Data management method and device, computer equipment and storage medium

Abstract

The embodiment of the invention provides a data management method and device, computer equipment and a storage medium. The data management method comprises the following steps: obtaining audio and video data to be transmitted and a sensitivity level corresponding to the audio and video data; at least according to the sensitivity level corresponding to the audio and video data, a matched transmission strategy is configured for the audio and video data, and the matched transmission strategy comprises an encrypted transmission strategy or a non-encrypted transmission strategy; the parameters of the encryption transmission strategy comprise at least one of the encryption algorithm type, the encryption algorithm complexity, the encryption key length and the encryption key complexity; and transmitting the audio and video data based on the matched transmission strategy.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to, but are not limited to, the field of audio and video technology, and in particular, to a data management method and apparatus, a computer device, and a storage medium. Background Art

[0002] With the rapid development of information technology and the Internet, electronic devices such as smart phones and tablets have long been very popular. In order to ensure the security of audio and video data, encryption technology is usually used in the data transmission process. Traditional encryption methods usually use fixed encryption algorithms. The diversity of electronic devices will increase the difficulty of audio and video encryption. Overly simple encryption processes will reduce data security and may cause serious losses to users. Overly complex encryption processes require more system resources and may cause delays, freezes, or increased power consumption, affecting users' instant access to audio and video data and smooth playback. To this end, how to reasonably ensure the security and transmission performance of audio and video data is a problem that needs to be solved. Summary of the invention

[0003] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0004] In a first aspect, an embodiment of the present disclosure provides a data management method, comprising: obtaining audio and video data to be transmitted and a sensitivity level corresponding to the audio and video data; configuring a matching transmission strategy for the audio and video data at least according to the sensitivity level corresponding to the audio and video data, wherein the matching transmission strategy comprises: an encrypted transmission strategy or a non-encrypted transmission strategy, and the parameters of the encrypted transmission strategy comprise: at least one of an encryption algorithm type, an encryption algorithm complexity, an encryption key length, and an encryption key complexity; based on the matching transmission strategy, transmitting the audio and video data.

[0005] In some exemplary embodiments, configuring a matching transmission strategy for the audio and video data at least based on the sensitivity level corresponding to the audio and video data includes: obtaining at least one of a network security level of the network environment in which the audio and video data is located, a user behavior pattern of a target user corresponding to the audio and video data, and a resource status of system resources of a target client corresponding to the audio and video data; determining the matching transmission strategy based on at least one of the network security level of the network environment in which the audio and video data is located, a user behavior pattern of a target user corresponding to the audio and video data, and a resource status of system resources of a target client corresponding to the audio and video data, and the sensitivity level corresponding to the audio and video data.

[0006] In some exemplary embodiments, the matching transmission strategy is determined based on at least one of the network security level of the network environment in which the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data, and the sensitivity level corresponding to the audio and video data, including: determining an initial transmission strategy that matches the sensitivity level corresponding to the audio and video data based on the sensitivity level corresponding to the audio and video data; determining whether to adjust the initial transmission strategy based on at least one of the network security level of the network environment in which the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data; when it is determined that the initial transmission strategy is to be adjusted, adjusting the initial transmission strategy to obtain the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy; or, when it is determined that the initial transmission strategy is not to be adjusted, determining the initial transmission strategy as the matching transmission strategy.

[0007] In some exemplary embodiments, the adjusting of the initial transmission strategy includes at least one of adjusting from the encrypted transmission strategy to the non-encrypted transmission strategy, adjusting from the non-encrypted transmission strategy to the encrypted transmission strategy, and adjusting parameters of the encrypted transmission strategy.

[0008] In some exemplary embodiments, the obtaining of at least one of the network security level of the network environment in which the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data includes: obtaining at least one of the network environment data of the network environment in which the audio and video data is located, the user behavior data of the target user corresponding to the audio and video data, and the system resource data of the system resources of the target client corresponding to the audio and video data; using an artificial intelligence big model to analyze at least one of the network environment data of the network environment in which the audio and video data is located, the user behavior data of the target user corresponding to the audio and video data, and the system resource data of the system resources of the target client corresponding to the audio and video data, to obtain at least one of the network security level of the network environment in which the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data.

