CN112286758A - Information processing method, information processing device, electronic equipment and computer readable storage medium - Google Patents
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Abstract
The embodiment of the application discloses an information processing method, an information processing device, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring monitoring data sent by wearable equipment; generating a message to be pushed according to the monitoring data; analyzing the monitoring data to obtain the importance degree corresponding to the message to be pushed; and sending the message to be pushed to target equipment corresponding to the wearable equipment according to the importance degree. The information processing method, the information processing device, the electronic equipment and the computer readable storage medium can get rid of the limitation conditions such as the distance between the wearable equipment and the target equipment and the number of the target equipment, and accurately push the information to the target equipment, and the pushed information is more accurate and timely.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to an information processing method and apparatus, an electronic device, and a computer-readable storage medium.
Background
With the rapid development of electronic technology, more and more lightweight and portable wearable devices are on the market. By wearing the wearable device, the user can monitor various data (such as motion data, physiological health data and the like) of physical activities in real time through the wearable device, and different services such as communication and data transmission can be realized through the wearable device. At present, data collected by the wearable device can only be fed back to the wearable device, or fed back to devices such as a mobile phone and the like which establish communication connection (such as bluetooth and the like) with the wearable device, and message pushing is limited.
Disclosure of Invention
The embodiment of the application discloses an information processing method and device, electronic equipment and a computer readable storage medium, which can get rid of the limitation conditions such as the distance between wearable equipment and target equipment and the number of the target equipment, accurately push a message to the target equipment, and the pushed message is more accurate and timely.
The embodiment of the application discloses an information processing method, which comprises the following steps:
acquiring monitoring data sent by wearable equipment;
generating a message to be pushed according to the monitoring data;
analyzing the monitoring data to obtain the importance degree corresponding to the message to be pushed;
and sending the message to be pushed to target equipment corresponding to the wearable equipment according to the importance degree.
An embodiment of the application discloses an information processing apparatus, including:
the data acquisition module is used for acquiring monitoring data sent by the wearable equipment;
the message generating module is used for generating a message to be pushed according to the monitoring data;
the analysis module is used for analyzing the monitoring data to obtain the importance degree corresponding to the message to be pushed;
and the sending module is used for sending the message to be pushed to the target equipment corresponding to the wearable equipment according to the importance degree.
The embodiment of the application discloses an electronic device, which comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor is enabled to realize the method.
An embodiment of the application discloses a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method as described above.
According to the information processing method, the information processing device, the electronic equipment and the computer readable storage medium, the monitoring data sent by the wearable equipment are obtained, the message to be pushed is generated according to the monitoring data, the monitoring data are analyzed, the importance degree corresponding to the message to be pushed is obtained, the message to be pushed is sent to the target equipment corresponding to the wearable equipment according to the importance degree, the message can be pushed to the target equipment without establishing communication connection between the wearable equipment and the target equipment, the limiting conditions such as the distance between the wearable equipment and the target equipment and the number of the target equipment can be eliminated, the importance of the message to be pushed is determined by analyzing the monitoring data, and the message can be pushed to the target equipment more accurately and timely.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a diagram of an exemplary implementation of a method for processing information;
FIG. 2 is a flow diagram of a method of information processing in one embodiment;
fig. 3 is a flowchart illustrating analyzing the monitoring data to obtain the importance degree corresponding to the message to be pushed according to an embodiment;
fig. 4 is a schematic diagram illustrating obtaining importance levels corresponding to each type of message to be pushed in one embodiment;
FIG. 5A is an architecture diagram of a neural network in one embodiment;
FIG. 5B is a block diagram of an LSTM unit in one embodiment;
FIG. 6 is a flowchart of an information processing method in another embodiment;
FIG. 7 is a block diagram of an information processing apparatus according to an embodiment;
FIG. 8 is a block diagram of an electronic device in one embodiment.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It is to be noted that the terms "comprises" and "comprising" and any variations thereof in the examples and figures of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, a first communication channel may be referred to as a second communication channel, and similarly, a second communication channel may be referred to as a first communication channel, without departing from the scope of the present application. Both the first communication channel and the second communication channel are communication channels, but they are not the same communication channel.
Fig. 1 is a diagram illustrating an application scenario of an information processing method according to an embodiment. As shown in fig. 1, wearable device 10 may establish a communication connection with electronic device 20, and electronic device 20 may establish a communication connection with target device 30. The electronic device 20 may be a terminal device, which may include but is not limited to a mobile phone, a tablet, a PC (Personal Computer), a vehicle-mounted terminal, etc., or may be a server, which may be a Computer device capable of providing computing or application services, and may be a single server or a server cluster. The wearable device 10 may include, but is not limited to, a smart watch, a smart bracelet, smart glasses, and the like, the target device 30 may be a terminal device having a corresponding relationship with the wearable device 10, and the target device 30 may include, but is not limited to, a mobile phone, a tablet, a PC (Personal Computer), a vehicle-mounted terminal, and the like. The wearable device 10, the electronic device 20, and the target device 30 are not limited in the embodiments of the present application.
