A community intervention method for elderly people with dementia

Abstract

The present invention introduces a community intervention method for elderly people with dementia, and belongs to the field of cognitive diagnosis technology. The method includes: determining the accessibility of a first device to a target user based on a first sensor; determining the first device used for intervention based on the accessibility of the first device to the target user; determining target intervention data based on historical intervention data and normal data of the first device; determining cognitive characteristics according to the interactive behavior of the target user and the first device; and determining an intervention plan based on cognitive characteristics. The present invention solves the problem that the existing cognitive intervention methods are less effective. The present invention determines the target intervention data through the historical intervention data of the target user and the normal data of the first device, so that the target intervention data is more consistent with the current intervention process, the accuracy of the cognitive characteristics obtained is higher, and the intervention effect of the intervention plan determined by the cognitive characteristics is improved. The present invention is mainly used in the cognitive intervention process of the elderly.

Description

Technical Field

[0001] The present invention relates to the technical field of cognitive diagnosis, and in particular to a community intervention method for elderly people with dementia. Background Art

[0002] Cognition is the process by which the human brain receives external information, processes it, and converts it into internal psychological activities, thereby acquiring knowledge or applying knowledge. It includes memory, language, visual space, execution, calculation, and understanding and judgment. Cognitive impairment refers to the impairment of one or more of the above-mentioned cognitive functions, which affects the individual's daily or social abilities. The reasons are varied. In addition to organic diseases, most of them are caused by mental illness. Such as neurasthenia, hysteria, hypochondria, menopausal syndrome, depression, obsessive-compulsive disorder, schizophrenia, reactive psychosis, paranoid psychosis, mania, bipolar disorder, etc. Elderly people with cognitive impairment need to use training devices for rehabilitation training. In the current cognitive intervention process, a unified standard is used for different users, and there is no personalized cognitive intervention for different users, which affects the effect of cognitive intervention. Summary of the invention

[0003] At least one aspect or advantage of the invention will be set forth in part in the following description, or may be obvious from the description, or may be learned by practice of the disclosed subject matter.

[0004] According to a first aspect of the present invention, a community intervention method for elderly people with dementia comprises:

[0005] determining, based on the first sensor, reachability of the first device to the target user;

[0006] Determining a first device for intervention use based on the reachability of the first device to the target user;

[0007] determining target intervention data based on historical intervention data, wherein the target intervention data is generated based on normal data of the first device;

[0008] Displaying the target intervention data on the first device, and configuring the first device to prompt a first operation in response to an interaction of a target user in the first time domain;

[0009] Determine a first cognitive feature and a second cognitive feature according to an interaction behavior between a target user and a first device in a first time domain;

[0010] determining an intervention plan based on the first cognitive characteristic and / or the second cognitive characteristic;

[0011] The first cognitive feature is a response feature of the target user's interactive behavior to the target intervention data;

[0012] The second cognitive feature is a matching degree between the target user's operation and the first operation.

[0013] According to an embodiment of the present invention, the first cognitive feature includes determining a first duration for completing the interactive behavior based on the start time of the user interactive behavior;

[0014] The process of determining the first cognitive feature includes:

[0015] Determine an expected behavior pattern based on the target intervention data and the initial position of the user determined by the first sensor, wherein the starting point of the expected behavior pattern is the current position of the target user, and the end point of the expected behavior is the end point position of the first device;

[0016] An expected behavior point is determined based on the direction of the target user, and a first duration is determined based on the distance between the expected behavior point and the end point of the expected behavior and the user's historical walking speed;

[0017] determining a first spatial domain based on a start point and an end point of the expected behavior pattern, wherein the first spatial domain is configured to be related to the user interaction behavior;

[0018] The response characteristic of the target user interaction behavior to the target intervention data is the correlation between the user space position and the first space domain.

