Context analysis method and device, equipment, storage medium and product

Abstract

The invention discloses a situation analysis method and device, equipment, a storage medium and a product, and relates to the technical field of computers, and the situation analysis method comprises the steps: obtaining environment information collected by a smart watch; the environment information is input into a preset situation analysis model, the situation where the user is located is analyzed, a situation analysis result is obtained, and the preset situation analysis model is obtained through iterative training based on sample environment information and sample labels. According to the method, the environment information collected by the smart watch is automatically obtained, and the user situation analysis is automatically performed through the collected user information, so that the situation analysis result is obtained, manual situation setting is not needed, and the situation that in applications of wearable equipment such as the smart watch, situation setting can only be performed manually according to different situations, and the user experience is improved can be avoided. And the situation cannot be automatically analyzed, so that the use efficiency of the smart watch is reduced.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a situation analysis method, device, equipment, storage medium and product. Background Art

[0002] Contextual Analysis refers to the technology that identifies the user's current situation by analyzing the user's behavior, environment and other relevant information, and provides personalized services or adjusts device functions accordingly.

[0003] In the related art, in the application of wearable devices such as smart watches, different scenarios can only be set manually, and the scenarios cannot be automatically analyzed, which reduces the efficiency of using smart watches. Summary of the invention

[0004] The main purpose of this application is to provide a situation analysis method, device, equipment, storage medium and product, aiming to solve the technical problem that smart watches can only be set up manually for different situations and cannot automatically analyze the situations, which reduces the efficiency of using smart watches.

[0005] To achieve the above objectives, the present application proposes a situation analysis method, which includes:

[0006] Get environmental information collected by smart watches;

[0007] The environmental information is input into a preset situation analysis model, and the situation of the user is analyzed to obtain a situation analysis result, wherein the preset situation analysis model is obtained by iterative training based on sample environmental information and sample labels.

[0008] In one embodiment, before the step of inputting the environmental information into a preset situation analysis model to analyze the situation of the user and obtaining the situation analysis result, the step includes:

[0009] Acquire sample environment information of the user in different situations, wherein the sample environment information includes the same number of sample environment information in different situations;

[0010] Determining context weights of the user in different contexts based on the sample environment information;

[0011] Using the context corresponding to the sample environment information as a sample label;

[0012] Obtain an initial scenario analysis model;

[0013] Based on the sample environment information, the sample label and the context weight, the initial context analysis model is iteratively trained to obtain a preset context analysis model.

[0014] In one embodiment, before the step of using the context corresponding to the sample environment information as a sample label, the step includes:

[0015] Performing at least one context labeling on the sample environment information to obtain context information;

[0016] Calculating the number of times the context information corresponds to the same sample environment information to obtain an actual number of times of consistency;

[0017] Based on the actual number of consistency times, the context information is judged for consistency. If the context information is consistent, the context corresponding to the sample environment information is obtained, and the context corresponding to the sample environment information is used as a sample label.

[0018] In one embodiment, the step of iteratively training the initial context analysis model based on the sample environment information and the sample label to obtain a preset context analysis model includes:

[0019] Inputting the sample environment information into the initial situation analysis model to perform situation prediction and obtain an initial situation analysis result;

[0020] Calculate the difference between the initial situation analysis result and the sample label to obtain an error result;

[0021] Based on the error result, the parameters of the initial situation analysis model are adjusted until the error result meets a preset threshold and then the training is stopped to obtain a preset situation analysis model that meets the accuracy requirements.

[0022] In one embodiment, after the step of inputting the environmental information into a preset situation analysis model, analyzing the situation of the user, and obtaining the situation analysis result, the following steps are included:

[0023] Based on the situation analysis results, determining the target mode of the user;

[0024] Inputting the target mode into a preset strategy model, predicting the parameters of the smart watch in the target mode, and obtaining predicted parameter data;

[0025] Based on the parameter data, the parameters of the smart watch are adjusted to obtain the smart watch that adapts to the target mode after the parameters are adjusted.

[0026] In one embodiment, the step of inputting the target mode into a preset strategy model, predicting the parameters of the smart watch in the target mode, and obtaining predicted parameter data includes:

[0027] Inputting the target mode into a preset strategy model, predicting parameters of the smart watch in the target mode, and obtaining predicted parameter data;

[0028] The user's historical parameter data is obtained, and the predicted parameter data is adjusted to obtain the adjusted predicted parameter data.

