Display method, device and storage medium of controls on human-computer interaction interface

Abstract

The embodiment of the present application provides a method, device and storage medium for displaying controls on a human-computer interaction interface, which belongs to the field of industrial control technology; the method includes obtaining target user information and interface usage data of the currently logged-in target user; the interface usage data represents usage data generated by clicking on and using each preset control on the human-computer interaction interface; the target user information and the corresponding interface usage data are input into a preset operation prediction model to determine the prediction score of each preset control classified into each preset display category; according to each of the prediction scores and the current menu level of each of the controls, the corresponding target menu path of each of the controls is determined; according to the target menu path of each of the controls, each of the controls is rendered in a preset display interface. The embodiment of the present application can make the control layout on the human-computer interaction interface automatically adapt to the logged-in user to improve the operation efficiency of the controls on the human-computer interaction interface.

Description

Technical Field

[0001] The present application relates to the field of industrial control technology, and in particular to a method, device, and storage medium for displaying controls on a human-computer interaction interface. Background Art

[0002] In the field of industrial control technology, with the popularization of intelligence, interface operations are becoming more and more popular in industrial equipment control. However, in actual applications, due to the complexity of industrial scenarios, the corresponding industrial automation software is often more complicated. Therefore, when controlling industrial equipment in the human-computer interaction interface of the terminal device, the layout of the various controls on the human-computer interaction interface is often messy, and it is difficult for users to quickly obtain the corresponding information. Moreover, due to the different operating requirements of different types of users, the operation efficiency of the controls on the human-computer interaction interface is low in related technologies. Therefore, how to improve the display layout of the controls on the human-computer interaction interface to improve the operation efficiency of the controls on the human-computer interaction interface and improve the user experience is a technical problem that needs to be solved urgently. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to propose a display method, device and storage medium for controls on a human-computer interaction interface, which can automatically adapt the control layout on the human-computer interaction interface to the logged-in user to improve the operating efficiency of the controls on the human-computer interaction interface.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application provides a method for displaying controls on a human-computer interaction interface, the method comprising:

[0005] Obtain target user information and interface usage data of the currently logged-in target user; the interface usage data represents usage data generated by clicking on each preset control on the human-computer interaction interface;

[0006] Inputting the target user information and the corresponding interface usage data into a preset operation prediction model to determine the prediction score of each preset control classified into each preset display category; the operation prediction model is used to predict the prediction score of each control classified into each display category when used by different users on the same human-computer interaction interface;

[0007] Determining a target menu path for each of the controls according to the prediction scores and the current menu level of each of the controls;

[0008] Each of the controls is rendered in a preset display interface according to the target menu path of the control.

[0009] To achieve the above-mentioned purpose, the second aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the method for displaying controls on the human-computer interaction interface described in any one of the first aspects above.

[0010] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method for displaying controls on the human-computer interaction interface described in any one of the first aspects.

[0011] The display method, device and storage medium of controls on the human-computer interaction interface proposed in this application perform feature extraction on the target user information and interface usage data of the target user through an operation prediction model to obtain prediction scores for classifying each control on the human-computer interaction interface into each display category, so as to determine the controls that need to be re-divided into menu levels and display categories based on the prediction scores, and then determine the target menu path of each control. After the rendering is completed based on each target menu path, the layout of each rendered control is more in line with the habits of the target user, so the operation efficiency of the target user is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flow chart of a method for displaying controls on a human-computer interaction interface provided by an embodiment of the present application;

[0013] Figure 2 This is a schematic diagram of the principle of a single-layer perceptron system in the process of constructing a multi-layer perceptron in the method for displaying controls on a human-computer interaction interface provided by an embodiment of the present application;

[0014] Figure 3 This is a network architecture diagram of a multilayer perceptron in one embodiment of a method for displaying controls on a human-computer interaction interface provided by an embodiment of the present application;

[0015] Figure 4 This is a schematic diagram of changes in a control list in one embodiment of a method for displaying controls on a human-computer interaction interface provided by an embodiment of the present application;

[0016] Figure 5 This is a schematic diagram of an interface implementation of a control list in another embodiment of the method for displaying controls on a human-computer interaction interface provided by an embodiment of the present application;

[0017] Figure 6 It is a structural diagram of the hardware structure corresponding to the display method of controls on the human-computer interaction interface provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0019] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0021] The display method, device and storage medium of controls on the human-computer interaction interface provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the display method of controls on the human-computer interaction interface in the embodiments of the present application is described.