The wearable device 10 can monitor the physiological health status, the motion status, the application use condition, the geographical location, and the like of the user, and collect monitoring data. Wearable device 10 may transmit the collected monitoring data to electronic device 20. The electronic device 20 may obtain the monitoring data sent by the wearable device 10 and generate a message to be pushed according to the monitoring data. The electronic device 20 may analyze the message to be pushed to obtain the importance degree corresponding to the generated message to be pushed. After determining the importance level corresponding to the message to be pushed, the electronic device 20 may send the message to be pushed to the target device 30 corresponding to the wearable device 10 based on the importance level.
As shown in fig. 2, in one embodiment, an information processing method is provided, which is applicable to the electronic device 20 described above, and the method may include the following steps:
step 210, acquiring monitoring data sent by the wearable device.
In some embodiments, the wearable device may monitor one or more of a physiological state, a motion state, an application usage of the wearable device, a geographic location, a device state, and the like of the user, and acquire monitoring data, which may include one or more of physiological health data, motion data, application usage of the wearable device, location data, a device state data, and the like of the user, but is not limited thereto.
Alternatively, the physiological health data may be used to describe the physiological health status of the user, which may include, but is not limited to, human heart rate, human fat rate, blood oxygen content, and the like. Various physiological detection sensors can be arranged on the wearable device, such as a heart rate sensor, a body fat sensor, a blood oxygen content sensor and the like, and physiological health data of a user can be acquired through the various physiological detection sensors.
The athletic data may include, but is not limited to, data such as number of steps walked over a fixed period of time (e.g., within 1 day, within 12 hours, etc.), mileage run, calories burned, etc. The wearable device can be provided with an acceleration sensor, a gravity sensor and the like, and the motion state of the user can be detected through the acceleration sensor, the gravity sensor and the like. The acceleration data, the gravity data and the like collected by the acceleration sensor, the gravity sensor and the like can be obtained, and the acceleration change and the gravity change of the wearable device are determined according to the acceleration data, the gravity data and the like. If the acceleration change and the gravity change of the wearable device meet the preset change conditions corresponding to the walking state/running state, it can be determined that the user is in the walking state/running state. And corresponding motion data are generated according to acceleration data, gravity data and the like acquired by an acceleration sensor, a gravity sensor and the like.
The application usage data may refer to data of an application used by the user on the wearable device. The application usage data may include, but is not limited to, the type of application used, the length of each use, the frequency of use, and the like. The wearable device can record application running data such as application identification, running starting time and running ending time of the application program running each time, and count the recorded application running data to obtain application use data.
The location data refers to location information of the wearable device, and the location data may include, but is not limited to, latitude and longitude information of the wearable device, indoor location information, and the like, where the latitude and longitude information may be obtained through a GPS (Global Positioning System) chip provided on the wearable device, the indoor location information may be obtained by using a WiFi (Wireless Fidelity) indoor location technology, a bluetooth beacon location technology, and the like, and the indoor location information may include information of the number of floors, a specific indoor area, a room, and the like.
Device status data may be used to describe the status of the wearable device, which may include, but is not limited to, a wearing status, a hardware normal/abnormal status, a power status, and the like.
In some embodiments, the wearable device may also generate and output corresponding push information according to the acquired physiological health data, motion data, application usage data of the wearable device, location data, device status data, and the like. For example, the wearable device generates motion push information according to the collected motion data, or generates position push information according to the position data, and the like. The behavior data of the user on the push information can be collected at the same time, for example, whether the user performs confirmation operation, cancel operation, check operation, or no operation on the push information or not, and the monitoring data containing the behavior data is sent to the electronic device.
The wearable device may send the collected monitoring data to the electronic device. In some embodiments, the wearable device may send the monitoring data collected for a preset duration to the electronic device every preset duration, e.g., the monitoring data may be sent every 2 hours, 5 hours, etc. In other embodiments, the wearable device may also analyze the collected monitoring data, determine whether the collected monitoring data is abnormal, and when the collected monitoring data is abnormal, may send the abnormal monitoring data to the electronic device. For example, wearable equipment gathers user's human rhythm of the heart, when detecting human rhythm of the heart and being greater than the rhythm of the heart threshold value of setting for, confirms that human rhythm of the heart takes place unusually promptly, can send the human rhythm of the heart that appears unusually to electronic equipment. The abnormal monitoring data can be sent to the electronic equipment in time, and the electronic equipment can push the message for reminding the abnormality according to the abnormal monitoring data, so that the timeliness of message pushing can be improved.
The message to be pushed may be information related to the monitoring data and required to be pushed to the terminal device, and may include, but is not limited to, prompt information, advertisement information, news information, information, and the like, where the prompt information may include information of various prompt functions, for example, information of prompting abnormality (e.g., prompting abnormal heart rate of a human body, abnormal hardware of the wearable device, abnormal geographic position, and the like), information of prompting exercise data (e.g., prompting number of steps taken within 1 day, mileage run, calories consumed, or exercise ranking, and the like), and information of prompting communication information (e.g., language information, short message, and the like) received on the wearable device. The message to be pushed may include, but is not limited to, text information, video information, voice information, picture information, and the like.
