Operating system operation methods, devices and storage media

Abstract

This disclosure relates to a method, apparatus, and storage medium for running an operating system. The method includes: in response to determining that an operating system needs to be run on a chip of an electronic device, acquiring adaptation information for the chip to run the operating system, the adaptation information including code information and guidance information required for the chip to run the operating system; and running the code information according to the guidance information in the adaptation information to run the operating system on the chip of the electronic device. This disclosure enables automatic or assisted chip-operating system adaptation, improving the efficiency of chip-operating system adaptation.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence, and in particular to an operating system operation method, apparatus and storage medium. Background Technology

[0002] In related technologies, enabling a chip to run an operating system (OS) requires close cooperation between each chip manufacturer and the operating system manufacturer. Based on their familiarity with the adaptation guidelines and driver framework, they integrate the hardware capabilities into the operating system. This traditional manual adaptation method suffers from low adaptation efficiency and seriously affects the time and quality of operating system integration. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides an operating system running method, apparatus and storage medium.

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

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

[0006] In one implementation, the adaptation information is determined based on a model, where the input of the model is a knowledge vector and the output is configuration information. The knowledge vector is used to represent the feature vector of the configuration information corresponding to the chip and / or the operating system. Different chips have different feature vectors, different operating systems have 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] Obtain the configuration information of the chip; convert the configuration information into a first knowledge vector; and determine the second knowledge vector with the highest similarity to the first knowledge vector in the knowledge vector library.

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

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

[0011] Obtain a configuration information training set, which 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 validation set with the adaptation information, wherein the dimension represents the presentation form of the configuration information; train the model based on the knowledge vector training set and the validation set with the adaptation information.

[0012] In one implementation, the model is deployed on a server, and obtaining the chip's compatibility information with the operating system includes:

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

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

[0015] In response to an anomaly occurring while the operating system is running on the chip of the electronic device, an anomaly information is determined; the anomaly information is used as debugging information to optimize the model.

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

[0017] A processing unit is configured to, in response to determining that an operating system needs to be run on a chip of an electronic device, acquire adaptation information for the chip to run the operating system, the adaptation information including code information and guidance information required for the chip to run the operating system; and a running unit is configured 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 implementation, the adaptation information is determined based on a model, wherein the input of the model is a knowledge vector and the output is adaptation information; the knowledge vector is used to represent the feature vector of the configuration information corresponding to the chip and / or the operating system, different chips have different feature vectors, different operating systems have 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] Obtain the configuration information of the chip; convert the configuration information into a first knowledge vector; and determine the second knowledge vector with the highest similarity to the first knowledge vector in the knowledge vector library.

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

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

[0023] Obtain a configuration information training set, which 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 validation set with the configuration information, wherein the dimension represents the presentation form of the configuration information; train the model based on the knowledge vector training set and the validation set with the adaptation information.

[0024] In one implementation, the model is deployed on a server, and obtaining the chip's compatibility information with the operating system includes:

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

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

[0027] In response to an anomaly occurring while the operating system is running on the chip of the electronic device, an anomaly information is determined; the anomaly information is used as debugging information to optimize the model.

[0028] According to a third aspect of the present disclosure, a device information collection apparatus is provided, comprising:

[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 embodiment of the first aspect.

[0030] According to a fourth aspect of the present disclosure, a storage medium is provided, the storage medium storing instructions that, when executed by a processor of a terminal, enable the terminal to perform the method described in the first aspect or any one of the embodiments of the first aspect.

[0031] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: electronic devices can obtain the adaptation information of the chip-adapted operating system and achieve automatic or assisted generalization 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 should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

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

[0034] Figure 1 This is a flowchart illustrating an operating system operation method according to an exemplary embodiment.

[0035] Figure 2 This is a flowchart illustrating a configuration information determination device according to an exemplary embodiment.

[0036] Figure 3 This is a flowchart illustrating a model training method according to an exemplary embodiment.

[0037] Figure 4 This is a flowchart illustrating a chip-to-operating system adaptation method according to an exemplary embodiment.

[0038] Figure 5 This is a flowchart illustrating an operating system operation method according to an exemplary embodiment.

[0039] Figure 6 This is a block diagram illustrating an operating system running device according to an exemplary embodiment.

[0040] Figure 7 This is a block diagram illustrating an apparatus for running an operating system according to an exemplary embodiment. Detailed Implementation

[0041] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure.

[0042] In the accompanying drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of this disclosure. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure. The embodiments of this disclosure will be described in detail below with reference to the accompanying drawings.

