performing identity recognition on a patient after the patient activates an education machine and establishing an identity recognition result; a reading module for reading the patient's medical record information and nursing assessment result based on the identity recognition result and establishing a medical record reading result and a nursing assessment result; a concern establishment module for establishing a sequential education concern according to the medical record reading result and the nursing assessment result and configuring the concerned education content through the sequential education concern; a basic display module for classifying and organizing the concerned education content and displaying it on a display screen; a concern matching module for obtaining the patient's retrieval interaction, performing concern matching according to the retrieval interaction and the sequential education concern, and establishing a concern matching result; a matching display module for displaying the concern matching result on the display screen. The postoperative patient's palm-top intelligent education system disclosed by the present invention solves the technical problems of insufficient timeliness, lack of personalization and interactivity, and lack of feedback mechanism in education, and realizes the technical effects of improving the instantaneity, interactivity and personalization level of education and providing a feedback channel. Description of the Drawings

[0034] Figure 1Schematic diagram of the structure of a palm-top intelligent health education system for postoperative patients according to the present invention;

[0035] Figure 2 Schematic diagram of the structure of the attention establishment module 13 in a palm-top intelligent health education system for postoperative patients according to the present invention;

[0036] Figure 3 Schematic diagram of the structure of an exemplary health education machine according to the present invention.

[0037] Explanation of reference numerals: identity recognition module 11, reading module 12, attention establishment module 13, basic display module 14, attention matching module 15, matching display module 16, timing information sorting module 131, feature extraction module 132, feature aggregation module 133, body 1, switch 1-1, charging port 1-2, data module 2, recording button 2-1, playback button 2-2, video and audio module 3, video screen 3-1, first type of audio button 3-2, second type of audio button 3-3, third type of audio button 3-4, patient message board 3-5. Detailed implementation manners

[0038] The following will describe the above technical solutions in detail in combination with the specification drawings and specific implementation manners to better understand the above technical solutions. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments for explaining the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention. In addition, it should be noted that, for the sake of convenience of description, only the parts related to the present invention are shown in the drawings rather than all of them.

[0039] Embodiment 1

[0040] Figure 1 Schematic diagram of the structure of a palm-top intelligent health education system for postoperative patients according to the present invention, including:

[0041] An identity recognition module 11, configured to perform identity recognition on a patient after the patient starts the health education machine and establish an identity recognition result.

[0042] Specifically, first, confirm the patient's operation behavior through the health education machine start signal, perform patient identity confirmation, establish identity recognition data, and output the identity recognition result for use by subsequent modules; through the design of the identity recognition module, accurate patient identity verification can be achieved, providing basic support for personalized services and permission control of the health education machine.

[0043] Optionally, the technical means for identity recognition include: biometric recognition (collecting features such as the patient's face image, fingerprint, iris, etc.), patient account authentication (logging in by the patient entering the account and password or scanning a code), RFID / NFC recognition (identifying the patient's identity through a worn card or device), and scanning the QR code on the patient's wristband for recognition.

[0044] In some embodiments, the identity recognition module 11 further includes:

[0045] An image acquisition module, configured to perform patient image acquisition and establish a first recognition result; an interactive input module, configured to obtain the patient's input data, establish a second recognition result according to the input data, and perform identity recognition on the patient according to the first recognition result and the second recognition result to establish an identity recognition result.

[0046] Specifically, the image acquisition module is configured to acquire the patient's image data to establish a preliminary identity recognition result. Exemplarily, first, capture the patient's facial or other biometric images through a camera, and then, use image processing and biometric recognition algorithms (such as face recognition, pose detection) to extract the patient's image features; furthermore, compare the extracted patient image features with the identity database to generate a first recognition result (such as the patient identity ID).

[0047] Specifically, the interactive input module is configured to obtain the information actively input by the patient to assist or verify the image acquisition result; Exemplarily, first, provide an interactive interface (such as a touch screen, keyboard, or voice input) for the patient to input data, where the patient input data may include the patient name, password, personal identification number, scanned code information, etc.; then, parse the input data and compare it with the identity database to generate a second recognition result; next, perform matching verification according to the first recognition result and the second recognition result, where the processing logic includes: when the two results match, confirm the identity and establish an identity recognition result; when the two results do not match, prompt the patient to re-verify or transfer to the manual assisted recognition process.

[0048] By integrating the image acquisition module and the interactive input module, the accuracy and security of identity recognition are improved, enabling the identity recognition module 11 to achieve accurate and efficient identity recognition, providing a reliable patient identity basis for subsequent services.

