Operating system running method and device and storage medium

Abstract

The invention relates to an operating system running method and device and a storage medium. The operating system running method comprises the steps that in response to the fact that it is determined that an operating system needs to be run on a chip of the electronic equipment, adaptation information, matched with the operating system, of the chip is obtained, and the adaptation information comprises code information and guide information needed for the chip to run the operating system; and running the code information according to guide information in the adaptive information so as to run the operating system on a chip of the electronic equipment. According to the method and the device, the operation of automatically or assisting the chip to adapt to the operating system can be realized, and the adaptation efficiency of the chip and the operating system is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, and in particular to an operating system operation method, device and storage medium. Background Art

[0002] In related technologies, in order to enable a chip to run an operating system (OS), each chip manufacturer and operating system manufacturer needs to work closely together to connect hardware capabilities to the operating system based on the familiarity of the adaptation guidelines and driver framework. This traditional manual adaptation method has low adaptation efficiency and seriously affects the time and quality of operating system access. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides an operating system running method, device and storage medium.

[0004] According to a first aspect of an embodiment of the present disclosure, there is provided a method for operating an operating system, comprising:

[0005] In response to determining that an operating system needs to be run on a chip of an electronic device, adaptation information of the chip to adapt to the operating system is obtained, the adaptation information including code information and guidance information required for the chip to run the operating system; the code information is run according to the guidance information in the adaptation information to run the operating system on the chip of the electronic device.

[0006] In one embodiment, the adaptation information is determined based on a model, the input of the model is a knowledge vector, and the output is configuration information; the knowledge vector is used to represent a feature vector of the configuration information corresponding to the chip and / or operating system, different chips correspond to different feature vectors, different operating systems correspond to different feature vectors, and different feature vectors correspond to different adaptation information.

[0007] In one implementation, the adaptation information is determined based on the model in the following manner:

[0008] Acquire configuration information of the chip; convert the configuration information into a first knowledge vector; and determine a second knowledge vector having the highest similarity to the first knowledge vector in a knowledge vector library;

[0009] The second knowledge vector is input into the model to obtain adaptation information corresponding to the second knowledge vector as adaptation information for the chip to adapt to the operating system.

[0010] In one implementation, the model is determined in the following manner:

[0011] Acquire a configuration information training set, wherein the configuration information training set includes configuration information of at least one dimension; transform the training configuration information of the at least one dimension to obtain a knowledge vector training set and a verification set for the adaptation information, wherein the dimension represents the presentation form of the configuration information; and train the model based on the knowledge vector training set and the verification set for the adaptation information.

[0012] In one implementation, the model is deployed on a server, and obtaining the adaptation information of the chip to the operating system includes:

[0013] The configuration information of the chip is sent to the server; and the adaptation information determined and sent by the server based on the model is obtained.

[0014] In one embodiment, the method further comprises:

[0015] In response to an exception occurring when the operating system is run on the chip of the electronic device, abnormal information is determined; and the abnormal information is used as debugging information for optimizing the model.

[0016] According to a second aspect of an embodiment of the present disclosure, there is provided an operating system running device, including:

[0017] A processing unit is used to, in response to determining that an operating system needs to be run on a chip of an electronic device, obtain adaptation information for adapting the chip to the operating system, wherein the adaptation information includes code information and guidance information required for the chip to run the operating system; and an operating unit is used to run the code information according to the guidance information in the adaptation information to run the operating system on the chip of the electronic device.

[0018] In one embodiment, the adaptation information is determined based on a model, the input of the model is a knowledge vector, and the output is adaptation information; the knowledge vector is used to represent a feature vector of configuration information corresponding to the chip and / or operating system, different chips correspond to different feature vectors, different operating systems correspond to different feature vectors, and different feature vectors correspond to different adaptation information.

[0019] In one implementation, the adaptation information is determined based on the model in the following manner:

[0020] Acquire configuration information of the chip; convert the configuration information into a first knowledge vector; and determine a second knowledge vector having the highest similarity to the first knowledge vector in a knowledge vector library;

[0021] The second knowledge vector is input into the model to obtain adaptation information corresponding to the second knowledge vector as adaptation information for the chip to adapt to the operating system.

[0022] In one implementation, the model is determined in the following manner:

[0023] Acquire a configuration information training set, wherein the configuration information training set includes configuration information of at least one dimension; transform the training configuration information of the at least one dimension to obtain a knowledge vector training set and a configuration information verification set, wherein the dimension represents the presentation form of the configuration information; and train the model based on the knowledge vector training set and the adaptation information verification set.

