User behavior monitoring method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a user behavior monitoring method and device, equipment and a storage medium, and the method comprises the steps: obtaining the current behavior data of a user, the current behavior data comprises the current physiological data, motion data and position data of the user; according to the current behavior data of the user and a preset behavior evaluation model, a behavior evaluation result of the user is obtained, and the preset behavior evaluation model is determined according to historical behavior data of the user in a normal state; and when the behavior evaluation result indicates that the user is in the abnormal state, sending reminding information to the associated user. The accuracy of state monitoring of a user can be improved, and an associated user can pay attention to the state of the user in time.

Description

Technical Field

[0001] The embodiments of the present application relate to information processing technology, and relate to but are not limited to a user behavior monitoring method and apparatus, equipment, and storage medium. Background Art

[0002] With the rapid development of science and technology, the increasingly miniaturized wearable device industry has entered people's lives. Wearable devices such as Bluetooth bracelets and smart watches are increasingly being used by users.

[0003] Take smart watches as an example. Since they have the same communication functions as mobile electronic devices such as mobile phones, many users use them as small communication tools to communicate with other users. And many users can also monitor the behavior of users using wearable devices through electronic devices.

[0004] Currently, when monitoring the behavior of users using wearable devices, the focus is more on obtaining information such as the user's location and movement transmitted by the wearable device they carry, but it is not possible to monitor the user comprehensively and cannot detect the user's abnormal state in time. Summary of the invention

[0005] In view of this, the user behavior monitoring method, device, equipment, and storage medium provided in the embodiments of the present application can improve the accuracy of monitoring the status of the user, and enable the associated user to pay attention to the status of the user in a timely manner. The user behavior monitoring method, device, equipment, and storage medium provided in the embodiments of the present application are implemented as follows:

[0006] The user behavior monitoring method provided in the embodiment of the present application is applied to a wearable device, including:

[0007] Acquire current behavior data of the user, wherein the current behavior data includes current physiological data, motion data and location data of the user;

[0008] Obtaining a behavior evaluation result of the user according to the current behavior data of the user and a preset behavior evaluation model, wherein the preset behavior evaluation model is determined according to the historical behavior data of the user under normal conditions;

[0009] When the behavior evaluation result indicates that the user is in an abnormal state, a reminder message is sent to the associated user.

[0010] In some embodiments, before obtaining the current behavior data of the user, the method further includes:

[0011] Acquire historical behavior data of the user in a normal state, wherein the historical behavior data includes physiological data, motion data, and location data of the user;

[0012] The preset behavior evaluation model is constructed based on the historical behavior data.

[0013] In some embodiments, the method further comprises:

[0014] Count the occurrence frequency of the current behavior data of the user within a preset time period;

[0015] When the occurrence frequency is greater than a threshold, the preset behavior evaluation model is updated according to the current behavior data.

[0016] In some embodiments, when the behavior evaluation result indicates that the user is in an abnormal state, sending a reminder message to the associated user includes:

[0017] When the behavior evaluation result indicates that the user is in an abnormal state, generating and outputting first inquiry information, wherein the first inquiry information is used to inquire whether the user is in an abnormal state;

[0018] receiving feedback from the user regarding the first inquiry information;

[0019] When the feedback content is used to indicate that the user is in an abnormal state, a first reminder message is sent to the associated user.

[0020] In some embodiments, the method further comprises:

[0021] Predicting the current behavior of the user according to the current behavior data;

[0022] Accordingly, when the behavior evaluation result indicates that the user is in an abnormal state, sending a reminder message to the associated user includes:

[0023] When the behavior evaluation result indicates that the user is in an abnormal state, generating and outputting second inquiry information, wherein the second inquiry information is used to inquire whether the user is performing the current behavior;

[0024] receiving feedback from the user regarding the second inquiry information;

[0025] When the feedback content is used to represent that the user is performing the current behavior, second reminder information is sent to the associated user, where the second reminder information includes the current behavior.

[0026] In some embodiments, the associated user is a preset user or a user whose distance from the wearable device is less than a threshold.

[0027] In some embodiments, the physiological data includes at least one of the following: body temperature, blood pressure, heart rate, and respiratory rate.

