Auxiliary learning method and system for old people

Abstract

The invention relates to the technical field of interaction based on a graphical user interface, in particular to an auxiliary learning method and system for old people. The method comprises the following steps: deploying a front-end monitoring service in a user intelligent machine; deploying a simulation mirror image system in a back-end system; when intervention is carried out, the operation purpose of the elderly user is obtained, the simulation mirror image system retrieves a target operation path corresponding to the operation purpose from a pre-established path library, and the target operation path is implemented to carry out path verification: in the process of executing the simulation touch event, a jittering simulation touch event is added, and when the simulation touch event is executed, the jittering simulation touch event is executed; calling an addressing operator based on a large model to carry out addressing on the operation path so as to obtain a final operation path which considers a jitter factor and meets a final state; and feeding back the final operation path to the elderly user, so that the elderly user can learn on the intelligent machine according to the steps. The method can more accurately assist the elderly user in learning to use the smart phone.

Description

Technical Field

[0001] This application relates to the technical field of graphic user interface-based interaction, and particularly to an elderly-assisted learning method and system. Background Art

[0002] With the rapid development of mobile Internet technology, intelligent terminal applications have shown an evolutionary trend of diversified functional modules and complex interface interactions. Especially for the elderly user group, the physiological characteristics of the degradation of their ability to capture page information and the decline in the accuracy of touch operations are increasingly prominent in the adaptation contradiction with complex interaction interfaces.

[0003] Existing intelligent machine operation learning systems generally use a pre-generated static operation path library. When it detects that the user opens a specific application (such as a chat software), it automatically calls the corresponding operation flow chart to provide step-by-step prompts to guide the user to learn step by step.

[0004] However, due to the fact that current application interfaces and functions are updated faster and faster, and there are more and more advertisements that frequently trigger jumps such as shake-to-get advertisements, this method cannot adapt to the path bifurcations generated during dynamic interface updates (for example: a suddenly popped-up or jumped advertisement window cuts off the original process), and the guidance system completely loses its guiding ability. Summary of the Invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, this application provides an elderly-assisted learning method and system, which can more accurately assist elderly users in learning to use smart phones.

[0006] In a first aspect, this application provides an elderly-assisted learning method, which includes the following steps: Deploy a front-end monitoring service in the user's smart phone; Obtain target application information and historical usage data through the front-end monitoring service, and deploy a simulation mirror system in the back-end system according to the target application information; During the process of the elderly user using the smart phone, detect whether intervention is required through a preset intervention timing recognition program to assist them in learning to use the current application; When it is determined that intervention is required, obtain the operation purpose of the elderly user, and 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 for path verification: In the simulation mirror system, sequentially simulate touch events according to the target operation path to execute each operation step; During the execution of the simulated touch event, according to the jitter intensity value in the historical usage data, configure the sensor parameters for simulating the phone jitter in the simulated mirror system, and introduce a random coordinate offset based on the jitter intensity value during the simulated touch event to simulate the hand jitter during the actual usage of elderly users; If the operation path is inconsistent with the expected interface state during the execution of the simulated touch event after adding jitter, then call the addressing operator based on the large model to address the operation path to obtain the final operation path that takes into account the jitter factor and meets the final state; Feed back the final operation path to the elderly user so that they can learn according to the steps on the smart phone.

[0007] Optionally, conduct voice interaction with the elderly user through a voice recognition program to identify the operation purpose of the elderly user.

[0008] Optionally, the elderly assistance learning method further includes the following steps: In the simulated mirror system, for multiple page nodes of the target application, simulate different jitter intensities and record the mis-touch probability and error jump rate of the elderly user at each page node to identify the page nodes with a jitter sensitivity greater than the preset sensitivity threshold as high-risk nodes; According to the identified high-risk nodes, construct a jitter sensitivity table to quantitatively identify the usability of each page node under different jitter intensities; Use the jitter sensitivity table and the length of each operation path as the reference basis for the addressing operator during path correction, so that the addressing operator can automatically select the corresponding target operation path according to the current jitter intensity of different elderly users to guide the elderly user to avoid high-risk nodes, thereby improving the operation success rate.