[0019] According to one embodiment of the present invention, when the location of the target user is updated, the interval space domain in which the user has walked is updated, and the interval space domain is the first space domain between the current location of the target user and the starting point of the expected behavior pattern;

[0020] Re-determine the expected behavior point of the user at the next moment based on the user's behavior, and update the first duration based on the expected behavior point and the distance between the end point of the expected behavior and the user's historical walking speed;

[0021] The first spatial domain and the first temporal domain between the user's current position and the end point of the expected behavior pattern are re-determined based on the user's expected behavior point at the next moment and the end point of the expected behavior.

[0022] According to one embodiment of the present invention, when the first time domain is exhausted, a first correlation between the user walking path and the target user's interactive behavior is determined based on the target user's actual walking path and the first spatial domain, and the first correlation is the probability of the user and the first device performing an interactive behavior.

[0023] According to one embodiment of the present invention, in response to the first correlation being less than a first preset value, the reachability of the second device to the target user is determined based on the second sensor, and when the second device is reachable to the target user, second prompt information is displayed based on the second device, and the second prompt information is associated with the target intervention data, and the second prompt information is configured to be unrelated to user interactive operations.

[0024] According to one embodiment of the present invention, in response to the target user not performing any operation on the first device within the validity period of the second prompt information, a manual intervention task is generated based on the target user information collected in the first time domain and the environment in which the target user is located, and the manual intervention task is sent to the server via a network connection.

[0025] According to one embodiment of the present invention, in response to a first correlation being not less than a first preset value, response information of the target user to the target intervention data is determined based on the target user's behavior in the first time domain, an information category corresponding to the response information is determined based on the first operation, the response information is sampled based on the information category to obtain behavior data, and a second matching degree between the target user operation and the first operation is determined based on the behavior data and the target intervention data.

[0026] According to one embodiment of the present invention, in response to the second matching degree being lower than a second preset value, a manual intervention task is generated based on the target user information, user operation information and the target user's environment collected in the first time domain, and the manual intervention task is sent to the server via the network.

[0027] According to one embodiment of the present invention, in response to the first correlation being lower than a threshold and the presence of interactive operations between the target user and the first device, response information of the target user to the target intervention data is determined based on the target user's behavior in the first time domain, an information category corresponding to the response information is determined based on the first operation, the response information is sampled based on the information category to obtain behavior data, and a match between the target user operation and the first operation is determined based on the behavior data and the target intervention data.

[0028] According to one embodiment of the present invention, the process of generating the target intervention data includes:

[0029] A first intervention data set is obtained based on the target user's historical successful intervention data, recommended intervention data is obtained based on the user group corresponding to the target user, the first intervention data set and the recommended intervention data are screened based on the first device to obtain a candidate intervention data set that can be applied to the first device, and target intervention data is generated based on the user's scenario and the candidate intervention data set.

[0030] The present invention determines the target intervention data through the historical intervention data of the target user and the normal data of the first device, so that the target intervention data is more consistent with the current intervention process, the accuracy of the first cognitive feature and the second cognitive feature obtained is higher, and the intervention effect of the intervention plan determined by the first cognitive feature and the second cognitive feature is improved. In addition, compared with the traditional method of manual intervention by doctors, the present invention digitizes the cognitive intervention process, reduces manual participation, and reduces the impact of human factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of a community intervention method for elderly people with dementia in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The present disclosure will now be discussed with reference to several exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and thus implement the present disclosure, rather than implying any limitation on the scope of the present disclosure.

[0033] According to one embodiment of the present invention, a community intervention method for elderly people with dementia includes steps 1100-1600.

[0034] Step 1100: Determine the reachability of a first device to a target user based on a first sensor.

[0035] The target users are those who need cognitive function intervention. For example, the target users may be elderly people with certain cognitive impairments.

[0036] The reachability of the first device to the target user indicates whether the target user can interact with the first device. If the target user can interact with the first device, the reachability of the first device to the target user is reachable. If the target user cannot interact with the first device, the reachability of the first device to the target user is unreachable.

[0037] The reachability of the first device to the target user may be determined by the first sensor. For example, the location of the target user may be detected by the first sensor, and then whether the target user can interact with the first device may be determined based on the location of the target user and the location of the first device.

[0038] Step 1200: Determine a first device for intervention based on the reachability of the first device to the target user.

