Information processing method, device, storage medium and electronic device

Abstract

The present application discloses an information processing method, apparatus, storage medium, and electronic device. The information processing method includes: obtaining multiple first information; storing the multiple first information in a first memory; clustering the first information in the first memory to obtain multiple information sets and index information corresponding to each information set; storing the index information corresponding to each information set in a second memory, and searching for the corresponding first information based on the index information. This method can accelerate the retrieval of the first information.

Description

Technical Field

[0001] The present application relates to the field of electronic technology, and in particular to an information processing method, device, storage medium and electronic device. Background Art

[0002] With the advancement of electronic technology, electronic devices such as smartphones are becoming increasingly intelligent. These devices can process data using a variety of algorithms and models, providing users with a variety of functions. For example, electronic devices can learn user behavioral characteristics based on algorithmic models to provide personalized services. Summary of the Invention

[0003] Embodiments of the present application provide an information processing method, apparatus, storage medium, and electronic device, which can accelerate the retrieval speed of information collected by the electronic device.

[0004] In a first aspect, an embodiment of the present application provides an information processing method, comprising:

[0005] obtaining a plurality of first information;

[0006] storing the plurality of first information in a first memory;

[0007] Clustering the first information in the first memory to obtain multiple information sets and index information corresponding to each information set;

[0008] The index information corresponding to each of the information sets is stored in a second memory, so as to search for the corresponding first information according to the index information.

[0009] In a second aspect, an embodiment of the present application further provides an information processing device, comprising:

[0010] An acquisition module, configured to acquire a plurality of first information;

[0011] A first storage module, configured to store the plurality of first information in a first memory;

[0012] a processing module, configured to cluster the first information in the first memory to obtain multiple information sets and index information corresponding to each information set;

[0013] The second storage module is used to store the index information corresponding to each of the information sets in a second memory, so as to search for the corresponding first information according to the index information.

[0014] In a third aspect, an embodiment of the present application further provides a storage medium on which a computer program is stored. When the computer program is run on a computer, the computer executes the steps of the above-mentioned information processing method.

[0015] In a fourth aspect, an embodiment of the present application further provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the above-mentioned information processing method by calling the computer program stored in the memory.

[0016] The information processing method, device, storage medium and electronic device provided by the embodiments of the present application store the index information corresponding to each information set in the second memory, so that other modules of the system can find the corresponding first information in the first memory according to the index information. Among them, in a smart phone, the second memory can be understood as the running memory of the mobile phone. There is no need to store a large amount of first information in the second memory, only the corresponding index information needs to be stored. The multi-source heterogeneous first information is clustered in time series by a clustering method, which effectively compresses the original first information, reduces the redundant information of the first information, and realizes real-time indexing and access to the first information. Because the computing resources and storage resources of electronic devices are limited, reasonable access and allocation of the first information can speed up the retrieval speed of the first information. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0018] Figure 1 Schematic diagram of an application scenario of the information processing method provided in an embodiment of the present application.

[0019] Figure 2 A first flow chart of the information processing method provided in an embodiment of the present application.

[0020] Figure 3 A schematic diagram of another application scenario of the information processing method provided in an embodiment of the present application.

[0021] Figure 4 A second flow chart of the information processing method provided in an embodiment of the present application.

[0022] Figure 5 A third flow chart of the information processing method provided in an embodiment of the present application.

[0023] Figure 6 A schematic diagram of the structure of an information processing device provided in an embodiment of the present application.

[0024] Figure 7 This is a schematic diagram of the first structure of the electronic device provided in an embodiment of the present application.

[0025] Figure 8 A second structural diagram of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0027] refer to Figure 1 , Figure 1 Schematic diagram of an application scenario of the information processing method provided in an embodiment of the present application. The information processing method is applied to an electronic device. The electronic device is provided with a panoramic perception architecture. The panoramic perception architecture is the integration of hardware and software used to implement the information processing method in the electronic device.

[0028] Among them, the panoramic perception architecture includes information perception layer, data processing layer, feature extraction layer, scenario modeling layer and intelligent service layer.

[0029] The information perception layer is used to obtain information about the electronic device itself and / or information from the external environment. The information perception layer may include multiple sensors. For example, the information perception layer may include a distance sensor, a magnetic field sensor, a light sensor, an accelerometer, a fingerprint sensor, a Hall effect sensor, a position sensor, a gyroscope, an inertial sensor, a posture sensor, a barometer, a heart rate sensor, and other sensors.

[0030] Among them, the distance sensor can be used to detect the distance between the electronic device and an external object. The magnetic field sensor can be used to detect the magnetic field information of the electronic device's environment. The light sensor can be used to detect the light information of the electronic device's environment. The acceleration sensor can be used to detect the acceleration data of the electronic device. The fingerprint sensor can be used to collect the user's fingerprint information. The Hall sensor is a magnetic field sensor made based on the Hall effect and can be used to achieve automatic control of the electronic device. The position sensor can be used to detect the current geographical location of the electronic device. The gyroscope can be used to detect the angular velocity of the electronic device in various directions. The inertial sensor can be used to detect the motion data of the electronic device. The attitude sensor can be used to sense the attitude information of the electronic device. The barometer can be used to detect the air pressure of the electronic device's environment. The heart rate sensor can be used to detect the user's heart rate information.

[0031] The data processing layer is used to process the data obtained by the information perception layer. For example, the data processing layer can perform data cleaning, data integration, data transformation, data reduction, etc. on the data obtained by the information perception layer.