[0009] Optionally, the elderly assistance learning method further includes the following steps: Record the time required for the elderly user to return to the original page each time after triggering a jump 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 to return to the original page in the previous preset number of times, it is determined that the elderly user has the ability to handle this high-risk node; After determining that the elderly user has the ability to handle this high-risk node, add this risk node to the mastered node record table. The reference basis for the addressing operator during path correction also includes the mastered node record table, so that the addressing operator reduces the avoidance of this mastered high-risk node.

[0010] Optionally, the elderly assistance learning method further includes: Record the success rate of the current elderly user returning to the original page within a predetermined time each time a non-voluntary trigger jump occurs, and obtain the anti-interference ability of the current elderly user according to the success rate through a preset anti-interference ability calculation function; The anti-interference ability calculation function is set in such a way that the higher the success rate, the stronger the anti-interference ability of the current elderly user; When the anti-interference ability of the elderly is greater, the intervention timing recognition program determines that the probability of needing to intervene is smaller.

[0011] Optionally, when feeding back the final operation path to the elderly user, the addressing operator is also called to monitor and provide real-time feedback on the actual operation situation of the elderly.

[0012] Optionally, the elderly assistance learning method can also automatically operate the final operation path with successful verification through a simulated click program in the user's smart phone.

[0013] In a second aspect, the present application provides an elderly assistance learning system, which includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and 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 assistance learning method according to any one of the first aspects.

[0014] The technical solution provided by the present application has the following advantages compared with the prior art: In the elderly assistance learning method and system provided by the present application, by deploying a simulated mirror system at the back end and combining an addressing operator based on a large model, problems such as hand jitter, accidental touch jumps, and interference faced by elderly users during the actual operation of using a smart phone can be identified and processed more accurately and dynamically, so as to achieve personalized guidance and adaptive learning.

[0015] One of its beneficial effects and its working principle are that this application evaluates the hand jitter intensity of elderly users by collecting their historical usage data (including touch coordinate offsets and jitter data from the acceleration sensor). Then, in combination with the multiple verifications and records of different jitter scenarios for each page node in the simulated mirror system in advance, a jitter sensitivity table is constructed. It realizes detecting the jitter intensity of the current user, and through the addressing operator based on the large model comparing with the jitter sensitivity table, the addressing operator based on the large model will automatically avoid or reduce entering high-risk nodes that are extremely sensitive to jitter, reducing the probability of accidental touch and incorrect jumps. This adaptability to different jitter levels can locate the page nodes where current elderly users are prone to make mistakes, and can also specifically avoid or prompt adjustments to them, thereby improving the operation success rate and shortening the operation duration, and thus adaptively taking measures to reduce adverse factors to facilitate the learning of elderly users in different situations.

[0016] Another beneficial effect and its working principle are that this application also utilizes the powerful reasoning ability of the large model during path addressing, enabling the system to flexibly correct accidental page jumps caused by accidental touches: If it is found that the current page state does not match the expectation after introducing random coordinate offsets in the simulated touch event, the large model will combine the jitter sensitivity table and the length of the operation path, automatically adjust or replace specific path nodes to change the subsequent operation path, and construct a correction plan after incorrect operations, realizing the dynamic generation of a perfect operation process.

[0017] Compared with conventional path hardcoding or manual rules, the large model in this application can find more suitable alternative nodes or jump 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, improving the system's compatibility and adaptability to various scenarios.

[0018] Another beneficial effect and its working principle are that this application focuses on dynamically evaluating the skill level and anti-interference ability of elderly users to dynamically adjust the intervention sensitivity of the system. When it is found that an elderly user can quickly and accurately handle the original high-risk nodes after a certain number of operations, the system will mark them 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 cumbersome steps and long prompts brought by overprotection, but also effectively encourages users to learn independently and explore actively, improving their confidence and mastery of smartphone functions. At the same time, for users with weak anti-interference ability, the system will increase the intervention sensitivity to intervene in the first time, thereby minimizing the sense of frustration. Description of the Drawings

[0019] Figure 1 It is a schematic flowchart of the elderly-assisted learning method provided by the embodiment of this application. Detailed implementation manners

[0020] Next, the technical solutions in this application will be described with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a thorough understanding of this application, but this application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of this application, rather than all the embodiments. It should be noted that, without conflict, the embodiments of this application and the features in the embodiments may be combined with each other.

