Personalized display method, electronic device and storage medium for nurse terminal operation interface

Abstract

This application discloses a method, electronic device, and storage medium for personalized display of a nurse terminal operation interface. The method includes: obtaining account identity information in response to a nurse's login instruction; querying and obtaining corresponding operation habit information based on the account identity information; determining a rendering layout relationship between each display area and each control in a preset display interface based on the operation habit information; rendering in the display interface based on the rendering layout relationship; obtaining the nurse's current operation data during the current login, and updating the operation habit information at a preset first interval based on the current operation data. This application enables nurses to complete their work more efficiently and accurately.

Description

Technical Field

[0001] The present application relates to the technical field of hospital data management, and in particular to a personalized display method for a nurse terminal operation interface, an electronic device, and a storage medium. Background Art

[0002] In modern medical environments, nurses need to frequently use terminal devices to query patient information, record nursing status, etc. Existing nurse terminal operation interfaces mostly have a unified fixed layout, which cannot take into account the differences in operating habits of different nurses. In actual work, nurses have different operating habits. For example, some nurses are accustomed to placing the commonly used patient information query function in a prominent position on the interface, while some nurses prefer to place the nursing record function in a convenient location. However, a unified fixed interface layout cannot meet these personalized needs, resulting in nurses spending extra time to find the required controls and frequently switching between different functional modules during the operation process. This not only reduces work efficiency, but also easily leads to problems such as information entry errors or untimely inquiries due to inconvenient operation, which in turn affects the quality of care for patients and the timeliness of medical services. Summary of the Invention

[0003] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a personalized display method for a nurse terminal operation interface, an electronic device, and a storage medium, which can enable nurses to complete their work more efficiently and accurately.

[0004] In a first aspect, the present application provides a method for personalized display of a nurse terminal operation interface, comprising:

[0005] Responding to a login instruction from a nurse, obtaining account identity information;

[0006] According to the account identity information, query and obtain corresponding operation habit information;

[0007] Determining, based on the operation habit information, a rendering layout relationship between each display area and each control in a preset display interface;

[0008] Rendering in the display interface according to the rendering layout relationship;

[0009] Acquire the current operation data of the nurse during the current login, and update the operation habit information at a preset first interval based on the current operation data;

[0010] The operation habit information is obtained according to the following steps:

[0011] Obtaining historical operation data corresponding to the account identity information;

[0012] Inputting the historical operation data into an input layer of a preset perceptual neural network model, and determining a first score set for each display area on the display interface and a second score set for each of the controls through multiple hidden layers of the perceptual neural network model;

[0013] The first score set and the second score set are input into the output layer of the perceptual neural network model to obtain operation habit information.

[0014] According to the first embodiment of the present application, the personalized display method for the nurse terminal operation interface has at least the following beneficial effects: When a nurse issues a login command, the system quickly responds and obtains their account identity information. Based on the obtained account identity information, the system queries a pre-stored database to find the corresponding operating habit information. This operating habit information is obtained by collecting the nurse's historical operating data over a long period of time and analyzing it using a perceptual neural network model. Based on the retrieved operating habit information, the system further determines the rendering layout relationship between each display area and each control in the preset display interface. Based on the determined rendering layout relationship, the system renders the display interface. As the nurse uses the terminal device, the system continuously obtains the current operating data of the nurse's current login. At a preset first time interval, the system incorporates this current operating data into the analysis scope and updates the existing operating habit information. By obtaining and analyzing the nurse's operating habit information, the system places frequently used functional controls in the locations where the nurse typically operates, reducing the time the nurse spends searching for controls and switching functional modules. Furthermore, the mechanism for updating operating habit information in real time can keep pace with changes in the nurse's operating habits, further ensuring operational convenience and avoiding problems such as information entry errors and delayed queries due to operational inconvenience. Nurses no longer need to spend a lot of time searching for the required controls and switching functions in a fixed layout interface. They can quickly locate and operate commonly used functions, saving operation time and enabling nurses to complete more nursing tasks per unit time. In addition, the interface layout that suits personal operation habits reduces the risk of information entry errors caused by inconvenient operation. Nurses can focus more on the nursing work itself, rather than being distracted by the inconvenient operation interface, thereby improving the accuracy of nursing records and the reliability of medical information, allowing nurses to complete their work more efficiently and accurately, and patients to receive more timely and high-quality care services.

[0015] According to some embodiments of the first aspect of the present application, after the step of obtaining the account identity information in response to the login instruction of the nurse, the method further includes:

[0016] When the account identity information does not have corresponding operation habit information, generating a questionnaire and rendering the questionnaire on the display interface;

[0017] obtaining survey results completed by the nursing staff and generating intent layout information based on the survey results;

[0018] The controls are rendered in different display areas of the display interface according to a preset initial layout position.