[0024] In one implementation, the model is deployed on a server, and obtaining the adaptation information of the chip to the operating system includes:

[0025] The configuration information of the chip is sent to the server; and the adaptation information determined and sent by the server based on the model is obtained.

[0026] In one implementation, the processing unit is further configured to:

[0027] In response to an exception occurring when the operating system is run on the chip of the electronic device, abnormal information is determined; and the abnormal information is used as debugging information for optimizing the model.

[0028] According to a third aspect of an embodiment of the present disclosure, there is provided a device for collecting device information, including:

[0029] A processor; a memory for storing processor executable instructions; wherein the processor is configured to: execute the device information collection method described in the first aspect or any one of the implementations of the first aspect.

[0030] According to a fourth aspect of an embodiment of the present disclosure, a storage medium is provided, in which instructions are stored. When the instructions in the storage medium are executed by a processor of a terminal, the terminal can perform the method described in the first aspect or any one of the implementations of the first aspect.

[0031] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: the electronic device obtains the adaptation information of the chip to adapt to the operating system, and realizes automatic or assisted generalized adaptation to the chip platform through the adaptation information, which can improve the efficiency of the adaptation process required for the chip to run the operating system, shorten the time of operating system access and improve the quality of operating system access.

[0032] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0034] Figure 1 The present invention is a flowchart of a method for operating an operating system according to an exemplary embodiment.

[0035] Figure 2 The present invention is a flowchart of a device for determining configuration information according to an exemplary embodiment.

[0036] Figure 3 The figure is a flowchart of a model training method according to an exemplary embodiment.

[0037] Figure 4 The present invention is a flowchart showing a method for adapting a chip to an operating system according to an exemplary embodiment.

[0038] Figure 5 The present invention is a flowchart of a method for operating an operating system according to an exemplary embodiment.

[0039] Figure 6 The invention is a block diagram showing a device for running an operating system according to an exemplary embodiment.

[0040] Figure 7 The invention is a block diagram showing a device for operating a system according to an exemplary embodiment. DETAILED DESCRIPTION

[0041] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present disclosure.

[0042] In the accompanying drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limitations on the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present disclosure. The embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings.

[0043] The operating system running method provided in the embodiment of the present disclosure can be applied to the application scenario of automatically or assisting a chip to adapt an operating system. The chip is adapted by training a neural network language model so that a specific operating system can be run in the chip.

[0044] In related technologies, the process of chips running operating systems requires cooperation between chip manufacturers and operating system manufacturers. Based on the familiarity of adaptation guidelines and driver frameworks, hardware capabilities are connected to the operating system. This traditional method has low adaptation efficiency and seriously affects the time and quality of operating system access.

[0045] However, in the related art, the extraction of entity relationships and the training of AI models are achieved by combining a graph state recurrent neural network and a Transformer-based bidirectional encoder representation model (Bidirectional Encoder Representations from Transformers, BERT) model, and a first vector for representing the semantic features of the text and a second vector for representing the dependency features of the text are extracted from the text respectively, and the first vector and the second vector are concatenated and classified, so that the relationship extraction of entity pairs can achieve better accuracy in long sentence and cross-sentence application scenarios, thereby improving the problem of insufficient accuracy in application scenarios such as long sentences and cross-sentences in the prior art. In addition, in the model training stage, the present application produces a large amount of labeled data through remote supervision based on preset rules and pre-trained models, so that a large amount of relatively accurate training data can be obtained at a lower cost.

[0046] In view of this, an operating system operation method is provided in an embodiment of the present disclosure. In the operating system operation method provided by the present disclosure, based on a large language model (LLM) and word embedding (EMbedding), the chip in the device is adapted to the target operating system to improve the adaptation efficiency of the chip and the operating system.

[0047] Figure 1 is a flowchart of a method for operating an operating system according to an exemplary embodiment. Figure 1 As shown, the operating system running method includes the following steps.

[0048] In step S11, in response to determining that an operating system needs to be run on a chip of the electronic device, adaptation information of the chip adapting to the operating system is obtained.

[0049] In the disclosed embodiments, the electronic device may be an Internet of Things (IoT) device such as a sports watch, a smart speaker, and a camera, or may be a device with a small core or a small system such as a smart watch, a television, a mobile phone, and a car computer.

