Method and system for assisting learning of the elderly

Abstract

The application relates to the interactive technology field based on a graphical user interface, in particular to an old person auxiliary learning method and system.The method comprises the following steps: deploying a front-end monitoring service in a user smart machine; deploying a simulation mirror system in a back-end system; when intervention, obtaining an operation purpose of an old user, the simulation mirror system retrieving a target operation path corresponding to the operation purpose from a pre-established path library, and implementing the target operation path to perform path verification; in the process of executing a simulation touch event, adding a dithering simulation touch event, calling a large model-based addressing operator to address the operation path, so that a final operation path meeting an end state and considering a dithering factor is obtained; and feeding back the final operation path to the old user, so that the old user can learn according to the steps on the smart machine.The application can more accurately assist the old user in learning to use the smart phone.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of interaction based on graphical user interface, and particularly relates to an old person auxiliary learning method and system. BACKGROUND

[0002] With the rapid development of mobile internet technology, the application program of intelligent terminal presents the evolution trend of diversified function modules and complex interface interaction. Especially for the old user group, the physiological characteristics of the degradation of the ability to capture page information and the decline of the precision of touch operation, and the adaptation contradiction between the complex interaction interface is increasingly prominent.

[0003] The prior art intelligent machine operation learning system generally adopts a pre-generated static operation path library. When it is detected that the user opens a specific application (such as a chat software), the corresponding operation flowchart is automatically called to guide the user step by step.

[0004] However, due to the increasingly fast update of the current application interface and function, the shake-to-advertise advertisement and other frequent trigger jump advertisements are increasing, which leads to the fact that this method cannot adapt to the path bifurcation generated when the dynamic interface is updated (for example: the original flow is cut off by the sudden pop-up or jump advertisement window of the page), and the guiding system completely loses the guiding ability. SUMMARY

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides an old person auxiliary learning method and system, which can more accurately assist the old user to learn to use the smart phone.

[0006] In a first aspect, the present application provides an old person auxiliary learning method, which comprises the following steps:

[0007] Deploying a front-end monitoring service in the user's smart machine;

[0008] Obtaining target application information and historical use data through the front-end monitoring service, and deploying a simulation mirror system in a back-end system according to the target application information;

[0009] Through a preset intervention time recognition program, detecting whether intervention is needed in the process of the old user using the smart machine to assist the old user to learn to use the current application;

[0010] When it is determined that intervention is needed, obtaining the operation purpose of the old user, the simulation mirror system retrieves the target operation path corresponding to the operation purpose from the pre-established path library, and implements the target operation path to perform path verification:

[0011] In the simulation mirror system, each operation step is simulated and executed in sequence according to the target operation path;

[0012] In the process of executing the simulated touch event, sensor parameters for simulating the shaking of the mobile phone in the simulation mirror system are configured according to the shaking intensity value in the historical use data, and a random coordinate offset is introduced based on the shaking intensity value in the process of simulating the touch event, so as to simulate the hand shaking of the elderly user in the actual use process;

[0013] If the operation path is inconsistent with the expected interface state in the process of executing the simulated touch event after adding the shaking, the addressing operator based on the large model is called to address the operation path to obtain the final operation path that considers the shaking factor and meets the final state;

[0014] The final operation path is fed back to the elderly user, so that the elderly user can learn on the smart phone according to the steps.

[0015] Optionally, voice interaction is carried out with the elderly user through a voice recognition program to identify the operation purpose of the elderly user.

[0016] Optionally, the old person auxiliary learning method further includes the following steps:

[0017] In the simulation mirror system, different shaking intensities are simulated for a plurality of page nodes of the target application, and the mis-touch probability and the error jump rate of the elderly user at each page node are recorded to identify a page node with a shaking sensitivity greater than a preset sensitivity threshold as a high-risk node;

[0018] According to the identified high-risk node, a shaking sensitivity table is constructed to quantitatively identify the ease of use of each page node under different shaking intensities;

[0019] The shaking sensitivity table and the length of each operation path are used as reference for the addressing operator when correcting the path, so that the addressing operator can automatically optimize the corresponding target operation path according to the shaking intensity of the current elderly user to guide the elderly user to bypass the high-risk node, thereby improving the operation success rate.