[0029] In addition, to achieve the above purpose, the present application also proposes a situation analysis device, which includes:

[0030] An acquisition module is used to obtain environmental information collected by the smart watch;

[0031] The analysis module is used to input the environmental information into a preset situation analysis model, analyze the situation of the user, and obtain a situation analysis result, wherein the preset situation analysis model is obtained by iterative training based on sample environmental information and sample labels.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a situation analysis device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the situation analysis method described above.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the situational analysis method described above are implemented.

[0034] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the situation analysis method described above are implemented.

[0035] One or more technical solutions proposed in this application have at least the following technical effects:

[0036] Compared with the related art, in the application of wearable devices such as smart watches, the situation settings can only be performed manually for different situations, and the situation analysis cannot be automatically performed, which reduces the efficiency of using smart watches. Compared with the related art, in the application of wearable devices such as smart watches, the situation settings can only be performed manually for different situations, and the situation analysis cannot be automatically performed, which reduces the efficiency of using smart watches. The present application automatically obtains environmental information collected by the smart watch, and inputs the collected user information into a preset situation analysis model, automatically performs user situation analysis, and obtains situation analysis results without manual situation settings. Furthermore, it can avoid the problem that in the application of wearable devices such as smart watches, the situation settings can only be performed manually for different situations, and the situation analysis cannot be automatically performed, which reduces the efficiency of using smart watches. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 A flowchart of the first embodiment of the situation analysis method of the present application is provided;

[0040] Figure 2 A flow chart of the second embodiment of the situation analysis method of the present application;

[0041] Figure 3 This is a schematic diagram of the module structure of the situation analysis device according to an embodiment of the present application;

[0042] Figure 4 Schematic diagram of the device structure of the hardware operating environment involved in the situation analysis method in the embodiment of the present application.

[0043] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0044] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0045] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0046] The main solution of the embodiment of the present application is: to obtain environmental information collected by a smart watch; to input the environmental information into a preset situation analysis model, to analyze the situation in which the user is located, and to obtain a situation analysis result, wherein the preset situation analysis model is obtained by iterative training based on sample environmental information and sample labels.

[0047] In the related art, in the application of wearable devices such as smart watches, different scenarios can only be set manually, and the scenarios cannot be automatically analyzed, which reduces the efficiency of using smart watches.

[0048] The present application automatically obtains environmental information collected by a smart watch, and inputs the collected user information into a preset situational analysis model to automatically perform user situational analysis and obtain situational analysis results without the need for manual situational settings. Furthermore, the problem that in the application of wearable devices such as energy watches, only situational settings can be performed manually for different situations, and the situations cannot be automatically analyzed, which reduces the efficiency of using smart watches.

[0049] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, etc. The following takes the situation analysis device as an example to illustrate this embodiment and the following embodiments.

[0050] Based on this, the present application embodiment provides a situation analysis method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the scenario analysis method of the present application.

[0051] In this embodiment, the situation analysis method includes steps S100 to S200:

[0052] Step S100, obtaining environmental information collected by the smart watch;

[0053] It should be noted that the executor of this embodiment is a situational analysis device, which is provided with a smart watch. The smart watch is provided with multiple sensors, including motion sensors, ambient light sensors, heart rate detectors and other sensors. The sensors are used to collect various types of information about the user and perform situational analysis based on this information, so as to identify the current situation of the user.

[0054] It is understandable that the context analysis device obtains user information collected by the sensors of the smart watch. Specifically, after obtaining the user information, the context analysis device needs to clean the database of the user information. The purpose of data cleaning is to remove noise and outliers to ensure the quality of the data. Common data cleaning methods include:

[0055] Noise removal: Use filters (such as low-pass filters, median filters, etc.) to remove high-frequency noise from sensor data.

[0056] Filling missing values: For missing data points, you can use interpolation methods (such as linear interpolation, polynomial interpolation), forward filling (ffill) or backward filling (bfill) to fill them.

[0057] Outlier removal: Detect and remove outliers using statistical methods (e.g., Z-score, IQR) or machine learning methods (e.g., Isolation Forest, Local Outlier Factor).