[0050] In the disclosed embodiment, the adaptation information includes code information and guidance information required for the chip to run the operating system. The code information may be sample code, compiled code tools, etc. The guidance information may be the installation steps required for the chip to run the operating system, or a recommended parameter setting table, etc.

[0051] In step S12, the code information is executed according to the guidance information in the adaptation information to run the operating system on the chip of the electronic device.

[0052] In the disclosed embodiment, a technician or an electronic device can automatically run the corresponding code information according to the guidance information to enable the specified operating system to run on the chip of the electronic device. For example, a technician can run the installation tool provided and included in the code information according to the guidance information obtained by the electronic device, and complete the installation according to the parameters provided in the guidance information.

[0053] In the disclosed embodiment, the adaptation information output by the model can be obtained by inputting a knowledge vector into the model, wherein the adaptation information output by the model is the adaptation information required for the chip on the electronic device to adapt to the specified operating system.

[0054] In the disclosed embodiment, the knowledge vector is used to represent the feature vector of the configuration information corresponding to the chip and / or operating system. Different chips correspond to different feature vectors, different operating systems correspond to different feature vectors, and different feature vectors correspond to different configuration information. Representing the configuration information of different chips or operating systems by knowledge vectors facilitates the management of configuration information, and has the advantages of structured and inherited.

[0055] In the disclosed embodiments, configuration information refers to the necessary hardware and software information required in the process of adapting a chip to an operating system. It should be understood that the configuration information required in the process of adapting a chip to an operating system may be the same or different.

[0056] In the embodiments of the present disclosure, a feature vector is used to describe the intrinsic properties and characteristics of data, and each element thereof represents a characteristic of the data. Therefore, different chips, operating systems, and configuration information in the embodiments of the present disclosure correspond to different feature vectors.

[0057] In the disclosed embodiment, the adaptation information is specifically obtained by inputting a knowledge vector corresponding to the configuration information into the model.

[0058] Figure 2 is a flowchart of a method for determining configuration information according to an exemplary embodiment. Figure 2 As shown, the configuration information determination method includes the following steps.

[0059] In step S21, the configuration information of the chip is obtained.

[0060] In the disclosed embodiment, the configuration information can be directly obtained by exporting a log of the electronic device, or by interacting with a technician of the electronic device in the form of a question and answer, and the configuration information of the chip can be obtained by extracting the interactive content.

[0061] In step S22, the configuration information is converted into a first knowledge vector.

[0062] In the embodiment of the present disclosure, the first knowledge vector can be obtained by embedding the configuration information to obtain a vector value. For example, text2vec-base-chinese can be used to embed the configuration information to obtain the first knowledge vector.

[0063] In step S23, a second knowledge vector having the highest similarity to the first knowledge vector is determined in the knowledge vector library.

[0064] In the disclosed embodiment, the second knowledge vector is a knowledge vector extracted by the model based on training data during the training process.

[0065] In an exemplary embodiment, the cosine value of the angle between each knowledge vector in the knowledge vector library and the first knowledge vector can be calculated, and the knowledge vector with the smallest cosine value of the angle can be selected as the second knowledge vector with the highest similarity to the first knowledge vector. It should be understood that the use of the cosine value of the angle to determine the second knowledge vector in this exemplary embodiment is only for illustration, and the method for determining the second knowledge vector is not limited in the disclosed embodiments.

[0066] In the disclosed embodiment, several second knowledge vectors with the highest similarity to the first knowledge vector may also be determined. For example, the cosine value of the angle between each knowledge vector in the knowledge vector library and the first knowledge vector may be calculated, and the knowledge vector with the smallest cosine value of the angle may be selected as the ten second knowledge vectors with the highest similarity to the first knowledge vector. In step S24, the second knowledge vector is input into the model to obtain the adaptation information corresponding to the second knowledge vector as the adaptation information of the chip adaptation operating system.

[0067] In the disclosed embodiment, after the second knowledge vector is input into the model, the model obtains the adaptation information corresponding to the second knowledge vector and outputs the adaptation information as the adaptation information of the operating system.

[0068] In the disclosed embodiment, if multiple second knowledge vectors are obtained, multiple adaptation information corresponding to the multiple second knowledge vectors can be processed again through a large language model (LLM) to achieve summarization, generalization, formatting, deduplication, translation and other effects.