[0022] The display method of controls on the human-computer interaction interface of the present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0023] Refer to the following Figure 1 As shown, according to the method for displaying controls on a human-computer interaction interface provided by an embodiment of the present application, the method for displaying controls on a human-computer interaction interface includes:

[0024] S100, obtaining target user information and interface usage data of the currently logged-in target user; the interface usage data represents usage data generated by clicking on each preset control on the human-computer interaction interface;

[0025] S200: Inputting target user information and corresponding interface usage data into a preset operation prediction model to determine prediction scores for each preset control classified into each preset display category; the operation prediction model is used to predict prediction scores for each control used by different users on the same human-computer interaction interface classified into each display category;

[0026] S300, determining a target menu path for each control according to each prediction score and the current menu level of each control;

[0027] S400: Render each control in a preset display interface according to the target menu path of each control.

[0028] Therefore, by operating the prediction model, feature extraction is performed on the target user information and interface usage data of the target user, and the prediction score of each control on the human-computer interaction interface being classified into each display category is obtained. Based on the prediction score, the controls that need to be re-divided into menu levels and display categories can be determined, and then the target menu path of each control can be determined. After the rendering based on each target menu path is completed, the layout of each rendered control is more in line with the habits of the target user, so the operation efficiency of the target user is higher.

[0029] The interface usage data is the usage data generated by the target user during the preset time before logging into the human-computer interaction interface or the first N logins. This embodiment of the application does not limit the preset time or number of logins, and can be selectively set according to actual needs. This embodiment of the application does not limit the type of interface usage data, such as the operation frequency of each control within the preset operation time threshold, the duration of a single operation, etc.

[0030] The target user information is used to identify the target user, and can be an account number or an index of the target user. This embodiment of the present application does not impose any restrictions on this.

[0031] Display categories can represent the operational attributes of different controls, such as system management, application management, network management, data management, time configuration, etc. Display categories are pre-configured in the terminal device, and those skilled in the art can selectively set them according to actual needs.

[0032] Each display category corresponds to a predicted score range. Therefore, based on the predicted scores belonging to each display category, a display category with a higher score can be selected as the target display category of the corresponding control. The embodiment of the present application does not display the menu hierarchy under each display category. In some embodiments, each display category has a hierarchy level, and each control is displayed in a list under the corresponding display category. At this time, after the target display category of each control is determined, the target menu path can also be determined. In other embodiments, multiple hierarchies can be set under each display category. At this time, the corresponding categories can be divided according to the range of predicted scores, so that the target menu path can be determined. When the predicted scores do not meet the predicted score range corresponding to each display category, a new display category is created based on the control for display. For each control of the same display category, in some embodiments, the order of arrangement can be set according to the predicted scores of each control in the same display category.

[0033] The embodiment of the present application does not limit how each target menu path is rendered, and those skilled in the art can selectively set it according to actual needs.

[0034] In some embodiments, step S100 may be triggered only after a preset interface update condition is met. The embodiment of the present application does not limit the interface update condition, and those skilled in the art may selectively set it according to actual needs, such as when the usage permissions of the same user change or the display interface corresponding to the logged-in user account lasts for more than a preset time threshold.

[0035] It is understandable that in the embodiments of the present application, in implementation scenarios such as:

[0036] Scenario 1: Initialize the categories of various controls for users with different roles. If one of the users frequently operates controls on another path while maintaining the same role, and the control search time is long, the controls can be rearranged through steps S100 to S400, thereby shortening the operation time of the controls that take a long time to search and improving the operation speed.

[0037] Scenario 2: The user role of the same user has changed, such as a job title change or the addition of a temporary task, etc., and the controls that need to be operated will also change. The controls can be rearranged through the above steps S100 to S400 to improve operation efficiency.

[0038] It is understandable that the parameters related to the human-computer interaction interface rendered in step S400 will be saved in the database, so that when logging in next time, when it is determined that the interface update conditions are not met, the human-computer interaction interface displayed last time can be displayed.