[0028] The user behavior monitoring device provided in the embodiment of the present application includes:

[0029] An acquisition module, used to acquire current behavior data of the user, wherein the current behavior data includes current physiological data, motion data and position data of the user;

[0030] An evaluation module, used to obtain a behavior evaluation result of the user according to the current behavior data of the user and a preset behavior evaluation model, wherein the preset behavior evaluation model is determined according to the historical behavior data of the user under normal conditions;

[0031] The sending module is used to send a reminder message to the associated user when the behavior evaluation result indicates that the user is in an abnormal state.

[0032] The computer device provided in the embodiment of the present application includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the method described in the embodiment of the present application is implemented.

[0033] The computer-readable storage medium provided in the embodiment of the present application stores a computer program thereon, and when the computer program is executed by a processor, the method provided in the embodiment of the present application is implemented.

[0034] The user behavior monitoring method, device, computer equipment and computer-readable storage medium provided in the embodiments of the present application obtain the current behavior data of the user, which includes the current physiological data, motion data and location data of the user; and obtain the behavior evaluation result of the user based on the current behavior data of the user and the preset behavior evaluation model, which is determined based on the historical behavior data of the user under normal conditions; when the behavior evaluation result indicates that the user is in an abnormal state, a reminder message is sent to the associated user. In this way, the user behavior is monitored based on the user's multiple behavior data, and when it is determined that the user is in an abnormal state, the associated user is reminded, thereby improving the accuracy of the user's status monitoring, and enabling the associated user to pay attention to the user's status in a timely manner, solving the technical problems raised in the background technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application and are used together with the specification to illustrate the technical solution of the present application.

[0036] Figure 1 An application scenario diagram of the user behavior monitoring method provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of the implementation flow of the user behavior monitoring method provided in the embodiment of the present application;

[0038] Figure 3 A schematic diagram of the implementation flow of the method for constructing a behavior assessment model provided in an embodiment of the present application;

[0039] Figure 4 A schematic diagram of the implementation process of updating a preset behavior evaluation model provided in an embodiment of the present application;

[0040] Figure 5 A schematic diagram of the implementation flow of the user behavior monitoring method provided in the embodiment of the present application;

[0041] Figure 6 A schematic diagram of an implementation process of sending reminder information provided in an embodiment of the present application;

[0042] Figure 7 A schematic diagram of an interface display of a wearable device provided in an embodiment of the present application;

[0043] Figure 8 A schematic diagram of an interface display of a wearable device provided in an embodiment of the present application;

[0044] Fig. 9 A schematic diagram of another implementation process of sending reminder information provided in an embodiment of the present application;

[0045] Fig.10 A schematic diagram of an interface display of another wearable device provided in an embodiment of the present application;

[0046] Fig.11 A schematic diagram of the structure of a user behavior monitoring device provided in an embodiment of the present application;

[0047] Fig.12 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the specific technical solution of the present application will be further described in detail below in conjunction with the drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0050] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0051] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present application are used to distinguish similar or different objects, and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0052] With the rapid development of urbanization and the increasingly obvious trend of population aging, more and more elderly people or children have to live alone at home because adults work away from home. Adults who are far away from home cannot pay attention to the behavior of the elderly or children at home in real time.

[0053] At present, most of the technical solutions to the above problems are mainly focused on recording the location or fall information of the wearable devices carried by users, but this cannot cover all the situations of concern to the elderly or children who are far away. For example, the elderly may feel suffocated when lying in bed at home, feel lonely and anxious, etc. Only relying on the location and movement information transmitted by wearable devices cannot fully monitor the user, and cannot detect the abnormal status of the user in time.

[0054] In view of this, an embodiment of the present application provides a user behavior monitoring method, which is applied to a wearable device. Figure 1 FIG. 1 is an application scenario diagram of a user behavior monitoring method provided in an embodiment. Figure 1 As shown, the user can carry, wear or use a wearable device 10, which may include but is not limited to a smart watch, a smart bracelet, smart glasses, etc. The function implemented by the method can be implemented by calling a program code by a processor in the wearable device. Of course, the program code can be stored in a computer storage medium. It can be seen that the wearable device at least includes a processor and a storage medium. The wearable device 10 can record various behaviors performed by the user and transmit them to a database server and / or server through a network.