[0058] Specifically, after data cleaning, the situational analysis device needs to integrate the sensor data to form a comprehensive data set. Common data fusion methods include:

[0059] Time synchronization: ensuring that data from different sensors are synchronized in time, usually by aligning timestamps or using fixed time windows.

[0060] Data merging: Merge the data from different sensors into a unified data frame in chronological order for subsequent processing.

[0061] Feature combination: The features of different sensors are combined to form a new feature vector to better reflect the user's activity status.

[0062] Specifically, after data fusion, the context analysis device needs to extract useful information from the raw data so that the model can better identify the user's activities or environment. Common feature extraction methods include:

[0063] Statistical features: Extract statistical features from sensor data, such as maximum, minimum, mean, standard deviation, median, etc.

[0064] Frequency domain features: Perform Fourier transform (FFT) on sensor data to extract frequency domain features, such as main frequency, power spectrum density, etc.

[0065] Time domain features: Extract time domain features, such as zero-crossing rate, energy, entropy, etc.

[0066] Custom features: Design specific features such as gait cycle, stride length, etc. according to specific application requirements.

[0067] Specifically, after feature extraction, the situational analysis device needs data standardization. The purpose of data standardization is to make the data from different sensors comparable and ensure that the model can learn better during the training process.

[0068] Step S200, input the environmental information into a preset situation analysis model, analyze the situation of the user, and obtain a situation analysis result, wherein the preset situation analysis model is obtained by iterative training based on sample environmental information and sample labels.

[0069] It should be noted that the situation analysis result is the situation the user is in. The preset situation analysis model is a trained model that can predict the input data to obtain the situation analysis result. The situation analysis device inputs the pre-processed user information into the preset situation analysis model, uses the trained model to perform situation prediction, and obtains the predicted situation analysis result.

[0070] In a feasible implementation manner, the following steps are performed before the step of inputting the environmental information into a preset situation analysis model to analyze the situation of the user and obtaining the situation analysis result:

[0071] Acquire sample environment information of the user in different situations, wherein the sample environment information includes the same number of sample environment information in different situations;

[0072] It should be noted that the sample environment information is the sensor data of the user in different situations. This data can be obtained from smart watches or other wearable devices. The sample environment information in different situations should be equal. When the number of samples in different categories varies greatly, the model may tend to predict the category with a larger number of samples. The sample environment information in different situations should be equal, which can improve the accuracy of model prediction. The sample environment information includes but is not limited to:

[0073] Location information: The user's geographic location is obtained through GPS or Wi-Fi positioning system.

[0074] Motion sensor data: such as accelerometer and gyroscope, used to detect the user's motion state and posture.

[0075] Ambient light sensor: Measures the light intensity of the surrounding environment to determine whether the user is outdoors or indoors.

[0076] Temperature and humidity sensors: Understand environmental conditions.

[0077] Heart rate monitor: Monitors the user's heart rate via a photoplethysmography (PPG) sensor.

[0078] Galvanic Skin Response Sensor (GSR): Measures skin conductivity and is used to assess emotional state.

[0079] Other biometric data: such as blood oxygen saturation (SpO2), etc.

[0080] It is understandable that the situation analysis device obtains the above information and uses the above information as sample environment information for model training.

[0081] Determining context weights of the user in different contexts based on the sample environment information;

[0082] It should be noted that the contextual weight reflects the degree of influence of different contextual factors on user behavior and needs. First, it is necessary to define the contextual factors that play an important role in the user environment. These factors may include but are not limited to:

[0083] Time factor: time of day (morning, noon, evening).

[0084] Location factors: The user's geographical location (home, office, outdoors).

[0085] Activity factors: ongoing activities (such as work, exercise, rest).

[0086] Device status: device battery level, connection status, etc.

[0087] Health factors: user's health status (such as heart rate, sleep quality).

[0088] It is understandable that the context analysis device analyzes the impact of different contextual factors on user behavior and needs based on the collected sample environmental information, and determines the weight of each contextual factor based on the analysis results. The weight reflects the importance of the contextual factor in different user contexts.