[0032] Data cleaning refers to the cleaning of large amounts of data acquired by the information perception layer to eliminate invalid and duplicate data. Data integration involves integrating multiple single-dimensional data acquired by the information perception layer into a higher or more abstract dimension for comprehensive processing. Data transformation involves converting the data type or format of the data acquired by the information perception layer to meet processing requirements. Data reduction involves maximizing the reduction of data while preserving its original appearance as much as possible.

[0033] The feature extraction layer is used to extract features from the data processed by the data processing layer to extract features from the data. The extracted features can reflect the state of the electronic device itself, the state of the user, or the environmental state of the electronic device.

[0034] Among them, the feature extraction layer can extract features or process the extracted features through methods such as filtering, packaging, and integration.

[0035] Filtering involves filtering extracted features to remove redundant feature data. Wrapping is used to filter extracted features. Integration combines multiple feature extraction methods to create a more efficient and accurate feature extraction method.

[0036] The scenario modeling layer is used to construct a model based on the features extracted by the feature extraction layer. The resulting model can be used to represent the state of the electronic device, the user, or the environment. For example, the scenario modeling layer can construct a key-value model, a pattern identification model, a graph model, an entity relationship model, an object-oriented model, etc. based on the features extracted by the feature extraction layer.

[0037] The intelligent service layer is used to provide intelligent services to users based on the models built by the scenario modeling layer. For example, the intelligent service layer can provide users with basic application services, perform intelligent system optimization for electronic devices, and provide users with personalized intelligent services.

[0038] In addition, the panoramic perception architecture can also include multiple algorithms, each of which can be used to analyze and process data. These multiple algorithms can form an algorithm library. For example, the algorithm library can include Markov algorithm, latent Dirichlet distribution algorithm, Bayesian classification algorithm, support vector machine, K-means clustering algorithm, K-nearest neighbor algorithm, conditional random field, residual network, long short-term memory network, convolutional neural network, recurrent neural network, etc.

[0039] The present application provides an information processing method that can be applied to electronic devices. The electronic devices may include smartphones, tablet computers, gaming devices, AR (augmented reality) devices, automobiles, data storage devices, audio and video playback devices, laptops, desktop computing devices, and wearable devices such as watches, glasses, helmets, electronic bracelets, electronic necklaces, and electronic clothing.

[0040] refer to Figure 2 , Figure 2 This is a first flow chart of the information processing method provided in the embodiment of the present application. The information processing method includes the following steps:

[0041] 110. Obtain multiple first information.

[0042] First information is obtained through various functional components of the electronic device. The first information may include operating information of the electronic device, configuration information of the electronic device, user information, current environmental information, etc. Specifically, the current environmental information may be obtained through sensors. For example, the current environmental information and electronic device related information may be obtained through at least one of a distance sensor, a magnetic field sensor, a light sensor, an accelerometer, a fingerprint sensor, a Hall effect sensor, a position sensor, a gyroscope, an inertial sensor, a gesture sensor, a barometer, a blood pressure sensor, a pulse sensor, a heart rate sensor, etc. The current environmental information includes user physical information such as blood pressure, pulse, and heart rate. The electronic device related information includes operating information of the electronic device, configuration information of the electronic device, and user information stored within the electronic device. User information includes user identity information, personal hobbies, browsing history, personal favorites, and other human-computer interaction information. The operating information of the electronic device includes power-on time, power-off time, standby time, memory usage at various time points, main chip usage at various time points, information about currently running programs, information about background running programs, the running time of each program, and the download volume of each program.

[0043] 120. Store multiple first information in a first memory.

[0044] After obtaining multiple first information, store them in a first memory. For example, multiple first information can be stored in a hard disk. Among them, a database can be set up to store multiple first information in the database, and the database is stored in the hard disk. It should be noted that the database can store not only the currently obtained first information, but also the previously stored first information. For smart phones, smart phones include mobile phone running memory and mobile phone non-running memory. The first memory can be understood as the mobile phone non-running memory, that is, multiple first information is stored in the mobile phone non-running memory.

[0045] 130 , clustering the first information in the first memory to obtain multiple information sets and index information corresponding to each information set.

[0046] All first information in the first storage is clustered, and first information of the same type is aggregated to form an information set, thereby obtaining multiple information sets of multiple types of first information. In addition, index information corresponding to each information set is obtained.

[0047] The first information can be categorized based on hardware attributes, such as information related to the main chip, display screen, hard drive, memory, and various sensors. The first information can also be categorized based on corresponding applications, such as information related to system applications and information related to installed applications. Information related to installed applications can be further categorized based on specific applications, such as information related to instant messaging applications, map applications, and shopping applications.

[0048] The time information of the multiple first information in each information set is processed to obtain an index of each first information, that is, the index information of each first information in each information set is obtained, and the corresponding first information can be quickly found through the index information. The index information corresponding to the multiple first information in each information set can be arranged in chronological order.

[0049] The index information provides pointers to data values ​​to be processed stored at designated locations in the first memory, and the pointers are then sorted according to a chronological order. The index information is used to find specific first information.

[0050] 140. Store the index information corresponding to each information set in a second memory for searching for the corresponding first information according to the index information.

[0051] The index information corresponding to each information set is stored in the second memory so that other modules of the system can find the corresponding first information in the first memory according to the index information. Among them, in a smart phone, the second memory can be understood as the running memory of the mobile phone. There is no need to store a large amount of first information in the second memory, only the corresponding index information needs to be stored. The multi-source heterogeneous first information is clustered in time series by clustering, which effectively compresses the original first information, reduces the redundant information of the first information, and realizes real-time indexing and access to the first information. Because the computing resources and storage resources of electronic devices are limited, reasonable access and allocation of the first information can speed up the retrieval of the first information.