[0055] Figure 2The following is a schematic diagram of the implementation process of the user behavior monitoring method provided in the embodiment of the present application. Figure 2 As shown, the method may include the following steps 201 to 203:

[0056] Step 201 : obtaining the current behavior data of the user, where the current behavior data includes the current physiological data, motion data and location data of the user.

[0057] It should be noted that the components of the wearable device provided in the embodiments of the present application may include: a processor, a power supply, a wireless communication module, a physiological sensing module, a motion sensor, a satellite positioning, a microphone, a speaker and a touch screen.

[0058] Understandably, children's daily routines are generally composed of fixed activities such as going to school, and adults' daily routines are generally composed of fixed activities such as going to work. After retirement, the elderly have a relatively fixed daily routine due to physical decline and long-term habits. For example, the elderly wash their faces in the morning and evening, read newspapers and listen to the news, take walks and exercise, and have three meals a day. The behaviors and times of occurrence are relatively fixed.

[0059] In view of the above principles, users usually perform fixed activities at fixed time periods throughout the day, thereby generating relatively fixed physiological data, motion data, and location data.

[0060] Based on this, in an embodiment of the present application, the current physiological data, motion data, and location data of the user can be obtained through the wearable device to monitor the behavioral status of the user based on the above data.

[0061] In the embodiment of the present application, there is no limitation on the specific type of physiological data. For example, the physiological data may include at least one of the following: body temperature, blood pressure, heart rate, and respiratory rate.

[0062] In some embodiments, when collecting and using the user's heart rate and breathing rate, it can be achieved through the built-in photoelectric volume pulse wave (PPG) detection module in the wearable device.

[0063] In the embodiment of the present application, there is no limitation on the specific type of motion data. For example, the motion data may include the collected acceleration data and angular velocity data of the user, which can be collected by the inertial measurement unit (IMU) built into the wearable device.

[0064] In an embodiment of the present application, the user's location data can be determined by a satellite positioning module built into the wearable device.

[0065] Step 202, obtaining a behavior evaluation result of the user based on the current behavior data of the user and a preset behavior evaluation model, wherein the preset behavior evaluation model is determined based on the historical behavior data of the user under normal conditions.

[0066] In an embodiment of the present application, after the current behavior data of the user is obtained, the current behavior of the user can be evaluated based on the data and a preset behavior evaluation model to obtain a behavior evaluation result, which can indicate whether the user is currently in a normal state or an abnormal state.

[0067] The behavior assessment model may be pre-constructed, as shown in steps 301 to 302 in the following embodiment, which provides a method for constructing a behavior assessment model:

[0068] Step 301 : obtaining historical behavior data of a user in a normal state, wherein the historical behavior data includes physiological data, motion data, and location data of the user.

[0069] Here, the historical behavior data of the user under normal conditions obtained refers to the behavior data of the user in various time periods within a certain period of time in the past.

[0070] In some embodiments, obtaining the historical behavior data of the user in a normal state may be to collect the historical behavior data of the user in each time period from morning to night every day in the past month. When collecting data, the whole day of the user may be segmented, and the historical behavior data in a time period may be stored as a data set. For example, the physiological data, motion data, and location data of the user between 8:00 and 8:30 every day are collected as data set 1; the physiological data, motion data, and location data of the user between 16:00 and 17:00 every day are collected as data set 2, etc.

[0071] Step 302: construct a preset behavior evaluation model based on historical behavior data.

[0072] In the embodiment of the present application, the preset behavior evaluation model can be constructed according to the historical behavior data in the following manner:

[0073] According to the collection period corresponding to the acquired historical behavior data, the historical behavior data is divided into a plurality of candidate data sets; the plurality of candidate data sets are normalized, and the plurality of data sets after the normalization are divided into a training data set and a test data set.

[0074] An initial behavior assessment model is constructed, the constructed initial behavior assessment model is trained using the training data set, and the constructed initial behavior assessment model is tested using the test data set until the accuracy reaches a preset accuracy threshold, and the current initial behavior assessment model is determined to be the preset behavior assessment model.

[0075] Here, there is no limitation on the type of the behavior assessment model, such as the behavior assessment model can be a convolutional neural network model. Of course, in the embodiment of the present application, there is no limitation on the structure of the convolutional neural network model, such as the convolutional neural network model can be composed of three convolutional layers, three pooling layers, one fully connected layer and one softmax layer.