[0049] A reading module 12, configured to read the patient's medical record information based on the identity recognition result, establish a medical record reading result, and synchronously obtain a nursing assessment result.

[0050] Specifically, based on the identity recognition result of the identity recognition module 11, the reading module 12 accurately locates the medical record information corresponding to the patient, completes information reading, and establishes a medical record reading result and a nursing assessment result. Exemplarily, first, the identity recognition result is used as a query keyword to initiate a query request to the medical record information and nursing information database, and the patient's medical record information and nursing assessment items are retrieved according to the query conditions, including medical record number, medical history, diagnosis record, treatment plan, nursing plan, etc. Then, according to the query result, the medical data related to the patient is extracted from the medical record information database, and the established reading result includes basic information (such as name, gender, age), historical medical record data, current nursing and treatment plans, etc.

[0051] Optionally, format and parse the read data to establish a standardized medical record reading result data set. Exemplarily, the read information is structured into a standard data format (such as JSON, XML, etc.); if the corresponding record is not found, prompt that the patient's identity is abnormal or the database information is missing.

[0052] Through the reading module 12, the education machine can efficiently obtain the patient's medical record information, ensure that the subsequent education content matches the actual situation of the patient, and improve the personalization and professionalism of the service.

[0053] The attention establishment module 13 is used to establish a time-series education attention based on the medical record reading result and the nursing assessment result, and configure the attention education content through the time-series education attention.

[0054] Specifically, the attention establishment module 13 is used to establish a time-series education attention based on the medical record reading result and the nursing assessment result, and configure the relevant education content according to this attention, so as to provide customized education content according to the patient's condition and needs. Among them, the time-series education attention is personalized time-series education information established according to information such as the patient's condition, treatment cycle, recovery plan, and nursing plan.

[0055] In some embodiments, as Figure 2 shown, the attention establishment module 13 includes:

[0056] The time-series information sorting module 131 is used to extract time-series data from the medical record reading result and the nursing assessment result, and establish a time-series data set, and the time-series data set includes time point data and time period data; the feature extraction module 132 is used to perform multi-modal feature extraction on the time-series data set to establish a multi-modal feature extraction result; the feature aggregation module 133 is used to perform feature clustering on the multi-modal feature extraction result based on the time-series data set to establish a feature clustering result, and use the feature clustering result to establish a time-series education attention.

[0057] Specifically, the timing information collation module 131 is used to extract time-series data from the data in the medical record reading result and the nursing assessment result, and construct a time-series data set. Exemplarily, first, extract the data containing time-related information from the medical record reading result and the nursing assessment result; then, sort out the extracted data according to the time dimension, and divide the information into time-point data (such as vital signs data and examination results at a specific time) and time-period data (such as treatment cycles, recovery periods, etc.) to form a structured time-series data set.

[0058] Specifically, perform multi-modal feature extraction on the time-point data and time-period data in the time-series data set, including quantitative feature extraction, qualitative feature extraction, and dynamic feature extraction (trend, periodic or sudden features), and summarize all the extracted features to form a multi-modal feature extraction result as the basis for subsequent analysis.

[0059] Specifically, the feature aggregation module 133 is used to perform feature clustering on the multi-modal feature extraction result, thereby generating timing education attention; Exemplarily, first, based on the time-series data set, perform cluster analysis on the multi-modal feature extraction result, extract the rules and relationships between similar features, and then identify the relevant health status and treatment stages; In other words, the feature clustering result will provide a cluster center point for each health status to help identify and determine the key nodes of attention. Such as certain specific usage cycles; then, generate timing education attention according to the feature clustering result. Each attention point in the timing education attention corresponds to a specific health status or treatment stage in a cluster, and the education content will be customized according to this status, such as the medication effect, precautions, dietary precautions, and prevention of concurrent symptoms at specific risk points in a certain stage.

[0060] Through the above-mentioned time-series data extraction and feature clustering analysis, automatically establish timing education attention related to the patient's health status and treatment plan, which helps to improve the efficiency and accuracy of education, and can dynamically adjust the timing attention according to the patient's condition changes to adapt to different treatment stages and health management needs.

[0061] The basic display module 14 is used to classify and sort out the attention education content and display it on the display screen.