[0039] For example, refer to Figure 2As shown, in the terminal display interface, after logging in, user 1 will display the first-level menu of function A, function B, function C, function D and function E, wherein function A, function B, function C, function D and function E respectively correspond to a display category. When it is determined based on the above steps S100 to S400 that user 1's predicted score for control B.5 under function B does not belong to function A, function B, function C, function D and function E, that is, the operation on control B.5 is more frequent, and functions A, function B, function C, function D and function E are more frequent, the predicted score of control B.5 under function B does not belong to function A, function B, function C, function D and function E, that is, the operation on control B.5 is more frequent, and functions A, function B, function C, function D and function E are more frequent. The operation frequencies of most display categories in Function E are similar; then B.5 is directly used as a new display category and displayed in parallel with Function A, Function B, Function C, Function D and Function E, and based on the above steps S100 to S400, it is determined that the predicted scores of each control under Function C all show that they can be classified into Function B (such as operating the controls under Function B will often be followed by operating the controls under Function C, that is, the controls under Function B and the controls under Function C are strongly related), then the entire Function C is used as a sublist of Function B.

[0040] It is understood that the interface usage data includes multiple interface usage records corresponding to each control; the operation prediction model includes at least two hidden layers, which input the target user information and the corresponding interface usage data into the preset operation prediction model to determine the prediction score of each preset control classified into each preset display category, including:

[0041] Determining first neurons corresponding to the target user information and each interface usage record in the first hidden layer; wherein at least some of the first neurons correspond to different interface usage records, and the weights configured in the first neurons that input the same interface usage record are different;

[0042] Extracting features from the target user information and the corresponding interface usage records through the first neuron to obtain a first feature that corresponds one-to-one to the first neuron;

[0043] Input the first feature into the second neuron corresponding to the first neuron in the second hidden layer;

[0044] Extract the corresponding first feature through the second neuron to obtain a second feature that corresponds one-to-one with the second neuron;

[0045] According to each second feature, a prediction score of each control being classified into each preset display category is determined; wherein the combination of second neurons corresponding to each display category is different.

[0046] Interface usage records may include the number of operations per unit time and control search duration data. The search duration data is calculated as the duration from the time a user dwells on a menu list in the human-computer interaction interface for a period greater than a preset first duration threshold and less than a preset second duration threshold until the control is triggered, serving as the search duration for the triggered control. The first and second duration thresholds can be set selectively based on actual circumstances. For example, the length of time experienced users spend on a menu list during operation and the length of time novice users spend on a menu list during operation can be counted to determine the first and second duration thresholds, respectively.

[0047] By combining different interface usage records and user information and assigning them to different first neurons for calculation, feature learning can be performed on interface usage records under different combinations, and the weights of the first neurons for the same interface usage records are different. Feature extraction of the first features of different combinations is performed through the second neurons, so that the hidden layer can perform multi-dimensional feature extraction on each interface usage record and user information, thereby ensuring that the predicted score of the display category of each control based on the interface usage record and user information can be strongly correlated with the target user information, thereby further ensuring that the rendered page is more in line with the usage habits of the target user, and improving the target user's operating efficiency on the terminal display interface.

[0048] It is understood that the interface usage data includes usage permissions, a list of common controls, operation frequency data corresponding to each control, device maintenance records, and device operation records; the operation prediction model includes an input layer, a first hidden layer, a second hidden layer, and an output layer; the target user information and the corresponding interface usage data are input into the preset operation prediction model to determine the prediction score of each preset control classified into each preset display category, including:

[0049] The target user information, usage permissions, common control list, operation frequency data, device maintenance records, and device operation records are all input into the input layer; wherein the input layer combines the target user information with the usage permissions, common control list, operation frequency data, device maintenance records, and device operation records to form multiple first record groups; the input layer is further used to combine the target user information, usage permissions, and operation frequency data to obtain a second record group, and the input layer is further used to combine the target user information, common control list, and device maintenance records to obtain a third record group.

[0050] Inputting each of the first record group, the second record group, the third record group, and the target user information into at least one first neuron in the first hidden layer through the input layer to obtain a first feature corresponding to the first neuron one by one;

[0051] Inputting each first feature into a second neuron corresponding to the first neuron in the second hidden layer, wherein each second neuron corresponds to at least two first neurons, and the combination of the first neurons in any two second neurons is different;

[0052] Each second feature is input into the output layer to obtain the prediction score of each control being classified into each preset display category.

[0053] Usage permissions record the target user's permissions for each control set on the human-computer interaction interface. For example, ordinary technicians and senior technicians are allowed to operate different controls.