[0009] In some exemplary embodiments, the determining whether to adjust the initial transmission strategy based on at least one of the network security level of the network environment in which the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data includes: when the network security level of the network environment in which the audio and video data is located is a preset first network security level for characterizing the existence of security risks, the user behavior pattern of the target user corresponding to the audio and video data is a preset first behavior pattern for characterizing the existence of abnormal behavior, and the resource status of the system resources of the target client corresponding to the audio and video data is at least one of the preset first resource states for characterizing the existence of system resource shortages, determining to adjust the initial transmission strategy to obtain the adjusted transmission strategy; or, when the network security level of the network environment in which the audio and video data is located is a preset second network security level for characterizing the absence of security risks, the user behavior pattern of the target user corresponding to the audio and video data is a preset second behavior pattern for characterizing the absence of abnormal behavior, and the resource status of the system resources of the target client corresponding to the audio and video data is at least one of the preset second resource states for characterizing the absence of system resource shortages, determining not to adjust the initial transmission strategy.

[0010] In some exemplary embodiments, the method further includes: obtaining user behavior data of the target user corresponding to the audio and video data, wherein the user behavior data includes account login data and key usage data, the account login data includes: any one or more of user ID, login time, operation time, operation frequency, operation type, commonly used devices and geographic location, and the key usage data includes any one or more of key ID, usage time, usage frequency and usage location; determining whether the target user has abnormal behavior based on the user behavior data; when it is determined that the target user has abnormal behavior, sending an identity authentication message for instructing the user to perform an identity authentication operation to the target client corresponding to the audio and video data to determine whether the target user is an authorized user; when it is determined that the target user is an authorized user, distributing keys to the target client corresponding to the audio and video data; wherein the identity authentication operation includes: any one or more of password verification operation, fingerprint recognition operation, facial recognition operation, voiceprint recognition operation and iris recognition operation.

[0011] In some exemplary embodiments, the transmitting of the audio and video data based on the matching transmission strategy includes: determining whether the sensitivity level corresponding to the audio and video data is a preset sensitivity level; when the sensitivity level corresponding to the audio and video data is the preset sensitivity level, sending an identity authentication message for instructing the user to perform an identity authentication operation to the target client corresponding to the audio and video data to determine whether the target user corresponding to the audio and video data is an authorized user; when it is determined that the target user is an authorized user, sending the audio and video data to the target client corresponding to the audio and video data based on the matching transmission strategy; wherein the identity authentication operation includes: any one or more of a password verification operation, a fingerprint recognition operation, a facial recognition operation, a voiceprint recognition operation, and an iris recognition operation.

[0012] In some exemplary embodiments, the method further includes: obtaining key usage data corresponding to each of a plurality of pre-stored keys, wherein the key usage data of each key includes any one or more of a key ID, usage time, usage frequency, and usage location; analyzing the key usage data corresponding to each of the plurality of keys to determine the key usage modes corresponding to each of the plurality of keys; when the key usage mode corresponding to at least one of the plurality of keys is an abnormal usage mode, identifying the abnormal keys from the plurality of keys based on the key usage data corresponding to each of the plurality of keys, and deleting the abnormal keys; or, when the key usage modes corresponding to each of the plurality of keys are all normal usage modes, obtaining the usage time corresponding to each of the plurality of keys, and determining the update time corresponding to each of the plurality of keys based on the usage time corresponding to each of the plurality of keys and a pre-set key life cycle; and updating the plurality of keys respectively based on the update time corresponding to each of the plurality of keys.

[0013] In some exemplary embodiments, the method further includes: monitoring the life cycles of a plurality of pre-stored keys, wherein the life cycles of at least two of the plurality of keys are different; when it is determined that there is a target key whose life cycle has ended among the plurality of keys, generating a new key corresponding to the target key, wherein the new key is different from the target key; and updating the target key to the new key.

[0014] In a second aspect, an embodiment of the present disclosure provides a data management device, comprising: an artificial intelligence agent, wherein the agent is configured to execute the steps of the data management method described in any one of the above exemplary embodiments.

[0015] In a third aspect, an embodiment of the present disclosure provides a computer device, comprising: a processor, and a memory storing a computer program executable on the processor, wherein the processor is configured to execute the steps of the data management method described in any one of the above exemplary embodiments.