[0039] In a scenario, there may be multiple first devices for cognitive intervention, some of which can interact with the target user, and others cannot. Then the device that can interact with the target user is determined as the first device used for intervention.

[0040] Step 1300: Determine target intervention data based on historical intervention data, wherein the target intervention data is generated based on normal data of the first device.

[0041] The historical intervention data is the intervention data corresponding to the intervention process of the target user before the current intervention. The first device is the device used for intervention, and the normal data of the first device is the intervention data corresponding to the intervention process using the first device. In this intervention process, the target intervention data is determined by combining the historical intervention data of the target user and the normal data of the first device, so that the target intervention data is highly matched with the target user and the first device.

[0042] Step 1400: Display target intervention data on a first device, and configure the first device to prompt a first operation in response to an interaction of a target user in a first time domain.

[0043] During the cognitive intervention process, the target user needs to complete a specified operation. The first operation is the operation that the target user needs to complete. The target user can be evaluated by the time and accuracy of the target user completing the specified operation, and the target intervention data may include the accuracy and time. During the intervention process, the target intervention data is displayed on the first device.

[0044] During the process of the target user performing the corresponding operation, the first device detects the operation performed by the target user in real time. When the first device detects that the operation performed by the target user is inconsistent with the first operation, the first device prompts the target user to complete the first operation. For example, the first device can prompt the target user by voice broadcast.

[0045] Step 1500: Determine a first cognitive feature and a second cognitive feature based on the interaction behavior between the target user and the first device in the first time domain, wherein the first cognitive feature is a response feature of the target user's interaction behavior to the target intervention data, and the second cognitive feature is a matching degree between the target user's operation and the first operation.

[0046] The target user interacts with the first device in a first time domain, and a first cognitive feature and a second cognitive feature are determined according to the interaction behavior.

[0047] Step 1600: Determine an intervention plan based on the first cognitive feature and / or the second cognitive feature.

[0048] The first cognitive feature and the second cognitive feature can reflect the degree of cognitive dysfunction of the target user. For example, the lower the matching degree between the target user's operation and the first operation, the higher the degree of cognitive dysfunction of the target user. The corresponding intervention plan is determined according to the degree of cognitive dysfunction of the target user.

[0049] The present invention determines the target intervention data through the historical intervention data of the target user and the normal data of the first device, so that the target intervention data is more consistent with the current intervention process, the accuracy of the first cognitive feature and the second cognitive feature obtained is higher, and the intervention effect of the intervention plan determined by the first cognitive feature and the second cognitive feature is improved. In addition, compared with the traditional method of manual intervention by doctors, the present invention digitizes the cognitive intervention process, reduces manual participation, and reduces the impact of human factors.

[0050] According to an embodiment of the present invention, the first cognitive feature includes determining a first duration of completing the interactive behavior based on the start time of the user interactive behavior. The process of determining the first cognitive feature includes:

[0051] Determine an expected behavior pattern based on the target intervention data and the initial position of the user determined by the first sensor, wherein the starting point of the expected behavior pattern is the current position of the target user, and the end point of the expected behavior is the end point position of the first device;

[0052] An expected behavior point is determined based on the direction of the target user, and a first duration is determined based on the distance between the expected behavior point and the end point of the expected behavior and the user's historical walking speed;

[0053] determining a first spatial domain based on a start point and an end point of the expected behavior pattern, wherein the first spatial domain is configured to be related to the user interaction behavior;

[0054] The response characteristic of the target user interaction behavior to the target intervention data is the correlation between the user space position and the first space domain.

[0055] The initial position of the target user is detected by the first sensor. The expected behavior pattern indicates the behavior pattern of the target user, including the process of the target user moving from the initial position to the final position. For example, there is an obstacle between the initial position and the final position of the target user, and the target user needs to bypass the obstacle to reach the final position. Then the expected behavior pattern can be: the target user first moves to the vicinity of the obstacle, then bypasses the obstacle, and then moves to the final position.