[0054] The common control list records a collection of various controls that are allowed to be displayed to the target user on the human-computer interaction interface.

[0055] Operation frequency data records the number of times each control is used and / or the search time on the human-computer interaction interface.

[0056] The device operation record records the execution order of different controls when the target user operates the device based on the human-computer interaction interface. The device maintenance record records the target user's device maintenance on the human-computer interaction interface to assist in identifying the user role.

[0057] It is understandable that the target user information and usage permissions, common control lists, operation frequency data, device maintenance records, and device operation records are combined to form multiple first record groups, thereby extracting features that are strongly correlated with the target user information from the usage permissions, common control lists, operation frequency data, device maintenance records, and device operation records, thereby further ensuring that the obtained predicted score matches the target user's actual usage habits. At the same time, by extracting features from the target user information separately and combining the first features of the first record group, the second record group, and the third record group through different second neurons, the predicted score can be made more accurate.

[0058] The present embodiment of the present application does not limit the number of first neurons corresponding to the first record group, the second record group, the third record group, and the target user information. Those skilled in the art can selectively set them according to actual needs. For example, if the first record group is input into two first neurons, and the weights on the two first neurons are set differently, features of different dimensions can be extracted from the first record group, making the features more diverse.

[0059] In the field of industrial control, due to the differences in functional requirements of different user roles, the layout of controls displayed on the human-computer interaction interface for the same account will be different. For example, the user roles are divided into maintenance roles and operation roles. Figure 4As shown, neurons for extracting the first feature of equipment maintenance records and accounts (i.e., target user information) are added, so that the impact of different user roles and user behavior habits on the display of controls on the human-computer interaction interface can be comprehensively considered. For example, Figure 4 As shown, the first hidden layer is provided with 12 first neurons, and the second hidden layer is provided with 12 second neurons. Figure 4 As shown, the second record group and the third record group each correspond to a first neuron, the target user information corresponds to a first neuron, and the first record group, except for the record group consisting of equipment maintenance records and target user information, each record group corresponds to a first neuron. The record group consisting of equipment maintenance records and target user information corresponds to two first neurons. Figure 4 As shown, the first feature of the third recording group corresponds to one second neuron, and the remaining second neurons combine at least two different first features for feature extraction.

[0060] It is understandable that the operation prediction model is trained through the following steps:

[0061] Acquire user information of each user to be registered, multiple sample interface usage data corresponding to the user information, and operation interface expected view data corresponding to the user information;

[0062] Determine the expected display category of each control corresponding to the user information one by one according to the expected view data of the operation interface;

[0063] Obtaining an initial operational prediction model;

[0064] Each user information and the corresponding sample interface usage data are input as a set of sample data into the initial operation prediction model for training, and the error is calculated based on the training results and the display expected category corresponding to the input sample data to determine the prediction error corresponding to each user information;

[0065] When each prediction error satisfies a preset error threshold or the number of training times is greater than or equal to a preset number threshold, the training of the operation prediction model is stopped to obtain a trained operation prediction model.

[0066] The expected view data of the operation interface is a drawn display view. By converting the drawn expected view data into a display expected category, the convenience of sample data collection can be improved.

[0067] It is understandable that the operation prediction model is set to a multi-layer perceptron, and the operation steps of the operation prediction model also include:

[0068] When the prediction error does not meet the error threshold and the number of training times is less than the number threshold, the prediction error is gradient-solved using the preset stochastic gradient algorithm to determine the weight update gradient;

[0069] Get the preset learning rate, perform weighted processing on the learning rate and weight update gradient, and obtain the target weight update gradient;

[0070] Updates the configured weights in the multilayer perceptron according to the target weight update gradient.

[0071] By combining the gradient algorithm with the multi-layer perceptron, rapid convergence can be achieved during training.

[0072] It is understandable that in industrial scenarios, the number of users registered in the same human-computer interaction interface is often large. Therefore, there is a lot of sample interface usage data generated by users. Therefore, how to improve the speed of convergence during training is very critical. At the same time, considering that the frequency of changes in the control requirements of the same user in industrial scenarios is low, the learning rate can be directly introduced to increase the amplitude of each gradient change, so that the prediction accuracy can be ensured while meeting the convergence requirements. The embodiment of the present application does not limit the specific value of the learning rate, and it can be adjusted synchronously through simulation experiments or during the training process (such as initially setting a larger value and determining it by gradually reducing it).