[0062] Specifically, the basic display module 14 is used to classify and sort out the above-mentioned attention education content, and visually through such as Figure 3The display screen embedded in the education machine as shown is used for display, so as to provide an intuitive and easy-to-operate interface to help patients understand health management suggestions, treatment guidance, warning information, etc., ensuring the effective transmission of education information and the timely response of patients. Exemplarily, first, receive the generated sequential education attention content from the attention establishment module; then, classify it according to the type of education content (such as treatment guidance, health reminder, drug management, precautions, etc.); next, sort the classified content by priority to ensure that the most important and urgent content is displayed first; finally, convert the classified and sorted education content into a format suitable for display (such as text, graphics, charts, etc.).

[0063] Optionally, in the case of a large amount of content, provide split-screen display or a scroll bar to ensure that patients can view the complete content.

[0064] Optionally, visualize the sorted education content to ensure that patients can obtain information intuitively, including: selecting a suitable display mode (such as list, chart, card layout, etc.) for different education content, typesetting and displaying the content; when displaying the content of each category, different elements such as fonts, colors, backgrounds, etc. can be used for distinction so that patients can quickly identify; set different display priorities, and use eye-catching colors or large fonts to display important health reminders.

[0065] Optionally, if the education content involves time requirements or timeliness (such as medication reminder, regular inspection, etc.), a countdown or a time progress bar is added to the display interface to help patients understand the time limit for taking actions.

[0066] Optionally, provide simple operation buttons or interactive designs on the visualization interface to allow patients to perform information confirmation, feedback or other necessary operations (such as "viewed", "set reminder", etc.).

[0067] Specifically, as Figure 3 The education machine shown includes a body 1, a data module 2, a video and audio module 3. Among them, the body 1 includes a switch 1-1 and a charging port 1-2. When in use, press "on" to turn on the education machine, and press "off" to turn off the education machine after use; the data module 2 includes a recording button 2-1 and a playback button 2-2. By pressing the 2-1 recording button and selecting the corresponding audio button for the corresponding category, the corresponding matters can be input into the education machine.

[0068] Specifically, the video and audio module 3 includes: a video screen 3-1, multiple audio buttons (first category audio button 3-2, second category audio button 3-3, third category audio button 3-4) and a patient message board 3-5, wherein the first category audio button 3-2, the second category audio button 3-3, and the third category audio button 3-4 correspond to different education types, exemplarily including diet, exercise, and other chapters.

[0069] The above-mentioned basic display module 14 enables patients to quickly and conveniently obtain important health management information through a clear and intuitive display interface, and enhances patients' sense of participation through interactive functions, thereby ensuring the effective communication and implementation of educational content.

[0070] In some embodiments, the basic display module 14 further includes:

[0071] The patient reading feature extraction module is used to adapt the reading comprehension information of the patient and establish an adaptation feature set; the display specialization module is used to optimize the personalized statement of the concerned education content based on the adaptation feature set, establish the optimization result, and classify and organize the optimization result and then display it on the display screen.

[0072] Specifically, the patient reading feature extraction module is used to obtain a personalized adaptation feature set for the patient. Optionally, the adaptation feature set includes reading habits (such as font size, information presentation mode), understanding difficulty (such as simplified language, increase or decrease explanation), interaction method (such as picture guidance, audio prompts), etc.

[0073] Exemplarily, first, conduct patient behavior analysis to monitor the patient's dwell time, sliding speed, browsing history, etc. during the reading process, conduct reading speed analysis, and at the same time track the patient's click frequency, mouse hovering time, option selection and other behaviors when interacting with information to evaluate the depth of their understanding of the information; then, establish a health content model of the patient's preferences based on the patient's past interaction records, such as frequently clicked health issues, types of diseases of concern, preferred health advice, etc.; then, adjust the font, color tone, information display method, etc. according to the patient's preference characteristics to ensure that the content presentation is in line with their reading habits.

[0074] Specifically, the display specialization module is used to optimize the display of educational content according to the patient's adaptation feature set, improve the information communication effect and patient experience, including content difficulty optimization, information presentation method adjustment, content sorting and organization, multimodal information configuration, dynamic adjustment and feedback mechanism.

[0075] Exemplarily, for elderly patients, the use of professional terms should be reduced, and more diagrams and short explanations should be provided; if the patient prefers graphical or chart information display, relevant diagrams should be provided; if the patient prefers text information, the image content should be reduced, and the text or table should be centrally displayed; when presenting content, important and urgent information (such as treatment guidance, medication reminders, etc.) should be placed first, followed by background information or long-term suggestions; for visually or hearing-impaired patients, a voice reading function or large font mode should be provided to ensure barrier-free access; the display content should be adjusted according to the patient's real-time feedback, especially during long-term reading, and dynamic adjustments should be provided, such as adding interactive Q&A, pop-up prompts, etc., to ensure that the information is effectively understood; further prompts or explanations should be triggered according to the patient's operation behavior (such as staying on a certain part for a long time, frequently returning to a certain part of the content, etc.) to ensure that the patient fully understands.