[0056] The expected behavior point is a point on the target user's direction, indicating the point that the user may reach when walking in the current direction. The target user needs to walk to the end point of the expected behavior. During the walking process, the target user first walks from the current position to the expected behavior point, and then walks from the expected behavior point to the end point of the expected behavior. The first duration indicates the duration required for the target user to walk to the end point of the expected behavior, which can be determined based on the target user's historical walking pace.

[0057] The user interaction behavior represents the user's walking process. During the user's walking process, the user's spatial position constantly changes. The correlation between the user's spatial position and the first spatial domain also changes with the change of the user's spatial position. For example, when the user's spatial position is within the first spatial domain, the correlation between the user's spatial position and the first spatial domain is high. When the user's spatial position is outside the first spatial domain, the correlation between the user's spatial position and the first spatial domain is low.

[0058] According to one embodiment of the present invention, when the location of the target user is updated, the interval space domain in which the user has walked is updated, and the interval space domain is the first space domain between the current location of the target user and the starting point of the expected behavior pattern;

[0059] Re-determine the expected behavior point of the user at the next moment based on the user's behavior, and update the first duration based on the expected behavior point and the distance between the end point of the expected behavior and the user's historical walking speed;

[0060] The first spatial domain and the first temporal domain between the user's current position and the end point of the expected behavior pattern are re-determined based on the user's expected behavior point at the next moment and the end point of the expected behavior.

[0061] The target user's location will be updated as the target user walks. The target user's already walked interval space domain will change with the target user's location. The target user's already walked interval space domain represents the space domain between the target user's current location and the starting point of the expected behavior pattern.

[0062] During the user's walking process, the user's position keeps changing. The expected behavior point also changes as the user walks. The first duration also changes as the expected behavior point changes. In this case, the first duration is updated.

[0063] According to one embodiment of the present invention, when the first time domain is exhausted, a first correlation between the user walking path and the target user's interactive behavior is determined based on the target user's actual walking path and the first spatial domain, and the first correlation is the probability of the user and the first device performing an interactive behavior.

[0064] The probability of the user interacting with the first device is determined based on the actual walking path of the user and the first spatial domain. For example, the degree of deviation between the target user and the first device can be determined based on the actual walking path of the user and the first spatial domain. The higher the degree of deviation, the lower the probability of the target user interacting with the first device.

[0065] According to one embodiment of the present invention, in response to the first correlation being less than a first preset value, the reachability of the second device to the target user is determined based on the second sensor, and when the second device is reachable to the target user, second prompt information is displayed based on the second device, and the second prompt information is associated with the target intervention data, and the second prompt information is configured to be unrelated to user interactive operations.

[0066] If the probability of the user interacting with the first device is less than the first preset value, it means that the user is unlikely to interact with the first device. In order to ensure the smooth progress of the intervention process, the second device is enabled in this case, and the second device is also a device for cognitive intervention.

[0067] The reachability of the second device to the target user can be determined by the second sensor. If the reachability of the second device to the target user is reachable, the second device is used for this cognitive intervention process. The second prompt information is displayed on the second device. The second prompt information is information related to the target intervention data.

[0068] According to one embodiment of the present invention, in response to the target user not performing any operation on the first device within the validity period of the second prompt information, a manual intervention task is generated based on the target user information collected in the first time domain and the environment in which the target user is located, and the manual intervention task is sent to the server via a network connection.

[0069] If the target user does not operate the first device within the validity period of the second prompt information, then the first cognitive feature and the second cognitive feature cannot be determined, and thus the intervention plan cannot be determined. In this case, a manual intervention task is generated, and the relevant staff performs manual intervention on the target user. The manual intervention task is generated based on the target user information collected in the first time domain and the environment in which the target user is located. The target user information collected in the first time domain may include information such as the target user's walking path and walking duration.

[0070] According to one embodiment of the present invention, in response to a first correlation being not less than a first preset value, response information of the target user to the target intervention data is determined based on the target user's behavior in the first time domain, an information category corresponding to the response information is determined based on the first operation, the response information is sampled based on the information category to obtain behavior data, and a second matching degree between the target user operation and the first operation is determined based on the behavior data and the target intervention data.