[0052] In some embodiments, the method further includes: obtaining a data volume of the index information; and when the data volume is greater than a preset storage threshold, storing the index information exceeding the preset storage threshold in the first memory.

[0053] All programs in an electronic device are executed in the second memory, which has limited storage space. Therefore, the storage of index information needs to be managed and controlled. When the amount of data detected exceeds a preset storage threshold, the index information exceeding the preset storage threshold is stored in the first memory, which can save storage space in the second memory. The second memory has much larger storage space than the first memory, and the second memory does not serve as a carrier for the operation of electronic device programs.

[0054] refer to Figure 3 , Figure 3 Another application scenario diagram of the information processing method provided in an embodiment of the present application. Among them, data1, data 2, ..., data n correspond to n first information obtained by each functional component. Specifically, n first information can be obtained through n sensors. Then, the n first information are stored in the corresponding primary storage (such as a hard disk) and backed up in the secondary storage (such as a hard disk or a cloud server). The clustering module clusters the first information in the primary storage and obtains the corresponding index information, and stores the index information in the memory module and the database. The intelligent service layer can search for the corresponding first information through the index information, and the user can search for the corresponding first information through the intelligent service layer or the database. When storing index information in the memory, if the sequence of the index information exceeds the preset length value of the sequence, the excess part will be saved in the cluster storage (such as a hard disk).

[0055] refer to Figure 4 , Figure 4 This is a second flow chart of the information processing method provided in the embodiment of the present application. The information processing method includes the following steps:

[0056] 210. Obtain multiple first information.

[0057] Multiple first information can be acquired in real time. The first information may include operation information of the electronic device, configuration information of the electronic device, user information, current environment information, etc.

[0058] In some embodiments, obtaining the plurality of first information may specifically include:

[0059] Real-time acquisition of electronic equipment operating information and current environmental information collected by multiple different sensors;

[0060] Obtain configuration information and user information of electronic devices;

[0061] The operation information, configuration information, user information and current environment information are taken as the first information.

[0062] Current environmental information can be obtained through sensors. For example, current environmental information and electronic device-related information can be obtained through at least one of a distance sensor, magnetic field sensor, light sensor, accelerometer, fingerprint sensor, Hall effect sensor, position sensor, gyroscope, inertial sensor, attitude sensor, barometer, blood pressure sensor, pulse sensor, and heart rate sensor. Current environmental information includes user physical information, such as blood pressure, pulse, and heart rate. Electronic device-related information includes operating information, configuration information, and user information stored within the electronic device. User information includes user identity information, personal preferences, browsing history, personal favorites, and other human-computer interaction information. Electronic device operating information includes power-on time, power-off time, standby time, memory usage at various time points, main chip usage at various time points, information about currently running programs, information about background running programs, the running time of each program, and the download volume of each program.

[0063] 220. Store multiple first information in a first memory.

[0064] After obtaining multiple first information, store them in a first memory. For example, multiple first information can be stored in a hard disk. Among them, a database can be set up to store multiple first information in the database, and the database is stored in the hard disk. It should be noted that the database can store not only the currently obtained first information, but also the previously stored first information. For smart phones, smart phones include mobile phone running memory and mobile phone non-running memory. The first memory can be understood as the mobile phone non-running memory, that is, multiple first information is stored in the mobile phone non-running memory.

[0065] 230 , cluster the first information in the first memory to obtain multiple information sets.

[0066] All first information in the first storage is clustered, and first information of the same type is aggregated together to form an information set, thereby obtaining multiple information sets of multiple types of first information.

[0067] In some embodiments, clustering the first information in the first memory may specifically include:

[0068] The first information in the first memory is clustered according to the category of the information to obtain a plurality of information sets and index information corresponding to each information set, wherein different categories of information correspond to different information sets.

[0069] The first information can be categorized based on information categories, such as hardware attributes, such as information related to the main chip, display screen, hard drive, memory, and various sensor types. The first information can also be categorized based on categories, such as corresponding applications, such as information related to system applications and information related to installed applications. The information related to installed applications can be further categorized based on specific applications, such as information related to instant messaging applications, map applications, and shopping applications.

[0070] In some embodiments, clustering the first information in the first memory may specifically include:

[0071] The first information in the first memory is clustered according to the type of the sensor to obtain multiple information sets and index information corresponding to each information set, wherein different sensors correspond to different information sets.

[0072] The electronic device obtains different types of first information based on the type of sensor, and treats the first information obtained by sensors of the same type as an information set. For example, an information set may be obtained for each of the distance sensor, magnetic field sensor, light sensor, acceleration sensor, fingerprint sensor, Hall sensor, position sensor, gyroscope, inertial sensor, attitude sensor, barometer, blood pressure sensor, pulse sensor, and heart rate sensor.

[0073] 240 , integrating the same target first information obtained at different time points in each information set to obtain a target first information and corresponding time point information.

[0074] In each information set, the same target first information obtained at different time points is integrated, and only one target first information and the corresponding time point information are retained.

[0075] 250. Obtain index information of the information set according to the target first information and the time point information.

[0076] After obtaining the target first information and the time point information, the index information of the target first information is obtained according to the time point information.