[0020] Optionally, the old person auxiliary learning method further includes the following steps:

[0021] The time required by the elderly user to return to the original page after triggering the jump at the high-risk node each time is recorded;

[0022] When it is detected that the average time required by the elderly user to return to the original page for a certain high-risk node is shortened compared with the average time of returning to the original page within the previous preset number of use rounds, it is determined that the elderly user has the ability to cope with the high-risk node;

[0023] After determining that the elderly user has the ability to cope with the high-risk node, the risk node is added to the mastered node record table, and the reference basis for the addressing operator when correcting the path further includes the mastered node record table, so that the addressing operator reduces the avoidance of the mastered high-risk node.

[0024] Optionally, the old person auxiliary learning method further includes:

[0025] The success rate of the current old person user returning to the original page within a predetermined time is recorded each time the non-active triggered jump is triggered, and the anti-interference ability of the current old person user is obtained through a preset anti-interference ability calculation function according to the success rate;

[0026] The anti-interference ability calculation function is set in such a way that the higher the success rate is, the stronger the anti-interference ability of the current old person user is;

[0027] When the anti-interference ability of the old person is greater, the intervention opportunity recognition program determines that the probability of intervention is smaller.

[0028] Optionally, when the final operation path is fed back to the elderly user, the actual operation of the addressing operator on the old person is monitored and real-time feedback is called.

[0029] Optionally, the old person auxiliary learning method can automatically operate the verified final operation path through a simulated clicking program in the user's smart machine.

[0030] In a second aspect, the present application provides an old person auxiliary learning system, which includes a processor and a memory, and the memory stores at least one instruction, at least one program, a code set or an instruction set, which is loaded and executed by the processor to realize the old person auxiliary learning method as described in any of the first aspect.

[0031] The technical solution provided by the present application has the following advantages compared with the prior art:

[0032] In the old person auxiliary learning method and system provided by the present application, by deploying a simulation mirror system in the back end and combining a large model-based addressing operator, more accurate and dynamic identification and processing of hand shaking, accidental touch jump and interference and other problems faced by the elderly user in the actual operation process of using a smart phone can be realized, thereby realizing personalized guidance and adaptive learning.

[0033] One of its beneficial effects and its working principle is that the application assesses the hand tremor intensity of the elderly user by collecting their historical usage data, including touch coordinate offset and acceleration sensor jitter data. Then, in combination with the multiple verifications and records of different jitter scenarios in the simulated mirror system, a jitter sensitivity table is constructed. This realizes the detection of the current user's jitter intensity, and through the comparison of the jitter sensitivity table based on the large model addressing operator, the large model addressing operator will automatically avoid or reduce entering the high-risk nodes that are extremely sensitive to jitter, reducing the probability of accidental touch and error jump. This adaptive ability for different jitter levels can locate the page nodes that the elderly user is prone to make mistakes, and can also avoid or prompt adjustments accordingly, thereby improving the operation success rate and shortening the operation time, and thus adaptively taking measures to reduce adverse factors to facilitate learning by elderly users in different situations.

[0034] The second beneficial effect and its working principle is that the application also utilizes the powerful reasoning capability of the large model during path addressing, enabling the system to flexibly correct accidental page jumps caused by accidental touch: if the simulated touch event finds that the current page state does not match the expected state after introducing random coordinate offset, the large model will automatically adjust or replace certain path nodes to change the subsequent operation path, and construct a correction scheme after the operation error, realizing the dynamic generation of a perfect operation process.

[0035] Compared with conventional path hardcoding or manual rules, the large model in the application can find more suitable replacement nodes or jump paths in more complex interface structures, and can adaptively take into account user habits, application version differences, and changes in mobile phone sensor parameters, improving the system's compatibility and adaptability to various scenarios.