[0016] In a fourth aspect, an embodiment of the present disclosure provides a non-volatile computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the steps of the data management method described in any one of the above exemplary embodiments.

[0017] The data management method and apparatus, computer equipment and storage medium provided by the exemplary embodiments of the present disclosure can, after acquiring the audio and video data to be transmitted and the sensitivity level corresponding to the audio and video data, configure a matching transmission strategy for the audio and video data at least according to the sensitivity level corresponding to the audio and video data, and then transmit the audio and video data based on the matching encryption transmission strategy, so as to reasonably ensure the security and transmission performance of the audio and video data. Thus, it is convenient for the widespread application and development of the audio and video data industry.

[0018] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. Other advantages of the present disclosure can be realized and obtained by the schemes described in the description and the drawings.

[0019] Other aspects will be apparent upon reading and understanding the drawings and detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide an understanding of the technical solution of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the technical solution of the present disclosure and do not constitute a limitation on the technical solution of the present disclosure. The shapes and sizes of the components in the accompanying drawings do not reflect the actual proportions and are only intended to illustrate the contents of the present disclosure.

[0021] Figure 1 A schematic diagram of a system architecture involved in a data management method in an exemplary embodiment of the present disclosure; Figure 2 A flowchart of a data management method in an exemplary embodiment of the present disclosure; Figure 3 Another flowchart of a data management method in an exemplary embodiment of the present disclosure is shown; Figure 4 It is another flowchart of the data management method in the exemplary embodiment of the present disclosure; Figure 5 It is another flowchart of the data management method in the exemplary embodiment of the present disclosure; Figure 6 It is another flowchart of the data management method in the exemplary embodiment of the present disclosure; Figure 7 It is another flowchart of the data management method in the exemplary embodiment of the present disclosure; Figure 8 It is another flowchart of the data management method in the exemplary embodiment of the present disclosure; Fig. 9 It is another flowchart of the data management method in the exemplary embodiment of the present disclosure; Fig.10 It is another flowchart of the data management method in the exemplary embodiment of the present disclosure; Fig.11 It is another flowchart of the data management method in the exemplary embodiment of the present disclosure; Fig.12 It is a schematic diagram of the structure of a computer device in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] The present disclosure describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present disclosure. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0023] When describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As will be appreciated by those of ordinary skill in the art, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as a limitation on the claims. In addition, the claims for the method and / or process should not be limited to the steps that are performed in the order written, and those skilled in the art can easily understand that these orders can be changed and still remain within the spirit and scope of the disclosed embodiments.

[0024] In the exemplary embodiments of the present disclosure, ordinal numbers such as "first", "second" or "third" and similar words are provided to avoid confusion of constituent elements, rather than to limit them in terms of quantity, order or importance. They should not be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. "Include" or "comprises" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Exemplarily", "for example" or "for example" are used to indicate examples, illustrations or explanations, and should not be interpreted as being more preferred or more advantageous than other embodiments or design schemes.

[0025] In the embodiments of the present disclosure, descriptions such as "when..." and "under the circumstances of..." all refer to that the device will make corresponding processing under certain objective circumstances, and do not limit the time, nor do they require that the device must have a judgment action when implementing, nor do they mean that there are other limitations. Depending on the context, the words "if" and "if" used herein can be interpreted as "when..." or "when..." or "in response to determination" or "in response to detection". Similarly, depending on the context, the phrases "if it is determined" or "if (the stated condition or event) is detected" can be interpreted as "when it is determined" or "in response to determination" or "when (the stated condition or event) is detected" or "in response to detection (the stated condition or event)". In the embodiments of the present disclosure, the term "determine" includes many kinds of actions. For example, "determine" can include calculation, operation, processing, derivation, investigation, query (for example, query in a table, database or other data structure), assertion, etc. Alternatively, "determine" can also include receiving (for example, receiving information), accessing (for example, accessing data in a memory), etc. Alternatively, "determine" can also include solving, selecting, choosing, establishing, etc. Information and data may be represented using any of a variety of different technologies and methods. For example, data, instructions, commands, information, signals, etc. mentioned in the description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0026] To facilitate understanding, some nouns or technical terms involved in the embodiments of the present disclosure are exemplified below.