[0077] Taking the first information as WiFi sensor information as an example, specifically, the WiFi sensor information is first collected, wherein the information can be recorded in the order of the time when the WiFi sensor is turned on. For example, on the five working days of a week, the user will connect to the company WiFi signal every morning and the home WiFi signal every evening, and use the mobile network signal at other times. In order to be able to quickly and in real time retrieve the user's WiFi activation status, if the default method is used, five WiFi record information will be queried, each WiFi record information including the target first information (WiFi activation feature) and time point information (WiFi activation time point).

[0078] The five records are then clustered and their corresponding index information is assigned. The WiFi on feature in the WiFi record corresponds to database record s1. The same record s1 appears at times t1, t8, t11, t12, t50, and t110. The clustering algorithm only records s1 and its corresponding index information. This simplifies storage, as only one s1 is needed, rather than five. Furthermore, by sorting by index information, s1 and its corresponding on time can be quickly found.

[0079] 260. Store the index information corresponding to each information set in the second memory in the form of a queue, so as to search for the corresponding first information according to the index information.

[0080] The index information corresponding to each information set is stored in a second memory (such as a memory) in the form of a queue, so that other modules of the system can find the corresponding first information in the first memory based on the index information. The second memory does not need to store a large amount of first information, only the corresponding index information. In a smartphone, the second memory can be understood as the mobile phone's running memory.

[0081] By clustering heterogeneous multi-source first information into time series, the original first information is effectively compressed, reducing redundancy while enabling real-time indexing and access. Because electronic devices have limited computing and storage resources, rationally accessing and allocating first information can accelerate retrieval.

[0082] 270, obtain the length of the queue formed by the index information.

[0083] Get the length of the queue formed by index information in real time.

[0084] 280. When the length is greater than a preset length value, store the index information exceeding the preset length value in the first memory.

[0085] All programs in the electronic device are executed in the second memory, which has limited storage space. Therefore, when it is detected that the length of the queue formed by the index information in the second memory exceeds a preset length value, the index information exceeding the preset length value is stored in the first memory, which can save storage space in the second memory. The storage space of the second memory is much larger than the storage space of the first memory, and the second memory does not serve as a carrier for the operation of electronic device programs.

[0086] In some embodiments, all first information in the first memory is redundantly backed up.

[0087] In order to ensure the security of the data to be processed, the first information in the first memory is redundantly backed up. Specifically, the redundant backup can be in another first memory, or another location in the first memory, or in a server, or in a cloud server.

[0088] If the first memory is a hard disk, the hard disk may be divided into at least two areas, the first information is stored in one area, and redundant backup is stored in the other area.

[0089] If the first memory is a hard disk and the electronic device includes at least two hard disks, redundant backup can be performed on the other hard disk. The two hard disks can be of the same type, such as a mechanical hard disk, a solid-state drive, or a hybrid hard disk. Alternatively, the two hard disks can be of different types, such as two of the same type.

[0090] If the first storage device is a hard disk, redundant backup can also be performed on a cloud server.

[0091] It should be noted that the redundant backup in this embodiment can be backed up in one copy or in multiple copies. Among them, the multiple copies can be backed up in the same way or in different ways, such as including another hard disk backup and a cloud server backup.

[0092] refer to Figure 5 , Figure 5 This is a third flow chart of the information processing method provided in the embodiment of the present application. The information processing method includes the following steps:

[0093] 310. Obtain multiple first information.

[0094] The information perception layer obtains first information. The first information may include operating information of the electronic device, configuration information of the electronic device, user information, current environmental information, and the like. Specifically, the current environmental information may be obtained through sensors. For example, the current environmental information and electronic device-related information may be obtained through at least one of a distance sensor, a magnetic field sensor, a light sensor, an accelerometer, a fingerprint sensor, a Hall effect sensor, a position sensor, a gyroscope, an inertial sensor, a gesture sensor, a barometer, a blood pressure sensor, a pulse sensor, and a heart rate sensor. The current environmental information includes user physical information such as blood pressure, pulse, and heart rate. The electronic device-related information includes operating information, configuration information, and user information stored within the electronic device. User information includes user identity information, personal preferences, browsing history, personal favorites, and other human-computer interaction information. The operating information of the electronic device includes power-on time, power-off time, standby time, memory usage at various points in time, main chip usage at various points in time, information about currently running programs, information about background running programs, the runtime of each program, and the number of downloads of each program.

[0095] 320. Store multiple first information in a first memory.

[0096] After obtaining the multiple first information, the data processing layer stores it in the first memory. For example, the multiple first information can be stored in the hard disk. Among them, a database can be set up to store the multiple first information in the database, and the database is stored in the hard disk. It should be noted that the database can store not only the currently obtained first information, but also the previously stored first information. For a smartphone, the smartphone includes a mobile phone running memory and a mobile phone non-running memory. The first memory can be understood as the mobile phone non-running memory, that is, the multiple first information is stored in the mobile phone non-running memory.

[0097] 330 , using the historical first information in the first memory as a training sample.

[0098] 340 , train the prediction model according to the training samples to obtain a trained prediction model, and set the trained prediction model in the electronic device.

[0099] The scenario modeling layer uses the historical first information stored in the first memory as a training sample, trains the prediction model according to the training sample, and obtains a trained prediction model.

[0100] In some embodiments, while obtaining the trained prediction model, the importance level corresponding to each type of historical first information is obtained; and then the sampling frequency of each type of historical first information is set according to the importance level.

[0101] 350 , cluster the first information in the first memory to obtain multiple information sets and index information corresponding to each information set.

[0102] The data processing layer clusters all first information in the first storage, aggregates first information of the same type to form an information set, thereby obtaining multiple information sets of multiple types of first information, and obtains index information corresponding to each information set.