[0071] If the first correlation is not less than the first preset value, it means that the target user will interact with the first device. In the case where there is an interactive operation between the target user and the first device, it is necessary to determine the matching degree between the target user's operation and the first operation.

[0072] The first operation is an operation that the target user needs to complete. The first operation can be a physical movement, such as stretching out both hands, and the corresponding action can be displayed on the first device, and the target user can be prompted to complete the action. The first operation can also be that the target user needs to read aloud specified text content, such as displaying text content that the target user needs to read aloud on the first device.

[0073] The target user's response information to the target intervention data represents the user's behavior information in the first time domain obtained by the first device. Different behaviors correspond to different information categories of the response information. For example, the user makes a body movement, and the first device obtains a picture of the user's body movement. In this case, the information category of the response information is the image category. Alternatively, the user reads aloud the text content displayed on the first device, and the first device obtains the audio of the user's reading, and the information category of the response information is the audio category.

[0074] For different types of response information, different sampling methods are used to obtain the user's behavior data. For example, when the response information category is an image category, the behavior data can be obtained by image detection. For another example, when the response information category is an audio category, the behavior data can be obtained by editing the captured audio data.

[0075] After the behavior data is obtained, the matching degree between the target user operation and the first operation is determined according to the behavior data and the target intervention data. The behavior data corresponds to the user operation, and the target intervention data corresponds to the first operation. The matching degree between the target user operation and the first operation can be determined by comparing the matching degree between the behavior data and the target intervention data.

[0076] According to one embodiment of the present invention, in response to the second matching degree being lower than a second preset value, a manual intervention task is generated based on the target user information, user operation information and the target user's environment collected in the first time domain, and the manual intervention task is sent to the server via the network.

[0077] If the second matching degree is lower than the second preset value, it means that the actual operation of the target user has a low matching degree with the first operation that the target user needs to complete. In this case, a manual intervention task is generated, and the relevant staff performs manual intervention on the target user. The manual intervention task is generated based on the target user information, target user operation information, and the target user's environment collected in the first time domain.

[0078] According to one embodiment of the present invention, in response to the first correlation being lower than a threshold and the presence of interactive operations between the target user and the first device, response information of the target user to the target intervention data is determined based on the target user's behavior in the first time domain, an information category corresponding to the response information is determined based on the first operation, the response information is sampled based on the information category to obtain behavior data, and a match between the target user operation and the first operation is determined based on the behavior data and the target intervention data.

[0079] When the first correlation is lower than the threshold, the target user may still interact with the first device. The threshold here is a first preset value. When there is an interactive operation between the target user and the first device, it is necessary to determine the matching degree between the target user's operation and the first operation.

[0080] The first operation is an operation that the target user needs to complete. The first operation can be a physical movement, such as stretching out both hands, and the corresponding action can be displayed on the first device, and the target user can be prompted to complete the action. The first operation can also be that the target user needs to read aloud specified text content, such as displaying text content that the target user needs to read aloud on the first device.

[0081] The target user's response information to the target intervention data represents the user's behavior information in the first time domain obtained by the first device. Different behaviors correspond to different information categories of the response information. For example, the user makes a body movement, and the first device obtains a picture of the user's body movement. In this case, the information category of the response information is the image category. Alternatively, the user reads aloud the text content displayed on the first device, and the first device obtains the audio of the user's reading, and the information category of the response information is the audio category.

[0082] For different types of response information, different sampling methods are used to obtain the user's behavior data. For example, when the response information category is an image category, the behavior data can be obtained by image detection. For another example, when the response information category is an audio category, the behavior data can be obtained by editing the captured audio data.

[0083] After the behavior data is obtained, the matching degree between the target user operation and the first operation is determined according to the behavior data and the target intervention data. The behavior data corresponds to the user operation, and the target intervention data corresponds to the first operation. The matching degree between the target user operation and the first operation can be determined by comparing the matching degree between the behavior data and the target intervention data.