[0076] Step 203: When the behavior evaluation result indicates that the user is in an abnormal state, a reminder message is sent to the associated user.

[0077] In an embodiment of the present application, the acquired current behavior data may be input into a preset behavior evaluation model, and the current behavior data may be analyzed via the behavior evaluation model to obtain an evaluation result of the current status of the user.

[0078] Here, if the behavior evaluation result indicates that the user is in an abnormal state, it means that the current behavior of the user deviates from the normal behavior in the past period of time. For example, the normal behavior of the user from 8:00 to 8:30 every day in the past period of time is washing the face, but the preset behavior evaluation model analysis shows that the current behavior of the user is climbing a mountain from 8:00 to 8:30, it can be known that the current behavior of the user deviates from the normal behavior, and the user can be considered to be in an abnormal state.

[0079] If the behavior evaluation result indicates that the user is in a normal state, it means that the current behavior of the user is basically the same as the normal behavior in the past period of time. For example, if the normal behavior of the user from 8:00 to 8:30 every day in the past period of time is washing the face, and the preset behavior evaluation model analysis shows that the current behavior of the user is also washing the face from 8:00 to 8:30, it can be known that the current behavior of the user is basically the same as the normal behavior, and the user can be considered to be in a normal state.

[0080] In an embodiment of the present application, when it is detected that the user is in an abnormal state, a reminder message can be sent to an associated user. The associated user can be a preset user or a user whose distance from the wearable device is less than a threshold. In this way, when it is detected that the user is in an abnormal state, a reminder message can be sent to the contact person who is pre-set in the wearable device by the user. Alternatively, if it is detected that the user is in an abnormal state, such as when it is detected that the current physiological data of the user is abnormal, a reminder message can be sent to a user who is closer to the user, so as to rescue the user.

[0081] Understandably, the daily behavior habits of users may change, such as users who rarely climbed mountains before may develop a habit of climbing mountains recently; or users who rarely rode bicycles before may develop a habit of riding bicycles recently. Accordingly, during the time period when the user is in the above behavior, the current behavior data collected will be different from the historical behavior data.

[0082] Therefore, in some embodiments, steps 401 to 402 in the following embodiments may also be performed to update the preset behavior evaluation model:

[0083] Step 401, counting the occurrence frequency of the user's current behavior data within a preset time period.

[0084] Here, there is no limitation on the preset time length, for example, the preset time length may be one week, one month, etc.

[0085] Here, counting the occurrence frequency of the current behavior data of the user within a preset time period is to count the occurrence frequency of the current behavior corresponding to the current behavior data of the user within the preset time period.

[0086] Step 402: When the occurrence frequency is greater than a threshold, a preset behavior evaluation model is updated according to current behavior data.

[0087] Here, updating the preset behavior evaluation model according to the current behavior data may be adding the current behavior corresponding to the current behavior data to the preset behavior evaluation model.

[0088] In this way, by timely updating the preset behavior evaluation model according to the user's living habits, the user's behavior status monitoring can be performed more accurately, further improving the accuracy of the user's status monitoring.

[0089] In the embodiment of the present application, by obtaining the current behavior data of the user, the current behavior data includes the current physiological data, motion data and location data of the user; and according to the current behavior data of the user and the preset behavior evaluation model, the behavior evaluation result of the user is obtained, and the preset behavior evaluation model is determined according to the historical behavior data of the user in a normal state; when the behavior evaluation result indicates that the user is in an abnormal state, a reminder message is sent to the associated user. In this way, the user behavior is monitored based on the various behavior data of the user, and when it is determined that the user is in an abnormal state, the associated user is reminded, thereby improving the accuracy of the status monitoring of the user, and enabling the associated user to pay attention to the status of the user in a timely manner.

[0090] Figure 5The following is a schematic diagram of the implementation process of the user behavior monitoring method provided in the embodiment of the present application. Figure 5 As shown, the method may include the following steps 501 to 503:

[0091] Step 501: Acquire the current behavior data of the user, where the current behavior data includes the current physiological data, motion data and position data of the user.