[0076] Furthermore, the final display content will be sorted and presented according to the results of optimization and adjustment. Exemplarily, the education content will be divided into several modules such as health management, treatment plan, life guidance, warning information, etc., and will be displayed in order of importance; at the same time, different types of content should be presented independently, using clear titles, icons, and colors to distinguish, to help patients quickly locate the content they care about.

[0077] Through the design of the patient reading feature extraction module and the display specialization module, the health education content can be dynamically optimized and adjusted according to the specific needs and preferences of the patient; through concise, intuitive, and personalized display, it is ensured that the education content is effectively understood by the patient and prompts them to respond in a timely manner, thereby improving the effect of health management and treatment guidance.

[0078] In some implementation manners, the display specialization module further includes:

[0079] A first optimization module, configured to obtain the patient education level feature in the adaptation feature set, perform personalized statement optimization of the concerned education content according to the education level feature, and establish a first optimization result; a second optimization module, configured to obtain the language feature set in the adaptation feature set, perform personalized statement optimization of the first optimization result according to the language feature set, and establish a second optimization result, where the language feature set includes dialect features and speech rate features; a third optimization module, configured to obtain the patient defect feature in the adaptation feature set, perform personalized statement optimization of the second optimization result according to the patient defect feature, and establish a third optimization result, and establish an optimization result according to the third optimization result.

[0080] Specifically, the purpose of the first optimization module is to optimize the personalized statement of the education content according to the patient's educational background. By analyzing the patient's education level feature, an appropriate expression method, term depth, and information organization method can be selected to facilitate the efficient transmission of the education information.

[0081] Exemplarily, first, analyze the patient's educational background information, such as degree (primary school, junior high school, senior high school, junior college, undergraduate, master, etc.); then, adjust the language complexity of the education content according to the educational level. For example, for patients with a low educational level, professional terms need to be simplified and more straightforward expressions should be used. For patients with a high educational level, technical or professional content can be simplified in the presentation. Higher educated patients may prefer data charts or more detailed medical knowledge, while lower educated patients may be more receptive to concise, graphic-based presentation methods.

[0082] Specifically, the second optimization module is used to further adjust the education content according to the patient's language characteristics, aiming to make the language of the presented content closer to the patient's habits and improve the patient's understanding and acceptance effect. Among them, the language characteristics include the patient's dialect, speech rate, accent, etc. The language style of the education content optimized by the language characteristics conforms to the patient's auditory and language habits, and the output is the second optimization result.

[0083] Exemplarily, analyze the patient's language preferences, including dialect characteristics (such as Mandarin, Cantonese, Sichuan dialect, etc.) and speech rate characteristics (such as fast speech rate, slow speech rate, etc.), and adaptively optimize the voice output or text content to make the language used closer to the patient's habits. For example, use the patient's familiar dialect pronunciation during voice playback, or adapt dialect words in the text, and adjust the voice playback speed according to the patient's habit of speech rate.

[0084] Specifically, the third optimization module is used to further customize the education content according to the patient's defect characteristics (such as visual impairment, hearing impairment, cognitive impairment, etc.) to ensure that the patient can better receive information, especially for patient groups with special needs. Exemplarily, for patients with visual impairment, adjust the font size and color contrast of the text, use the large font mode, or provide voice prompts; for patients with hearing impairment, a text-to-speech function can be provided or visual indicators can be increased to help them obtain information; for patients with cognitive impairment, simplify the information expression, provide more diagrams, illustrations and step-by-step instructions, and avoid complex medical terms to ensure that the information is clear and easy to understand.

[0085] Through the first optimization module, the second optimization module and the third optimization module, the display specialization module can carefully optimize the presentation form of the education content according to the characteristics such as the patient's educational background, language habits and health defects, ensure the efficiency and understandability of information transmission, improve the patient's acceptance and response ability, and thus improve the health management and treatment effect.

[0086] The attention matching module 15 is used to obtain the patient's retrieval interaction, perform attention matching according to the retrieval interaction and the time-series education attention, and establish an attention matching result.