[0084] According to one embodiment of the present invention, the process of generating the target intervention data includes: obtaining a first intervention data set based on the historical intervention success data of the target user, obtaining recommended intervention data based on the user group corresponding to the target user, screening the first intervention data set and the recommended intervention data based on the first device to obtain a candidate intervention data set that can be applied to the first device, and generating the target intervention data based on the scenario in which the user is located and the candidate intervention data set.

[0085] Before the current cognitive intervention is performed on the target user, the target user may have been subjected to multiple cognitive interventions. Each cognitive intervention on the target user corresponds to corresponding intervention success data. A first intervention data set is generated based on the above intervention success data. The first intervention data set includes the historical intervention success data of the target user.

[0086] The target users are users with certain cognitive dysfunction. Cognitive dysfunction includes many different types, such as perceptual impairment, memory impairment, and thinking impairment. Different types of cognitive dysfunction require different cognitive intervention methods. Users can be divided into different user groups according to the type of cognitive dysfunction, and different user groups correspond to different recommended intervention data.

[0087] Each type of intervention data corresponds to a different intervention device, and different intervention devices have different functions. The first device is the device used in this cognitive intervention process. If the function of the first device does not match a certain type of intervention data, then the intervention data cannot be used in this cognitive intervention process. The intervention data in the candidate intervention data set is consistent with the function of the first device.

[0088] Since each intervention process is carried out in a specific scenario, the candidate intervention data is further screened by the scenario in which the user is in this intervention process, and the target intervention data is found from the candidate intervention data. The scenario corresponding to the target intervention data is the same as the scenario in which the user is in this intervention process.

[0089] For example, the first intervention data set includes intervention data 1, intervention data 2, and intervention data 3, and the recommended intervention data obtained according to the user group corresponding to the target user is intervention data 4. Among them, the intervention data that conforms to the function of the first device includes intervention data 1 and intervention data 4, and the candidate intervention data set obtained after screening the above intervention data based on the first device includes intervention data 1 and intervention data 4. Then, intervention data 1 and intervention data 4 are screened according to the scene where the user is located, wherein the scene corresponding to intervention data 1 is the same as the scene where the user is located, and the scene corresponding to intervention data 4 is different from the scene where the user is located, and then the target intervention data obtained in the end is intervention data 1.

[0090] In this embodiment, the first intervention data set and the recommended intervention data are screened by the first device to obtain a candidate intervention data set, so that the intervention data in the candidate intervention data set conforms to the first device used in this intervention process. Then, the candidate intervention data set is further screened according to the scenario in which the user is located, and the target intervention data obtained not only conforms to the first device used in this intervention process, but also the scenario corresponding to the target intervention data is the same as the scenario in which the user is located, so that the target intervention data is highly matched with this intervention process, thereby improving the effect of this intervention.

[0091] Those of ordinary skill in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0093] In the embodiments provided in the present 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 only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules 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 modules, which can be electrical, mechanical or other forms.

[0094] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0095] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0096] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the energy-saving signal sending / receiving method of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0097] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the present application.

[0098] It should be understood that the size of the sequence number of each step in the content of the invention and the embodiments of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. For the purpose of example and description, the foregoing description of the implementation of the present disclosure has been given. The foregoing description is not exhaustive and is not intended to limit the present disclosure to the exact form disclosed. Various deformations and modifications may exist according to the above teachings, or various deformations and modifications may be obtained from the practice of the present disclosure. These embodiments are selected and described to illustrate the principles of the present disclosure and its practical application, so that those skilled in the art can use the present disclosure in various embodiments and various modifications suitable for the specific purpose conceived.