[0069] In the disclosed embodiment, ChatCompletion can be used to extract context from historical configuration information and perform analysis and processing.

[0070] Figure 3 is a flow chart of a model training method according to an exemplary embodiment. Figure 3 As shown, the model training method includes the following steps.

[0071] In step S31, a configuration information training set is obtained, where the configuration information training set includes training configuration information of at least one dimension.

[0072] In the disclosed embodiment, the configuration information training set is a set of training samples used for model training, which may be pictures, texts, codes, examples, etc. containing configuration information content.

[0073] In step S32, the training configuration information of at least one dimension is converted to obtain a knowledge vector training set and an adaptation information verification set.

[0074] In the disclosed embodiment, the dimension represents the presentation form of the configuration information, and the presentation form includes a variety of different information forms. For example, a screenshot of a technical forum belongs to visual information, text such as a system development guide belongs to language information, and an open source code computer program language belongs to language information, so the dimensions of the above configuration information are different.

[0075] In an exemplary embodiment, the configuration information training set for generating knowledge vectors can come from different dimensions such as pictures, text, code, and examples: there is information extracted from photo screenshot data and analyzed and parsed into knowledge vectors (such as from "Technical Forum Screenshots"); there is key information extracted from summary adaptation documents and analyzed and parsed into knowledge vectors (such as from: System Development Guide); there is code analysis from source code, parsed into knowledge vectors (such as from open source code in technical forums); and there are also examples and sample codes from emulators, which are parsed into knowledge vectors.

[0076] In the disclosed embodiment, natural language processing (NLP) may be added during the training process of the model to increase the weight of existing configuration information in the model and enhance cognition and understanding generation.

[0077] In step S33, a model is trained based on the knowledge vector training set and the configuration information verification set.

[0078] In the disclosed embodiment, the model includes at least one neural network model and at least one natural language model. Deep learning training is performed through the neural network model to enhance information retrieval identification and accuracy and generalize it to new tasks, and the information weight of the results is annotated through the natural language model.

[0079] In an exemplary embodiment, the knowledge vector training set is trained in parallel using a distributed graphics processing unit (GPU) through a Transformer model, and deep learning training is performed using neural network models such as a recurrent neural network (RNN) and a convolutional neural network (CNN). In addition, multiple natural language models such as chatGLM and chatGPT3 are integrated for training to finally obtain a model.

[0080] In the disclosed embodiment, the model processing results with the information weights marked are organized and stored in a custom vector database, which can provide vector similarity search by providing special indexes, reduce feature value extraction, and increase accuracy and the speed of retrieving information. For example, the Embedding interface of text2vec-base-chinese can be used to convert the knowledge training set into a second knowledge vector, which can be saved in a custom database together with the corresponding configuration information, and vector similarity search can be provided through special indexes such as k-nearest neighbor algorithm (k-NN) index. The custom vector database can store knowledge vectors of multiple dimensions and corresponding adaptation information, where the key value in the custom vector database is the second knowledge vector; the specific value is the adaptation information content corresponding to the second knowledge vector, which can also be called the original record of the knowledge point.

[0081] In the disclosed embodiment, the granularity of the second knowledge vector can be modified by sharding and word embedding operations. By generating a word segmenter for the adaptation information content and performing a sharding operation, the adaptation information content is subdivided to achieve control of the granularity of the adaptation information. By word embedding the sharded adaptation information, it is possible to obtain a second knowledge vector corresponding to the sharded adaptation information. It should be understood that the granularity can be distinguished from fine to coarse using punctuation, paragraphs, chapters, etc. If the sharding granularity is too fine, the knowledge points will be too fragmented and affect their relationships; if the sharding granularity is too coarse, redundant information may be carried during matching, and the efficiency of operations such as word embedding, processing, and indexing will also be affected.

[0082] Figure 4 is a flowchart of a method for adapting a chip to an operating system according to an exemplary embodiment. Figure 4 As shown, the chip adaptation operating system method includes the following steps.

[0083] In step S41, the configuration information of the chip is sent to the server.

[0084] In the disclosed embodiment, for the scenario where the model is deployed on the server, the content containing configuration information sent by the electronic device to the server can be in a multi-dimensional presentation form, for example, natural language content, configuration screenshots, configuration information files, etc. can be uploaded.

[0085] In step S42, the adaptation information determined and sent by the server based on the model is obtained.