[0092] Step 502, obtaining a behavior evaluation result of the user based on the current behavior data of the user and a preset behavior evaluation model, wherein the preset behavior evaluation model is determined based on the historical behavior data of the user under normal conditions.

[0093] Step 503: Send reminder information to the associated user based on the behavior evaluation result.

[0094] In the embodiment of the present application, the behavior evaluation result may indicate whether the user is currently in a normal state or an abnormal state. After obtaining the behavior evaluation result of the user, the content of the reminder information sent to the associated user may be adjusted according to the content of the behavior evaluation result.

[0095] For example, in some embodiments, when the behavior evaluation result indicates that the user is in an abnormal state, step 503 may be implemented by executing steps 601 to 603 as follows:

[0096] Step 601, generating and outputting first inquiry information, where the first inquiry information is used to inquire whether the user is in an abnormal state.

[0097] In an embodiment of the present application, when it is determined that the user is currently in an abnormal state, first inquiry information can be generated and output on the wearable device carried by the user to allow the user to determine whether he is in an abnormal state.

[0098] For example, Figure 7 As shown, if it is determined that the user is currently in a falling state, a first inquiry message "Are you abnormal?" may be generated and output on the display screen of the wearable device carried by the user.

[0099] Step 602: receiving feedback from the user regarding the first inquiry information.

[0100] Here, the user's feedback content for the first inquiry information may be that the user is in an abnormal state or is not in an abnormal state. In the embodiment of the present application, there is no limitation on the user's feedback method for the first inquiry information, which may be touch feedback, voice feedback, gesture feedback, etc. For example, Figure 8As shown, the first inquiry information can be displayed on the display screen of the wearable device carried by the user, and touch buttons "yes" and "no" can also be displayed for the user to provide feedback.

[0101] Step 603: When the feedback content is used to indicate that the user is in an abnormal state, a first reminder message is sent to the associated user.

[0102] Here, the specific content of the first reminder information is not limited. For example, the first reminder information may include the current behavior of the user, such as "user A is climbing a mountain", and of course, the current behavior can be determined based on the current behavior data of the user; or the first reminder information may also include the abnormal state of the user, such as "user A's heart rate is abnormal, please pay attention".

[0103] In other embodiments, when the behavior evaluation result indicates that the user is in an abnormal state, step 503 may also be implemented by executing the following steps 901 to 904:

[0104] Step 901, predicting the current behavior of the user based on the current behavior data.

[0105] In the embodiment of the present application, there is no limitation on the implementation method of predicting the current behavior of the user according to the current behavior data. For example, the current behavior data may be analyzed by a preset behavior evaluation model to obtain the current behavior of the user.

[0106] Step 902: Generate and output second inquiry information, where the second inquiry information is used to inquire whether the user is currently performing a current action.

[0107] In an embodiment of the present application, when it is predicted that the current behavior of the user is abnormal, that is, the current behavior of the user deviates from the normal behavior in the past period of time, a second inquiry message is generated and output to inquire whether the user is performing the current behavior.

[0108] For example, Fig.10 As shown, if it is determined that the user is currently climbing a mountain, and the climbing behavior is different from the normal behavior of the user in the current time period (such as the normal behavior is that the user is in a resting state in the current time period), then it can be determined that the user is in an abnormal state, thereby generating and outputting a second inquiry message "Are you climbing a mountain?" on the display screen of the wearable device carried by the user.

[0109] Step 903: receiving the user's feedback on the second inquiry information.

[0110] Similarly, the user's feedback content for the second query information may also be that the user is performing the current behavior or not performing the current behavior. In the embodiment of the present application, there is no limitation on the user's feedback method for the second query information, which may be touch feedback, voice feedback, gesture feedback, etc.

[0111] Step 904: When the feedback content is used to represent that the user is performing the current behavior, second reminder information is sent to the associated user, and the second reminder information includes the current behavior.

[0112] Here, the specific content of the first and second reminder information is not limited, but the second reminder information at least includes the current behavior of the user. If it is predicted that the current behavior of the user is mountain climbing, the second reminder information at least includes the words "user A is climbing a mountain".

[0113] Of course, in the embodiment of the present application, if it is monitored that the behavior of the user is in a normal state, there is no need to send reminder information to the associated user to avoid frequently disturbing the associated user.