[0103] The first information can be categorized based on hardware attributes, such as information related to the main chip, display screen, hard drive, memory, and various sensors. The first information can also be categorized based on corresponding applications, such as information related to system applications and information related to installed applications. Information related to installed applications can be further categorized based on specific applications, such as information related to instant messaging applications, map applications, and shopping applications.

[0104] The time information of the multiple first information in each information set is processed to obtain an index of each first information, that is, the index information of each first information in each information set is obtained, and the corresponding first information can be quickly found through the index information. The index information corresponding to the multiple first information in each information set can be arranged in chronological order.

[0105] The index information provides pointers to data values ​​to be processed stored at designated locations in the first memory, and the pointers are then sorted according to a chronological order. The index information is used to find specific first information.

[0106] In some embodiments, the clustering methods for different data may be different, and the clustering devices may also be different. Therefore, the corresponding clustering methods can be retrieved for different first information through the time series database. Specifically, the time series database can be expressed in the form of a table. For example, the current data is the information of the sensor wifi, and its corresponding name in the time series database is wifi. The corresponding clustering method is wifi cluster. Therefore, the wifi cluster clustering method will be used for the first information, thereby obtaining the index information corresponding to the name wifi in the time series database.

[0107] In some embodiments, different data can be clustered in different ways. The following uses light sensor information as an example to illustrate the clustering method. For example, if the light sensor samples light sensor information at a sampling frequency of 50 Hz per second, 50 sampled data points will be generated per second. If the current environment is dark for 5 seconds (i.e., the light sensor data does not change), the 250 time series data points generated within 5 seconds are merged to obtain an index of the corresponding light sensor information.

[0108] 360. Store the index information corresponding to each information set in the second memory.

[0109] The data processing layer stores the index information corresponding to each information set in a second memory (such as a memory), so that other modules of the system can find the corresponding first information in the first memory based on the index information. The second memory does not need to store a large amount of first information, only the corresponding index information. In a smartphone, the second memory can be understood as the phone's operating memory.

[0110] By clustering heterogeneous multi-source first information into time series, the original first information is effectively compressed, reducing redundancy while enabling real-time indexing and access. Because electronic devices have limited computing and storage resources, rationally accessing and allocating first information can accelerate retrieval.

[0111] For example, when performing panoramic modeling of panoramic activities or panoramic scenes, it is necessary to extract data from the feature extraction layer, cluster the data, and obtain the corresponding index information. The data can be compressed, and a large amount of data interaction, transmission, and callback will not be generated between the server and the terminal client. Due to the requirements of the effectiveness of network services and the real-time nature of data processing, the less data is transmitted, the higher its stability will be.

[0112] 370. Search for corresponding first information according to the index information, and input the first information into the trained prediction model to obtain a prediction result.

[0113] The feature extraction layer searches for the corresponding first information according to the index information, and then inputs the first information into the trained prediction model to obtain a prediction result.

[0114] For example, first information is obtained, which is WiFi sensor information, and the obtained WiFi sensor information is input into the trained prediction model to obtain a prediction result. The WiFi sensor information includes WiFi activation characteristics and activation time information of the WiFi sensor in the past week.

[0115] In some embodiments, searching for corresponding first information according to the index information and inputting the first information into the trained prediction model may specifically include:

[0116] Searching for the corresponding first information to be processed according to the index information, and determining a target sampling frequency that matches the first information to be processed;

[0117] The first information to be processed obtained according to the target sampling frequency is input into the trained prediction model.

[0118] The input data of the trained prediction model can be reduced, thereby reducing the amount of calculation and improving reaction efficiency.

[0119] 380, controlling the electronic device according to the prediction result.

[0120] The intelligent service layer controls the electronic device based on the prediction results. The prediction results include the Wi-Fi sensor activation signature and the corresponding probability. If the corresponding probability is greater than a preset probability, such as 80%, the intelligent service layer controls the electronic device to activate the Wi-Fi sensor.

[0121] It should be noted that, depending on actual usage, steps 330 and 340 in this embodiment may be set before the step 310 or before the step 370. The prediction model training process may be performed in the electronic device. The prediction model training process may also be performed in a cloud server, and the trained prediction model may then be ported to the electronic device.

[0122] It should be understood that in the embodiments of the present application, terms such as "first" and "second" are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. The objects described in this way can be interchangeable under appropriate circumstances.

[0123] During specific implementation, the present application is not limited by the execution order of the various steps described. If no conflict occurs, some steps can be performed in other orders or simultaneously.

[0124] In some embodiments, the information processing method may specifically include: first, obtaining information about the user's electronic device (such as electronic device operation information, user behavior information, information obtained by various sensors, electronic device status information, electronic device display content information, electronic device download information, etc.) through the information perception layer, then processing the electronic device information through the data processing layer (such as deleting invalid data, etc.), and then extracting the required first information from the information processed by the data processing layer through the feature extraction layer (the first information can be specifically described in the above embodiment). The data processing layer can store the first information in a first memory (such as a non-operating memory) and simultaneously back up the first information to another memory (another area of ​​the non-operating memory, such as the other of the two non-operating memory chips, or a server). The data processing layer also clusters the first information stored in the first memory to obtain multiple information sets and index information corresponding to each information set, and then stores the index information in a second memory (such as an operating memory) so that the scenario modeling layer and the intelligent service layer can quickly find the required first information through the index information in the second memory. In this case, the prediction model in the scenario modeling layer can be trained or predicted based on the first information, and the intelligent service layer can quickly retrieve the first information required by the user. As can be seen from the above, the information processing method provided by the embodiment of the present application first obtains multiple first information; then stores the multiple first information in the first memory; then clusters the first information in the first memory to obtain multiple information sets and index information corresponding to each information set; finally, stores the index information corresponding to each information set in the second memory to find the corresponding first information according to the index information. The index information corresponding to each information set is stored in the second memory (such as memory) so that other modules of the system can find the corresponding first information in the first memory according to the index information. There is no need to store a large amount of first information in the second memory, only the corresponding index information needs to be stored. The multi-source heterogeneous first information is clustered in time series by a clustering method, which effectively compresses the original first information, reduces the redundant information of the first information, and realizes real-time indexing and access to the first information. Because the computing resources and storage resources of electronic devices are limited, reasonable access and allocation of the first information can speed up the retrieval speed of the first information.