[0073] It is understandable that the initial multilayer perceptron can be constructed by the following steps:

[0074] Step 1: First, build a system operation as a sensor in the terminal device:

[0075] Suppose a single perceptron with n-dimensional input is Figure 3 As shown, to are the components of the n-dimensional input vector (i.e., the parameters and user information in the sample interface usage data), to are the weights connecting each input component to the perceptron, is the bias, f(.) is the transfer function, is the expected output. Output The mathematical description is:

[0076] ;

[0077] in , and f(x) is an antisymmetric sign function defined as: ;

[0078] in, is the weight; if the weight of an input value of the current neuron is zero, then whether the current neuron is excited or not has nothing to do with this input value; if the weight of an input value is positive, it has a positive effect on the excitation value of the current neuron. Conversely, if the weight is negative, it has a negative effect on the excitation value.

[0079] in, The bias defines the spatial offset of the firing thresholds of each neuron in the hidden layer. This has a translational effect on the decision boundary, much like the effect of a constant on a linear or quadratic function. When a perceptron is represented as the inner product of an input vector and a weight vector, the bias is extended to represent the weight, and the corresponding input value is 1.

[0080] Step 2: Build a multi-layer perceptron based on the single-layer perceptron in step 1, as follows:

[0081] Using the back-propagation algorithm and gradient descent convergence process, adding at least one hidden layer to the perceptron in step 1 can enhance the learning ability of the multilayer perceptron model terminal. The gradient propagation algorithm is a gradient descent method. As long as the direction of the gradient can be calculated, the weights of the multilayer perceptron can be directed in the direction with the fastest energy decrease.

[0082] Step 3: Determine the direction of gradient descent, as follows:

[0083] Output error is defined as the error between the output obtained based on any set of weight vectors and the expected output. For example, the error is calculated using mean square error, where the formula for calculating mean square error is as follows:

[0084] ;

[0085] In the above formula, D represents all input conditions, and d represents a sample instance. represents the output of the perceptron, Represents the expected output; Indicates the output error.

[0086] Based on finding a set of weights such that The idea of minimizing the error value is to take the derivative of the error with respect to the weight as the gradient. In this case, the gradient can be expressed by the following gradient formula:

[0087] ;

[0088] Step 4: Calculate the convergence of the gradient, as follows:

[0089] Update the gradient to stochastic gradient to update the weight according to each individual training sample. At this time, the following gradient formula can be obtained:

[0090] ;

[0091] in, Indicates the expected output, represents the output of the perceptron.

[0092] At the same time, in order to further improve the convergence speed, the learning rate can be set. At this time, the update formula for each weight can be obtained as follows:

[0093] ;

[0094] ;

[0095] in, represents the learning rate, represents the current weight, represents the updated weight. ; Indicates input, represents the target segmentation function. is a real number, which is the result calculated based on the training sample data during the weight update process, so that Determine And update the weights.

[0096] It is understandable that, according to the target menu path of each control, each control is rendered in the preset display interface, including:

[0097] Compare the target menu path of each control with the original menu path to determine the target control to which the menu path is moved upward;

[0098] Obtain preset enhanced rendering parameters, and render the target control according to the enhanced rendering parameters; the enhanced rendering parameters include at least one of a format parameter and an effect parameter.

[0099] It is understandable that formatting parameters such as size, font, bold, etc.

[0100] Effect parameters such as adding marks or dynamically displaying text on controls.

[0101] For example, refer to Figure 5 As shown, the hardware performance test is the target control that moves up the menu path. At this time, the font of the target control is bolded and a mark is added on the side. At this time, when the target user enters the system maintenance submenu, the control can be quickly obtained.

[0102] By enhancing rendering to make the changed target controls more conspicuous, users can be more quickly familiar with the updated human-computer interaction interface, thereby improving operational efficiency.

[0103] It is understandable that, after rendering each control in the preset display interface, the method further includes:

[0104] The rendered display interface is saved to the storage record corresponding to the target user in the preset interface storage database;

[0105] When the target user logs in again, the display interface in the storage record corresponding to the target user is retrieved from the interface storage database and displayed;

[0106] When there is a target control with enhanced rendering parameters, determining the display duration of the target control;

[0107] Cancel the enhanced rendering parameters of the target control based on the display duration.