[0043] The operating system running method provided in this disclosure can be applied to application scenarios where automatic or assisted chip adaptation to an operating system is possible. By training a neural network language model to adapt the chip, a specific operating system can be run on the chip.

[0044] In related technologies, the process of running an operating system on a chip requires cooperation between chip manufacturers and operating system manufacturers. Based on the compatibility guidelines and familiarity with the driver framework, hardware capabilities are integrated into the operating system. This traditional method has low compatibility efficiency and seriously affects the time and quality of operating system integration.

[0045] However, in related technologies, entity relationship extraction and AI model training are achieved by combining graph-state recurrent neural networks and Bidirectional Encoder Representations from Transformers (BERT) models. A first vector representing the semantic features of the text and a second vector representing the dependency relationship features of the text are extracted from the text, and the first and second vectors are concatenated and classified. This allows the entity pair relationship extraction to achieve better accuracy in application scenarios with long sentences and cross sentences, improving the problem of insufficient accuracy in application scenarios with long sentences and cross sentences in existing technologies. In addition, in the model training stage, this application generates a large amount of labeled data through remote supervision based on preset rules and pre-trained models, which can obtain a large amount of relatively accurate training data at a low cost.

[0046] In view of this, this disclosure provides an operating system running method. In the operating system running method provided in this disclosure, the chip in the device is adapted to the target operating system based on the Large Language Model (LLM) and word embedding (EMbedding), thereby improving the adaptation efficiency between the chip and the operating system.

[0047] Figure 1 This is a flowchart illustrating an operating system operation method according to an exemplary embodiment, such as... Figure 1 As shown, the operating system operation method includes the following steps.

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

[0049] In this embodiment of the disclosure, the electronic device can be an Internet of Things (IoT) device such as a sports watch, a smart speaker, and a camera, or a device with a small core or small system such as a smart watch, a TV, a mobile phone, and an in-vehicle system.

[0050] In this embodiment of the disclosure, 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, pre-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 run according to the guidance information in the adaptation information to run the operating system on the chip of the electronic device.

[0052] In this embodiment of the disclosure, technicians or electronic devices can automatically run the corresponding code information according to the guidance information, thereby enabling the specified operating system to run on the chip of the electronic device. For example, technicians can run the installation tool contained 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 this embodiment of the disclosure, the adaptation information output by the model can be obtained by inputting knowledge vectors into the model. 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 this embodiment of the disclosure, knowledge vectors are used to represent feature vectors of configuration information corresponding to chips and / or operating systems. Different chips have different feature vectors, different operating systems have different feature vectors, and different feature vectors correspond to different configuration information. Representing the configuration information of different chips or operating systems using knowledge vectors facilitates the management of configuration information and has advantages such as structure and inheritance.

[0055] In this embodiment of the disclosure, configuration information refers to the necessary hardware and software information required during the chip adaptation to the operating system. It should be understood that the configuration information required during the adaptation of different chips and operating systems may be the same or different.

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

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

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

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

[0060] In this embodiment of the disclosure, the configuration information can be obtained directly from the log exported by the electronic device, or it can be obtained by interacting with the technical personnel of the electronic device in the form of a question and answer, and the configuration information of the chip can be extracted from the interaction content.

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

[0062] In this embodiment of the disclosure, the first knowledge vector can be obtained by word embedding of 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, the second knowledge vector with the highest similarity to the first knowledge vector is determined in the knowledge vector base.

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

[0065] In one exemplary embodiment, the cosine of the angle between each knowledge vector in the knowledge vector base and the first knowledge vector can be calculated, and the knowledge vector with the smallest cosine value 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 to determine the second knowledge vector in this exemplary embodiment is only for illustrative purposes, and the method for determining the second knowledge vector in this disclosure is not limited.

[0066] In this embodiment of the disclosure, several second knowledge vectors with the highest similarity to the first knowledge vector can also be determined. For example, the cosine 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 can be selected as the ten second knowledge vectors with the highest similarity to the first knowledge vector. In step S24, the second knowledge vectors are input into the model to obtain the adaptation information corresponding to the second knowledge vectors, which is used as the adaptation information for the chip to adapt to the operating system.

[0067] In this embodiment of the disclosure, 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 this embodiment of the disclosure, if multiple second knowledge vectors are obtained, the multiple adaptation information corresponding to the multiple second knowledge vectors can be further processed through a large language model (LLM) to achieve effects such as summarization, generalization, formatting, deduplication, and translation.

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

[0070] Figure 3 This is a flowchart illustrating a model training method according to an exemplary embodiment, such as... Figure 3 As shown, the model training method includes the following steps.