[0019] According to some embodiments of the first aspect of the present application, inputting the historical operation data into an input layer of a preset perceptual neural network model, and determining a first score set for each display area on the display interface and a second score set for each of the controls through multiple hidden layers of the perceptual neural network model, includes:

[0020] Obtaining the intent layout information corresponding to the account identity information;

[0021] Inputting the historical operation data and the intended layout information into an input layer of a preset perceptual neural network model, and assigning a first confidence level and a second confidence level to the historical operation data and the intended layout information respectively through the input layer;

[0022] The historical operation data, the first confidence level, the intended layout information and the second confidence level are fused through the multiple hidden layers of the perceptual neural network model to obtain a first score set for each display area of ​​the display interface and a second score set for each of the controls.

[0023] According to some embodiments of the first aspect of the present application, assigning a first confidence level and a second confidence level to the historical operation data and the intended layout information, respectively, through the input layer, includes:

[0024] Obtaining, through the input layer, a first timestamp of the last update of the historical operation data and a second timestamp of establishing the intended layout information;

[0025] Establishing an assignment rule table; wherein the assignment rule table is used to represent the relationship between the difference between the first timestamp and the second timestamp and the ratio between the first confidence level and the second confidence level;

[0026] Based on the assignment rule table and according to the first timestamp and the second timestamp, a first confidence level of the historical operation data and a second confidence level of the intended layout information are determined.

[0027] According to some embodiments of the first aspect of the present application, the historical operation data includes: the historical usage time and historical usage area of ​​each of the controls, and the intended layout information includes control usage requirements and customary usage areas;

[0028] The historical operation data, the first confidence level, the intended layout information, and the second confidence level are fused through the multiple hidden layers of the perceptual neural network model to obtain a first score set for each display area of ​​the display interface and a second score set for each control, including:

[0029] Determining a first usage score value for each of the controls through a first hidden layer of the perceptual neural network model according to the historical usage time of each of the controls, the first confidence level, the control usage demand, and the second confidence level;

[0030] Determining a second usage score value for each of the display areas through a second hidden layer of the perceptual neural network model according to the historical usage area, the first confidence level, the habitual usage area, and the second confidence level;

[0031] A first score set is obtained based on the first usage score values, and a second score set is obtained based on the second usage score values.

[0032] According to some embodiments of the first aspect of the present application, the perceptual neural network model is trained according to the following steps:

[0033] Establish an initial perceptual neural network model;

[0034] Acquire sample operation data and expected display results corresponding to the sample operation data;

[0035] Inputting the sample operation data into the perceptual neural network model to obtain an actual display result; wherein the actual display result includes the actual controls to be displayed and the actual display area of ​​each actual control;

[0036] Obtaining a training loss value according to the actual display result and the expected display result;

[0037] The parameters of the perceptual neural network model are adjusted according to the training loss value until the training loss value meets the preset convergence condition or the number of training times is greater than or equal to the preset number threshold, and the training of the perceptual neural network model is stopped to obtain a trained perceptual neural network model.

[0038] According to some embodiments of the first aspect of the present application, the perceptual neural network model is further trained according to the following steps:

[0039] When the training loss value does not meet the convergence condition and the number of training times is less than the number threshold, performing a gradient solution on the parameters of the perceptual neural network model according to a preset gradient algorithm and the training loss value to obtain a gradient vector;

[0040] Obtaining a learning rate parameter according to the number of training times of the perceptual neural network model and a preset learning rate decay function;

[0041] Updating the gradient vector according to the learning rate parameter;

[0042] The parameters of the perceptual neural network model are adjusted according to the updated gradient vector.

[0043] According to some embodiments of the first aspect of the present application, determining, based on the operation habit information, a rendering layout relationship between each display area and each control in a preset display interface includes:

[0044] Determining, based on the operation habit information, a first priority of each control corresponding to the current account identity information and a second priority of each display area;

[0045] According to the first priority and the second priority, a rendering layout relationship between each of the display areas and each of the controls in a preset display interface is determined.

[0046] In a second aspect, the present application further provides an electronic device, comprising:

[0047] at least one memory;

[0048] at least one processor;

[0049] at least one program;

[0050] The program is stored in the memory, and the processor executes at least one of the programs to implement the personalized display method of the nurse terminal operation interface as described in any embodiment of the first aspect.

[0051] In a third aspect, the present application also provides a computer-readable storage medium, which stores a computer-executable signal, and the computer-executable signal is used to execute the personalized display method of the nurse terminal operation interface as described in any embodiment of the first aspect.

[0052] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Additional aspects and advantages of the present application will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0054] Figure 1 A flowchart of a method for personalized display of a nurse terminal operation interface provided in an embodiment of the present application;

[0055] Figure 2 For this application Figure 1 Regarding the flowchart after step S100;

[0056] Figure 3 For this application Figure 1 Flowchart regarding step S620;

[0057] Figure 4 For this application Figure 3 Flowchart regarding step S650;

[0058] Figure 5 For this application Figure 3 Flowchart regarding step S660;

[0059] Figure 6 This is a flowchart of an embodiment of the training steps of the perceptual neural network model of the present application;

[0060] Figure 7 This is a flowchart of another embodiment of the perceptual neural network model training steps of the present application;

[0061] Figure 8 For this application Figure 1 Flowchart about step S300 in FIG. DETAILED DESCRIPTION

[0062] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0063] In the description of this application, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on this application.