[0114] In the embodiment of the present application, by obtaining the current behavior data of the user, the current behavior data includes the current physiological data, motion data and location data of the user; and according to the current behavior data of the user and the preset behavior evaluation model, the behavior evaluation result of the user is obtained, and the preset behavior evaluation model is determined according to the historical behavior data of the user in a normal state; and according to the behavior evaluation result, a reminder message is sent to the associated user. In this way, the user behavior is monitored based on the multiple behavior data of the user, and the reminder message is sent to the associated user according to the behavior evaluation result of the user's current behavior, thereby improving the accuracy of the status monitoring of the user, and enabling the associated user to pay attention to the status of the user in a timely manner.

[0115] It should be understood that, although the steps in the above-mentioned flowcharts are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above-mentioned flowcharts may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0116] Based on the foregoing embodiments, an embodiment of the present application provides a user behavior monitoring device, which includes the modules included and the units included in the modules, which can be implemented by a processor; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA), etc.

[0117] Fig.11 A schematic diagram of the structure of a user behavior monitoring device provided in an embodiment of the present application is shown in FIG. Fig.11 As shown, the device 1100 includes an acquisition module 1101, an evaluation module 1102 and a sending module 1103, wherein:

[0118] An acquisition module 1101 is used to acquire current behavior data of a user, wherein the current behavior data includes current physiological data, motion data and location data of the user;

[0119] An evaluation module 1102 is used to obtain a behavior evaluation result of the user according to the current behavior data of the user and a preset behavior evaluation model, wherein the preset behavior evaluation model is determined according to the historical behavior data of the user in a normal state;

[0120] The sending module 1103 is used to send a reminder message to the associated user when the behavior evaluation result indicates that the user is in an abnormal state.

[0121] In some embodiments, the device also includes a construction module, which is used to obtain historical behavior data of the user under normal conditions, wherein the historical behavior data includes physiological data, motion data, and location data of the user; and construct the preset behavior assessment model based on the historical behavior data.

[0122] In some embodiments, the device also includes a statistical module and an update module. The statistical module is used to count the frequency of occurrence of the current behavior data of the user within a preset time period; the update module is used to update the preset behavior evaluation model according to the current behavior data when the occurrence frequency is greater than a threshold.

[0123] In some embodiments, the device also includes a generating module and a receiving module. The generating module is used to generate and output a first inquiry message when the behavior evaluation result indicates that the user is in an abnormal state, and the first inquiry message is used to inquire whether the user is in an abnormal state; the receiving module is used to receive feedback from the user on the first inquiry message; the sending module 1103 is specifically used to send a first reminder message to the associated user when the feedback content is used to characterize that the user is in an abnormal state.

[0124] In some embodiments, the device also includes a prediction module, which is used to predict the current behavior of the user based on the current behavior data; the generation module is also used to generate and output a second inquiry message when the behavior evaluation result indicates that the user is in an abnormal state, and the second inquiry message is used to inquire whether the user is performing the current behavior; the receiving module is also used to receive feedback from the user on the second inquiry message; the sending module 903 is specifically used to send a second reminder message to the associated user when the feedback content is used to characterize that the user is performing the current behavior, and the second reminder message includes the current behavior.

[0125] In some embodiments, the associated user is a preset user or a user whose distance from the wearable device is less than a threshold.

[0126] In some embodiments, the physiological data includes at least one of the following: body temperature, blood pressure, heart rate, and respiratory rate.

[0127] The description of the above device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiment of the present application, please refer to the description of the method embodiment of the present application for understanding.

[0128] It should be noted that in the embodiments of this application Fig.11 The division of modules in the user behavior monitoring device shown is schematic and is only a logical functional division. There may be other division methods in actual implementation. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. It may also be implemented in the form of a combination of software and hardware.

[0129] It should be noted that in the embodiment of the present application, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application can be essentially or partly embodied in the form of a software product that contributes to the relevant technology. The computer software product is stored in a storage medium, including several instructions to enable an electronic device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0130] The embodiment of the present application provides a computer device, which may be a server, and its internal structure diagram may be as follows: Fig.12 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0131] An embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the method provided in the above embodiment are implemented.

[0132] An embodiment of the present application provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the steps of the method provided in the above method embodiment.