[0086] In the disclosed embodiment, the server receives the configuration information sent by the device, determines the first knowledge vector corresponding to the configuration information, obtains at least one second knowledge vector with the highest similarity to the first knowledge vector through the custom vector database, determines the adaptation information based on the second knowledge vector, and returns the adaptation information to the device. For example, the server uses text2vec-base-chinese to embed the configuration information of the device to obtain the first knowledge vector. The first knowledge vector is searched in the custom vector database to obtain several second knowledge vectors with the highest similarity to the first knowledge vector, and the adaptation information corresponding to the several second knowledge vectors is returned to the device. In an exemplary embodiment, the device inputs the question to the server, and the server returns the adaptation information retrieved by the above model calculation to the client, downloads the trained minimalist model, and provides text steps and sample code. Furthermore, the device parses the returned results, automatically installs relevant build and simulation running tools, runs the sample code to demonstrate on the simulator, or burns it to the terminal device to give the running process, results and further prompts, guidelines and summaries.

[0087] Figure 5 is a flowchart of a method for operating an operating system according to an exemplary embodiment. Figure 5 As shown, the operating system running method includes the following steps.

[0088] In step S51, in response to an abnormality in running an operating system on a chip of an electronic device, abnormality information is determined.

[0089] In the embodiment of the present disclosure, the abnormal information is configuration information for reporting abnormal situations. For example, the abnormal information may include abnormal type, abnormal time, abnormal location, abnormal data, and other contents.

[0090] In step S52, the abnormal information is used as debugging information of the optimization model.

[0091] In the disclosed embodiment, the debugging information is used to help the model analyze and locate errors. The device sends the exception information to the server, and the server inputs the exception information into the model as debugging information, and the model performs optimization operations based on the debugging information.

[0092] In an exemplary embodiment, the model is on the server side, and the server side calculates the model and feeds back the adaptation information provided by the custom vector library on the device side. After an exception occurs on the device side, the configuration information, adaptation information and exception information are closed-loop fed back to the server side. The server side adapts the exception during the operation of the type of device side according to the received information to automatically solve the problem and give a prompt; the server side model can provide sample code, interactively debug and update the training according to the feedback, and update the updated second knowledge vector and the corresponding adaptation information into the custom vector database. In addition, after the server completes the installation of the operating system on the device side, the acceptance case in the vector database will be issued to the user for verification and acceptance, and finally an acceptance report will be provided to the device side. In the embodiment of the present disclosure, the electronic device obtains the adaptation information of the chip of the electronic device and the operating system, and automatically or assistedly realizes the operation of the operating system on the chip of the electronic device through the adaptation information, which greatly shortens the time required for information retrieval in the process of southbound interface adaptation of the chip manufacturer, breaks the information barrier between the chip manufacturer and the operating system development team, and improves the stickiness between the chip manufacturer and the operating system platform. Shorten the development cycle of the operating system manufacturer, improve the efficiency of chip adaptation development, and help solve the problem of information fragmentation.

[0093] Based on the same concept, an embodiment of the present disclosure also provides an operating system running device.

[0094] It is understandable that the operating system running device provided by the embodiment of the present disclosure includes hardware structures and / or software modules corresponding to the execution of each function in order to realize the above functions. In combination with the units and algorithm steps of each example disclosed in the embodiment of the present disclosure, the embodiment of the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiment of the present disclosure.

[0095] Figure 6 FIG. 1 is a block diagram of an operating system running device 100 according to an exemplary embodiment. Figure 6 The device includes a processing unit 101 and an operating unit 102.

[0096] The processing unit 101 is used for obtaining the adaptation information of the chip to adapt to the operating system in response to determining that the operating system needs to be run on the chip of the electronic device, where the adaptation information includes code information and guidance information required for the chip to run the operating system.

[0097] The running unit 102 is used to run the code information according to the guidance information in the adaptation information, so as to run the operating system on the chip of the electronic device.

[0098] In one embodiment, the adaptation information is determined based on a model, the input of the model is a knowledge vector, and the output is the adaptation information; the knowledge vector is used to represent the feature vector of the configuration information corresponding to the chip and / or operating system, different chips correspond to different feature vectors, different operating systems correspond to different feature vectors, and different feature vectors correspond to different configuration information.