[0064] In the description of this application, if there is a description of first or second, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0065] In the description of this application, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in this application based on the specific content of the technical solution.

[0066] In modern medical environments, nurses need to frequently use terminal devices to query patient information, record nursing status, etc. Existing nurse terminal operation interfaces mostly have a unified fixed layout, which cannot take into account the differences in operating habits of different nurses. In actual work, nurses have different operating habits. For example, some nurses are accustomed to placing the commonly used patient information query function in a prominent position on the interface, while some nurses prefer to place the nursing record function in a convenient location. However, a unified fixed interface layout cannot meet these personalized needs, resulting in nurses spending extra time to find the required controls and frequently switching between different functional modules during the operation process. This not only reduces work efficiency, but also easily leads to problems such as information entry errors or untimely inquiries due to inconvenient operation, which in turn affects the quality of care for patients and the timeliness of medical services.

[0067] Based on this, the present application provides a personalized display method for a nurse terminal operation interface, an electronic device and a storage medium to solve the above-mentioned technical problems. The technical solutions provided by the present application are described in detail one by one below.

[0068] First, refer to Figure 1 , this application provides a personalized display method for a nurse terminal operation interface, which may include but is not limited to the following steps:

[0069] Step S100: Responding to the login instruction of the nurse, obtaining the account identity information.

[0070] Step S200: According to the account identity information, query and obtain corresponding operation habit information.

[0071] Step S300: determining a rendering layout relationship between each display area and each control in a preset display interface according to the operation habit information.

[0072] Step S400: Rendering is performed in the display interface according to the rendering layout relationship.

[0073] Step S500: Acquire the current operation data of the nurse during the current login, and update the operation habit information at a preset first time interval based on the current operation data.

[0074] The operation habit information in step S300 is obtained according to the following steps:

[0075] Step S610: Obtain historical operation data corresponding to the account identity information.

[0076] Step S620: Input the historical operation data into the input layer of the preset perceptual neural network model, and determine the first score set for each display area of ​​the display interface and the second score set for each control through multiple hidden layers of the perceptual neural network model.

[0077] Step S630: Input the first score set and the second score set into the output layer of the perceptual neural network model to obtain operation habit information.

[0078] In steps S100 to S500 and S610 to S630, when a nurse issues a login command, the system quickly responds and obtains their account identity information. Based on the obtained account identity information, the system queries a pre-stored database to find the corresponding operating habit information. This operating habit information is derived from the nurse's historical operating data collected over a long period of time and analyzed using a perceptual neural network model. Based on the retrieved operating habit information, the system further determines the rendering layout relationship between each display area and each control in the preset display interface. For example, if a nurse frequently uses the patient information query function and tends to place it prominently on the left side of the screen, the system will determine the rendering layout of this control in the left area of ​​the display interface based on this habit. Based on this determined rendering layout relationship, the system renders the control in the display interface. As the nurse uses the terminal device, the system continuously obtains the current operating data of the current login. At a preset first time interval, the system incorporates this current operating data into the analysis scope and updates the existing operating habit information. By obtaining and analyzing the nurse's operating habit information, frequently used function controls are placed in the locations where the nurse tends to operate, reducing the time the nurse spends searching for controls and switching between functional modules. At the same time, the mechanism for updating operating habit information in real time can keep pace with possible changes in nurses' operating habits, further ensuring the convenience of operation and avoiding problems such as information entry errors and untimely inquiries caused by inconvenient operation. Nurses no longer need to spend a lot of time searching for the required controls and switching functions in a fixed layout interface. They can quickly locate and operate commonly used functions, saving operation time and enabling nurses to complete more nursing work tasks per unit time. On the other hand, the interface layout that suits personal operating habits reduces the risk of information entry errors caused by inconvenient operation. Nurses can focus more on the nursing work itself, rather than being distracted by the inconvenient operating interface, thereby improving the accuracy of nursing records and the reliability of medical information, enabling nurses to complete their work more efficiently and accurately, and patients to receive more timely and high-quality nursing services.

[0079] Reference Figure 2 After step S100, the following steps may also be included but are not limited to:

[0080] Step S710: When the account identity information does not have corresponding operation habit information, generate a questionnaire and render the questionnaire on the display interface.

[0081] Step S720: Obtain the survey results filled out by the nurses, and generate the intended layout information based on the survey results.

[0082] Step S730: Rendering the controls in different display areas of the display interface according to the preset initial layout positions.