After the electronic equipment acquires the monitoring data sent by the wearable equipment, the monitoring data can be analyzed, and corresponding information to be pushed is generated. Illustratively, the monitoring data can be analyzed, whether the monitoring data is abnormal or not can be judged, if the monitoring data is detected to be abnormal, prompt information can be generated according to the monitoring data with the abnormal condition, and the prompt information can be used as a message to be pushed. For example, the location information of the wearable device may be analyzed, and when the location information is detected not to be within a preset normal geographic range, a prompt message of location abnormality may be generated.
In some embodiments, the electronic device may also obtain the behavior habit characteristics of the user by analyzing the monitoring data, and generate the message to be pushed according to the behavior habit characteristics. For example, the monitoring data is analyzed to count that the frequency of music applications that the user uses the wearable device in the past period is high, so that information of push music information or information of push music song list can be generated. It is to be understood that the above-mentioned several ways of generating a message to be pushed are only used for illustrating the embodiments of the present application, and are not used to limit the specific way of generating the message to be pushed.
And step 230, analyzing the monitoring data to obtain the importance degree corresponding to the message to be pushed.
The importance degree can be used to represent the importance of the message to be pushed and the degree of urgency, and the higher the importance degree is, the stronger the importance of the message to be pushed is, and the higher the degree of urgency is. In some embodiments, the monitoring data may be analyzed to obtain an analysis result, where the analysis result may include the type of the monitoring data, whether an exception occurs, user behavior (such as the confirmation operation, the cancellation operation, the check operation, and no operation described above), and determine the importance degree corresponding to the message to be pushed according to the analysis result. Alternatively, the different types of monitoring data may correspond to different degrees of importance, for example, the physiological health data of the user may correspond to a higher degree of importance than the exercise data, the exercise data may correspond to a higher degree of importance than the application usage data, and so on. The importance degree corresponding to the abnormal monitoring data may be higher than that corresponding to the normal monitoring data, and the like. The importance levels corresponding to different user behaviors may be different, for example, the importance levels corresponding to the checking operation and the confirming operation may be higher than the importance levels corresponding to the canceling operation and the no operation, and the like.
Different weights can be distributed to various contents contained in the analysis result, various contents contained in the analysis result are converted into important score numerical values, weighting and calculation are carried out according to the corresponding weights, and scores corresponding to the monitoring data are obtained and can be used for representing the importance degree corresponding to the message to be pushed, and the higher the score is, the higher the importance degree can be, and the like.
Furthermore, the importance degree corresponding to the message to be pushed can be determined in an auxiliary manner according to the message attribute of the message to be pushed. The message attribute of the message to be pushed may include a message type of the message to be pushed, a content of the message to be pushed, and the like, and the message type may include a prompt message, an advertisement message, an information message, and the like, wherein the importance degree of the prompt message may be the highest, and the importance degree of the advertisement message may be the lowest, and further, the importance degree of prompting physiological health abnormality, prompting location abnormality, and the like in the prompt message may be higher than that of the ordinary prompt message.
It should be noted that step 230 may also be executed before step 220, and the electronic device may first analyze the monitoring data to obtain the importance degree and then generate the message to be pushed.
And 240, sending a message to be pushed to a target device corresponding to the wearable device according to the importance degree.
The electronic device may obtain each terminal device bound to the wearable device, and the electronic device may store a binding relationship between a device identifier of each wearable device and a device identifier of another terminal device, where the device identifier may be any one of a Media Access Control (MAC) address, a device number, and the like. The first device identifier of the wearable device sending the monitoring data can be acquired, and the second device identifier having a binding relationship with the first device identifier is searched according to the first device identifier, wherein the second device identifier having the binding relationship is the device identifier of the terminal device bound with the wearable device. The target devices can be selected from the bound terminal devices, the number of the target devices can be one or more, one wearable device can be bound with a plurality of terminal devices at the same time, and the electronic device can send information to be pushed to the plurality of target devices at the same time.
In some embodiments, the messages to be pushed with different importance degrees may correspond to different message pushing rules, and the message pushing rules may include the number of target devices to be pushed, a communication channel for sending the messages to be pushed, pushing time, a prompting manner of the messages to be pushed on the target devices, and the like. The message to be pushed can be sent to the target device corresponding to the wearable device according to the pushing rule corresponding to the importance degree of the message to be pushed. Taking the push time as an example, the message to be pushed with higher importance can be sent to the target device in time, and the message to be pushed with lower importance can be sent to the target device at a fixed time or at a certain time interval, so that the condition that the message to be pushed is too much to cause bad impression of the user can be prevented.
In some embodiments, the manner in which messages to be pushed of different importance are prompted on the target device may be different. Step 240 may include: determining a prompt mode according to the importance degree of the message to be pushed, generating a prompt instruction according to the prompt mode, and sending the message to be pushed and the prompt instruction to target equipment corresponding to the wearable equipment, wherein the prompt instruction is used for indicating the target equipment to prompt the message to be pushed according to the prompt mode.