[0027] 1. Symmetric encryption algorithm refers to an algorithm that uses the same key for encryption and decryption. Common symmetric encryption algorithms include: Advanced Encryption Standard (AES), which is a block encryption standard widely used in audio and video encryption; Data Encryption Standard (DES), which is a block algorithm using key encryption and was widely used in the past; Triple Data Encryption Algorithm (TDEA, Triple DEA), or 3DES (Triple DES), which is a symmetric key encryption block cipher, equivalent to applying the Data Encryption Standard (DES) algorithm three times to each data block; RC4 (Rivest Cipher 4), which is a stream encryption algorithm with a variable key length, etc.

[0028] 2. Asymmetric encryption algorithm refers to an algorithm in which the encryption key for encryption and the decryption key for decryption are different, or in other words, one key cannot be derived from the other. It uses a pair of keys, including a public key and a private key. The public key is used for encryption and the private key is used for decryption. This method provides an additional layer of security when transmitting keys, because even if the public key is intercepted, the data cannot be decrypted. Commonly used asymmetric encryption algorithms are: RSA (Rivest–Shamir–Adleman), used for secure data transmission, including key exchange; Elliptic Curve Cryptography (ECC), an asymmetric encryption based on elliptic curves, uses a shorter key length than RSA at the same level of security.

[0029] 3. In the field of artificial intelligence (AI), artificial intelligence agent (AI agent), also known as intelligent agent, refers to an intelligent software entity that can perceive the environment, reason, make decisions and perform actions to perform a specific task or a series of tasks. These tasks may include but are not limited to data analysis, decision making, automated workflow, customer service, speech recognition, natural language processing, image recognition, etc. AI Agent is designed to imitate human intelligent behavior, improve its performance through learning and adaptation, and can operate without direct human intervention. The components of an AI Agent may include: (1) a perception module: configured to obtain information about the external environment, which can perceive its environment through sensors or data input; (2) a decision module: configured to make decisions based on the perceived information through a corresponding big data model (this may involve simple "if-else" logic rules, or may involve complex machine learning algorithms); (3) an actuator module: configured to convert decisions into actual actions or outputs; (4) a learning module: configured to reason about audio, vision, and text through AI language models such as GPT (Generative Pre-training), GPT-4O, etc., to learn and improve from experience and determine whether the formulated strategy is satisfied.

[0030] 4. Natural Language Processing (NLP) technology refers to the process in which a computer accepts input in the form of natural language from a user and processes and calculates it internally through algorithms defined by humans to simulate human understanding of natural language and return the results expected by the user. NLP technology can be divided into two types: natural language understanding (NLU) and natural language generation (NLG). NLU focuses on how to understand text, such as text classification, named entity recognition, syntactic analysis, machine reading and decoding, and NLG focuses on how to generate natural text after understanding the text, such as automatic summarization.

[0031] 5. Big voice model: refers to a big data model in the AI ​​field that can process and learn complex language patterns and analyze information such as voice features, categories, and voice files through learning. It can handle tasks such as speech recognition, speech synthesis, language understanding, and translation based on AI technology (such as deep learning technology).

[0032] 6. Anomaly detection big model: refers to a big data model in the AI ​​field that can formulate corresponding plans based on learning strategies and perform corresponding anomaly detection operations. It can realize anomaly detection based on AI technology.

[0033] 7. Data-aware big model: refers to a big data model in the AI ​​field that can process and understand large-scale data sets, extract useful information from them, and make accurate predictions or decisions. It can extract information from various types of data such as images, voice, text and structured data based on AI technology (such as deep learning technology, neural networks, etc.).

[0034] 8. Optical Character Recognition (OCR) model: It is a technology for searching, extracting, identifying and manipulating text in images. It determines the shape by detecting dark and light patterns, and then uses character recognition methods to translate the shape into computer text.