[0089] Using the context corresponding to the sample environment information as a sample label;

[0090] It is understandable that the sample tag records the context represented by the sample environment information. Tags may include but are not limited to:

[0091] Activity status: walking, running, driving, stationary, etc.

[0092] Environmental conditions: indoor, outdoor, office, home, etc.

[0093] Obtain an initial scenario analysis model;

[0094] It should be noted that the initial context analysis model is a pre-trained model. The context analysis device obtains a pre-selected initial context analysis model, which can be used to predict the user's behavior or preference in a specific situation.

[0095] Based on the sample environment information, the sample label and the context weight, the initial context analysis model is iteratively trained to obtain a preset context analysis model.

[0096] It is understandable that the situational analysis device uses sample environmental information as a training set and sample labels as a test set, uses the training set data to train the model, evaluates the model performance on the test set, and makes accurate predictions about the user's situation based on the situational weights. The model is adjusted based on its performance on the test set, and the training process is repeated until the model performance reaches expectations, thereby obtaining a preset situational analysis model.

[0097] In a feasible implementation manner, the step of using the context corresponding to the sample environment information as a sample label includes the following steps:

[0098] Performing at least one context labeling on the sample environment information to obtain context information;

[0099] It should be noted that the sample labels are obtained through annotation. In the case of multiple annotations, the consistency of the labels needs to be tested, which can effectively improve the reliability and accuracy of the annotation results. The context analysis device uses the same standard and category to perform multiple context annotations on the sample environment information to obtain context information.

[0100] Calculating the number of times the context information corresponds to the same sample environment information to obtain an actual number of times of consistency;

[0101] It is understandable that in the case of two annotations, the actual number of consistent times is the number of consistent annotations in the case of multiple annotations. The context analysis device counts the number of consistent times of the two annotations to obtain the actual number of consistent times.

[0102] Based on the actual number of consistency times, the context information is judged for consistency. If the context information is consistent, the context corresponding to the sample environment information is obtained, and the context corresponding to the sample environment information is used as a sample label.

[0103] It should be noted that the context analysis device determines the actual number of consistency times. If it exceeds a preset threshold, the context information is determined to be consistent, and the context corresponding to the sample environment information is used as the sample label.

[0104] In a feasible implementation manner, based on the sample environment information and the sample label, the step of iteratively training the initial context analysis model to obtain a preset context analysis model includes the following steps:

[0105] Inputting the sample environment information into the initial situation analysis model to perform situation prediction and obtain an initial situation analysis result;

[0106] It should be noted that the initial situation analysis result is the situation of the user predicted by the initial situation analysis model. The situation analysis device inputs the sample environment information into an initially set situation analysis model, and the model generates a prediction result based on the input data, thereby obtaining the initial situation analysis result.

[0107] Calculate the difference between the initial situation analysis result and the sample label to obtain an error result;

[0108] It is understandable that the error result reflects the current performance of the model. The smaller the error, the closer the model prediction is to the true value. The sample label represents the actual observation value. The situational analysis device measures the gap between the model prediction result and the actual observation value through the loss function.

[0109] Specifically, different loss functions can be selected according to different tasks:

[0110] Regression tasks: Usually the mean squared error (MSE) is used, which calculates the average of the sum of the squares of the differences between the predicted values ​​and the true values.

[0111] Classification task: For binary or multi-classification problems, cross-entropy loss is often used, which measures the difference between probability distributions.

[0112] Based on the error result, the parameters of the initial situation analysis model are adjusted until the error result meets a preset threshold and then the training is stopped to obtain a preset situation analysis model that meets the accuracy requirements.

[0113] It should be noted that the error result is the loss. Once the situational analysis device calculates the loss, the gradient of the loss function L with respect to the model parameter θ can be calculated through the chain rule. Specifically, for each layer of weights and biases, its corresponding partial derivative needs to be calculated, and the gradient of the loss relative to the model parameters is calculated through backpropagation. This process involves the chain rule. Starting from the output layer, the derivative of the loss function L with respect to the output of the layer is calculated, and the derivative is passed back to the previous layer. The derivative of the weight and bias of the previous layer with respect to the loss function is calculated, and the derivative of the loss function with respect to the weight of each layer is passed back in turn, and finally the gradient of the parameters of each layer is obtained. After the gradient of the parameters of each layer is obtained, the parameters of the model can be updated according to the optimization algorithm, so that the model can better fit the data in future predictions. This process is repeated until the model converges or reaches the predetermined stop condition, and then a preset situational analysis model that meets the accuracy requirements is obtained.