The prompting modes can comprise a popup prompting mode, a vibration prompting mode, a ringing prompting mode, a default set information prompting mode under the target equipment and the like. The higher the importance degree is, the stronger the prompting mode of the prompting effect can be corresponded. As an embodiment, the importance level of the message to be pushed may be divided by an importance level, a plurality of importance levels may be preset, and the higher the importance level is, the higher the importance level is. For example, the importance levels may include 4 levels of super importance, sub importance, common importance, light importance, etc., wherein the order of the importance levels corresponding to the 4 importance levels is super importance > sub importance > common importance > light importance. The prompt mode corresponding to the super importance may be vibration prompt + ring prompt, the prompt mode corresponding to the secondary importance may be vibration prompt, the prompt mode corresponding to the common importance may be an information prompt mode set by default under the target device, and the prompt mode corresponding to the importance may be pop-up prompt, but is not limited thereto.
The target device can output the message to be pushed after receiving the message to be pushed and the instruction, determine a prompting mode according to the instruction, and prompt the output message to be pushed according to the prompting mode. The method and the device can select a proper prompting mode to prompt according to the importance degree of the message to be pushed, can prompt a user of the target device to check the message with high importance degree in time, prevent the user from missing the important message, and can not generate great influence on the user of the target device for the message to be prompted is more flexible and convenient and meets different requirements of the user.
In the embodiment of the application, monitoring data sent by a wearable device is obtained, a message to be pushed is generated according to the monitoring data, the monitoring data is analyzed, the importance degree corresponding to the message to be pushed is obtained, the message to be pushed is sent to a target device corresponding to the wearable device according to the importance degree, the message can be pushed to the target device without establishing communication connection between the wearable device and the target device, limitation conditions such as the distance between the wearable device and the target device and the number of the target device can be eliminated, the importance of the message to be pushed is determined by analyzing the monitoring data, and the message can be pushed to the target device more accurately and timely.
As shown in fig. 3, in an embodiment, the step of analyzing the monitoring data to obtain the importance degree corresponding to the message to be pushed may include the following steps:
The data characteristics may be used to describe the actual information expressed by the monitoring data. Optionally, the monitoring data sent by the wearable device may include monitoring information acquired by the wearable devices at different acquisition times, the monitoring information may be arranged in sequence according to the sequence of the acquisition times, each monitoring information may be analyzed one by one, invalid monitoring information may be filtered, and data characteristics of the monitoring data with the invalid monitoring information filtered are extracted. The invalid monitoring information can be monitoring information meeting invalid conditions, the invalid conditions can be set according to actual requirements, and the invalid conditions set by different wearable devices can be different.
For example, taking application usage data of the wearable device as an example, a plurality of running records of the wearable device running the application in a certain period of time may be obtained, for example, in 12: 00-12: 30 running music application a, at 13: 12-13: 25 running social application B etc. Of the plurality of operation records, the operation record with the operation duration not greater than the set duration may be filtered out as invalid monitoring information, for example, the operation record with the operation duration not greater than 5 minutes may be filtered out. Invalid monitoring information is filtered, and then data features are extracted, so that the extracted data features are more accurate.
In some embodiments, since the monitoring data collected by the wearable device is in natural language, in order to better analyze the monitoring data, a feature vector corresponding to the monitoring data may be generated. Optionally, each piece of monitoring information in the monitoring data may be mapped to a vector through a Word2vec model or the like, the mapped vector is a feature of the monitoring information, and an embedding feature vector with multiple dimensions may be obtained, the embedding feature vector may be used as a data feature of the monitoring data, and a vector of each dimension in the embedding feature vector may be used to represent a piece of monitoring information. Furthermore, the monitoring information in the monitoring data is sequentially arranged according to the sequence of the acquisition time, so that the vector of each dimension in the generated embedding embedded feature vector has a certain time sequence relation.
And 304, analyzing the data characteristics through the trained neural network to obtain the importance level corresponding to the message to be pushed.
In the embodiment of the present application, the Neural Network may be a Recurrent Neural Network (RNN), an LSTM (Long Short-Term Memory) Neural Network, a GRU (Gated Recurrent Neural) Neural Network, or the like, but is not limited thereto. The neural network can be obtained through training of a large amount of monitoring sample data of wearable equipment, each monitoring sample data can be marked with a corresponding important grade, the monitoring sample data and the marked important grade can be input into the neural network to be trained during training, the neural network to be trained analyzes the monitoring sample data, and the estimated important grade is output. The parameters of the neural network can be continuously adjusted according to the distance between the estimated importance level and the marked importance level until the distance between the estimated importance level and the marked importance level meets the expected value, and the training of the neural network is completed.
In some embodiments, before the monitoring sample data is input into the neural network to be trained, the monitoring sample data may also be mapped into a multidimensional embedding feature sample vector through a model such as Word2vec, and the multidimensional embedding feature sample vector is input into the neural network to be trained to train the neural network to be trained.