[0035] Compared with text data, the images and sounds in audio and video data can bring users an intuitive and immersive information acquisition experience, which makes the demand for audio and video data grow rapidly. Encryption technology is one of the important means to ensure the security of data transmission. By encrypting data, the security of data can be guaranteed during the data transmission process. At present, the encryption technology of audio and video data can generally be divided into symmetric encryption technology and asymmetric encryption technology. Among them, symmetric encryption technology, such as AES and 3DES, has the characteristics of fast encryption speed and high encryption strength, but there is a problem of complex key management; asymmetric encryption technology, such as RSA, although the key distribution process is relatively simple, the encryption and decryption process is relatively time-consuming, and there are problems of slow encryption speed and low encryption efficiency when processing large amounts of data. In addition, there are some specific encryption technologies, such as DRM (Digital Rights Management) system, which are applied to audio and video data protection, but their implementation is complex and often requires dedicated hardware support or software environment, which limits their wide application.

[0036] With the popularization of mobile Internet, electronic devices have been widely used. The diversity and openness of electronic devices (such as Android devices) have increased the difficulty of audio and video encryption. Usually, encryption and decryption operations require a certain amount of processing power. Different encryption algorithms have different requirements for the central processing module (CPU) and memory of electronic devices (such as Android devices). Different electronic devices (such as Android devices) may have different performance levels, operating system versions, and third-party applications. These factors will affect the compatibility and efficiency of encryption algorithms. In addition, the resources of mobile devices (such as CPU performance, memory, and battery life) are limited, which puts higher requirements on encryption algorithms. In the selection and implementation of encryption technology, traditional encryption methods usually use fixed encryption algorithms. Overly simple encryption processes will reduce data security and may cause serious losses to users. Overly complex encryption technology may lead to excessive system resource consumption, delays, freezes, or power consumption, which will affect users' instant access to audio and video data and smooth playback. Therefore, how to balance the security and transmission performance of audio and video data and maintain a good user experience has become a problem that needs to be solved.

[0037] The disclosed embodiment provides a data management method, which uses efficient AI Agent technology to determine the encryption transmission strategy that matches the audio and video data based at least on the sensitivity level corresponding to the audio and video data to be transmitted, and then transmits and stores the audio and video data based on the matching encryption transmission strategy, thereby achieving efficient, secure and easy-to-implement data management, and reasonably ensuring the security and transmission performance of the audio and video data. Thereby, it is convenient for the widespread application and development of the audio and video data industry. Compared with the method of using fixed encryption algorithms in some technologies, it has a certain flexibility in practical applications, and can adaptively and dynamically adjust the encryption transmission strategy according to actual scenarios (such as the sensitivity level corresponding to the audio and video content, client device performance, network security and other factors), and has higher practical application value.

[0038] The following is an exemplary description of the system architecture involved in the exemplary embodiments of the present disclosure.

[0039] The exemplary embodiments of the present disclosure provide a data management system. In practical applications, the data management system can be applied to application scenarios that require management of audio and video data, such as live broadcast, video call, video conference, audio and video playback, commercial exhibition hall operation, large conference, and large press conference.

[0040] Figure 1 FIG. 1 is a schematic diagram of an exemplary system architecture involved in the data management method in the exemplary embodiment of the present disclosure. Figure 1 As shown, the data management system may include: a client 10, a data management device 11, and a server 12. For example, here, Figure 1 The number of the client 10, data management device 11 and server 12 in the figure is only for illustration. According to the actual application scenario, any number of the client 10, data management device 11 and server 12 can be set. Here, the embodiment of the present disclosure does not limit this.

[0041] Among them, Figure 1 As shown, the client 10 is configured to exchange information with the data management device 11 through the network. For example, after the user operates, the client can send the user input instruction for obtaining audio and video data to the data management device 11 according to the user operation. The client can receive the audio and video data processed by the data management device 11 based on the encryption transmission strategy. The server 12 is configured to store audio and video data; it can exchange information with the data management device 11 through the network. For example, it can be implemented by using a storage server, a data server or a business server.