[0036] The third beneficial effect and its working principle is that the application focuses on dynamic assessment of the skill level and anti-interference ability of elderly users to dynamically adjust the intervention sensitivity of the system. When the elderly user can quickly and accurately deal with the originally high-risk nodes after a certain number of operations, the system will mark it as mastered and gradually reduce the avoidance or additional prompts for that node, guiding the user to use a shorter or more direct operation path. This not only avoids the tedious steps and lengthy prompts caused by overprotection, but also effectively encourages users to self-study and actively explore, improving their confidence and mastery of smart phone functions. At the same time, for users with weak anti-interference ability, the system will increase the sensitivity of intervention to intervene at the first time, thereby minimizing frustration. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The flowchart of the old person assisted learning method provided by the embodiment of the application. DETAILED DESCRIPTION

[0038] The technical solutions in the present application will be described below with reference to the accompanying drawings.

[0039] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other manners different from those described herein; obviously, the embodiments described in the specification are only a part of the embodiments of the present application, and not all the embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0040] In a first aspect, the present application provides an old person assisted learning method, as shown in the following formula: Figure 1 The old person assisted learning method comprises the following steps:

[0041] S101: Deploying a front-end monitoring service in a user's smart machine, for collecting the user's operation actions, the historical use data of the smart machine and the version information of the target application (i.e. target application information);

[0042] Specifically, the front-end monitoring service is deployed in the user's Android or iOS device in the form of an application program.

[0043] The user's operation actions include touch coordinates and touch time such as the user's clicks, swipes and long presses on the screen.

[0044] The historical use data of the smart machine includes the average jitter amplitude recorded by the acceleration sensor and the page identifier of each access.

[0045] The version information of the target application can call the system API or read the Metadata (such as version number, Build number, etc.) of the target application itself to identify the current version.

[0046] Among them, the target application is an application in a set of commonly used software set by a person.

[0047] S102: According to the version information of the target application, deploying a simulation mirror system in the back-end system;

[0048] Specifically, receiving the target application version information uploaded from the front-end monitoring service;

[0049] Querying the back-end application version library to obtain the application installation package (APK / IPA) matched with the version information;

[0050] In the back-end server, through an automatic testing platform (such as Appium) or a containerized simulation environment (such as an Android simulator), the simulation mirror system of the corresponding version is automatically deployed or started to keep consistent with the real user interface.

[0051] S103: Through the preset intervention timing recognition program, whether intervention is needed is detected in the process that the old user uses the smart machine, to assist him in learning to use the current application;

[0052] The used page identifiers in a preset sliding time window length of the old user are acquired through the front-end monitoring service, and specifically, the preset sliding time window length is 10 and the step is 1 second in the embodiment of the application;

[0053] If the number of times that a certain page identifier repeatedly appears in the preset time window exceeds a preset intervention threshold, in the embodiment of the application, the intervention threshold is 6. It is determined that intervention is needed at present, otherwise it is determined that intervention is not needed.

[0054] Specifically, the old person assisted learning method further comprises:

[0055] The success rate of the old person user returning to the original page within a predetermined time each time the non-active triggered jump is recorded, and the anti-interference ability of the current old person user is acquired through a preset anti-interference ability calculation function according to the success rate.

[0056] Specifically, the non-active triggered jump is any non-button triggered page adjustment when the average jitter amplitude recorded by the acceleration sensor is less than a preset intensity jitter threshold.

[0057] For example: the shaking intensity of any page is abnormal (the average jitter amplitude detected by the acceleration sensor is less than the preset intensity jitter threshold) and the shaking jump.

[0058] These can be acquired through the front-end monitoring service, for example, the front-end application changes can be acquired through the front-end monitoring service directly calling the getRunningTasks() interface of the Android system to monitor the cross-page jump.

[0059] The anti-interference ability calculation function is set in such a way that the higher the success rate is, the stronger the anti-interference ability of the current old person user is;

[0060] Specifically, the anti-interference ability calculation function is:

[0061]

[0062] Wherein, is the success rate of the old person user returning to the original page within a predetermined time, is the maximum value of the anti-interference ability, and are preset experience parameters, respectively.