[0125] refer to Figure 6 , Figure 6 Schematic diagram of the structure of the information processing device provided in an embodiment of the present application. The information processing device 400 can be integrated into an electronic device, and includes an acquisition module 401, a first storage module 402, a processing module 403, and a second storage module 404.

[0126] An acquisition module 401 is configured to acquire a plurality of first information;

[0127] A first storage module 402 is configured to store a plurality of first information in a first memory;

[0128] The processing module 403 is configured to cluster the first information in the first memory to obtain multiple information sets and index information corresponding to each information set;

[0129] The second storage module 404 is configured to store the index information corresponding to each information set in the second memory, so as to search for the corresponding first information according to the index information.

[0130] In some embodiments, the first information in the first memory is clustered to obtain multiple information sets and index information corresponding to each information set, and the processing module 403 is further configured to:

[0131] Clustering the first information in the first memory to obtain multiple information sets;

[0132] Integrate the same target first information obtained at different time points in each information set to obtain a target first information and corresponding time point information;

[0133] The index information of the information set is obtained according to the target first information and the time point information.

[0134] In some embodiments, the index information corresponding to each information set is stored in the second memory, and the second storage module 404 is further used to:

[0135] The index information corresponding to each information set is stored in the second memory in the form of a queue.

[0136] In some embodiments, after storing the index information corresponding to each information set in the second memory in the form of a queue, the second storage module 404 is further configured to:

[0137] Get the length of the queue formed by the index information;

[0138] When the length is greater than a preset length value, the index information exceeding the preset length value is stored in the first memory.

[0139] In some embodiments, after storing the index information corresponding to each information set in the second memory in the form of a queue, the second storage module 404 is further configured to:

[0140] Get the data volume of index information;

[0141] When the data volume is greater than a preset storage threshold, the index information exceeding the preset storage threshold is stored in the first memory.

[0142] In some embodiments, the device further includes a backup module configured to perform redundant backup of all first information in the first memory.

[0143] In some embodiments, in acquiring multiple pieces of first information, the acquiring module 401 is further configured to:

[0144] Collect current environmental information in real time through multiple different sensors;

[0145] The current environment information is used as the first information.

[0146] In some embodiments, the first information in the first memory is clustered to obtain multiple information sets and index information corresponding to each information set. The processing module 403 is also used to cluster the first information in the first memory according to the type of sensor to obtain multiple information sets and index information corresponding to each information set, wherein different types of sensors correspond to different information sets.

[0147] In some embodiments, the device further includes a training module, which is used to use the historical first information in the first memory as a training sample; train the prediction model according to the training sample to obtain a trained prediction model, and set the trained prediction model in the electronic device.

[0148] After storing the index information corresponding to each information set in the second memory, the processing module 403 is further used to search for the corresponding first information according to the index information, and input the first information into the trained prediction model to obtain a prediction result; and control the electronic device according to the prediction result.

[0149] In some embodiments, after obtaining the trained prediction model, the processing module 403 is further configured to:

[0150] Obtain the trained prediction model and the importance level of each type of historical first information;

[0151] Set the sampling frequency of various historical first information according to the importance level;

[0152] Searching for the corresponding first information to be processed according to the index information, and determining a target sampling frequency that matches the first information to be processed;

[0153] The first information to be processed obtained according to the target sampling frequency is input into the trained prediction model.

[0154] As can be seen from the above, an embodiment of the present application provides an information processing device, wherein first, an acquisition module 401 acquires a plurality of first information; then a first storage module 402 stores the plurality of first information in a first memory; then a processing module 403 clusters the first information in the first memory to obtain a plurality of information sets and index information corresponding to each information set; finally, a second storage module 404 stores the index information corresponding to each information set in a second memory for searching for the corresponding first information according to the index information. The index information corresponding to each information set is stored in a second memory (such as a memory) so that other modules of the system can find the corresponding first information in the first memory according to the index information. A large amount of first information does not need to be stored in the second memory, only the corresponding index information needs to be stored. The multi-source heterogeneous first information is clustered in time series by a clustering method, which effectively compresses the original first information, reduces the redundant information of the first information, and realizes the indexing and access of the first information in real time. Because the computing resources and storage resources of electronic devices are limited, reasonable access and allocation of the first information can speed up the retrieval speed of the first information.

[0155] The present application also provides an electronic device. The electronic device may be a smartphone, a tablet computer, a gaming device, an augmented reality (AR) device, a car, a data storage device, an audio playback device, a video playback device, a notebook, a desktop computing device, a wearable device such as a watch, glasses, a helmet, an electronic bracelet, an electronic necklace, or electronic clothing. The electronic device is provided with an algorithm model, the algorithm model including a first algorithm module, which is used to process a preset task.