[0108] By recording the display duration of the target control, the normal display of the interface can be restored when the target user has adapted to the new interface.

[0109] When the display duration exceeds the preset display threshold, the enhanced rendering parameters of the target control can be canceled. Display duration statistics can be calculated from the first time the target control is triggered when the target user logs in, and end when the target user logs out. When the cumulative display duration of the same target user exceeds the display threshold, the enhanced rendering parameters of the target control can be canceled.

[0110] It is understandable that obtaining the target user information and interface usage data of the currently logged-in target user includes:

[0111] When the preset interface update conditions are met, the target user information and interface usage data of the currently logged-in target user are obtained;

[0112] The interface update conditions include at least one of the following:

[0113] The similarity between the currently operated device list and the historical device list corresponding to the same target user is less than a preset first threshold;

[0114] The usage rights of the same target user are changed and the degree of change is greater than a preset second threshold;

[0115] The shortening trend of the operation time of the same target user in the same display interface is smaller than the preset operation trend curve.

[0116] The present application does not limit how similarity is calculated. Those skilled in the art can selectively set it according to actual needs, such as using feature comparison or direct type comparison. For example, for a series of devices of the same model, most of their functions are not very different. Therefore, when the model of the device operated by the same target user changes, the interface needs to be updated. The same method can also be used to determine usage permissions.

[0117] The operation trend curve can be set according to actual needs. Different devices can set different operation trend curves, and users of different levels can also set different thresholds, thereby eliminating the impact caused by device differences and user skills.

[0118] In actual use, when the interface layout of the control is relatively reasonable, as the user becomes more familiar with the interface, the change in operation time should gradually shorten and stabilize. If the operation time changes repeatedly, it can be determined that the operation is more complicated due to the control interface layout.

[0119] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method for displaying controls on a human-computer interaction interface. The electronic device can be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0120] See also Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0121] The processor 601 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0122] The memory 602 may be a NAND flash. The relevant program code is stored in the memory 602 and is called by the processor 601 to execute the method for displaying controls on the human-computer interaction interface according to the embodiment of the present application.

[0123] Input / output interface 603, used to implement information input and output;

[0124] Communication interface 604, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0125] Bus 605 , which transmits information between various components of the device (e.g., processor 601 , memory 602 , input / output interface 603 , and communication interface 604 );

[0126] The processor 601 , the memory 602 , the input / output interface 603 and the communication interface 604 are connected to each other in communication within the device via a bus 605 .

[0127] An embodiment of the present application also provides a computer-readable storage medium, which is a computer-readable storage medium that stores a computer program. When the computer program is executed by a processor, it implements the display method of the controls on the above-mentioned human-computer interaction interface.

[0128] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor 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.

[0129] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0130] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0131] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0132] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0133] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for displaying controls on a human-computer interaction interface, characterized in that: The method comprises: Obtain target user information and interface usage data of the currently logged-in target user; the interface usage data represents usage data generated by clicking on each preset control on the human-computer interaction interface; Inputting the target user information and the corresponding interface usage data into a preset operation prediction model to determine the prediction score of each preset control classified into each preset display category; the operation prediction model is used to predict the prediction score of each control classified into each display category when used by different users on the same human-computer interaction interface; Determining a target menu path for each of the controls according to the prediction scores and the current menu level of each of the controls; Rendering each of the controls in a preset display interface according to the target menu path of each of the controls; The operation prediction model is trained through the following steps: Acquire user information of each user to be registered, multiple sample interface usage data corresponding to the user information, and operation interface expected view data corresponding to the user information; Determining, according to the expected view data of the operation interface, the expected display category of each of the controls corresponding one-to-one to the user information; Obtaining an initial operational prediction model; Inputting each of the user information and the corresponding sample interface usage data as a set of sample data into the initial operation prediction model for training, and performing error calculation based on the training results and the expected display category corresponding to the input sample data to determine the prediction error corresponding to each of the user information; When each of the prediction errors satisfies a preset error threshold or the number of training times is greater than or equal to a preset number threshold, the training of the operation prediction model is stopped to obtain a trained operation prediction model; The interface usage data includes a plurality of interface usage records corresponding to each of the controls; the operation prediction model includes at least two hidden layers, and inputting the target user information and the corresponding interface usage data into a preset operation prediction model to determine a prediction score for classifying each preset control into each preset display category includes: Determining first neurons corresponding to the target user information and each of the interface usage records in a first hidden layer; wherein at least some of the first neurons correspond to different interface usage records, and weights configured in the first neurons that input the same interface usage record are different; Performing feature extraction on the target user information and the corresponding interface usage record through the first neuron to obtain a first feature corresponding to the first neuron one-to-one; Inputting the first feature into a second neuron corresponding to the first neuron in a second hidden layer; Extracting the first feature corresponding to the second neuron through the second neuron to obtain a second feature corresponding to the second neuron one-to-one; Based on each of the second features, a prediction score for classifying each control into each preset display category is determined; wherein the combination of second neurons corresponding to each display category is different.