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

[0072] In this embodiment of the disclosure, the configuration information training set is a set of training samples used for model training, which may include images, text, code, examples, etc. containing configuration information content.

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

[0074] In this embodiment of the disclosure, the dimension represents the presentation format of the configuration information, which includes various different information formats. For example, screenshots of technical forums belong to visual information, text such as system development guidelines belong to language information, and open-source code belongs to computer programming languages. Therefore, the dimensions of the above-mentioned configuration information are all different.

[0075] In one exemplary embodiment, the training set of configuration information for generating knowledge vectors can come from different dimensions such as images, text, code, and examples: information can be extracted from screenshots of photos and analyzed into knowledge vectors (e.g., screenshots from technical forums); key information can be extracted from summary adaptation documents and analyzed into knowledge vectors (e.g., from system development guides); code analysis can be performed on source code and parsed into knowledge vectors (e.g., open-source code from technical forums); and examples and sample code from emulators can be parsed into knowledge vectors.

[0076] In this embodiment of the disclosure, Natural Language Processing (NLP) can be added during the training process of the model to increase the weight of the existing configuration information in the model and enhance cognition, understanding and generation.

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

[0078] In this embodiment of the disclosure, the model includes at least one neural network model and at least one natural language model. The neural network model is trained through deep learning to enhance the information retrieval identification and accuracy and generalize to new tasks. The natural language model is used to label the results with information weights.

[0079] In one 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 Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). The model is then trained by combining multiple natural language models such as chatGLM and chatGPT3 to finally obtain the model.

[0080] In this embodiment, the model processing results, labeled with information weights, are organized and stored in a custom vector database. Vector similarity searching can be provided through specialized indexes, reducing feature extraction and increasing accuracy and information retrieval speed. For example, the text2vec-base-chinese Embedding interface can be used to convert the knowledge training set into a second knowledge vector, which, along with the corresponding configuration information, is stored in the custom database. Vector similarity searching is then provided through specialized indexes such as the k-nearest neighbor (k-NN) algorithm. The custom vector database can store knowledge vectors of multiple dimensions and their corresponding adaptation information. The key in the custom vector database is the second knowledge vector; the value is the adaptation information content corresponding to the second knowledge vector, also known as the original record of the knowledge point.

[0081] In this embodiment, the granularity of the second knowledge vector can be modified through segmentation and word embedding operations. By generating a word segmenter from the adapted information content and performing segmentation operations, the adapted information content is subdivided, thereby controlling the granularity of the adapted information. By performing word embedding on the segmented adapted information, the second knowledge vector corresponding to the segmented adapted information can be obtained. It should be understood that the granularity can be distinguished from fine to coarse using punctuation marks, paragraphs, chapters, etc. If the segmentation granularity is too fine, the knowledge points will be too fragmented, affecting the relationships between them; if the segmentation granularity is too coarse, redundant information may be carried during matching, and the efficiency of word embedding, processing, indexing, and other operations will also be affected.

[0082] Figure 4 This is a flowchart illustrating a chip-to-operating system adaptation method according to an exemplary embodiment, such as... Figure 4 As shown, the chip-to-operating system adaptation method includes the following steps.

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

[0084] In this embodiment of the disclosure, for scenarios where the model is deployed on the server, the content containing configuration information sent by the electronic device to the server can be presented in a multi-dimensional form, such as uploading natural language content, configuration screenshots, configuration information files, etc.

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

[0086] In this embodiment, the server receives configuration information sent by the device, determines a 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 a custom vector database, determines 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 perform word embedding on the configuration information of the device to obtain the first knowledge vector. The server searches the custom vector database to obtain several second knowledge vectors with the highest similarity to the first knowledge vector, and returns the adaptation information corresponding to the several second knowledge vectors to the device. In an exemplary embodiment, the device inputs a question to the server, and the server returns the adaptation information retrieved by the above model to the client, downloads the trained simplified model, and provides text steps and example code. Further, the device parses the returned results, automatically installs relevant building and simulation tools, runs and demonstrates the example code on the simulator, or burns it to the terminal device, providing the running process, results, further prompts, and summaries.

[0087] Figure 5 This is a flowchart illustrating an operating system operation method according to an exemplary embodiment, such as... Figure 5 As shown, the operating system operation method includes the following steps.

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

[0089] In this embodiment of the disclosure, the exception information includes configuration information for reporting exceptions. For example, the exception information may include exception type, exception time, exception location, and exception data.