[0042] Among them, Figure 1As shown, the data management device 11 is provided with an artificial intelligence agent (AI Agent), and the AI ​​Agent is configured to receive user instructions sent by one or more clients; according to the user instructions, one or more audio and video data are obtained from one or more service terminals, and a suitable transmission strategy (such as an encrypted transmission strategy) is formulated for the one or more audio and video data at least according to the sensitivity level corresponding to the one or more audio and video data, and the one or more audio and video data are correspondingly transmitted to one or more clients based on the suitable transmission strategy (such as the encrypted transmission strategy). For example, a client requests to play the first audio and video data and the second audio and video data, and determines that the sensitivity level of the first audio and video data is low (such as low data importance or low data confidentiality), then, when formulating the transmission strategy, an encryption algorithm with a shorter key length can be used to encrypt the audio and video data, and the sensitivity level of the second audio and video data is high (such as high data importance or high data confidentiality), then, when formulating the transmission strategy, an encryption algorithm with a longer key length can be used to encrypt the audio and video data, so that the security of data transmission and the smoothness of audio and video playback can be guaranteed. For another example, the performance of the first client and the second client are different, among which, relatively speaking, the performance of the first client is relatively poor, and the performance of the second client is relatively good. Considering the poor performance of the first client, when the data management device formulates a suitable transmission strategy for the audio and video data required by the first client, a lower complexity encryption algorithm can be used, and considering the better performance of the second client, when formulating a suitable transmission strategy (such as an encrypted transmission strategy) for the audio and video data required by the second client, a higher complexity encryption algorithm can be used. In this way, the security of data transmission and the smoothness of audio and video playback can be guaranteed. In this way, compared with the method of using fixed encryption algorithms based on experience in some technologies, it has certain flexibility in practical applications, and can adaptively and dynamically adjust the encryption transmission strategy according to actual scenarios (such as the sensitivity of audio and video content, client device performance, network security and other factors), and has higher practical application value.

[0043] In some exemplary embodiments, the data management device 11 may include an artificial intelligence agent (AI Agent), and the artificial intelligence agent (AI Agent) may include: a data management module and a key management module, the data management module is configured to formulate a matching transmission strategy for audio and video data, and the key management module is configured to dynamically manage keys.

[0044] In some exemplary embodiments, the client may be a device using an Android operating system, such as an intelligent mobile terminal based on the Android system. In some exemplary embodiments, the client may include, but is not limited to: intelligent mobile devices, intelligent home devices, wearable devices, intelligent medical devices, PCs (Personal Computers), various business terminals, etc. Among them, intelligent mobile devices may include mobile devices with audio and video playback functions such as mobile phones, tablet computers, laptops, PDAs (Personal Digital Assistants), Internet cars, etc. Intelligent home devices may include intelligent home appliances, such as smart TVs and other home appliances with audio and video playback functions. Wearable devices may include devices with audio and video playback functions such as smart watches, smart glasses, VR (Virtual Reality) devices, AR (Augmented Reality), mixed reality devices (i.e., devices that can support virtual reality and augmented reality), etc.

[0045] In some exemplary embodiments, the number of the client 10 may be one or more, and the number of the server 12 may be one or more.

[0046] In some exemplary embodiments, the server 12 may be a computer terminal, or a server, or a server cluster consisting of several servers, or a cloud computing service system or other computer products with strong computing capabilities. The cloud computing service system is also called a cloud computing server, a cloud server or a cloud host, etc. It is a host product in the cloud computing service system to solve the defects of difficult management and weak service scalability in traditional physical hosts and virtual private servers (VPS) services.

[0047] In some exemplary embodiments, the data management device 11 may be a computer terminal, or may be a server, or may be a server cluster composed of several servers, or may be a computer product with strong computing capabilities such as a cloud computing service system.

[0048] In some exemplary embodiments, the data management device 11 and the server 12 can be respectively set as independent servers, independent server clusters or independent cloud computing service systems. Alternatively, the data management device 11 and the server 12 can be set in the same server, the same server cluster or the same cloud computing service system.

[0049] The system architecture and business scenarios described in the exemplary embodiments of the present disclosure are intended to more clearly illustrate the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art will appreciate that with the evolution of the system architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present disclosure are equally applicable to similar technical problems.

[0050] The exemplary embodiments of the present disclosure provide a data management method. The exemplary embodiments of the present disclosure can be applied to scenarios of how to dynamically manage audio and video data and dynamically manage keys based on artificial intelligence.

[0051] In some exemplary embodiments, the data management method may be executed by a data management device, which may be implemented in hardware and / or software and may be configured in an electronic device with data management functions. In other exemplary embodiments, the data management method may be applicable to electronic devices, such as computer terminals, servers or other devices. The electronic device may deploy a pre-developed AI Agent so that the user can use the AI ​​Agent to implement scenario tasks. For example, the execution subject of the data management method may be the electronic device, such as the AI ​​Agent configured in the electronic device.