[0022] In a first aspect, this application provides an elderly-assisted learning method, as Figure 1 shown, the elderly-assisted learning method includes the following steps: S101: Deploy a front-end monitoring service in the user's smart phone to collect the user's operation actions, the historical usage data of the smart phone, and the version information of the target application (i.e., target application information); Specifically, the front-end monitoring service is deployed in the user's Android or iOS device in the form of an application program.

[0023] The user's operation actions include touch coordinates and touch time such as clicks, swipes, and long presses on the screen by the user.

[0024] The historical usage data of the smart phone includes the average jitter amplitude recorded by the acceleration sensor and the page identifier for each access; The version information of the target application can call the system API or read the Metadata of the target application itself (such as version number, Build number, etc.) to identify the current version.

[0025] Among them, the target application is an application in a set of commonly used software set by a person.

[0026] S102: According to the version information of the target application, deploy a simulation mirror system in the back-end system; Specifically, receive the version information of the target application uploaded from the front-end monitoring service; Query the back-end application version library to obtain an application installation package (APK / IPA) that matches the version information; In the back-end server, through an automated testing platform (such as Appium) or a containerized simulation environment (such as an Android emulator), automatically deploy or start a simulation mirror system of the corresponding version to make it consistent with the real user interface.

[0027] S103: Through a preset intervention timing identification program, during the process of an elderly user using a smart phone, detect whether intervention is required to assist them in learning to use the current application; Obtain the page identifiers used within a sliding time window of a preset sliding time window length for elderly users through the front-end monitoring service. Specifically, in the embodiment of this application, the preset sliding time window length is 10 and the step size is 1 second; If, within the preset time window, the number of occurrences of a certain page identifier repeats more than the preset intervention threshold, in the embodiment of this application, the intervention threshold is 6. Then it is determined that intervention is required currently, otherwise it is determined that no intervention is required.

[0028] Specifically, the elderly assisted learning method further includes: Record the success rate of the current elderly user returning to the original page within a predetermined time each time a non-actively triggered jump occurs, and obtain the anti-interference ability of the current elderly user through a preset anti-interference ability calculation function according to the success rate; Specifically, the non-actively triggered jump is any page adjustment that is not button-triggered when the average jitter amplitude recorded by the acceleration sensor is less than the preset intensity jitter threshold.

[0029] For example: The shake-to-jump of any page with abnormal shaking intensity (the average jitter amplitude detected by the acceleration sensor is less than the preset intensity jitter threshold).

[0030] All of these can be obtained through the front-end monitoring service. For example, the front-end monitoring service can directly call the getRunningTasks() interface of the Android system to obtain the changes of the front-end application to monitor cross-page jumps.

[0031] The setting method of the anti-interference ability calculation function is such that the higher the success rate, the stronger the anti-interference ability of the current elderly user; Specifically, the anti-interference ability calculation function is: Wherein, is the success rate of the elderly user returning to the original page within a predetermined time, is the artificially set maximum value of the anti-interference ability, and are respectively preset empirical parameters.

[0032] When the anti-interference ability of the elderly is greater, the probability that the intervention timing recognition program determines that intervention is required is smaller.

[0033] In the embodiment of this application, there are multiple preset gradients for the anti-interference ability, and each gradient is provided with a preset intervention threshold. The larger the value of the anti-interference ability gradient, the larger the intervention threshold.

[0034] S104: When it is determined that intervention is needed, obtain the operation purpose of the elderly user and send the operation purpose to the simulation mirror system; Specifically, conduct voice interaction with the elderly user through a voice recognition program to identify the operation purpose of the elderly user.

[0035] S105: 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 for path verification: The path library is a jump linear path that is artificially collected in advance for each page from the start page of the target application to the final complete realization of a certain function.

[0036] The target operation path is the jump linear path corresponding to the function of this operation purpose. It should be noted that there may be multiple paths to achieve the same function.

[0037] In the simulation mirror system, sequentially simulate touch events according to the target operation path to execute each operation step; S1051: During the execution of the simulated touch event, configure the sensor parameters for simulating the phone jitter in the simulation mirror system according to the jitter intensity value in the historical usage data, and introduce a random coordinate offset based on the jitter intensity value during the simulated touch event to simulate the hand jitter during the actual use of the elderly user; Specifically, the jitter intensity value is the average jitter amplitude recorded by the acceleration sensor.