Claims

1. A community intervention method for elderly people with dementia, characterized in that: include: determining, based on the first sensor, reachability of the first device to the target user; Determining a first device for intervention use based on the reachability of the first device to the target user; determining target intervention data based on historical intervention data, wherein the target intervention data is generated based on normal data of the first device; Displaying the target intervention data on the first device, and configuring the first device to prompt a first operation in response to an interaction of a target user in the first time domain; Determine a first cognitive feature and a second cognitive feature according to an interaction behavior between a target user and a first device in a first time domain; determining an intervention plan based on the first cognitive characteristic and / or the second cognitive characteristic; The first cognitive feature is a response feature of the target user's interactive behavior to the target intervention data; The second cognitive feature is a matching degree between the target user's operation and the first operation; The first cognitive feature includes determining a first duration for completing the interactive behavior based on the start time of the user interactive behavior; The process of determining the first cognitive feature includes: Determine an expected behavior pattern based on the target intervention data and the initial position of the user determined by the first sensor, wherein the starting point of the expected behavior pattern is the current position of the target user, and the end point of the expected behavior is the end point position of the first device; An expected behavior point is determined based on the direction of the target user, and a first duration is determined based on the distance between the expected behavior point and the end point of the expected behavior and the user's historical walking speed; determining a first spatial domain based on a start point and an end point of the expected behavior pattern, wherein the first spatial domain is configured to be related to the user interaction behavior; The response characteristic of the target user interaction behavior to the target intervention data is the correlation between the user space position and the first space domain.

2. A community intervention method for elderly people with dementia as claimed in claim 1, characterized in that: When the location of the target user is updated, the interval space domain in which the user has walked is updated, where the interval space domain is the first space domain between the current location of the target user and the starting point of the expected behavior pattern; Re-determine the expected behavior point of the user at the next moment based on the user's behavior, and update the first duration based on the expected behavior point and the distance between the end point of the expected behavior and the user's historical walking speed; The first spatial domain and the first temporal domain between the user's current position and the end point of the expected behavior pattern are re-determined based on the user's expected behavior point at the next moment and the end point of the expected behavior.

3. A community intervention method for elderly people with dementia as claimed in claim 1, characterized in that: When the first time domain is exhausted, a first correlation between the user walking path and the target user's interactive behavior is determined based on the target user's actual walking path and the first spatial domain, where the first correlation is a probability of the user interacting with the first device.

4. A community intervention method for elderly people with dementia as claimed in claim 3, characterized in that: In response to the first correlation being less than a first preset value, the reachability of the second device to the target user is determined based on the second sensor, and when the second device is reachable to the target user, second prompt information is displayed based on the second device, the second prompt information is associated with the target intervention data, and the second prompt information is configured to be unrelated to user interactive operations.

5. A community intervention method for elderly people with dementia as claimed in claim 4, characterized in that: In response to the target user not operating the first device within the validity period of the second prompt information, a manual intervention task is generated based on the target user information collected in the first time domain and the target user's environment, and the manual intervention task is sent to the server through a network connection.

6. A community intervention method for elderly people with dementia as claimed in claim 3, characterized in that: In response to the first correlation being not less than a first preset value, determining the target user's response information to the target intervention data based on the target user's behavior in the first time domain, determining an information category corresponding to the response information based on the first operation, sampling the response information based on the information category to obtain behavior data, and determining a second matching degree between the target user operation and the first operation based on the behavior data and the target intervention data.

7. A community intervention method for elderly people with dementia as claimed in claim 6, characterized in that: In response to the second matching degree being lower than a second preset value, a manual intervention task is generated based on the target user information, user operation information and the target user's environment acquired in the first time domain, and the manual intervention task is sent to the server via a network.

8. A community intervention method for elderly people with dementia as claimed in claim 3, characterized in that: In response to the first correlation being lower than a threshold and the presence of interactive operations between the target user and the first device, response information of the target user to the target intervention data is determined according to the target user's behavior in the first time domain, an information category corresponding to the response information is determined according to the first operation, the response information is sampled based on the information category to obtain behavior data, and a match degree between the target user operation and the first operation is determined based on the behavior data and the target intervention data.

9. A community intervention method for elderly people with dementia as claimed in claim 1, characterized in that: The generation process of the target intervention data includes: A first intervention data set is obtained based on the target user's historical successful intervention data, recommended intervention data is obtained based on the user group corresponding to the target user, the first intervention data set and the recommended intervention data are screened based on the first device to obtain a candidate intervention data set that can be applied to the first device, and target intervention data is generated based on the user's scenario and the candidate intervention data set.

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