[0063] The greater the anti-interference ability of the old person, the smaller the probability of intervention determined by the intervention opportunity identification program.

[0064] In the embodiments of the present application, the anti-interference ability has multiple preset gradients, and each gradient is provided with a preset intervention threshold value. The greater the value of the anti-interference ability gradient, the greater the intervention threshold value.

[0065] S104: When it is determined that intervention is needed, the operation purpose of the old user is obtained, and the operation purpose is sent to the simulation mirroring system.

[0066] Specifically, the voice recognition program interacts with the old user through voice to identify the operation purpose of the old user.

[0067] S105: The simulation mirroring system retrieves a target operation path corresponding to the operation purpose from a pre-established path library, and implements the target operation path to perform path verification:

[0068] The path library is a linear path that is artificially collected in advance from each page of a target application starting page to a final complete implementation of a certain function.

[0069] The target operation path is a linear path corresponding to the function of the operation purpose. It should be noted that there can be multiple paths to implement the same function.

[0070] In the simulation mirroring system, each operation step is simulated according to the target operation path in sequence;

[0071] S1051: In the process of executing the simulated touch event, the sensor parameters for simulating the shaking of the mobile phone in the simulation mirroring system are configured according to the shaking intensity value in the historical use data, and a random coordinate offset is introduced based on the shaking intensity value when the simulated touch event is simulated, so as to simulate the hand shaking in the actual use process of the old user.

[0072] Specifically, the shaking intensity value is the average shaking amplitude recorded by the acceleration sensor.

[0073] The acceleration sensor for detecting the shaking of the mobile phone is initialized in the simulation mirroring system, and the basic shaking value, shaking frequency or noise level of the simulation environment is set according to the shaking intensity value.

[0074] If the shaking intensity value is high, the sensor simulates a large amplitude of acceleration change in the simulation environment; if the shaking intensity is low, only a slight fluctuation is added.

[0075] When the simulation mirroring system performs an automatic click or sliding event, a random offset (Δx, Δy) is added to the originally accurate touch position (determined by the target operation path).

[0076] The offset is determined by the jitter intensity value, and can be generated in the following manner:

[0077] In the embodiments of the present application, a Gaussian distribution is adopted, i.e., centered on μ = 0, and the variance is proportional to the jitter intensity value.

[0078] Alternatively, in other embodiments, a uniform distribution can also be adopted: random sampling in the range of [−r, r], where r is in linear or nonlinear relationship with the jitter intensity.

[0079] It should be noted that the random offset not only acts on the click event, but also can simulate the jitter of the trajectory of complex gestures such as long press and sliding, so that the simulation environment is closer to the physiological characteristics of the old user in real use.

[0080] S1052: If the operation path is inconsistent with the expected interface state during the execution of the simulated touch event after adding the jitter, a large model-based addressing operator is called to address the operation path to obtain a final operation path that considers the jitter factor and meets the final state.

[0081] Specifically, the operation path is composed of a series of interface jumps and click or sliding actions required to achieve the corresponding jump.

[0082] Specifically, in the embodiments of the present application, the large model-based addressing operator can be implemented by the operator provided by OpenAI company and by loading a specially designed knowledge base. The operator is an artificial intelligence agent launched by OpenAI. However, those skilled in the art should realize that it is actually to use a large model to obtain an action intention, and then call a specific python script to realize the real control function, so other companies can also provide a multi-modal visual model to realize the actual operation by combining adaptive code.

[0083] The core of the invention in the embodiments of the present application is how to build a specially designed knowledge base, which includes a path library, the identification of each page, the length of each operation path, a jitter sensitivity table, and a mastered node record table.

[0084] Specifically:

[0085] In the simulation mirror system, different jitter intensities are simulated for multiple page nodes of the target application, and the mis-touch probability and error jump rate of the old user at each page node are recorded to identify a page node with a jitter sensitivity greater than a preset sensitivity threshold as a high-risk node;

[0086] According to the identified high-risk nodes, a jitter sensitivity table is constructed to quantitatively identify the usability of each page node under different jitter intensities.