[0038] Initialize the acceleration sensor for detecting phone jitter in the simulation mirror system and set the basic jitter value, jitter frequency or noise level of the simulation environment according to the jitter intensity value.

[0039] If the jitter intensity value is high, make the sensor simulate a large-amplitude acceleration change in the simulation environment; if the jitter intensity is low, only add slight fluctuations.

[0040] When the simulation mirror system executes an automated click or swipe event, add a random offset (Δx, Δy) on the basis of the originally accurate touch position (determined by the target operation path).

[0041] This offset is determined by the jitter intensity value and can be generated in the following specific way: In the embodiments of the present application, a Gaussian distribution is adopted, that is, centered on μ = 0 and the variance is proportional to the jitter intensity value.

[0042] Alternatively, in other embodiments, a uniform distribution can also be adopted: randomly sample within the range of [−r, r], where r has a linear or non-linear relationship with the jitter intensity.

[0043] It should be noted that the random offset not only affects click events, but can also perform corresponding jitter simulations on the trajectories of complex gestures such as long presses and swipes, making the simulation environment closer to the physiological characteristics of elderly users during actual use.

[0044] S1052: If the operation path is inconsistent with the expected interface state during the execution of the simulated touch event after adding jitter, then call the large model-based addressing operator to address the operation path to obtain the final operation path that takes into account the jitter factor and meets the final state.

[0045] Specifically, the operation path consists of a series of interface jumps and click or swipe actions required to achieve the corresponding jumps.

[0046] Specifically, in the embodiments of the present application, the large model-based addressing operator can be implemented by using the operator provided by OpenAI and by loading a special knowledge base. Among them, Operator is an artificial intelligence agent launched by OpenAI. However, those skilled in the art should be aware that it actually uses the large model to obtain the action intention and then calls a specific python script to implement the actual control function. Therefore, other companies' multi-modal visual models can also be applied in this field to combine with the adaptive code to implement the actual operation.

[0047] The invention core of the embodiments of the present application lies in how to construct a special knowledge base, and the special knowledge base includes a path library, the identifier of each page, the length of each operation path, a jitter sensitivity table, and a mastered node record table.

[0048] Specifically: In the simulated mirror system, for multiple page nodes of the target application, simulate different jitter intensities and record the mis-touch probability and error jump rate of the elderly users at each page node to identify the page nodes with a jitter sensitivity greater than the preset sensitivity threshold as high-risk nodes; According to the identified high-risk nodes, construct a jitter sensitivity table to quantitatively identify the usability of each page node under different jitter intensities.

[0049] Specifically, the jitter sensitivity table is constructed through the following steps: From each page node of the target application, screen out the page nodes that have jumped due to jitter.

[0050] For each page node that has jumped, record the jitter amplitude of the acceleration sensor when it jumps; Set all the jitter amplitudes of the acceleration sensor when jumping that are less than the preset intensity jitter threshold as high-risk nodes; Group and record the page identifiers of high-risk nodes and the jitter amplitude when corresponding jumps occur, thereby constituting a jitter sensitivity table.

[0051] Use the jitter sensitivity table and the length of each operation path together as a reference basis for the addressing operator during path correction, enabling the addressing operator to automatically optimize the corresponding target operation path according to the jitter intensity of different current elderly users, so as to guide the elderly users to avoid high-risk nodes and thus improve the operation success rate.

[0052] Specifically, when an unexpected page jump occurs, the primary purpose of the addressing operator is to construct the returned path. After returning to the original page through the reasoning ability of the large model itself, the addressing operator continues to use the reasoning ability to construct the path until the final operation purpose is completed.

[0053] Send the final constructed path (including the operation path up to the point where the addressing operator intervenes, the operation path for returning after misoperation, and the operation path for achieving the final operation purpose after returning) to the smart phone of the elderly user.

[0054] Specifically, the elderly-assisted learning method further includes the following steps: Record the time required for the elderly user to return to the original page each time after entering a high-risk node and triggering a jump. 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 in the previous preset number of times, it is determined that the elderly user already has the ability to handle this high-risk node. After determining that the elderly user already has the ability to handle this high-risk node, add this risk node to the mastered node record table. The reference basis for the addressing operator during path correction also includes the mastered node record table, so that the addressing operator can reduce the avoidance of this mastered high-risk node.