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

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

[0092] In one exemplary embodiment, the model resides on the server side. The server calculates and feeds back adaptation information provided by a custom vector library to the device. When an anomaly occurs on the device, it sends a closed-loop feedback of configuration information, adaptation information, and anomaly information to the server. The server automatically resolves the anomaly during the adaptation process for that type of device based on the received information, providing prompts. The server-side model can provide sample code, interactively debug, and update training based on feedback, updating the second knowledge vector and corresponding adaptation information into the custom vector database. Furthermore, after the operating system installation is completed on the device, the server sends acceptance test cases from the vector database to the user for verification and acceptance, ultimately providing an acceptance report to the device. In this embodiment, the electronic device obtains the adaptation information between its chip and the operating system, automatically or assistedly enabling the operating system to run on the chip. This significantly shortens the information retrieval time required for chip manufacturers during southbound interface adaptation, breaks down information barriers between chip manufacturers and operating system development teams, and improves the stickiness between chip manufacturers and operating system platforms. It also shortens the development cycle for operating system manufacturers, improves chip adaptation development efficiency, and helps solve the problem of information fragmentation.

[0093] Based on the same concept, embodiments of this disclosure also provide an operating system running device.

[0094] It is understood that the operating system running device provided in this disclosure includes hardware structures and / or software modules corresponding to each function in order to achieve the above-mentioned functions. In conjunction with the units and algorithm steps of the various examples disclosed in this disclosure, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by 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 beyond the scope of the technical solutions of this disclosure.

[0095] Figure 6 This is a block diagram illustrating an operating system running device 100 according to an exemplary embodiment. (Refer to...) Figure 6 The device includes a processing unit 101 and an operating unit 102.

[0096] The processing unit 101 is configured to, in response to determining that an operating system needs to be run on the chip of the electronic device, obtain the chip's operating system adaptation information, which includes code information and guidance information required for the chip to run the operating system.

[0097] The execution unit 102 is used to run code information according to the guidance information in the adaptation information in order 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 the operating system. Different chips have different feature vectors, different operating systems have 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] Obtain the chip's configuration information; convert the configuration information into a first knowledge vector; in the knowledge vector base, determine the second knowledge vector that has the highest similarity to the first knowledge vector;

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

[0102] In one embodiment, the model is determined in the following manner:

[0103] Obtain a configuration information training set, which includes configuration information of at least one dimension; transform the training configuration information of at least one dimension to obtain a knowledge vector training set and a validation set with adaptation information, where the dimension represents the presentation form of the configuration information; train the model based on the knowledge vector training set and the validation set with adaptation information.

[0104] In one embodiment, the model is deployed on a server to obtain chip-compatible operating system adaptation information, including:

[0105] Send the chip's 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 anomaly occurring in the operating system running on the chip of an electronic device, the anomaly information is determined; the anomaly information is used as debugging information for the optimization model.

[0108] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0109] Figure 7 This is a block diagram illustrating a device 200 for running an operating system according to an exemplary embodiment. For example, device 200 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

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

[0111] Processing component 202 typically controls the overall operation of device 200, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 202 may include one or more processors 220 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 202 may include one or more modules to facilitate interaction between processing component 202 and other components. For example, processing component 202 may include a multimedia module to facilitate interaction between multimedia component 208 and processing component 202.

[0112] Memory 204 is configured to store various types of data to support the operation of device 200. Examples of such data include instructions for any application or method operating on device 200, contact data, phonebook data, messages, pictures, videos, etc. 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 storage, flash memory, magnetic disk, or optical disk.

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

[0114] 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 touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 208 includes a front-facing camera and / or a rear-facing camera. When the device 200 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0115] Audio component 210 is configured to output and / or input audio signals. For example, audio component 210 includes a microphone (MIC) configured to receive external audio signals when device 200 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 204 or transmitted via communication component 216. In some embodiments, 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, home buttons, volume buttons, power buttons, and lock buttons.

[0117] Sensor assembly 214 includes one or more sensors for providing status assessments of various aspects of device 200. For example, sensor assembly 214 may detect the on / off state of device 200, the relative positioning of components such as the display and keypad of device 200, changes in the position of device 200 or a component of device 200, the presence or absence of user contact with device 200, the orientation or acceleration / deceleration of device 200, and temperature changes of device 200. Sensor assembly 214 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 214 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 214 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0118] Communication component 216 is configured to facilitate wired or wireless communication between device 200 and other devices. Device 200 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 216 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 216 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may 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 methods described above.