[0133] Those skilled in the art will understand that Fig.12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0134] In one embodiment, the user behavior monitoring device provided by the present application can be implemented in the form of a computer program. The computer program can be Fig.12The computer device shown in the figure is run. The memory of the computer device can store various program modules constituting the above-mentioned device. The computer program composed of various program modules enables the processor to execute the steps in the method of each embodiment of the present application described in this specification.

[0135] It should be noted here that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium, storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0136] It should be understood that "one embodiment" or "an embodiment" or "some embodiments" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in one embodiment" or "in some embodiments" appearing throughout the specification may not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and 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 application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments. The above description of each embodiment tends to emphasize the differences between the various embodiments, and the same or similar aspects can be referenced to each other. For the sake of brevity, this article will not repeat them.

[0137] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, object A and / or object B can represent three situations: object A exists alone, object A and object B exist at the same time, and object B exists alone.

[0138] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0139] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The 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 can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical, mechanical or other forms.

[0140] The modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules; they may be located in one place or distributed on multiple network units; some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0141] In addition, all functional modules in the embodiments of the present application may be integrated into one processing unit, or each module may be a separate unit, or two or more modules may be integrated into one unit; the above-mentioned integrated modules may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0142] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, etc., various media that can store program codes.

[0143] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling an electronic device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0144] The methods disclosed in several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0145] The features disclosed in several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0146] The features disclosed in several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0147] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A user behavior monitoring method, characterized in that: Applied to a wearable device, the method comprises: Acquire current behavior data of the user, wherein the current behavior data includes current physiological data, motion data and location data of the user; Obtaining a behavior evaluation result of the user according to the current behavior data of the user and a preset behavior evaluation model, wherein the preset behavior evaluation model is determined according to the historical behavior data of the user under normal conditions; When the behavior evaluation result indicates that the user is in an abnormal state, a reminder message is sent to the associated user.

2. The method according to claim 1, characterized in that Before obtaining the current behavior data of the user, the method further includes: Acquire historical behavior data of the user in a normal state, wherein the historical behavior data includes physiological data, motion data, and location data of the user; The preset behavior evaluation model is constructed based on the historical behavior data.

3. The method according to claim 1, characterized in that The method further comprises: Count the occurrence frequency of the current behavior data of the user within a preset time period; When the occurrence frequency is greater than a threshold, the preset behavior evaluation model is updated according to the current behavior data.

4. The method according to claim 1, characterized in that: When the behavior evaluation result indicates that the user is in an abnormal state, sending a reminder message to the associated user includes: When the behavior evaluation result indicates that the user is in an abnormal state, generating and outputting first inquiry information, wherein the first inquiry information is used to inquire whether the user is in an abnormal state; receiving feedback from the user regarding the first inquiry information; When the feedback content is used to indicate that the user is in an abnormal state, a first reminder message is sent to the associated user.

5. The method according to claim 4, characterized in that The method further comprises: Predicting the current behavior of the user according to the current behavior data; Accordingly, when the behavior evaluation result indicates that the user is in an abnormal state, sending a reminder message to the associated user includes: When the behavior evaluation result indicates that the user is in an abnormal state, generating and outputting second inquiry information, wherein the second inquiry information is used to inquire whether the user is performing the current behavior; receiving feedback from the user regarding the second inquiry information; When the feedback content is used to represent that the user is performing the current behavior, second reminder information is sent to the associated user, where the second reminder information includes the current behavior.

6. The method according to claim 1, characterized in that The associated user is a preset user or a user whose distance from the wearable device is less than a threshold.

7. The method according to claim 1, characterized in that The physiological data includes at least one of the following: body temperature, blood pressure, heart rate, and respiratory rate.

8. A user behavior monitoring device, characterized in that: include: An acquisition module, used to acquire current behavior data of the user, wherein the current behavior data includes current physiological data, motion data and position data of the user; An evaluation module, used to obtain a behavior evaluation result of the user according to the current behavior data of the user and a preset behavior evaluation model, wherein the preset behavior evaluation model is determined according to the historical behavior data of the user under normal conditions; The sending module is used to send a reminder message to the associated user when the behavior evaluation result indicates that the user is in an abnormal state.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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