2. The method for displaying controls on a human-computer interaction interface according to claim 1, characterized in that: The interface usage data includes usage permissions, a list of common controls, operation frequency data corresponding to each control, device maintenance records, and device operation records; the operation prediction model also includes an input layer and an output layer; the first hidden layer is a first hidden layer, and the second hidden layer is a second hidden layer; the target user information and the corresponding interface usage data are input into a preset operation prediction model to determine the prediction score of each preset control classified into each preset display category, including: The target user information, the usage permission, the common control list, the operation frequency data, the device maintenance record, and the device operation record are all input into the input layer; wherein the input layer combines the target user information with the usage permission, the common control list, the operation frequency data, the device maintenance record, and the device operation record to form multiple first record groups; the input layer is further used to combine the target user information, the usage permission, and the operation frequency data to obtain a second record group, and the input layer is further used to combine the target user information, the common control list, and the device maintenance record to obtain a third record group, Inputting each of the first record group, the second record group, the third record group, and the target user information into at least one first neuron in the first hidden layer through the input layer to obtain a first feature corresponding to each of the first neurons; Inputting each of the first features into a second neuron in the second hidden layer corresponding to the first neuron to obtain a second feature corresponding one-to-one to the second neuron, wherein each of the second neurons corresponds to at least two first neurons, and the combination of the first neurons in any two of the second neurons is different; Each of the second features is input into the output layer to obtain a prediction score for classifying each control into each preset display category.

3. The method for displaying controls on a human-computer interaction interface according to claim 1, characterized in that: The operation prediction model is set as a multi-layer perceptron, and the operation steps of the operation prediction model further include: When the prediction error does not meet the error threshold and the number of training times is less than the number threshold, performing a gradient solution on the prediction error using a preset stochastic gradient algorithm to determine a weight update gradient; Obtaining a preset learning rate, performing weighted processing on the learning rate and the weight update gradient to obtain a target weight update gradient; Each weight configured in the multilayer perceptron is updated according to the target weight update gradient.

4. The method for displaying controls on a human-computer interaction interface according to claim 1, wherein: Rendering each of the controls in a preset display interface according to the target menu path of each of the controls includes: Comparing the target menu path of each control with the original menu path to determine the target control to which the menu path is moved upward; Acquire preset enhanced rendering parameters, and render the target control according to the enhanced rendering parameters; the enhanced rendering parameters include at least one of a format parameter and an effect parameter.

5. The method for displaying controls on a human-computer interaction interface according to claim 4, characterized in that: After rendering each of the controls in the preset display interface, the method further includes: The rendered display interface is saved in a storage record corresponding to the target user in a preset interface storage database; When the target user logs in again, the display interface in the storage record corresponding to the target user is retrieved from the interface storage database and displayed; When there is a target control with enhanced rendering parameters, determining the display duration of the target control; According to the display duration, the enhanced rendering parameters of the target control are canceled.

6. The method for displaying controls on a human-computer interaction interface according to claim 1, characterized in that: The step of obtaining target user information and interface usage data of the currently logged-in target user includes: When the preset interface update conditions are met, the target user information and interface usage data of the currently logged-in target user are obtained; The interface update condition includes at least one of the following: The similarity between the currently operated device list and the historical device list corresponding to the same target user is less than a preset first threshold; The usage permission of the target user is changed and the degree of change is greater than a preset second threshold; The duration of operation of the same target user in the same display interface has a shorter duration than a preset operation trend curve.

7. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method for displaying controls on a human-computer interaction interface according to any one of claims 1 to 6 when executing the computer program.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for displaying controls on a human-computer interaction interface according to any one of claims 1 to 6 is implemented.

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