[0099] In one embodiment, the adaptation information is determined based on the model in the following manner:

[0100] Obtaining chip configuration information; converting the configuration information into a first knowledge vector; determining a second knowledge vector having the highest similarity to the first knowledge vector in a knowledge vector library;

[0101] The second knowledge vector is input into the model to obtain adaptation information corresponding to the second knowledge vector as adaptation information for the chip to adapt to the operating system.

[0102] In one embodiment, the model is determined as follows:

[0103] A configuration information training set is obtained, wherein the configuration information training set includes configuration information of at least one dimension; the training configuration information of at least one dimension is converted to obtain a knowledge vector training set and an adaptation information verification set, wherein the dimension represents the presentation form of the configuration information; a model is obtained by training based on the knowledge vector training set and the adaptation information verification set.

[0104] In one embodiment, the model is deployed on the server side, and the adaptation information of the chip adaptation operating system is obtained, including:

[0105] Send the chip configuration information to the server; obtain the adaptation information determined and sent by the server based on the model.

[0106] In one embodiment, the processing unit is further configured to:

[0107] In response to an abnormality in running an operating system on a chip of an electronic device, abnormality information is determined; and the abnormality information is used as debugging information of an optimization model.

[0108] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0109] Figure 7 2 is a block diagram of an apparatus 200 for operating system operation according to an exemplary embodiment. For example, the apparatus 200 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0110] Reference Figure 7 , the device 200 may include one or more of the following components: a processing component 202 , a memory 204 , a power component 206 , a multimedia component 208 , an audio component 210 , an input / output (I / O) interface 212 , a sensor component 214 , and a communication component 216 .

[0111] The processing component 202 generally controls the overall operation of the device 200, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 202 may include one or more processors 220 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 202 may include one or more modules to facilitate interaction between the processing component 202 and other components. For example, the processing component 202 may include a multimedia module to facilitate interaction between the multimedia component 208 and the processing component 202.

[0112] The memory 204 is configured to store various types of data to support operations on the device 200. Examples of such data include instructions for any application or method operating on the device 200, contact data, phone book data, messages, pictures, videos, etc. The memory 204 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0113] The power component 206 provides power to the various components of the device 200. The power component 206 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 200.

[0114] The multimedia component 208 includes a screen that provides an output interface between the device 200 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 208 includes a front camera and / or a rear camera. When the device 200 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0115] The audio component 210 is configured to output and / or input audio signals. For example, the audio component 210 includes a microphone (MIC), and when the device 200 is in an operation mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 204 or sent via the communication component 216. In some embodiments, the audio component 210 also includes a speaker for outputting audio signals.

[0116] I / O interface 212 provides an interface between processing component 202 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0117] The sensor assembly 214 includes one or more sensors for providing various aspects of the status assessment of the device 200. For example, the sensor assembly 214 can detect the open / closed state of the device 200, the relative positioning of components, such as the display and keypad of the device 200, the sensor assembly 214 can also detect the position change of the device 200 or a component of the device 200, the presence or absence of user contact with the device 200, the orientation or acceleration / deceleration of the device 200 and the temperature change of the device 200. The sensor assembly 214 can include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 214 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 214 can also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor or a temperature sensor.

[0118] The communication component 216 is configured to facilitate wired or wireless communication between the device 200 and other devices. The device 200 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 216 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 216 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0119] In an exemplary embodiment, the apparatus 200 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0120] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 204 including instructions, and the instructions can be executed by the processor 220 of the device 200 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0121] It is to be understood that in the present disclosure, "plurality" refers to two or more than two, and other quantifiers are similar. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. The singular forms "a", "the" and "the" are also intended to include plural forms, unless the context clearly indicates other meanings.

[0122] It is further understood that the terms "first", "second", etc. are used to describe various information, but such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other, and do not indicate a specific order or degree of importance. In fact, the expressions "first", "second", etc. can be used interchangeably. For example, without departing from the scope of the present disclosure, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information.

[0123] It should be further understood that the terms "center", "longitudinal", "lateral", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present embodiment and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation.

[0124] It can be further understood that, unless otherwise specified, “connection” includes a direct connection without other components between the two, and also includes an indirect connection with other components between the two.

[0125] It is further understood that, although the operations are described in a specific order in the drawings in the embodiments of the present disclosure, it should not be understood as requiring the operations to be performed in the specific order shown or in a serial order, or requiring the execution of all the operations shown to obtain the desired results. In certain environments, multitasking and parallel processing may be advantageous.