In some embodiments, after acquiring the monitoring data sent by the wearable device, the electronic device may classify the monitoring data to obtain N types of monitoring data, where N may be a positive integer. The N types of monitoring data may include one or more of: physiological health data of the user, motion data of the user, application usage data of the wearable device, location data of the wearable device, device status data of the wearable device, and the like. After the monitoring data is classified, a message to be pushed corresponding to each type may be generated according to the monitoring data of each type, for example, a physiological health prompting message may be generated according to the physiological health data of the user, an exercise prompting message may be generated according to the exercise data of the user, an application information pushing message may be generated according to the application usage data of the wearable device, or an entertainment pushing message, an advertisement message, and the like, which is not limited herein.
For each type of monitoring data, the electronic device may extract data features of each type of monitoring data to obtain a feature vector corresponding to each type of monitoring data. The monitoring data of each type can be analyzed through the neural network obtained through training, and the importance level corresponding to the message to be pushed of each type is obtained.
Fig. 4 is a schematic diagram illustrating obtaining importance levels corresponding to each type of message to be pushed in one embodiment. As shown in fig. 4, the electronic device classifies the monitoring data of the wearable device into different types of monitoring data (e.g., physiological health data, motion data, application usage data … …, etc. in fig. 4). For each type of monitoring data, the data feature of each type of monitoring data can be extracted and input into the neural network, and the neural network can analyze the input data feature and output an importance level (such as super importance, sub importance, common importance or light importance in fig. 4), which can be used to indicate the importance degree of the message to be pushed corresponding to the input type. It is understood that the importance level can be expressed by other means, such as numbers 1-4, or letters A-D, and the like, and is not limited herein.
For example, taking the first type of monitoring data as an example, the first type may be any one of N types. The first type of monitoring data may include monitoring information arranged in order of the acquisition time. Each piece of monitoring information contained in the first type of monitoring data can be mapped to a vector through a Word2vec model and the like, and then a multidimensional first feature vector is formed according to the vector mapped by each piece of monitoring information. The first feature vector contains a vector for each dimension, which can be used to represent a feature of each piece of monitoring information.
For example, the monitoring data may include application usage data, the application usage data may include multiple application records that operate at different times within a certain period of time, the application identifiers recorded in each application record may be obtained, and the application identifiers are sequentially arranged according to the sequence of operation, for example, the application identifiers according to the sequence of operation within a certain period of time of the wearable device include APP _2, APP _1, and APP _ 0. The obtained application identifications can be mapped into vectors, wherein APP _2 is mapped into [2, 1, 2 ]]APP _1 is mapped to [1, 2]APP _0 is mapped to [2, 1]. The 3 vectors obtained by mapping can be combined to form a feature vector corresponding to the application use data as
Wherein the vector of each dimension of the feature vector is used to represent the feature of each application record.
It should be noted that, for some types of monitoring data, the adopted data is numbers, for example, acquired human heart rate data, the recorded data is the human heart rate detected each time, the value acquired each time can be mapped into a fixed value interval range, for example, a range of [0, 1], and then the feature vector is generated according to the value combination obtained by mapping.
After the first feature vector of the first type of monitoring data is obtained, the first feature vector may be input into a neural network obtained by training, the first feature vector is subjected to cyclic processing by neurons in the neural network, and an importance level corresponding to the first type of message to be pushed is output.
Illustratively, FIG. 5A is a real worldArchitecture diagram of neural network in the example. As shown in fig. 5A, the neural network may include a cyclic unit a, which may be an LSTM unit, a GRU unit, or the like. XtFor data input to the neural network at time T, htIt represents the data that the cyclic unit a outputs at time T. For a first feature vector of a first type, the first feature vector includes multidimensional vectors, and since monitoring information included in the monitoring data is arranged in sequence according to the acquisition time, each vector in the first feature vector has a certain time sequence, and each dimension vector can be used as input of the cyclic unit a at different times in sequence according to the sequence of each dimension vector in the first feature vector. The circulation unit a may obtain data output at the current time according to a vector input at the current time, data output by the circulation unit a at the previous time, and the like, and input the data output at the current time into the circulation unit a at the next time. The process is circulated until the vector input at the current moment is the last one-dimensional vector in the first feature vector, and the neural network can classify according to the data output at each moment to obtain the corresponding importance level.
Using the feature vector as
To illustrate, where the vector [2, 1, 2 ]]Can be used as X0Input to the circulation unit A at time T0 to obtain output h at time T00Vector [1, 2 ]]Can be used as X1At time T1, the signal is input to the cycle unit A, which is based on X1And output h at time T00To obtain an output h at the time T11Vector [2, 1]]Can be used as X2At time T2, the signal is input to the cycle unit A, which is based on X2And output h at time T11To obtain an output h at the time T22. Can be according to h0、h1、h2And (5) classifying to obtain corresponding importance levels.