[0087] Specifically, the retrieval interaction of a patient generally refers to a query or request made by the patient through input (such as text, voice, gesture, etc.), corresponding to the patient's initiative to query certain health information, symptoms, treatment methods, or other relevant content; the purpose of attention matching is to compare the patient's retrieval interaction with the content in the chronological health education attention, and generate a list of matching results as the attention matching result.

[0088] By obtaining the patient's retrieval interaction and matching it with the chronological health education attention, it can be ensured that the patient receives the most relevant and personalized health management information.

[0089] The matching display module 16 is used to display the attention matching result through the display screen.

[0090] Specifically, the result after attention matching is also presented to the patient through the display screen to ensure that the patient can clearly and understandably see the educational content related to their needs; optionally, the display content includes: the title of the attention content, the matching degree score, relevant suggestions or content summaries, guiding operations or recommendations, etc.

[0091] By presenting the most relevant health education content in an intuitive, clear, and personalized way, it is ensured that the patient can quickly find and understand the information related to their health needs, improving the educational effect and the patient's response ability.

[0092] In some implementation manners, the system further includes:

[0093] The record recognition module is used to judge whether the attention matching result is an unsuccessful matching result. If the attention matching result is an unsuccessful matching result, a retrieval interaction record is generated; the reminder module is used to send a medical staff trigger set to the patient when the retrieval interaction record is generated, establish a medical staff association according to the patient's feedback, and generate a reminder instruction when the educational machine detects that the corresponding medical staff is less than the preset distance.

[0094] Specifically, the function of the record recognition module is to ensure that the educational machine can effectively process the unsuccessfully matched attention content and generate relevant records for subsequent operations; first, evaluate each attention matching result to judge whether it meets the patient's needs or preset criteria. If the matching result does not meet the expectation (that is, the relevant educational content fails to be successfully matched), a retrieval interaction record is automatically generated, including the patient's query content or behavior, the unmatched attention content, the generation time and status, etc., to help understand the gap in the patient's needs and provide a basis for subsequent recommendation optimization.

[0095] Specifically, the reminder module ensures that when the patient needs further support, they can get the help of medical staff in a timely manner through real-time monitoring and analysis. The reminder module realizes the above functions by sending reminder instructions to medical staff.

[0096] Specifically, when a retrieval interaction record is generated during the patient's use of the education machine, the reminder module will establish an association with specific medical staff based on the patient's feedback and interaction situation; then, through positioning technologies (such as GPS, indoor positioning systems, etc.), it is determined whether the medical staff is approaching the patient to determine whether to remind the medical staff; when the system detects that the distance between the medical staff and the patient is less than a preset distance threshold (i.e., it can be considered that the medical staff is on duty), the reminder module automatically generates a reminder instruction to notify the medical staff to immediately respond to the patient's needs.

[0097] Through the record recognition module and the reminder module, the intelligent response ability of the education machine is improved, making it more personalized and efficient in the process of patient health management.

[0098] In some implementation manners, the system further includes:

[0099] A linkage warning module, which is used to establish a physical sign trigger warning threshold based on the time-series education and attention, perform trigger analysis of the physical sign trigger warning threshold by reading the real-time physical sign data of the patient, establish a linkage warning according to the trigger analysis result, and issue a warning.

[0100] Optionally, the system further includes a linkage warning module, which is used to realize intelligent physical sign warning according to the real-time physical sign data of the patient and in combination with the set physical sign trigger warning threshold. In other words, this module obtains the real-time health status of the patient and issues a warning when the physical sign data is abnormal, thereby improving the patient's health management and nursing response ability.

[0101] In some implementation manners, the system further includes:

[0102] A message module, which is used to record the patient's message and feedback the message to the mobile terminal of the medical staff for message interaction between the patient and the medical staff.

[0103] Specifically, the message module is used to record the patient's message and feedback the message to the mobile terminal of the medical staff to realize message interaction between the patient and the medical staff. This message module promotes communication in health management, ensuring that the patient can timely feedback problems or consult opinions to the medical staff, and the medical staff can also respond in a timely manner to provide necessary support and help.

[0104] Optionally, the message module includes functions such as patient message recording, message storage, message classification and marking, message feedback notification and push, and message priority reminder.