[0083] In steps S710 to S730, after the nurse issues a login instruction, the system quickly obtains their account identity information and immediately searches the database to determine whether corresponding operating habits exist for the account identity information. If the system determines that no corresponding operating habits exist for the account identity information, it automatically generates a questionnaire. The questionnaire focuses on the nurse's layout preferences for the terminal interface, asking, for example, where they would like commonly used function buttons (such as patient information query and nursing record) to be placed on the screen, and the order they prefer for different information display areas (such as basic information area and test report area). After generating the questionnaire, the system immediately renders it on the display interface and presents it to the nurse. After viewing the questionnaire on the display interface, the nurse fills it out based on their operating preferences and habits, and submits the survey results. Based on the obtained survey results, the system uses a pre-set algorithm to analyze and process the data. For example, the system calculates the frequency of selection of each function button in different position options and determines the intended position of each function button on the display interface based on the frequency. The system renders each control in different display areas of the display interface based on the preset initial layout position and the generated intention layout information. In the above steps, for new nurses or nurses who use the terminal system for the first time, their personalized needs for the operation interface can be quickly collected through questionnaires, which avoids the operational confusion of new users due to facing unfamiliar and non-personalized interfaces, and enables them to have an interface layout that conforms to their initial operating habits in a relatively short period of time, thereby improving the new user's acceptance and efficiency of the system. The collected survey results and the generated intention layout information serve as important reference data for the subsequent system to learn the nurse's operating habits, so as to achieve more accurate learning and optimization of the nurse's operating habits and achieve deeper personalized customization.

[0084] Reference Figure 3 It is understood that step S620 may include but is not limited to the following steps:

[0085] Step S640: Obtaining the intent layout information corresponding to the account identity information;

[0086] Step S650: inputting the historical operation data and the intended layout information into the input layer of the preset perceptual neural network model, and assigning a first confidence level and a second confidence level to the historical operation data and the intended layout information respectively through the input layer;

[0087] Step S660: Through the multiple hidden layers of the perception neural network model, the historical operation data, the first confidence, the intention layout information and the second confidence are integrated to obtain a first score set for each display area in the display interface and a second score set for each control.

[0088] In steps S640 to S660, before inputting historical operation data into the preset perceptual neural network model, the system first retrieves the corresponding intended layout information from the database based on the nurse's account identity information. This intended layout information is the nurse's feedback on their expectations for the user interface layout when they first use the terminal system by completing a questionnaire. The system inputs both the acquired historical operation data and the intended layout information into the input layer of the preset perceptual neural network model. The input layer assigns a first confidence level and a second confidence level to each of these two different types of data. The first confidence level is used to measure the reliability of the historical operation data in subsequent analysis. Its value is determined by comprehensively considering factors such as the data's time span and the stability of the operation frequency. For example, recent data with a stable operation frequency may be assigned a higher first confidence level. The second confidence level is determined based on the intended layout information, taking into account factors such as the nurse's initial diligence and completeness in completing the questionnaire, to reflect the importance of this intended layout information in the model analysis. After processing the data at the input layer—namely, historical operation data with a first confidence level and intended layout information with a second confidence level—then enter the multiple hidden layers of the perceptual neural network model. The hidden layers perform a deep fusion of this data through a series of complex nonlinear transformations and weight adjustments. For example, neurons in the hidden layer perform a weighted sum of the input data according to a preset weight matrix and perform nonlinear mapping using an activation function. During this process, the model comprehensively considers the nurses' actual operating habits, as reflected by the historical operation data, and their initial expectations, as reflected by the intended layout information. This determines a first set of scores for each display area on the display interface, as well as a second set of scores for each control. The first set of scores represents the degree to which each display area is suited to the nurses' operating habits, while the second set of scores represents the importance of each control in the nurses' operating process. Introducing intended layout information as a model input ensures that the model's analysis of nurses' operating habits not only relies on actual operating data but also takes into account the individual needs initially expressed by the nurses. By assigning confidence levels to different types of data, the model can more reasonably weigh the role of historical operational data and intended layout information in the analysis process, avoiding analytical bias due to the limitations of a single data source, thereby significantly improving the accuracy of determining the scoring set and achieving deep personalized customization from operating habits to interface layout.

[0089] Reference Figure 4 It is understood that step S650 may include but is not limited to the following steps:

[0090] Step S651: Obtain the first timestamp of the last update of the historical operation data and the second timestamp of the intention layout information through the input layer.

[0091] Step S652: establishing an assignment rule table; wherein the assignment rule table is used to represent the relationship between the difference between the first timestamp and the second timestamp and the ratio between the first confidence level and the second confidence level.

[0092] Step S653: Based on the assignment rule table and the first timestamp and the second timestamp, determine the first confidence level of the historical operation data and the second confidence level of the intended layout information.