[0052] Figure 2 FIG. 1 is a flow chart of a data management method in an exemplary embodiment of the present disclosure, as shown in FIG. Figure 2 As shown, the data management method may include the following steps: Step 201: Obtain audio and video data to be transmitted and the sensitivity level corresponding to the audio and video data; The audio and video data may refer to data containing at least one of audio information and video information. For example, depending on different application scenarios, the audio and video data may be an audio and video stream in an online streaming service, or the audio and video data may be a locally stored multimedia file.

[0053] In some exemplary embodiments, the sensitivity level can be set in advance according to actual needs (such as the importance, confidentiality or sensitivity of audio and video data). For example, the sensitivity level can be "high level sensitivity", "medium level sensitivity", "low level sensitivity", etc., or the sensitivity level can be "first level sensitivity", "second level sensitivity", "third level sensitivity", etc., or the sensitivity level can be "higher sensitivity", "lower sensitivity", "normal", etc., or the sensitivity level can be "high sensitivity", "higher sensitivity", "lower sensitivity", "lower sensitivity", "normal", etc., or the sensitivity level can be "sensitive", "normal". The number of sensitivity levels can be more or less, and the embodiments of the present disclosure are not limited to this.

[0054] For example, data with a high sensitivity level may refer to data that you wish to control or limit exposure to, and data that will cause serious losses to individuals or companies if leaked, such as private data, confidential data, or important data. For example, private data may include identity information, financial information, health information, or location information, among which identity information may include name, ID number, phone number (including mobile phone number or landline number), address, unit name, or certificate type, financial information may include bank card number, payment password, health information may include medical records, medication records, or physical examination reports, and location information may include movement trajectories, global positioning system (GPS) positioning, etc. For example, confidential data may include commercial confidential documents or corporate confidential data, etc. For example, important data may include information with commercial value or customer information, etc.

[0055] For example, data with a sensitivity level set to a lower sensitivity may refer to data that is not recommended to be widely disseminated, such as major news information, data from large commercial exhibition halls, and PPT content for public conferences.

[0056] For example, data with a sensitivity level set to normal may refer to data that can be widely disseminated, such as entertainment information, basic information that can be found on the public network, etc.

[0057] In some exemplary embodiments, the sensitivity level may be related to the video type corresponding to the audio and video data to be transmitted. For example, the sensitivity level corresponding to the audio and video data of the financial type may be of high sensitivity, and the sensitivity level corresponding to the audio and video data of the entertainment type may be of low sensitivity or normal sensitivity. Alternatively, the sensitivity level may be related to the content in the audio and video data to be transmitted, for example, the sensitivity level corresponding to the audio and video data containing information such as transaction amount or identity information may be of high sensitivity, and the sensitivity level corresponding to the audio and video data containing advertising information may be of low sensitivity or normal sensitivity.

[0058] Step 202: configuring a matching transmission strategy for the audio and video data at least according to the sensitivity level corresponding to the audio and video data; The transmission strategy includes: an encrypted transmission strategy or a non-encrypted transmission strategy. The parameters of the encrypted transmission strategy include: at least one of the encryption algorithm type, encryption algorithm complexity, encryption key length and encryption key complexity.

[0059] For example, according to the encryption algorithm type, the encryption algorithm may be a symmetric encryption algorithm or an asymmetric encryption algorithm. For example, taking the RSA algorithm as an example, according to the complexity of the encryption algorithm, the encryption algorithm may be an RSA-128 algorithm or an RSA-256 algorithm, etc. For example, according to the encryption key length, the encryption key may be 128 bits, 256 bits, 512 bits, 1024 bits, etc. For example, according to the complexity of the encryption key, the encryption key may be a randomly generated encryption key, a fixed encryption key, an encryption key composed of numbers, an encryption key composed of letters, or an encryption key composed of multiple character combinations, etc.

[0060] Step 203: Transmit the audio and video data based on the matched transmission strategy.