[0055] 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 this mastered high-risk node, thereby avoiding over-avoidance that leads to operation complexity and facilitating the learning and mastery of the elderly user.

[0056] S106: Feed back the final operation path to the elderly user so that they can learn according to the steps on the smart phone.

[0057] Through the front-end business system, when the elderly user uses the target application, find the position that needs to be clicked or swiped on the current page according to the final operation path, and dynamically overlay a guiding "floating layer" or "arrow" identifier on its interface to highlight the button or input box that needs to be clicked currently.

[0058] It is also possible to directly prompt the name of the button to be clicked through voice.

[0059] Optionally, in other embodiments, when the final operation path is fed back to the elderly user, the addressing operator is also called to monitor and provide real-time feedback on the actual operation situation of the elderly.

[0060] Specifically, if the "real-time monitoring and feedback" function based on the addressing operator is enabled, the front-end monitoring service can continuously detect the click coordinates and interface status during the process of the user actually executing the operation path; Once it is detected that the user deviates from the predetermined path of the final operation path or accidentally touches a high-risk node, a correction prompt, voice correction, or updated path suggestion will be promptly popped up on the mobile phone side to ensure that the user can quickly return to the correct process.

[0061] For example: when the user needs to send a photo in a social App but accidentally enters the system permission settings interface, after the system detects the interface anomaly, it immediately broadcasts "You have entered the settings interface. Do you need to return or set access permissions?" and provides a one-key return or new guidance.

[0062] Optionally, in other embodiments, the elderly assistance learning method can also automatically operate the finally verified operation path through a simulated click program in the user's smart phone.

[0063] After the user clicks on full automation, the system can also directly execute the first pass of the process of the final operation path on the user's smart phone through the "simulated click program", allowing the elderly user to intuitively understand the complete steps in a "bystander" manner.

[0064] After the demonstration is completed, the user can choose to try the operation by themselves. If difficulties occur, the system can enable the prompt assistance again.

[0065] The technical solution provided by this application has the following advantages compared with the prior art: In the elderly assistance learning method and system provided by this application, by deploying a simulated mirror system at the back end and combining it with an addressing operator based on a large model, it is possible to more accurately and dynamically identify and handle problems such as hand tremors, accidental touch jumps, and interference faced by elderly users during the actual operation process of using a smart phone, thereby realizing personalized guidance and adaptive learning.

[0066] One of its beneficial effects and its working principle are as follows. This application evaluates the hand tremor intensity of elderly users by collecting their historical usage data (including touch coordinate offsets and jitter data from the acceleration sensor). Then, in combination with the multiple verifications and records of different jitter scenarios for each page node in the simulated mirror system in advance, a jitter sensitivity table is constructed. It realizes detecting the current user's tremor intensity, and through the large model-based addressing operator comparing with the jitter sensitivity table, the large model-based addressing operator will automatically avoid or reduce entering high-risk nodes that are extremely sensitive to jitter, reducing the occurrence probability of accidental touches and incorrect jumps. This adaptability to different jitter levels can locate the page nodes where current elderly users are prone to make mistakes, and can also specifically avoid or prompt adjustments for them, thereby improving the operation success rate and shortening the operation duration, and thus adaptively taking measures to reduce adverse factors to facilitate the learning of elderly users in different situations.

[0067] Another beneficial effect and its working principle are as follows. This application also utilizes the powerful reasoning ability of the large model during path addressing, enabling the system to flexibly correct accidental page jumps caused by accidental touches: If it is found that the current page state does not match the expectation after introducing random coordinate offsets in the simulated touch event, the large model will combine the jitter sensitivity table and the length of the operation path, automatically adjust or replace specific path nodes to change the subsequent operation path, and construct a correction plan after incorrect operations to achieve the dynamic generation of a perfect operation process.

[0068] Compared with conventional path hard coding or manual rules, the large model in this application can find more suitable alternative nodes or jump 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, improving the system's compatibility and adaptability to various scenarios.