[0093] In steps S651 to S653, the input layer of the system starts working, which will accurately locate and extract key time information related to the data. For historical operation data, the first timestamp of its last update is obtained. This timestamp records the moment when the nurse last performed an operation, reflecting the timeliness of the historical operation data. At the same time, the second timestamp of establishing the intention layout information is obtained. This timestamp marks the time point when the nurse first expressed his expectations for the interface layout through the questionnaire. An assignment rule table is pre-constructed within the system. The rule table is a carefully designed set of data mapping relationships. It defines in detail the corresponding relationship between the difference between the first timestamp and the second timestamp and the ratio between the first confidence level and the second confidence level. For example, when the difference between the first and second timestamps is small, it indicates a short time interval between the historical operation data and the intended layout information, potentially leading to a high degree of correlation between the two. In this case, the rule table may set the ratio of the first and second confidence levels to be relatively close to 1 or less than 1. A larger difference indicates a significant temporal shift between the historical operation data and the initial intended layout information, suggesting significant changes in the nurse's operating habits. The rule table will adjust the confidence ratio accordingly, assigning a higher confidence level to the more current historical operation data. Specifically, the first confidence level will be significantly higher than the second confidence level. Based on the established assignment rule table, the system substitutes the acquired first and second timestamps for calculation and analysis. By searching the rule table for the corresponding timestamp difference interval, the system can accurately determine the first confidence level for the historical operation data and the second confidence level for the intended layout information. For example, if the calculated timestamp difference falls within a specific interval in the rule table, the system determines the specific first and second confidence levels based on the confidence ratio setting corresponding to that interval, combined with a preset confidence range (e.g., 0-1). Confidence is determined by timestamps, fully considering the timeliness of historical operational data and intent layout information. This allows the perceptual neural network model to assign different weights based on the freshness of the data when processing data, allowing the model to automatically adapt to operational changes and ensure that the model output is more in line with the actual needs of nurses at the moment.

[0094] Reference Figure 5It is understood that the historical operation data includes: the historical usage time and historical usage area of ​​each control, and the intended layout information includes the control usage requirements and customary usage area. In step S660, the following steps may be included but not limited to:

[0095] Step S661: Determine a first usage score value for each control through a first hidden layer of a perceptual neural network model based on the historical usage time, the first confidence level, the control usage demand, and the second confidence level of each control;

[0096] Step S662: Determine a second usage score value for each display area through the second hidden layer of the perceptual neural network model based on the historical usage area, the first confidence level, the habitual usage area, and the second confidence level;

[0097] Step S663: Obtain a first score set according to the plurality of first usage score values, and obtain a second score set according to the plurality of second usage score values.

[0098] In step S661, the historical usage time of each control, along with its corresponding first confidence level, as well as the control usage demand and second confidence level, are input into the first hidden layer of the perceptual neural network model. Historical usage time reflects the nurse's actual frequency of use of the control, while the first confidence level reflects the reliability of historical operation data. The control usage demand represents the nurse's expectations for the control in the initial intended layout, while the second confidence level reflects the importance of the intended layout information. In the first hidden layer, neurons perform a weighted summation of the input data based on a preset weight matrix to obtain a first usage score for each control.

[0099] In step S662, the historical usage area and the first confidence level, as well as the habitual usage area and the second confidence level, are input into the second hidden layer. The historical usage area shows the interface locations of controls that nurses frequently use in actual operations, while the habitual usage area represents the locations of controls that nurses express their desired usage in the questionnaire. The second hidden layer also performs a weighted summation of these data using the weight matrix to obtain a second usage score for each display area.

[0100] In step S663, the system collects the first usage scores of all controls and organizes them into a first score set according to specific rules (e.g., sorting from high to low). This score set comprehensively presents the importance of each control in relation to nurses' operating habits and needs. The second usage scores of all display areas are processed in the same manner to produce a second score set, which reflects the ranking of each display area's suitability for nurses' operations. Through layered processing and multi-dimensional analysis, the model can better adapt to the diverse operating habits and needs of different nurses.

[0101] Reference Figure 6It is understood that the perceptual neural network model in step S620 can be based on but not limited to the following steps:

[0102] Step S810: establishing an initial perceptual neural network model;

[0103] Step S820: Obtain sample operation data and the expected display result corresponding to the sample operation data;

[0104] Step S830: Inputting the sample operation data into the perceptual neural network model to obtain an actual display result; wherein the actual display result includes the actual controls to be displayed and the actual display area of ​​each actual control;

[0105] Step S840: Obtaining a training loss value based on the actual display result and the expected display result;

[0106] Step S850: Adjust the parameters of the perceptual neural network model according to the training loss value until the training loss value meets the preset convergence condition or the number of training times is greater than or equal to the preset number threshold, stop the training of the perceptual neural network model, and obtain a trained perceptual neural network model.