[0105] In summary, the postoperative patient palm-top intelligent education system provided by the present invention has the following technical effects:

[0106] After the patient starts the education machine, the patient is identified and an identity identification module is established for the identification result; a reading module is used to read the patient's medical record information based on the identity identification result, and simultaneously obtain the nursing assessment result, and establish the medical record reading result and the nursing assessment result; a module for establishing a time-series education focus according to the medical record reading result, and configuring the focus on the education content through the time-series education focus; a basic display module is used to display the education content on the display screen after classification and organization; a module for obtaining the patient's retrieval interaction, matching the focus according to the retrieval interaction and the time-series education focus, and establishing the focus matching result; a matching display module is used to display the focus matching result through the display screen. The present invention discloses a handheld intelligent education system for postoperative patients that solves the technical problems of insufficient timeliness, insufficient personalization and interactivity, and lack of feedback mechanism, thereby achieving the technical effect of improving the immediacy, interactivity and personalization of education and providing a feedback channel.

[0107] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A handheld intelligent education system for postoperative patients, characterized in that: The system is applied to a propaganda machine, and the system comprises: The identity recognition module is used to identify the patient and establish the identity recognition result after the patient starts the education machine; A reading module, used to read the patient's medical record information based on the identity recognition result, establish the medical record reading result, and simultaneously obtain the nursing assessment result; A focus establishment module, used to establish a time-series education focus according to the medical record reading result and the nursing assessment result, and configure the education content through the time-series education focus; A basic display module, used to classify and organize the educational content of interest and display it on a display screen; An attention matching module is used to obtain the patient's search interaction, perform attention matching according to the search interaction and the time-series education attention, and establish an attention matching result; The matching display module is used to display the focus matching result through the display screen.

2. The handheld intelligent education system for postoperative patients as claimed in claim 1, characterized in that: The attention establishment module includes: A time series information sorting module is used to extract time series data from the medical record reading results and the nursing assessment results to establish a time series data set, wherein the time series data set includes time point data and time period data; A feature extraction module, used to perform multimodal feature extraction on the time series data set and establish a multimodal feature extraction result; A feature aggregation module is used to perform feature clustering on the multimodal feature extraction results based on the time series data set, establish feature clustering results, and use the feature clustering results to establish time series education focus.

3. The handheld intelligent education system for postoperative patients as claimed in claim 1, characterized in that: The system further comprises: A record identification module, used to determine whether the focus matching result is an unmatched success result, and if the focus matching result is an unmatched success result, generate a search interaction record; The reminder module is used to send a medical staff trigger set to the patient after the retrieval interaction record is generated, establish a medical staff association based on the patient's feedback, and generate a reminder instruction when the education machine detects that the corresponding medical staff is less than a preset distance.

4. The postoperative patient handheld intelligent education system according to claim 1, characterized in that: The identity recognition module also includes: An image acquisition module, used to perform patient image acquisition and establish a first recognition result; The interactive input module is used to obtain the patient's input data, establish a second recognition result based on the input data, identify the patient according to the first recognition result and the second recognition result, and establish an identity recognition result.

5. The handheld intelligent education system for postoperative patients as claimed in claim 1, characterized in that: The basic display module also includes: A patient reading feature extraction module, used to adapt the patient's reading comprehension information and establish an adaptation feature set; The display specialization module is used to optimize the personalized statement of the concerned education content based on the adaptation feature set, establish the optimization result, classify and organize the optimization result, and then display it on the display screen.

6. The handheld intelligent education system for postoperative patients as claimed in claim 5, characterized in that: The display specific module also includes: A first optimization module is used to obtain the educational background characteristics of the patient in the adaptation feature set, optimize the personalized statement of the education content according to the educational background characteristics, and establish a first optimization result; A second optimization module is used to obtain a language feature set in the adaptation feature set, perform personalized statement optimization of the first optimization result according to the language feature set, and establish a second optimization result, wherein the language feature set includes a dialect feature and a speech speed feature; The third optimization module is used to obtain the patient defect characteristics in the adaptation feature set, perform personalized statement optimization of the second optimization result according to the patient defect characteristics, establish a third optimization result, and establish an optimization result according to the third optimization result.

7. The handheld intelligent education system for postoperative patients as claimed in claim 1, characterized in that: The system further comprises: The linkage warning module is used to establish a physical sign trigger warning threshold based on the time series education focus, perform trigger analysis of the physical sign trigger warning threshold by reading the real-time physical sign data of the patient, establish a linkage warning according to the trigger analysis result, and issue a warning alarm.

8. The handheld intelligent education system for postoperative patients as claimed in claim 1, characterized in that: The system further comprises: The message module is used to record the patient's message and feed the message back to the medical staff's mobile terminal to enable message interaction between the patient and the medical staff.

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