[0114] In this embodiment, the context analysis device iteratively trains the initial context analysis model to obtain a preset context analysis model that meets the accuracy requirements, automatically analyzes the user information, obtains the context analysis results, and improves the efficiency of using the smart watch.

[0115] The present application embodiment provides a situation analysis method, referring to Figure 2 , Figure 2 This is a flow chart of the second embodiment of the scenario analysis method of the present application.

[0116] In a feasible implementation manner, step S200 includes steps A10 to A30:

[0117] Step A10, determining the target mode of the user based on the situation analysis result;

[0118] It is understandable that the target mode is the user's behavior mode in different situations. The situation analysis device can define the target mode based on the analysis results, that is, how the user wants the smart watch to behave or what functions it should provide in a specific situation. The following are examples of target modes in several situations: outdoor mode, sports mode, work mode, rest mode and social mode.

[0119] Step A20, inputting the target mode into a preset strategy model, predicting the parameters of the smart watch in the target mode, and obtaining predicted parameter data;

[0120] It should be noted that the preset strategy model will calculate a series of parameter data according to the input target mode. The situation analysis device inputs the previously determined target mode into the preset strategy model, and the model can predict the most suitable smart watch parameter configuration under the target mode according to the target mode.

[0121] In particular, in order to better manage and implement these policies, the situational analysis device can preset a policy model. The policy model based on machine learning can dynamically adjust various parameters of the smart watch by predicting the user's future behavior, thereby providing a more personalized user experience. The following are the specific steps to implement this policy model: collect user sensor data, such as heart rate, steps, location, sleep cycle, etc., extract useful features from the original data, such as average heart rate, step change trend, location change frequency, etc., select features that are highly related to user behavior to improve the accuracy and efficiency of the prediction model, pre-process the data after feature extraction, remove noise and outliers, fill in missing values, and standardize or normalize the data to make it suitable for the requirements of the machine learning algorithm, input the data set into the initial policy model, iteratively train the initial policy model, and then obtain a policy model that meets the accuracy requirements, so that the model can learn the user's habits and preferences, deploy the trained model on the smart watch, predict the user's future behavior in real time, and dynamically adjust various parameters of the smart watch according to the prediction results to optimize the user experience.

[0122] Step A30: adjusting the parameters of the smart watch based on the parameter data to obtain the smart watch adapted to the target mode after the parameters are adjusted.

[0123] It is understandable that the situation analysis device can adjust the parameters of the smart watch according to the predicted parameter data corresponding to the target mode. Specifically, the following are several common modes and their corresponding smart watch parameter adjustment solutions:

[0124] 1. Outdoor Mode

[0125] High light environment: When the sunlight is strong, a higher screen brightness is required.

[0126] Sports needs: Users may exercise outdoors and need to monitor heart rate and other data in real time.

[0127] Parameter Adjustment

[0128] Screen brightness: Automatically adjust the screen brightness based on ambient light sensor data to ensure clear display even in strong light.

[0129] Heart rate monitoring: Increase the sampling rate of the heart rate sensor to monitor heart rate changes in real time.

[0130] GPS positioning: Turn on the GPS positioning function to record movement trajectory.

[0131] Notification frequency: Reduce non-urgent notifications to avoid interrupting your exercise.

[0132] Battery Management: Optimize battery management strategies to ensure sufficient power during long outdoor activities.

[0133] When the target mode is outdoor mode, first, the data of the current ambient light sensor is obtained in real time on the smart watch. These data will be obtained in real time and used as the basis for adjusting the screen brightness. The screen brightness is dynamically adjusted according to the prediction results. If the prediction results show that the current ambient brightness is high (for example, in the sun), the screen brightness is increased to ensure that the user can still clearly see the screen content under strong light. If the ambient brightness is low (for example, indoors or at night), the screen brightness is lowered to reduce energy consumption and protect eyesight. In order to prevent the discomfort caused by sudden changes in screen brightness, a smooth transition effect can be set to gradually adjust the brightness to the target value.