[0156] refer to Figure 7 , Figure 7 This is a schematic diagram of a first structure of an electronic device provided in an embodiment of the present application. In particular, the electronic device 600 includes a processor 601 and a memory 602. The processor 601 is electrically connected to the memory 602.

[0157] The processor 601 is the control center of the electronic device 600. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or calling computer programs stored in the memory 602 and calling data stored in the memory 602, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole.

[0158] In this embodiment, the processor 601 in the electronic device 600 loads instructions corresponding to one or more computer program processes into the memory 602 according to the following steps, and the processor 601 runs the computer program stored in the memory 602 to implement various functions:

[0159] obtaining a plurality of first information;

[0160] storing a plurality of first information in a first memory;

[0161] Clustering the first information in the first memory to obtain multiple information sets and index information corresponding to each information set;

[0162] The index information corresponding to each information set is stored in the second memory, so as to search for the corresponding first information according to the index information.

[0163] In some embodiments, when clustering the first information in the first memory to obtain multiple information sets and index information corresponding to each information set, the processor 601 performs the following steps:

[0164] Clustering the first information in the first memory to obtain multiple information sets;

[0165] Integrate the same target first information obtained at different time points in each information set to obtain a target first information and corresponding time point information;

[0166] The index information of the information set is obtained according to the target first information and the time point information.

[0167] In some embodiments, when clustering the first information in the first memory to obtain multiple information sets and index information corresponding to each information set, the processor 601 performs the following steps:

[0168] In some embodiments, when storing the index information corresponding to each information set in the second memory, the processor 601 performs the following steps:

[0169] The index information corresponding to each information set is stored in the second memory in the form of a queue.

[0170] In some embodiments, after storing the index information corresponding to each information set in the second memory in the form of a queue, the processor 601 performs the following steps:

[0171] Get the length of the queue formed by the index information;

[0172] When the length is greater than a preset length value, the index information exceeding the preset length value is stored in the first memory.

[0173] In some embodiments, after storing the index information corresponding to each information set in the second memory, the processor 601 performs the following steps:

[0174] Get the data volume of index information;

[0175] When the data volume is greater than a preset storage threshold, the index information exceeding the preset storage threshold is stored in the first memory.

[0176] In some embodiments, the processor 601 performs the following steps:

[0177] All first information in the first memory is redundantly backed up.

[0178] In some embodiments, when acquiring a plurality of first information, the processor 601 performs the following steps:

[0179] Collect current environmental information in real time through multiple different sensors;

[0180] The current environment information is used as the first information.

[0181] In some embodiments, when clustering the first information in the first memory to obtain multiple information sets and index information corresponding to each information set, the processor 601 performs the following steps:

[0182] The first information in the first memory is clustered according to the type of the sensor to obtain multiple information sets and index information corresponding to each information set, wherein different types of sensors correspond to different information sets.

[0183] In some embodiments, the processor 601 performs the following steps:

[0184] Using the historical first information in the first memory as a training sample;

[0185] Training the prediction model according to the training samples to obtain a trained prediction model, and setting the trained prediction model in the electronic device;

[0186] Searching for corresponding first information according to the index information, and inputting the first information into the trained prediction model to obtain a prediction result;

[0187] Control electronic devices based on the prediction results.

[0188] In some embodiments, upon obtaining the trained prediction model, the processor 601 performs the following steps:

[0189] Obtain the trained prediction model and the importance level of each type of historical first information;

[0190] Set the sampling frequency of various historical first information according to the importance level;

[0191] Searching for the corresponding first information to be processed according to the index information, and determining a target sampling frequency that matches the first information to be processed;

[0192] The first information to be processed obtained according to the target sampling frequency is input into the trained prediction model.

[0193] In some embodiments, reference Figure 8 , Figure 8 A second structural diagram of the electronic device provided in an embodiment of the present application.

[0194] The electronic device 600 further includes a display screen 603, a control circuit 604, an input unit 605, a sensor 606, and a power supply 607. The processor 601 is electrically connected to the display screen 603, the control circuit 604, the input unit 605, the sensor 606, and the power supply 607.

[0195] The display screen 603 may be used to display information input by a user or information provided to a user, as well as various graphical user interfaces of the electronic device. These graphical user interfaces may be composed of images, texts, icons, videos, and any combination thereof.

[0196] The control circuit 604 is electrically connected to the display screen 603 and is used to control the display screen 603 to display information.

[0197] The input unit 605 may be configured to receive input digital, character information, or user feature information (e.g., fingerprint), and generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control. The input unit 605 may include a fingerprint recognition module.

[0198] Sensor 606 is used to collect information about the electronic device itself, the user, or the external environment. For example, sensor 606 may include a distance sensor, a magnetic field sensor, a light sensor, an acceleration sensor, a fingerprint sensor, a Hall sensor, a position sensor, a gyroscope, an inertial sensor, a posture sensor, a barometer, a heart rate sensor, and the like.

[0199] The power supply 607 is used to supply power to various components of the electronic device 600. In some embodiments, the power supply 607 can be logically connected to the processor 601 through a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system.

[0200] although Figure 8 Not shown in the figure, the electronic device 600 may further include a camera, a Bluetooth module, etc., which will not be described in detail here.