[0087] Specifically, the shaking sensitivity table is constructed by the following steps:

[0088] From each page node of the target application, a page node that jumps due to shaking is filtered out.

[0089] For each page node that jumps, the shaking amplitude of the acceleration sensor when it jumps is recorded;

[0090] All the shaking amplitudes of the acceleration sensor when it jumps are less than the preset intensity shaking threshold, and are set as high-risk nodes;

[0091] The page identifiers of the high-risk nodes and their corresponding shaking amplitudes when they jump are recorded in groups to form the shaking sensitivity table.

[0092] The shaking sensitivity table and the length of each operation path are used as a reference basis for the addressing operator to correct the path, so that the addressing operator can automatically optimize the corresponding target operation path according to the shaking intensity of the different current elderly users to guide the elderly users to bypass the high-risk nodes, thereby improving the operation success rate.

[0093] Specifically, when an unexpected page jump occurs, the primary goal of the addressing operator is to construct a return path. After the large model realizes the return to the original page through its own reasoning ability, the addressing operator continues to use the reasoning ability to construct the path until the final operation purpose is achieved.

[0094] The final constructed path (including the operation path before the addressing operator intervenes, the operation path after the return from the misoperation, and the operation path after the return to achieve the final operation purpose) is sent to the smart machine of the elderly user.

[0095] Specifically, the old person auxiliary learning method further includes the following steps:

[0096] The time required for the elderly user to return to the original page after triggering a jump at each high-risk node is recorded;

[0097] When it is detected that the average time taken by the elderly user to return to the original page for a certain high-risk node is shorter than the average time taken to return to the original page in the previous preset number of use rounds, it is determined that the elderly user has the ability to cope with the high-risk node;

[0098] After determining that the elderly user has the ability to cope with the high-risk node, the risk node is added to the mastered node record table, and the reference basis for the addressing operator to correct the path further includes the mastered node record table, so that the addressing operator reduces the avoidance of the mastered high-risk node.

[0099] Specifically, by constructing a mastered node record table and incorporating it into a special knowledge base for use by the large model during reasoning, the addressing operator can reduce the avoidance of the mastered high-risk node, thereby avoiding excessive avoidance and operational complexity, which facilitates learning and mastery by the elderly user.

[0100] S106: Feedback the final operation path to the elderly user, so that he can learn the steps on the smart machine.

[0101] Through the front-end business system, when the elderly user uses the target application, the location of the current page required to click or swipe is found according to the final operation path, and a guide "floating layer" or "arrow" mark is dynamically superimposed on the interface, highlighting the button or input box that needs to be clicked.

[0102] The button name required to be clicked can also be prompted directly through voice.

[0103] Optionally, in other embodiments, when the final operation path is fed back to the elderly user, the addressing operator also monitors and feeds back the actual operation of the old person in real time.

[0104] Specifically, if the "real-time monitoring and feedback" function based on the addressing operator is enabled at the same time, the front-end monitoring business can continuously detect the click coordinates and interface state during the user's actual execution of the operation path.

[0105] Once it is detected that the user deviates from the predetermined path of the final operation path or mistakenly touches the high-risk node, a correction prompt, voice correction or updated path suggestion is popped up on the mobile phone in time to ensure that the user can quickly return to the correct process.

[0106] For example, when the user needs to send a photo in a social app, but accidentally enters the system permission setting interface, the system detects the interface anomaly and immediately broadcasts "You have entered the setting interface, do you need to return or set access permissions?" and provides one-key return or new guidance.

[0107] Optionally, in other embodiments, the old person assisted learning method can also automatically operate the verified final operation path through a simulated click program in the user's smart machine.

[0108] After the user clicks on full automation, the system can also directly execute the first pass of the final operation path on the user's smart machine through the "simulated click program", allowing the elderly user to intuitively understand the complete steps in a "spectator" mode.