Taking the example of the loop unit a as the LSTM unit, fig. 5B is a schematic diagram of the structure of the LSTM unit in one embodiment. As shown in fig. 5B, the LSTM unit may include a forgetting layer, an input layer, and an output layer. Wherein, the forgetting layerFor output data h of last time LSTM cellt-1And input data X at the current timetFiltering to remove unimportant information, and the forgetting layer can obtain a number for representing the forgetting state through an activation function sigma and transfer the digital input word to C for representing the last unit statet-1In (1). The input layer can output data h from the last time LSTM unitt-1And input data X at the current timetSelects important information and changes the cell state from C according to the selected informationt-1Is updated to Ct. The output layer can process the updated unit state through the activation function tanh to obtain the output result h at the current momenttThen the output result h of the current time is obtainedtAnd cell state CtPassed to the LSTM unit at the next time instant.
In the embodiment of the application, the monitoring data can be analyzed through the neural network obtained through training to obtain the importance level corresponding to the message to be pushed, so that the accuracy of importance analysis can be improved, and the message can be pushed to the target device more accurately. And the importance analysis can be respectively carried out aiming at different types of monitoring data, so that the obtained importance level has pertinence, and the information push requirements of various different monitoring scenes of the wearable equipment are met. And furthermore, the monitoring data is analyzed by adopting the recurrent neural network with the time sequence information, so that the monitoring data is more attached to the monitoring data acquired by the wearable equipment, the analysis accuracy is improved, and the pushed message is more accurate and timely.
As shown in fig. 6, in one embodiment, another information processing method is provided, which is applicable to the electronic device described above. The method may comprise the steps of:
And step 604, generating a message to be pushed according to the monitoring data.
And 606, extracting data characteristics of the monitoring data.
And 608, analyzing the data characteristics through the trained neural network to obtain the importance level corresponding to the message to be pushed.
The descriptions of steps 602-608 refer to the related descriptions of the above embodiments, and are not repeated herein.
In some embodiments, after obtaining the importance degree corresponding to the message to be pushed, the electronic device may obtain at least one terminal device bound to the wearable device, determine at least one target device from the bound terminal devices according to the importance degree, and send the message to be pushed to the target device. The importance degrees corresponding to the messages to be pushed are different, and the target devices which select to send the messages to be pushed can be different.
Optionally, the number of target devices corresponding to different importance levels may be different, the number of target devices may be in a positive correlation with the importance levels, and the higher the importance level is, the more the number of determined target devices may be, which may ensure that the push message is known in time.
Optionally, device identifiers corresponding to different importance levels may also be set. For example, the message to be pushed with a high importance level may be a terminal device of a user's family (such as a parent) bound to the wearable device, the message to be pushed with a low importance level may be a device of the wearable device, such as a mobile phone of the user. Further, the target device may be selected in combination with the content of the message to be pushed, for example, if the message to be pushed is prompt information related to life safety of the user, the message to be pushed may be sent to the terminal device of the bound family, and the message to be pushed may be sent to the bound medical terminal, etc. Different message pushing scenes can be met, the accuracy and the timeliness of the pushed messages are guaranteed, and the requirements of users are met.
In some embodiments, the electronic device may send the message to be pushed to the target device corresponding to the wearable device through a communication channel corresponding to the importance level. The step of sending the message to be pushed to the target device corresponding to the wearable device through the communication channel corresponding to the importance degree can include steps 610-612.
In some embodiments, the first communication channel may be a TCP (Transmission Control Protocol) communication channel. The target device may have an application installed thereon, which may include an SDK (Software Development Kit) that receives a message to be pushed sent by the electronic device in relation to the wearable device. The application program on the target device can establish a TCP connection with the electronic device (such as a server) through the SDK, and the electronic device can send the message to be pushed to the target device through the TCP connection.
Further, in order to ensure stable transmission of the message to be pushed, after the electronic device establishes a TCP connection with the target device, the electronic device may send the message to be pushed to the target device in a long connection manner, and may continuously send a plurality of data packets during the TCP connection establishment process, and when the data packets are not sent, the electronic device may send a heartbeat packet to the target device, so that the TCP connection may be maintained without being disconnected.
After receiving a message to be pushed sent by the electronic device through the TCP connection, the target device may return feedback information to the electronic device, and if the electronic device receives the feedback information, it may be determined that the target device has received the message to be pushed. If the electronic device does not receive the feedback information returned by the target device, it can be determined that the target device does not receive the message to be pushed. The electronic device may determine whether the importance of the message to be pushed belongs to a target importance, where the target importance may be greater than an importance threshold. For example, if the importance level is expressed by a percentage value, it may be determined whether the importance level of the message to be pushed is greater than 80%, and if the importance level is expressed by an importance level, it may be determined whether the importance level of the message to be pushed is super-important, etc., but the present invention is not limited thereto.
If the importance degree of the message to be pushed belongs to the target importance degree, it can be shown that the message to be pushed is important and needs to be sent to the target device in time. The electronic device can send a message to be pushed to the target device through a second communication channel, wherein the data transmission stability of the second communication channel can be higher than that of the first communication channel. Optionally, the second communication channel may be an operator communication channel, and since the stability of the operator communication channel is higher than that of the TCP communication channel, it may be ensured that the message to be pushed can be sent to the target device in time.
In the embodiment of the application, the message to be pushed is sent to the target device by combining two different communication channels, so that the important pushed message can be ensured to be sent to the target device in time, and the timeliness of the pushed message is improved.