[0126] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modifications, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure.

[0127] It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the scope of the appended claims.

Claims

1. A method for operating an operating system, characterized in that: include: In response to determining that an operating system needs to be run on a chip of the electronic device, obtaining adaptation information of the chip for adapting the operating system, the adaptation information including code information and guidance information required for the chip to run the operating system; The code information is run according to the guidance information in the adaptation information to run the operating system on the chip of the electronic device.

2. The method according to claim 1, characterized in that The adaptation information is determined based on a model, the input of the model is a knowledge vector, and the output of the model is the adaptation information; The knowledge vector is used to represent the feature vector of the configuration information corresponding to the chip and / or operating system. Different chips correspond to different feature vectors, different operating systems correspond to different feature vectors, and different feature vectors correspond to different configuration information.

3. The method according to claim 2, characterized in that The configuration information is determined based on the model in the following manner: Obtaining configuration information of the chip; Converting the configuration information into a first knowledge vector; In the knowledge vector library, determining a second knowledge vector having the highest similarity to the first knowledge vector; The second knowledge vector is input into the model to obtain adaptation information corresponding to the second knowledge vector as adaptation information for the chip to adapt to the operating system.

4. The method according to claim 2 or 3, characterized in that: The model is determined in the following way: Acquire a configuration information training set, wherein the configuration information training set includes training configuration information of at least one dimension; Converting the training configuration information of the at least one dimension to obtain a knowledge vector training set and a validation set of the adaptation information, wherein the dimension represents a presentation form of the training configuration information; The model is trained based on the knowledge vector training set and the adaptation information verification set.

5. The method according to claim 2 or 3, characterized in that: The model is deployed on the server side, and the obtaining of the adaptation information of the chip to the operating system includes: Sending the configuration information of the chip to the server; Acquire the adaptation information determined and sent by the server based on the model.

6. The method according to claim 2 or 3, characterized in that: The method further comprises: In response to an abnormality occurring in running the operating system on the chip of the electronic device, determining abnormality information; The exception information is used as debugging information for optimizing the model.

7. An operating system running device, characterized in that: include: A processing unit, configured to, in response to determining that an operating system needs to be run on a chip of the electronic device, obtain adaptation information of the chip for adapting the operating system, wherein the adaptation information includes code information and guidance information required for the chip to run the operating system; An operating unit is used to operate the code information according to the guidance information in the adaptation information, so as to operate the operating system on the chip of the electronic device.

8. The device according to claim 7, characterized in that The adaptation information is determined based on a model, the input of the model is a knowledge vector, and the output of the model is the adaptation information; The knowledge vector is used to represent the feature vector of the configuration information corresponding to the chip and / or operating system. Different chips correspond to different feature vectors, different operating systems correspond to different feature vectors, and different feature vectors correspond to different configuration information.

9. The device according to claim 8, characterized in that The adaptation information is determined based on the model in the following manner: Obtaining configuration information of the chip; Converting the configuration information into a first knowledge vector; In the knowledge vector library, determining a second knowledge vector having the highest similarity to the first knowledge vector; The second knowledge vector is input into the model to obtain adaptation information corresponding to the second knowledge vector as adaptation information for the chip to adapt to the operating system.

10. The device according to claim 8 or 9, characterized in that The model is determined in the following way: Acquire a configuration information training set, wherein the configuration information training set includes configuration information of at least one dimension; Converting the training configuration information of the at least one dimension to obtain a knowledge vector training set and a validation set of the adaptation information, wherein the dimension represents the presentation form of the configuration information; The model is trained based on the knowledge vector training set and the adaptation information verification set.

11. The device according to claim 8 or 9, characterized in that The model is deployed on the server side, and the obtaining of the adaptation information of the chip to the operating system includes: Sending the configuration information of the chip to the server; Acquire the adaptation information determined and sent by the server based on the model.

12. The device according to claim 8 or 9, characterized in that The processing unit is also used for: In response to an abnormality occurring in running the operating system on the chip of the electronic device, determining abnormality information; The exception information is used as debugging information for optimizing the model.

13. A device information collection device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: Wherein, the processor is configured to: execute the method described in any one of claims 1 to 6.

14. A storage medium, characterized in that: The storage medium stores instructions, and when the instructions in the storage medium are executed by a processor of an electronic device, the terminal is enabled to execute the method according to any one of claims 1 to 6.

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