[0134] 2. Sports Mode

[0135] Real-time monitoring: Heart rate, steps, distance and other data need to be monitored in real time. Specifically, the heart rate sensor is enabled and its sampling rate is increased to collect data more frequently to ensure that changes in heart rate can be monitored in real time. This helps users understand their heart rate range and adjust exercise intensity. Accelerometers and gyroscopes are used to monitor users' steps and travel distance. Increasing the sampling rate of these sensors can more accurately record users' movement trajectory and pace information.

[0136] Personalized training suggestions: Provide personalized training plans and suggestions. Specifically, smart watches can be synchronized with fitness applications and use the user's exercise history data to develop personalized training plans. The application can provide customized exercise suggestions based on factors such as the user's age, gender, weight, fitness goals, etc., and enable posture recognition algorithms to identify the user's exercise posture by analyzing sensor data and promptly remind the user to adjust when a bad posture is detected. This helps prevent sports injuries and improve exercise efficiency.

[0137] Parameter Adjustment

[0138] Heart rate sensor sampling rate: Increase the sampling rate of the heart rate sensor to ensure real-time monitoring of heart rate changes. Specifically, the sampling rate of the heart rate sensor is increased to capture more subtle heart rate fluctuations, which is especially important for sports such as high-intensity interval training (HIIT) that require close attention to heart rate changes.

[0139] Accelerometer and gyroscope sampling rate: Increase the sampling rate to record motion data more accurately. Specifically, increase the sampling rate of these two sensors to more accurately capture the user's movements and movement speed. This is especially important for activities such as running and cycling.

[0140] Notification frequency: Reduce non-urgent notifications to avoid interrupting exercise. Specifically, reduce the push of non-urgent notifications to ensure that users are not disturbed during exercise. You can set notification rules in exercise mode to only receive urgent or important notifications.

[0141] Personalized training suggestions: Integrate with fitness apps to provide personalized training suggestions based on the user's historical data.

[0142] Posture Correction: Enable posture recognition algorithm to promptly remind users to adjust bad postures.

[0143] 3. Working Mode

[0144] Focus needs: Reduce distractions and improve work efficiency. Specifically, enable focus mode to block most non-urgent notifications and only keep notifications from important contacts or key applications. This helps users not be disturbed by irrelevant messages while working and concentrate on completing tasks.

[0145] Health reminders: Regularly remind users to take breaks and do healthy activities. Specifically, regular reminders are given to users to do necessary healthy activities, such as drinking water, doing simple stretching exercises, etc. These reminders can help users maintain good living habits and not forget to take care of their bodies during busy work.

[0146] Parameter Adjustment

[0147] Focus mode: Block non-urgent notifications and only keep reminders of important information.

[0148] Sedentary reminder: Set a reasonable reminder interval, such as a reminder every 30 minutes, to encourage users to get up and move around.

[0149] Health Tips: Regularly remind users to drink water or do some simple stretching exercises.

[0150] Data synchronization: Optimize data synchronization frequency to ensure data is updated in a timely manner without affecting battery life.

[0151] Battery management: Enable power saving mode to extend battery life.

[0152] 4. Rest Mode

[0153] Sleep monitoring: It is necessary to accurately monitor the user's sleep quality.

[0154] Smart Alarm Clock: Automatically adjusts the alarm time according to the user's sleep cycle.

[0155] Parameter Adjustment

[0156] Sleep Monitoring: Optimize the sleep monitoring algorithm to improve the accuracy of sleep stage recognition.

[0157] Smart alarm setting: Automatically adjust the alarm time according to the user's sleep cycle to ensure that the user wakes up in the best condition.

[0158] Relaxation guidance: Integrate meditation or breathing exercise apps to help users relax before bed.

[0159] Notification frequency: Reduce the frequency of notifications to reduce nighttime interruptions.

[0160] Screen brightness: Lower the screen brightness to reduce light interference.

[0161] 5. Social Mode

[0162] Frequent interaction: The need to respond quickly to messages on social apps.

[0163] Personalized reminders: Set different reminder methods based on user preferences.

[0164] Parameter Adjustment

[0165] Notification frequency: Increase the notification frequency of social applications to ensure that users receive messages in a timely manner.