[0107] During the system development phase, in steps S810 to S830, an initial perceptual neural network model is constructed. This model comprises an input layer, multiple hidden layers, and an output layer. Its architecture is designed based on an understanding and needs analysis of the personalized display tasks of the nurse terminal interface. Parameters such as the number of neurons, connection method, and initial weights in each layer are set according to pre-set rules. A large amount of sample operation data is collected. This data covers various operational behaviors of different nurses when using the terminal device. This data includes historical operation data such as the usage time and usage area of ​​each control, as well as information on the intended layout of control usage needs and habitual usage areas as reported by nurses through questionnaires. Simultaneously, the expected display results corresponding to these sample operation data are obtained. The expected display results are an ideal interface layout defined based on professional nursing process analysis and the actual needs of nurses, including the controls to be displayed and the ideal display area for each control. The sample operation data is input into the initialized perceptual neural network model. Starting from the input layer, the data is processed sequentially through multiple hidden layers. In the hidden layers, neurons perform a weighted summation of the input data using a pre-set weight matrix and perform nonlinear transformations using activation functions to extract key features from the data. Finally, the data processed by the hidden layer reaches the output layer, which outputs the actual display results, including the actual controls that need to be displayed and the actual display area of ​​each actual control.

[0108] In steps S840 and S850, the actual display result output by the model is compared and analyzed with the previously acquired expected display result. A specific loss function is used to calculate the difference between the two and obtain a training loss value. The training loss value reflects the degree of deviation between the current model output and the expected result. The smaller the loss value, the closer the model output is to the expected result. Based on the calculated training loss value, the backpropagation algorithm is used to adjust the parameters of the perceptual neural network model. The backpropagation algorithm propagates the loss value from the output layer to the input layer, calculating the contribution of each parameter to the loss value during the propagation process and adjusting the parameters accordingly, so that the model can output an actual display result closer to the expected result during the next prediction. The above steps are repeated continuously, namely, inputting new sample operation data, calculating the actual display result and the training loss value, and then adjusting the parameters until the training loss value meets the preset convergence conditions, such as the loss value being less than a minimum threshold or the number of training cycles being greater than or equal to a preset number of times. At this point, model training is terminated, resulting in a trained perceptual neural network model.

[0109] Reference Figure 7 It is understandable that the perceptual neural network model in step S620 can also be based on but not limited to the following steps:

[0110] Step S860: When the training loss value does not meet the convergence condition and the number of training times is less than the threshold, the parameters of the perceptual neural network model are gradient-solved according to the preset gradient algorithm and the training loss value to obtain a gradient vector.

[0111] Step S870: Obtain a learning rate parameter according to the number of training times of the perceptual neural network model and a preset learning rate decay function.

[0112] Step S880: Update the gradient vector according to the learning rate parameter.

[0113] Step S890: Adjust the parameters of the perceptual neural network model according to the updated gradient vector.

[0114] In step S860, according to the preset gradient algorithm and the training loss value L, the parameter θ of the perceptual neural network model is gradient-solved to obtain the gradient vector The stochastic gradient descent algorithm randomly selects a sample (or a small batch of samples) from the training data set to calculate the gradient at each iteration. Assume that the training data set is , for the tth training iteration, randomly select a sample , the gradient calculation formula of the loss function L with respect to the parameter θ is:

[0115] ;

[0116] in, Indicates that under the parameter θ, the sample The corresponding loss value.

[0117] In step S870, the learning rate parameter η is obtained according to the training times t of the perceptual neural network model and the preset learning rate decay function. t Here we use the exponential decay learning rate function, whose formula is:

[0118] ;

[0119] in:

[0120] Is the initial learning rate, which is a pre-set constant that controls the step size of the model parameter update in the early stage of training.

[0121] It is the decay coefficient, which usually ranges between , and is used to control the speed at which the learning rate decays with the number of training times.

[0122] k is the number of attenuation steps, that is, the learning rate decays once every k training iterations.

[0123] Represents the floor function.

[0124] In step S880, according to the learning rate parameter For the gradient vector Update and get the updated gradient vector, which is calculated as follows:

[0125] .

[0126] In steps S860 to S890, the learning rate parameters are dynamically adjusted based on the number of training runs using a preset learning rate decay function. In the early stages of training, a higher learning rate enables rapid updates of model parameters, accelerating the model's approach to the optimal solution and reducing overall training time. As the number of training runs increases, the learning rate gradually decreases, preventing the model from skipping the optimal solution due to excessive step sizes when approaching the optimal solution, resulting in oscillation near the optimal solution. This ensures the stability of model convergence. Simultaneously, a preset gradient algorithm is used to calculate the gradient vector, accurately determining the direction and magnitude of the model parameter update in the current state. Adjusting parameters based on the gradient vector allows the model to optimize along the direction where the loss function decreases most rapidly, improving convergence efficiency. Furthermore, dynamically adjusting the learning rate and gradient-based parameter updates help prevent model overfitting. In the later stages of training, a smaller learning rate allows for more refined updates of model parameters, which can, to a certain extent, prevent the model from overfitting the training data. This allows the model to perform better even when faced with unseen test data, thereby improving the model's generalization ability.

[0127] Reference Figure 8 It is understood that step S300 may include but is not limited to the following steps:

[0128] Step S310: Determine the first priority of each control corresponding to the current account identity information and the second priority of each display area according to the operation habit information.