[0201] As can be seen from the above, an embodiment of the present application provides an electronic device, in which a processor in the electronic device performs the following steps: first, obtaining a plurality of first information; then storing the plurality of first information in a first memory; then clustering the first information in the first memory to obtain a plurality of information sets, and index information corresponding to each information set; finally, storing the index information corresponding to each information set in a second memory, for searching for the corresponding first information according to the index information. The index information corresponding to each information set is stored in a second memory (such as a memory) so that other modules of the system can find the corresponding first information in the first memory according to the index information. There is no need to store a large amount of first information in the second memory, only the corresponding index information needs to be stored. The multi-source heterogeneous first information is clustered in time series by a clustering method, which effectively compresses the original first information, reduces the redundant information of the first information, and realizes real-time indexing and access to the first information. Because the computing resources and storage resources of the electronic device are limited, reasonable access and allocation of the first information can speed up the retrieval speed of the first information.

[0202] An embodiment of the present application further provides a storage medium, in which a computer program is stored. When the computer program runs on a computer, the computer executes the information processing method described in any of the above embodiments.

[0203] For example, in some embodiments, when the computer program is run on a computer, the computer performs the following steps:

[0204] obtaining a plurality of first information;

[0205] storing a plurality of first information in a first memory;

[0206] Clustering the first information in the first memory to obtain multiple information sets and index information corresponding to each information set;

[0207] The index information corresponding to each information set is stored in the second memory, so as to search for the corresponding first information according to the index information.

[0208] It should be noted that, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the storage medium may include but is not limited to: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0209] The above is a detailed introduction to the information processing method, device, storage medium, and electronic device provided in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only intended to help understand the method and core concept of the present application. At the same time, for those skilled in the art, based on the concept of the present application, there may be changes in the specific implementation methods and application scope. In summary, the contents of this specification should not be understood as limiting the present application.

Claims

1. An information processing method, characterized in that: include: obtaining a plurality of first information; storing the plurality of first information in a first memory; Clustering the first information in the first memory to obtain a plurality of information sets and index information corresponding to each information set, including: clustering the first information in the first memory to obtain a plurality of information sets; integrating the same target first information obtained at different time points in each of the information sets to obtain the target first information and corresponding time point information; and obtaining index information of the information set based on the target first information and the time point information; Storing the index information corresponding to each of the information sets in a second memory for searching for the corresponding first information according to the index information; wherein the first memory is a non-operating memory; and the second memory is an operating memory; Searching for the corresponding first information to be processed according to the index information, and determining a target sampling frequency that matches the first information to be processed; Inputting the first information to be processed obtained according to the target sampling frequency into the trained prediction model; Control electronic devices based on the prediction results.

2. The information processing method according to claim 1, wherein: Storing the index information corresponding to each information set in the second memory includes: The index information corresponding to each of the information sets is stored in a second memory in the form of a queue.

3. The information processing method according to claim 2, wherein: After storing the index information corresponding to each information set in the second memory in the form of a queue, the method further includes: Obtaining the length of the queue formed by the index information; When the length is greater than a preset length value, the index information exceeding the preset length value is stored in the first memory.

4. The information processing method according to claim 1, wherein: After storing the index information corresponding to each information set in the second memory, the method further includes: Obtaining the data volume of the index information; When the data amount is greater than a preset storage threshold, the index information exceeding the preset storage threshold is stored in the first memory.

5. The information processing method according to claim 1, wherein: After storing the plurality of first information in the first memory, the method further includes: All first information in the first memory is redundantly backed up.

6. The information processing method according to claim 1, wherein: The acquiring of a plurality of first information includes: The first information is collected in real time through a plurality of different sensors.

7. The information processing method according to claim 6, characterized in that: The clustering of the first information in the first memory to obtain multiple information sets and index information corresponding to each information set includes: The first information in the first memory is clustered according to the type of the sensor to obtain multiple information sets and index information corresponding to each information set, wherein different types of sensors correspond to different information sets.

8. The information processing method according to claim 1, wherein: The method further comprises: using the historical first information in the first memory as a training sample; The prediction model is trained according to the training samples to obtain a trained prediction model, and the trained prediction model is set in an electronic device.

9. The information processing method according to claim 8, characterized in that The trained prediction model includes: Obtain the trained prediction model and the importance level of each type of historical first information; Setting the sampling frequency of each type of historical first information according to the importance level; The searching for corresponding first information according to the index information and inputting the first information into the trained prediction model includes: Searching for corresponding first information to be processed according to the index information, and determining a target sampling frequency that matches the first information to be processed; The first information to be processed obtained according to the target sampling frequency is input into the trained prediction model.

10. An information processing device, characterized in that: include: An acquisition module, configured to acquire a plurality of first information; A first storage module, configured to store the plurality of first information in a first memory; a processing module, configured to cluster the first information in the first memory to obtain a plurality of information sets and index information corresponding to each information set, including: clustering the first information in the first memory to obtain a plurality of information sets; integrating the same target first information obtained at different time points in each of the information sets to obtain the target first information and corresponding time point information; and obtaining index information of the information set based on the target first information and the time point information; a second storage module, configured to store the index information corresponding to each of the information sets in a second memory, so as to search for the corresponding first information according to the index information; wherein the first memory is a non-operating memory; and the second memory is an operating memory; A processing module is used to search for the corresponding first information to be processed according to the index information and determine the target sampling frequency that matches the first information to be processed; input the first information to be processed obtained according to the target sampling frequency into the trained prediction model; and control the electronic device according to the prediction result.

11. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed on a computer, the computer is caused to execute the information processing method according to any one of claims 1 to 9.

12. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein a computer program is stored in the memory, and the processor is configured to execute the information processing method according to any one of claims 1 to 9 by calling the computer program stored in the memory.

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