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

[0121] It is understood that in this disclosure, "multiple" refers to two or more, and other quantifiers are similar. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. The singular forms "a," "the," and "the" are also intended to include the plural forms unless the context clearly indicates otherwise.

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

[0123] It is further understood that the terms “center,” “longitudinal,” “lateral,” “lower,” “left,” “right,” “vertical,” “horizontal,” “top,” “bottom,” “inner,” and “outer,” etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this embodiment and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation.

[0124] It can be further understood that, unless otherwise specified, "connection" includes both direct connections where no other components exist between the two parties and indirect connections where other components exist between them.

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

[0126] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein.

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

Claims

1. An operating system running method, characterized by, The method comprises: in response to determining that an operating system needs to be run on a chip of an electronic device, obtaining adaptation information of the chip adapting to the operating system, the adaptation information comprising code information required by the chip to run the operating system and guide information; running the code information according to the guide information in the adaptation information, so as to run the operating system on the chip of the electronic device; wherein the adaptation information is determined based on a knowledge vector and a model, the knowledge vector being used to represent a feature vector of configuration information corresponding to the chip and / or the operating system, the configuration information is determined in the following manner: obtaining configuration information of the chip; converting the configuration information into a first knowledge vector; in a knowledge vector library, determining a second knowledge vector with the highest similarity to the first knowledge vector; inputting the second knowledge vector into the model to obtain adaptation information corresponding to the second knowledge vector as the adaptation information of the chip adapting to the operating system.

2. The method of claim 1, wherein, The input of the model is a knowledge vector, and the output is adaptation information; wherein 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 1 or 2, characterized in that, The model is determined in the following manner: obtaining a configuration information training set, the configuration information training set comprising 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 an adaptation information validation set, wherein the dimension represents the presentation form of the training configuration information; training the model based on the knowledge vector training set and the adaptation information validation set.

4. The method according to claim 1 or 2, characterized in that, The model is deployed on a server, and the adaptation information of the chip adapting to the operating system comprises: sending the configuration information of the chip to the server; obtaining the adaptation information determined and sent by the server based on the model.

5. The method according to claim 1 or 2, characterized in that, The method further comprises: in response to an exception occurring when running the operating system on the chip of the electronic device, determining exception information; using the exception information as debugging information for optimizing the model.

6. An operating system running apparatus characterized by comprising: The device comprises: a processing unit configured to, in response to determining that an operating system needs to be run on a chip of an electronic device, obtain adaptation information of the chip adapting to the operating system, the adaptation information comprising code information required by the chip to run the operating system and guide information; a running unit configured to run the code information according to the guide information in the adaptation information, so as to run the operating system on the chip of the electronic device; wherein the adaptation information is determined based on a knowledge vector and a model, the knowledge vector being used to represent a feature vector of configuration information corresponding to the chip and / or the operating system, the configuration information is determined in the following manner: obtaining configuration information of the chip; converting the configuration information into a first knowledge vector; in a knowledge vector library, determining a second knowledge vector with the highest similarity to the first knowledge vector; inputting the second knowledge vector into the model to obtain adaptation information corresponding to the second knowledge vector as the adaptation information of the chip adapting to the operating system.

7. The apparatus of claim 6, wherein, An input of the model is a knowledge vector, and an output is adaptation information. Different chips correspond to different feature vectors, different operating systems correspond to different feature vectors, and different feature vectors correspond to different configuration information.

8. The apparatus of claim 6 or 7, wherein, The model is determined in the following manner: Obtain a configuration information training set, and the configuration information training set includes configuration information of at least one dimension; Convert the training configuration information of the at least one dimension to obtain a knowledge vector training set and the adaptation information verification set, wherein the dimension represents a presentation form of the configuration information; Based on the knowledge vector training set and the adaptation information verification set, the model is trained.

9. The apparatus of claim 6 or 7, wherein, The model is deployed on a server, and the adaptation information of the chip adapted to the operating system is obtained in the following manner: Send the configuration information of the chip to the server; Obtain the adaptation information determined and sent by the server based on the model.

10. The apparatus of claim 6 or 7, wherein, The processing unit is further configured to: In response to an exception occurring when the operating system runs on the chip of the electronic device, determine exception information; Use the exception information as debugging information for optimizing the model.

11. An apparatus information collecting device characterized by comprising: Comprise: A processor; A memory for storing processor-executable instructions; The processor is configured to: The processor is configured to execute the method of any one of claims 1 to 5.

12. A storage medium, characterized by The storage medium stores instructions, and when the instructions in the storage medium are executed by the processor of the electronic device, the terminal can execute the method of any one of claims 1 to 5.

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