[0129] Step S320: Determine a rendering layout relationship between each display area and each control in a preset display interface according to the first priority and the second priority.

[0130] In steps S310 to S320, the system reads operational habit information, which includes data on the nurse's usage frequency and duration of each control, as well as preferences for different display areas. Based on this data, the system calculates the first priority of each control. For example, if a nurse frequently uses the "Patient Information Search" control and uses it for a long time, the first priority of this control will be assigned a higher priority. Conversely, controls that are rarely used will have a lower priority. The second priority of each display area is also determined based on operational habit information. For example, if a nurse frequently views important information in the upper left corner of the screen, the second priority of this display area will be higher. After determining the first priority of each control and the second priority of each display area, the system matches them. Controls with higher priorities are preferentially placed in higher-priority display areas. For example, if the "Patient Information Search" control has a high priority, and the upper left corner display area also has a high priority, the system will place the "Patient Information Search" control in the upper left corner of the screen. Other controls and display areas are also matched and laid out in descending order of priority, determining the rendering layout relationship between each display area and each control in the preset display interface. Placing the controls nurses use most frequently in the high-priority display area, which is easiest to operate and view, allows nurses to quickly find and operate common functions when using the terminal device, reducing the number of steps and the time spent searching for controls. For example, in an emergency, a nurse can quickly find the "vital signs recording" control in the high-priority area and record the patient's key information in a timely manner, improving nursing efficiency.

[0131] In the second aspect, the present application also provides an electronic device comprising: at least one memory; at least one processor; at least one program; the program is stored in the memory, and the processor executes at least one program to implement a personalized display method for the nurse terminal operation interface as in any embodiment of the first aspect.

[0132] In this electronic device, when a nurse issues a login command, the system quickly responds and obtains their account identity information. Based on the obtained account identity information, the system queries a pre-stored database to find the corresponding operating habit information. This operating habit information is derived from the nurse's historical operating data collected over a long period of time and analyzed using a perceptual neural network model. Based on the retrieved operating habit information, the system further determines the rendering layout relationship between each display area and each control in the preset display interface. For example, if a nurse frequently uses the patient information query function and tends to place it prominently on the left side of the screen, the system will determine the rendering layout of this control in the left area of ​​the display interface based on this habit. Based on this determined rendering layout relationship, the system renders the control in the display interface. As the nurse uses the terminal device, the system continuously obtains the current operating data of the current login. At a preset first time interval, the system incorporates this current operating data into its analysis scope and updates the existing operating habit information. By obtaining and analyzing the nurse's operating habit information, frequently used function controls are placed in the locations where the nurse tends to operate, reducing the time the nurse spends searching for controls and switching between functional modules. At the same time, the mechanism for updating operating habit information in real time can keep pace with possible changes in nurses' operating habits, further ensuring the convenience of operation and avoiding problems such as information entry errors and untimely inquiries caused by inconvenient operation. Nurses no longer need to spend a lot of time searching for the required controls and switching functions in a fixed layout interface. They can quickly locate and operate commonly used functions, saving operation time and enabling nurses to complete more nursing work tasks per unit time. On the other hand, the interface layout that suits personal operating habits reduces the risk of information entry errors caused by inconvenient operation. Nurses can focus more on the nursing work itself, rather than being distracted by the inconvenient operating interface, thereby improving the accuracy of nursing records and the reliability of medical information, enabling nurses to complete their work more efficiently and accurately, and patients to receive more timely and high-quality nursing services.

[0133] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs, and signals, such as the program instructions / signals corresponding to the processing module in the embodiments of this application. The processor executes the non-transitory software programs, instructions, and signals stored in the memory to perform various functional applications and data processing, thereby implementing the personalized display method for the nurse terminal operation interface of the above-mentioned method embodiment.

[0134] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store relevant data of the personalized display method of the above-mentioned nurse terminal operation interface, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processing module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0135] One or more signals are stored in a memory, and when executed by one or more processors, the personalized display method of the nurse terminal operation interface in any of the above method embodiments is executed.

[0136] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. The computer program is executed by one or more processors, enabling the one or more processors to execute the personalized display method of the nurse terminal operation interface in the above method embodiment.

[0137] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected based on actual needs to achieve the objectives of this embodiment.

[0138] From the above description of the embodiments, one skilled in the art will appreciate that all or some of the steps and systems of the methods disclosed above can be implemented as software, firmware, hardware, or any suitable combination thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable signals, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable signals, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0139] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0141] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0143] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0144] The embodiments of the present application are described in detail above in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments. Various changes can be made within the scope of knowledge possessed by ordinary technicians in the relevant technical field without departing from the purpose of the present application.