[0166] Reminder method: Select different reminder methods according to user preferences, such as vibration, sound or screen prompt.

[0167] Personalization: Allows users to customize the types of notifications they want to receive.

[0168] Battery Management: Optimize battery management strategies to ensure that the battery is not depleted during frequent interactions.

[0169] In a feasible implementation manner, the target mode is input into a preset strategy model, and the parameters of the smart watch in the target mode are predicted. The step of obtaining the predicted parameter data includes the following steps:

[0170] Inputting the target mode into a preset strategy model, predicting parameters of the smart watch in the target mode, and obtaining predicted parameter data;

[0171] It is understandable that the preset strategy model can predict the parameters in the target mode to obtain predicted parameter data. The situation analysis device inputs the target mode into the preset strategy model, predicts the parameters in the target mode, and obtains predicted parameter data.

[0172] The user's historical parameter data is obtained, and the predicted parameter data is adjusted to obtain the adjusted predicted parameter data.

[0173] It should be noted that the user's historical parameter data is the user's historical usage habits. The context analysis device can adjust the predicted parameter data according to the user's historical usage habits to avoid the predicted parameter data exceeding the user's usage habits.

[0174] In this embodiment, the situation analysis device obtains the situation analysis results through a preset situation analysis model, determines the target mode of the user, and determines the parameter data corresponding to the target mode. Based on the parameter data corresponding to the target mode, the parameters of the smart watch are adjusted to obtain the smart watch that adapts to the target mode, which can improve the usage efficiency of the smart watch.

[0175] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the context analysis of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0176] This application also provides a situation analysis device, please refer to Figure 3 , the situation analysis device comprises:

[0177] An acquisition module 10 is used to acquire environmental information collected by a smart watch;

[0178] The analysis module 20 is used to input the environmental information into a preset situation analysis model, analyze the situation of the user, and obtain a situation analysis result.

[0179] Optionally, the analysis module includes:

[0180] The input module is used to input the environmental information into a preset situation analysis model, analyze the situation of the user, and obtain a situation analysis result, wherein the preset situation analysis model is obtained by iterative training based on sample environmental information and sample labels.

[0181] The determination module is used to determine a target mode based on the situation analysis result; and adjust parameters of the smart watch based on the target mode to obtain the smart watch adapted to the target mode.

[0182] Optionally, the analysis module includes:

[0183] The adjustment module is used to adjust the screen brightness of the smart watch if the target mode is the outdoor mode, so as to obtain a target screen brightness suitable for outdoor use.

[0184] Optionally, the input module includes:

[0185] The training module is used to obtain sample environment information of the user in different contexts; use the context corresponding to the sample environment information as a sample label; obtain an initial context analysis model; and iteratively train the initial context analysis model based on the sample environment information and the sample label to obtain a preset context analysis model.

[0186] Optionally, the training module includes:

[0187] A calculation module is used to input the sample environment information into the initial situation analysis model for situation prediction to obtain an initial situation analysis result; perform difference calculation on the initial situation analysis result and the sample label to obtain an error result; based on the error result, adjust the parameters of the initial situation analysis model until the error result meets a preset threshold and then stop training to obtain a preset situation analysis model that meets the accuracy requirements.

[0188] The situation analysis device provided by the present application adopts the situation analysis in the above embodiment to solve the technical problem of situation analysis. Compared with the prior art, the beneficial effects of the situation analysis device provided by the present application are the same as the beneficial effects of the situation analysis provided by the above embodiment, and the other technical features in the situation analysis device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0189] The present application provides a situation analysis device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the situation analysis in the above-mentioned embodiment one.

[0190] Reference below Figure 4 , which shows a schematic diagram of the structure of a situation analysis device suitable for implementing the embodiment of the present application. The situation analysis device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, tablet computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The context analysis device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0191] like Figure 4As shown, the situation analysis device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the situation analysis device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the situation analysis device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a situation analysis device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0192] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0193] The situation analysis device provided by the present application adopts the situation analysis in the above embodiment to solve the technical problem of situation analysis. Compared with the prior art, the beneficial effects of the situation analysis device provided by the present application are the same as the beneficial effects of the situation analysis provided by the above embodiment, and the other technical features in the situation analysis device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0194] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0195] The above is only a specific implementation 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.