[0069] A third beneficial effect and its working principle are as follows. This application focuses on dynamically evaluating the skill level and anti-interference ability of elderly users to dynamically adjust the intervention sensitivity of the system. When it is found that an elderly user can quickly and accurately handle the original high-risk nodes after a certain number of operations, the system will mark them 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 cumbersome steps and long prompts brought by overprotection, but also effectively encourages users to learn independently and explore actively, improving their confidence and mastery of smartphone functions. At the same time, for users with weak anti-interference ability, the system will increase the intervention sensitivity to intervene immediately, thereby minimizing the sense of frustration.

[0070] Second aspect, an embodiment of the present application provides an elderly-assisted learning system, which includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and 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 of the above embodiments.

[0071] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Additionally, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element. Moreover, in the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may represent A or B; "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. And, in the description of the embodiments of the present application, "a plurality of" means two or more than two.

[0072] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. An elderly assistance learning method, characterized in that, The elderly-assisted learning method includes the following steps: Deploy a front-end monitoring service in the user's smart phone; Obtain target application information and historical usage data through the front-end monitoring service. According to the target application information, deploy a simulation mirror system in the back-end system; During the process of the elderly user using the smart phone, detect whether intervention is needed through a preset intervention timing recognition program to assist them in learning to use the current application; When it is determined that intervention is needed, obtain the operation purpose of the elderly 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 for path verification, including the following steps: In the simulation mirror system, sequentially simulate touch events according to the target operation path to execute each operation step; During the execution of the simulated touch event, configure the sensor parameters for simulating the shaking of the mobile phone in the simulation mirror system according to the shaking intensity value in the historical usage data, and introduce a random coordinate offset based on the shaking intensity value during the simulated touch event to simulate the hand shaking during the actual use of the elderly user; If the situation where the operation path is inconsistent with the expected interface state occurs during the execution of the simulated touch event after adding shaking, call the addressing operator based on the large model to address the operation path to obtain the final operation path that takes into account the shaking factor and meets the final state; Feed back the final operation path to the elderly user so that they can learn according to the steps on the smart phone.

2. The elderly-assisted learning method according to claim 1, wherein, The elderly-assisted learning method further includes the following steps: In the simulation mirror system, simulate different shaking intensities for multiple page nodes of the target application, record the mis-touch probability and error jump rate of the elderly user at each page node, and identify the page nodes with a shaking sensitivity greater than the preset sensitivity threshold as high-risk nodes; According to the identified high-risk nodes, construct a shaking sensitivity table to quantitatively identify the usability of each page node under different shaking intensities; Use the shaking sensitivity table and the length of each operation path as the reference basis for the addressing operator during path correction, so that the addressing operator can automatically select the corresponding target operation path according to the shaking intensity of the current elderly user, so as to guide the elderly user to avoid high-risk nodes and improve the operation success rate.

3. The elderly assisted learning method according to claim 2, characterized in that, The elderly-assisted learning method further includes the following steps: Record the time required for the elderly user to return to the original page each time after triggering a jump 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 in the previous preset number of times, it is determined that the elderly user has the ability to handle this high-risk node; After determining that the elderly user has the ability to handle this high-risk node, add this risk node to the mastered node record table. The reference basis for the addressing operator during path correction also includes the mastered node record table, so that the addressing operator reduces the avoidance of this mastered high-risk node.

4. The elderly assisted learning method according to claim 1, characterized in that, The elderly-assisted learning method further includes: Record the success rate of the current elderly user returning to the original page within a predetermined time each time a non-initiated trigger jump occurs, and obtain the anti-interference ability of the current elderly user through a preset anti-interference ability calculation function according to the success rate; The anti-interference ability calculation function is set in such a way that the higher the success rate, the stronger the anti-interference ability of the current elderly user; When the anti-interference ability of the elderly is greater, the intervention timing recognition program determines that the probability of needing to intervene is smaller.

5. The elderly-assisted learning method according to claim 1, wherein When feeding back the final operation path to the elderly user, an addressing operator is also called to monitor and provide real-time feedback on the actual operation situation of the elderly.

6. The elderly assisted learning method according to any one of claims 1-5, characterized in that The elderly-assisted learning method can also automatically operate the finally verified operation path through a simulated click program in the user's smart phone.

7. The elderly assistance learning system is characterized in that, The elderly-assisted learning system includes a processor and a memory, and at least one instruction, at least one program, a code set or an instruction set is stored in the memory. 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 according to any one of claims 1-6.

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