[0109] After the demonstration is completed, the user can choose to try the operation himself, and if he encounters difficulties, the system can again enable prompt assistance.

[0110] The technical scheme provided in the application has the following advantages compared with the prior art.

[0111] In the old person auxiliary learning method and system provided in the application, through deploying a simulation mirror system in the back end and combining a large model-based addressing operator, more accurate and dynamic identification and processing can be performed on the hand shaking, accidental touch jumping and interference problems faced by the old user in the actual operation process of using a smart phone, thereby realizing personalized guidance and adaptive learning.

[0112] One of the beneficial effects and working principles thereof is that the application assesses the hand shaking intensity of the old user by collecting historical use data (including touch coordinate offset and shaking data of the acceleration sensor) of the old user. Then, in combination with the simulation mirror system, multiple verifications and records of each page node under different shaking scenarios are performed in advance, thereby constructing a shaking sensitivity table. The shaking intensity of the current user is detected, and the large model-based addressing operator automatically avoids or reduces entering high-risk nodes that are extremely sensitive to shaking by comparing the shaking sensitivity table, thereby reducing the probability of accidental touch and error jumping. This adaptive ability for different shaking levels can locate the page nodes that are prone to errors by the current old user and can also avoid or prompt adjustment thereof, thereby improving the operation success rate and shortening the operation time, thereby adaptively taking measures to reduce adverse factors to facilitate learning of the old user in different situations.

[0113] The second beneficial effect and working principle thereof is that the application also utilizes the strong reasoning capability of the large model in path addressing, so that the system can flexibly correct accidental page jumps caused by accidental touch: if the simulation touch event finds that the current page state does not match the expectation after introducing random coordinate offset, the large model will automatically adjust or replace specific path nodes to change the subsequent operation path in combination with the shaking sensitivity table and the length of the operation path, and construct a correction scheme after the operation error, thereby realizing dynamic generation of a perfect operation process.

[0114] Compared with conventional path hard coding or manual rules, the large model in the application can find more suitable replacement nodes or jumping paths in a relatively complex interface structure, and can adaptively take into account user habits, application version differences and changes in mobile phone sensor parameters, thereby improving the compatibility and adaptability of the system to various scenarios.

[0115] The third benefit and working principle thereof lies in that the application focuses on dynamic evaluation of the skill level and anti-interference ability of the elderly user to dynamically adjust the intervention sensitivity of the system. When it is found that the elderly user can quickly and accurately cope with the originally high-risk node after a certain number of operations, the system will mark it as mastered and gradually reduce the avoidance or additional prompt of the node, guiding the user to use a shorter or more direct operation path. This not only avoids the cumbersome steps and lengthy prompts brought by over-protection, but also effectively encourages the user to self-study and actively explore, improving their confidence and mastery of the function of the intelligent machine. At the same time, for users with weak anti-interference ability, the system will increase the sensitivity of intervention to intervene at the first time, thereby minimizing frustration.

[0116] In a second aspect, the embodiments of the present application provide an old person auxiliary learning system, the old person auxiliary learning system comprises a processor and a memory, the memory stores at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to realize the old person auxiliary learning method as any one of the above-mentioned embodiments.

[0117] It should be noted that, in this document, relational terms such as“first” and“second”, and / or the like, are used solely to distinguish one entity or action from another entity or action, without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms“comprises”,“comprising”, or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by“comprises a...” does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element. Furthermore, in the description of embodiments of the present application, unless otherwise stated, “ / ” means or, for example, A / B can mean A or B; “and / or” in this document is only a description of the associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, A and B, and B alone. In addition, in the description of embodiments of the present application, “multiple” means two or more than two.

[0118] The foregoing detailed description of the application has been presented for purposes of illustration and description. Various modifications and changes can be made to these embodiments without departing from the spirit and scope of the application. It is intended that the scope of the application should not be limited by the particular representative embodiments described above.