[0196] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the situation analysis in the above-mentioned embodiments.

[0197] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0198] The computer-readable storage medium may be included in the situation analysis device; or may exist independently without being assembled into the situation analysis device.

[0199] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the situation analysis device, the situation analysis device: obtains environmental information collected by the smart watch; inputs the environmental information into a preset situation analysis model, analyzes the situation of the user, and obtains a situation analysis result, wherein the preset situation analysis model is obtained by iterative training based on sample environmental information and sample labels.

[0200] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0201] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0202] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0203] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned situation analysis, and can solve the technical problems of situation analysis. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the situation analysis provided in the above-mentioned embodiments, and will not be described in detail here.

[0204] The present application also provides a computer program product, including a computer program, which implements the above-mentioned steps of situation analysis when executed by a processor.

[0205] The computer program product provided by this application can solve the technical problem of situation analysis. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the situation analysis provided by the above embodiment, which will not be repeated here.

[0206] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A situation analysis method, characterized in that: The situation analysis method includes: Get environmental information collected by smart watches; The environmental information is input into a preset situation analysis model, and the situation of the user is analyzed to obtain a situation analysis result, wherein the preset situation analysis model is obtained by iterative training based on sample environmental information and sample labels.

2. The situation analysis method according to claim 1, characterized in that: The step of inputting the environmental information into a preset situation analysis model, analyzing the situation of the user, and obtaining the situation analysis result includes: Acquire sample environment information of the user in different situations, wherein the sample environment information includes the same number of sample environment information in different situations; Determining context weights of the user in different contexts based on the sample environment information; Using the context corresponding to the sample environment information as a sample label; Obtain an initial scenario analysis model; Based on the sample environment information, the sample label and the context weight, the initial context analysis model is iteratively trained to obtain a preset context analysis model.

3. The situation analysis method according to claim 2, characterized in that: The step of using the context corresponding to the sample environment information as a sample label comprises: Performing at least one context labeling on the sample environment information to obtain context information; Calculating the number of times the context information corresponds to the same sample environment information to obtain an actual number of times of consistency; Based on the actual number of consistency times, the context information is judged for consistency. If the context information is consistent, the context corresponding to the sample environment information is obtained, and the context corresponding to the sample environment information is used as a sample label.

4. The situation analysis method according to claim 2, characterized in that: The step of iteratively training the initial situation analysis model based on the sample environment information and the sample label to obtain a preset situation analysis model includes: Inputting the sample environment information into the initial situation analysis model to perform situation prediction and obtain an initial situation analysis result; Calculate the difference between the initial situation analysis result and the sample label to obtain an error result; Based on the error result, the parameters of the initial situation analysis model are adjusted until the error result meets a preset threshold and then the training is stopped to obtain a preset situation analysis model that meets the accuracy requirements.

5. The situation analysis method according to claim 1, characterized in that: The step of inputting the environmental information into a preset situation analysis model, analyzing the situation of the user, and obtaining the situation analysis result comprises: Based on the situation analysis results, determining the target mode of the user; Inputting the target mode into a preset strategy model, predicting the parameters of the smart watch in the target mode, and obtaining predicted parameter data; Based on the parameter data, the parameters of the smart watch are adjusted to obtain the smart watch that adapts to the target mode after the parameters are adjusted.

6. The situation analysis method according to claim 5, characterized in that: The step of inputting the target mode into a preset strategy model, predicting the parameters of the smart watch in the target mode, and obtaining predicted parameter data comprises: Inputting the target mode into a preset strategy model, predicting parameters of the smart watch in the target mode, and obtaining predicted parameter data; The user's historical parameter data is obtained, and the predicted parameter data is adjusted to obtain the adjusted predicted parameter data.

7. A situation analysis device, characterized in that: The device comprises: An acquisition module is used to obtain environmental information collected by the smart watch; The analysis module is used to input the environmental information into a preset situation analysis model, analyze the situation of the user, and obtain a situation analysis result, wherein the preset situation analysis model is obtained by iterative training based on sample environmental information and sample labels.

8. A situation analysis device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the situation analysis method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the situation analysis method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the situation analysis method according to any one of claims 1 to 6 are implemented.

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