As shown in fig. 7, in one embodiment, an information processing apparatus 700 is provided, which is applicable to the electronic device described above. The information processing apparatus 700 may include a data acquisition module 710, a message generation module 720, an analysis module 730, and a sending module 740.
And a data obtaining module 710, configured to obtain monitoring data sent by the wearable device.
And a message generating module 720, configured to generate a message to be pushed according to the monitoring data.
The analysis module 730 is configured to analyze the monitoring data to obtain an importance degree corresponding to the message to be pushed.
The sending module 740 is configured to send a message to be pushed to a target device corresponding to the wearable device according to the importance degree.
In one embodiment, the sending module 740 includes a prompting unit and a sending unit.
And the prompting unit is used for determining a prompting mode according to the importance degree and generating a prompting instruction according to the prompting mode.
The sending unit is used for sending the message to be pushed and a prompt instruction to the target equipment corresponding to the wearable equipment, and the prompt instruction is used for indicating the target equipment to prompt the message to be pushed according to a prompt mode.
In the embodiment of the application, monitoring data sent by a wearable device is obtained, a message to be pushed is generated according to the monitoring data, the monitoring data is analyzed, the importance degree corresponding to the message to be pushed is obtained, the message to be pushed is sent to a target device corresponding to the wearable device according to the importance degree, the message can be pushed to the target device without establishing communication connection between the wearable device and the target device, limitation conditions such as the distance between the wearable device and the target device and the number of the target device can be eliminated, the importance of the message to be pushed is determined by analyzing the monitoring data, and the message can be pushed to the target device more accurately and timely.
In one embodiment, the analysis module 730 includes a feature extraction unit and an analysis unit.
And the characteristic extraction unit is used for extracting the data characteristics of the monitoring data.
And the analysis unit is used for analyzing the data characteristics through the trained neural network to obtain the important grade corresponding to the message to be pushed, the neural network is obtained through training of monitoring sample data of the wearable device, and the monitoring sample data is marked with the corresponding important grade.
In one embodiment, the information processing apparatus 700 further includes a classification module in addition to the data acquisition module 710, the message generation module 720, the analysis module 730, and the sending module 740.
And the classification module is used for classifying the monitoring data to obtain N types of monitoring data, wherein N is a positive integer.
The message generating module 720 is further configured to generate a message to be pushed corresponding to each type according to the monitoring data of each type.
And the feature extraction unit is further used for extracting the data features of the monitoring data of each type to obtain a feature vector corresponding to the monitoring data of each type.
The analysis unit is further configured to input a first feature vector corresponding to the first type of monitoring data into the trained neural network, perform cyclic processing on the first feature vector through a neuron in the neural network, and output an importance level corresponding to a first type of message to be pushed, where the first type is any one of the N types.
In one embodiment, the N types of monitoring data include one or more of: physiological health data of a user, motion data of the user, application usage data of the wearable device, location data of the wearable device, and device status data of the wearable device.
In the embodiment of the application, the monitoring data can be analyzed through the neural network obtained through training to obtain the importance level corresponding to the message to be pushed, so that the accuracy of importance analysis can be improved, and the message can be pushed to the target device more accurately. And the importance analysis can be respectively carried out aiming at different types of monitoring data, so that the obtained importance level has pertinence, and the information push requirements of various different monitoring scenes of the wearable equipment are met. And furthermore, the monitoring data is analyzed by adopting the recurrent neural network with the time sequence information, so that the monitoring data is more attached to the monitoring data acquired by the wearable equipment, the analysis accuracy is improved, and the pushed message is more accurate and timely.
In an embodiment, the sending module 740 is further configured to obtain at least one terminal device bound to the wearable device, determine at least one target device from the terminal devices according to the importance degree, and send a message to be pushed to the target device.
In one embodiment, the sending module 740 is further configured to send the message to be pushed to the target device corresponding to the wearable device through a communication channel corresponding to the importance level.
In one embodiment, the sending module 740 is further configured to send a message to be pushed to a target device corresponding to the wearable device through a first communication channel, and send the message to be pushed to the target device through a second communication channel if feedback information for the message to be pushed returned by the target device is not received and the importance degree belongs to a target importance degree, where the target importance degree is greater than an importance threshold.
In the embodiment of the application, the message to be pushed is sent to the target device by combining two different communication channels, so that the important pushed message can be ensured to be sent to the target device in time, and the timeliness of the pushed message is improved.
FIG. 8 is a block diagram of an electronic device in one embodiment. The electronic device may be a server or a server cluster, and may also be a terminal device such as a smart phone and a tablet computer. As shown in fig. 8, electronic device 800 may include one or more of the following components: a processor 810, a memory 820 coupled to the processor 810, wherein the memory 820 may store one or more computer programs that may be configured to be executed by the one or more processors 810 to perform the methods as described in the various embodiments above.
The Memory 820 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). The memory 820 may be used to store instructions, programs, code sets, or instruction sets. The memory 820 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like. The stored data area may also store data created during use by the electronic device 800, and the like.