Claims

1. A personalized display method for a nurse terminal operation interface, characterized in that: include: Responding to a login instruction from a nurse, obtaining account identity information; According to the account identity information, query and obtain corresponding operation habit information; Determining, based on the operation habit information, a rendering layout relationship between each display area and each control in a preset display interface; Rendering in the display interface according to the rendering layout relationship; Acquire the current operation data of the nurse during the current login, and update the operation habit information at a preset first interval based on the current operation data; The operation habit information is obtained according to the following steps: Obtaining historical operation data corresponding to the account identity information; Inputting the historical operation data into an input layer of a preset perceptual neural network model, and determining a first score set for each display area on the display interface and a second score set for each of the controls through multiple hidden layers of the perceptual neural network model; Inputting the first score set and the second score set into the output layer of the perceptual neural network model to obtain operation habit information; The step of inputting the historical operation data into an input layer of a preset perceptual neural network model and determining a first score set for each display area on the display interface and a second score set for each control through multiple hidden layers of the perceptual neural network model includes: Obtaining the intent layout information corresponding to the account identity information; Inputting the historical operation data and the intended layout information into an input layer of a preset perceptual neural network model, and assigning a first confidence level and a second confidence level to the historical operation data and the intended layout information respectively through the input layer; fusing the historical operation data, the first confidence level, the intended layout information, and the second confidence level through multiple hidden layers of the perceptual neural network model to obtain a first score set for each display area on the display interface and a second score set for each control; The assigning of a first confidence level and a second confidence level to the historical operation data and the intended layout information respectively through the input layer includes: Obtaining, through the input layer, a first timestamp of the last update of the historical operation data and a second timestamp of establishing the intended layout information; Establishing an assignment rule table; wherein the assignment rule table is used to represent the relationship between the difference between the first timestamp and the second timestamp and the ratio between the first confidence level and the second confidence level; Based on the assignment rule table and according to the first timestamp and the second timestamp, a first confidence level of the historical operation data and a second confidence level of the intended layout information are determined.

2. The personalized display method of the nurse terminal operation interface according to claim 1, characterized in that: After the step of obtaining the account identity information in response to the login instruction of the nurse, the method further includes: When the account identity information does not have corresponding operation habit information, generating a questionnaire and rendering the questionnaire on the display interface; obtaining survey results completed by the nursing staff and generating intent layout information based on the survey results; The controls are rendered in different display areas of the display interface according to a preset initial layout position.

3. The personalized display method of the nurse terminal operation interface according to claim 1 is characterized in that: The historical operation data includes: the historical usage time and historical usage area of ​​each control, and the intended layout information includes the control usage requirements and customary usage area; The historical operation data, the first confidence level, the intended layout information, and the second confidence level are fused through the multiple hidden layers of the perceptual neural network model to obtain a first score set for each display area of ​​the display interface and a second score set for each control, including: Determining a first usage score value for each of the controls through a first hidden layer of the perceptual neural network model according to the historical usage time of each of the controls, the first confidence level, the control usage demand, and the second confidence level; Determining a second usage score value for each of the display areas through a second hidden layer of the perceptual neural network model according to the historical usage area, the first confidence level, the habitual usage area, and the second confidence level; A first score set is obtained based on the first usage score values, and a second score set is obtained based on the second usage score values.

4. The personalized display method of the nurse terminal operation interface according to claim 1, characterized in that: The perceptual neural network model is trained according to the following steps: Establish an initial perceptual neural network model; Acquire sample operation data and expected display results corresponding to the sample operation data; Inputting the sample operation data into the perceptual neural network model to obtain an actual display result; wherein the actual display result includes the actual controls to be displayed and the actual display area of ​​each actual control; Obtaining a training loss value according to the actual display result and the expected display result; The parameters of the perceptual neural network model are adjusted according to the training loss value until the training loss value meets the preset convergence condition or the number of training times is greater than or equal to the preset number threshold, and the training of the perceptual neural network model is stopped to obtain a trained perceptual neural network model.

5. The personalized display method of the nurse terminal operation interface according to claim 4 is characterized in that: The perceptual neural network model is also trained according to the following steps: When the training loss value does not meet the convergence condition and the number of training times is less than the number threshold, performing a gradient solution on the parameters of the perceptual neural network model according to a preset gradient algorithm and the training loss value to obtain a gradient vector; Obtaining a learning rate parameter according to the number of training times of the perceptual neural network model and a preset learning rate decay function; Updating the gradient vector according to the learning rate parameter; The parameters of the perceptual neural network model are adjusted according to the updated gradient vector.

6. The personalized display method of the nurse terminal operation interface according to claim 3, characterized in that: The determining, based on the operation habit information, a rendering layout relationship between each display area and each control in a preset display interface includes: Determining, based on the operation habit information, a first priority of each control corresponding to the current account identity information and a second priority of each display area; According to the first priority and the second priority, a rendering layout relationship between each of the display areas and each of the controls in a preset display interface is determined.

7. An electronic device, characterized in that: include: at least one memory; at least one processor; at least one program; The programs are stored in the memory, and the processor executes at least one of the programs to implement the personalized display method of the nurse terminal operation interface according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer-executable signal, and the computer-executable signal is used to execute the personalized display method of the nurse terminal operation interface according to any one of claims 1 to 6.

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