Claims

1. A learning assistance method for the elderly, characterized in that, The method for assisting elderly people with learning includes the following steps: Deploy front-end monitoring services in users' smartphones; The system obtains target application information and historical usage data through front-end monitoring services, and deploys a simulated image system in the back-end system based on the target application information. By using a pre-defined intervention timing recognition program, the system detects whether intervention is needed during the use of smartphones by elderly users, in order to help them learn to use the current applications. When intervention is required, the system obtains the elderly user's operational purpose and simulates the mirror system to retrieve the target operation path corresponding to the operational purpose from a pre-established path library. The path library stores multiple operation paths, where each operation path refers to a linear path from the target application's start page to a specific page that ultimately achieves a certain function. Implementing the target operation path for path verification includes the following steps: In the simulated mirror system, sequentially simulating touch events to execute each operation step according to the target operation path; During the execution of simulated touch events, the sensor parameters used to simulate mobile phone shaking in the simulation mirror system are configured according to the shaking intensity value in the historical usage data, and a random coordinate offset is introduced based on the shaking intensity value during simulated touch events to simulate the hand shaking of elderly users during actual use. If, during the execution of a simulated touch event with added jitter, the target operation path is inconsistent with the expected interface state, then the addressing operator based on the large model is invoked to address the target operation path in order to obtain a final operation path that takes jitter into account and satisfies the final state. The final operation path is then fed back to elderly users, enabling them to learn by following the path on their smartphones.

2. The method for assisting elderly learning according to claim 1, characterized in that, The method for assisting the elderly in learning also includes the following steps: In the simulated mirroring system, different jitter intensities are simulated for multiple page nodes of the target application, and the probability of accidental touch and error jump rate of elderly users on each page node are recorded, so as to identify page nodes with jitter sensitivity greater than a preset sensitivity threshold as high-risk nodes. Based on the identified high-risk nodes, a jitter sensitivity table is constructed to quantify the usability of each page node under different jitter intensities. The jitter sensitivity table and the length of each operation path are used together as a reference for the addressing operator when correcting the path. This allows the addressing operator to automatically select the appropriate target operation path based on the jitter intensity of different elderly users, so as to guide the elderly users to avoid high-risk nodes and thus improve the success rate of the operation.

3. The method for assisting elderly people with learning according to claim 2, characterized in that, The method for assisting the elderly in learning also includes the following steps: Record the time required for elderly users to return to the original page after each redirect triggered when entering a high-risk node; When it is detected that within a preset number of usage rounds, the average time taken by the elderly user to return to the original page for a certain high-risk node is shorter than the average time taken to return to the original page within the previous preset number of rounds, it is determined that the elderly user has the ability to deal with the high-risk node. After determining that the elderly user has the ability to deal with the high-risk node, the high-risk node is added to the list of nodes that have been identified. The addressing operator also uses the list of nodes that have been identified as a reference when making path corrections, so that the addressing operator can reduce the avoidance of high-risk nodes in the list of nodes that have been identified as identified.

4. The method for assisting elderly people with learning according to claim 1, characterized in that, The methods for assisting elderly people with learning also include: Record the success rate of the current elderly user returning to the original page within a predetermined time each time a non-initiated redirect is made, and obtain the current elderly user's anti-interference ability based on the success rate using a preset anti-interference ability calculation function; The setting method of the anti-interference ability calculation function makes the anti-interference ability of the current elderly user stronger when the success rate is higher. The greater the elderly person's ability to resist interference, the lower the probability that the intervention timing recognition program will determine that intervention is necessary.

5. The method for assisting elderly learning according to claim 1, characterized in that, When providing the final operation path to the elderly user, the system also calls upon the addressing operator to monitor and provide real-time feedback on the elderly user's actual operation.

6. The method for assisting elderly learning according to any one of claims 1-5, characterized in that, The aforementioned learning assistance method for the elderly can also automatically operate the final operation path through a simulated click program on the user's smartphone.

7. An elderly-assisted learning system, characterized in that, The elderly-assisted learning system includes a processor and a memory. The memory stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the elderly-assisted learning method as described in any one of claims 1-6.

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