It is understood that the electronic device 800 may include more or less structural elements than those shown in the above structural block diagrams, for example, an input device, a communication module, etc., and is not limited herein.
The embodiment of the application discloses a computer readable storage medium, which stores a computer program, wherein the computer program realizes the method described in the above embodiments when being executed by a processor.
Embodiments of the present application disclose a computer program product comprising a non-transitory computer readable storage medium storing a computer program, and the computer program, when executed by a processor, implements the method as described in the embodiments above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), or the like.
Any reference to memory, storage, database, or other medium as used herein may include non-volatile and/or volatile memory. Suitable non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Those skilled in the art should also appreciate that the embodiments described in this specification are all alternative embodiments and that the acts and modules involved are not necessarily required for this application.
In various embodiments of the present application, it should be understood that the size of the serial number of each process described above does not mean that the execution sequence is necessarily sequential, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated units, if implemented as software functional units and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present application, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, may be embodied in the form of a software product, stored in a memory, including several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the above-described method of the embodiments of the present application.
The foregoing detailed description has provided a detailed description of an information processing method, an information processing apparatus, an electronic device, and a computer-readable storage medium, which are disclosed in the embodiments of the present application, and the detailed description has been provided to explain the principles and implementations of the present application, and the description of the embodiments is only provided to help understanding the method and the core idea of the present application. Meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (12)
1. An information processing method characterized by comprising:
acquiring monitoring data sent by wearable equipment;
generating a message to be pushed according to the monitoring data;
analyzing the monitoring data to obtain the importance degree corresponding to the message to be pushed;
and sending the message to be pushed to target equipment corresponding to the wearable equipment according to the importance degree.
2. The method of claim 1, wherein sending the message to be pushed to a target device corresponding to the wearable device according to the importance level comprises:
determining a prompting mode according to the importance degree, and generating a prompting instruction according to the prompting mode;
and sending the message to be pushed and the prompt instruction to target equipment corresponding to the wearable equipment, wherein the prompt instruction is used for indicating the target equipment to prompt the message to be pushed according to the prompt mode.
3. The method of claim 1, wherein sending the message to be pushed to a target device corresponding to the wearable device according to the importance level comprises:
and sending the message to be pushed to target equipment corresponding to the wearable equipment through a communication channel corresponding to the importance degree.
4. The method of claim 3, wherein sending the message to be pushed to a target device corresponding to the wearable device through a communication channel corresponding to the importance level comprises:
sending the message to be pushed to target equipment corresponding to the wearable equipment through a first communication channel;
and if the feedback information aiming at the message to be pushed returned by the target equipment is not received and the importance degree belongs to the target importance degree, the message to be pushed is sent to the target equipment through a second communication channel, wherein the target importance degree is greater than an importance threshold value.
5. The method according to any one of claims 1 to 4, wherein the analyzing the monitoring data to obtain the importance degree corresponding to the message to be pushed includes:
extracting data characteristics of the monitoring data;
analyzing the data characteristics through a neural network obtained through training to obtain the important grade corresponding to the message to be pushed, wherein the neural network is obtained through training of monitoring sample data of wearable equipment, and the monitoring sample data is marked with the corresponding important grade.
6. The method of claim 5, wherein after the obtaining monitoring data transmitted by the wearable device, the method further comprises:
classifying the monitoring data to obtain N types of monitoring data, wherein N is a positive integer;
the generating a message to be pushed according to the monitoring data includes:
and generating a message to be pushed corresponding to each type according to the monitoring data of each type.
7. The method of claim 6, wherein said extracting data characteristics of said monitoring data comprises:
extracting data characteristics of each type of monitoring data to obtain a characteristic vector corresponding to each type of monitoring data;
analyzing the data characteristics by the neural network obtained through training to obtain the importance level corresponding to the message to be pushed, including:
inputting a first feature vector corresponding to first type monitoring data into a trained neural network, performing cyclic processing on the first feature vector through a neuron in the neural network, and outputting an importance level corresponding to the first type of message to be pushed, wherein the first type is any one of the N types.
8. The method of claim 6 or 7, wherein the N types of monitoring data comprise one or more of: physiological health data of a user, motion data of the user, application usage data of the wearable device, location data of the wearable device, and device status data of the wearable device.
9. The method according to any one of claims 1 to 4, wherein the sending the message to be pushed to the target device corresponding to the wearable device according to the importance level comprises:
acquiring at least one terminal device bound with the wearable device;
and determining at least one target device from the terminal device according to the importance degree, and sending the message to be pushed to the target device.
10. An information processing apparatus characterized by comprising:
the data acquisition module is used for acquiring monitoring data sent by the wearable equipment;
the message generating module is used for generating a message to be pushed according to the monitoring data;
the analysis module is used for analyzing the monitoring data to obtain the importance degree corresponding to the message to be pushed;
and the sending module is used for sending the message to be pushed to the target equipment corresponding to the wearable equipment according to the importance degree.
11. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program that, when executed by the processor, causes the processor to carry out the method of any one of claims 1 to 9.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1 to 9.
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