[0061] In this way, the data management method provided by the exemplary embodiment of the present disclosure, during the audio and video data transmission process, after obtaining the audio and video data to be transmitted and the sensitivity level corresponding to the audio and video data, configures a matching transmission strategy for the audio and video data at least according to the sensitivity level corresponding to the audio and video data, and then transmits the audio and video data based on the matching encryption transmission strategy. In this way, different transmission strategies are adopted for audio and video data of different sensitivity levels, which improves the security of data transmission, takes into account resource consumption, and can more reasonably ensure the security and transmission performance of audio and video data. Thereby, it is convenient for the widespread application and development of the audio and video data industry.

[0062] The data management method provided by the exemplary embodiment of the present disclosure is described below by taking the AI ​​Agent configured in the electronic device as an example in which the execution subject of the data management method is an AI agent.

[0063] The following describes how to determine the sensitivity level corresponding to audio and video data.

[0064] In some exemplary embodiments, step 201 may include: step 2011: the AI ​​Agent obtains the audio and video data to be transmitted; step 2012: the AI ​​Agent analyzes the audio and video data to be transmitted and obtains the sensitivity level corresponding to the audio and video data.

[0065] In other exemplary embodiments, step 201 may include: step 2013: the AI ​​Agent obtains the audio and video data to be transmitted; step 2014: the AI ​​Agent analyzes the content of the audio and video data by using an AI big model (i.e., a big data model in the AI ​​field) to obtain data analysis results (such as video type, audio content analysis results, video content analysis results, etc.); step 2015: the AI ​​Agent determines the sensitivity level corresponding to the audio and video data based on the data analysis results.

[0066] For example, video types may include financial videos, entertainment videos, educational videos, general videos, and privacy videos, etc., and at least one of the audio content analysis results and the video content analysis results may include financial videos, entertainment videos, educational videos, general videos, and privacy videos, etc.

[0067] In some further exemplary embodiments, taking the case where the data analysis results include video type, audio content analysis results, and video content analysis results, step 201 may include: step 2016: the AI ​​Agent obtains the audio and video data to be transmitted; step 2017: the AI ​​Agent analyzes the audio information in the audio and video data based on the AI ​​big model to obtain the audio content analysis results, and the AI ​​Agent analyzes the video information in the audio and video data by using the AI ​​big model to obtain the video content analysis results and the video type; step 2018: the AI ​​Agent determines a first sensitivity level corresponding to the audio content analysis result, a second sensitivity level corresponding to the video content analysis result, and a third sensitivity level corresponding to the video category; step 2019: the AI ​​Agent determines the sensitivity level corresponding to the audio and video data to be transmitted based on the first sensitivity level corresponding to the audio content analysis result, the second sensitivity level corresponding to the video content analysis result, and the third sensitivity level corresponding to the video category.

[0068] In this way, AI Agent can comprehensively determine the sensitivity level corresponding to the audio and video data from three aspects: the audio content analysis results and the video content analysis results of the audio and video data, as well as the video category to which the audio and video data belongs. This can improve the rationality of the determined sensitivity level, and thus, based on the more reasonable sensitivity level, a reasonable transmission strategy can be formulated for the audio and video data to be transmitted, thereby avoiding excessive data delays and excessive system loads caused by overly complex transmission strategies, thereby improving users' audio and video service experience, or avoiding the risk of data being cracked and stolen due to overly simple transmission strategies, thereby improving data security.

[0069] In some exemplary embodiments, a corresponding sensitivity level value may be pre-set for each video type, audio content analysis result, and video content analysis result, and then, the sensitivity level value corresponding to the audio and video data to be transmitted is determined based on the sum of the sensitivity level value corresponding to the video type corresponding to the audio and video data to be transmitted, the sensitivity level value corresponding to the audio and video data to be transmitted, and the sensitivity level corresponding to the audio and video data to be transmitted is determined based on the sensitivity level value corresponding to the audio and video data to be transmitted. For example, taking the video type corresponding to the audio and video data to be transmitted as entertainment video, the audio content analysis result includes news video, and the video content analysis result includes privacy video as an example, then, according to the pre-set sensitivity level value, the first sensitivity level value corresponding to the entertainment video is 0, the second sensitivity level corresponding to the news video is 0, and the third sensitivity level corresponding to the privacy video is 2, then the sensitivity level value corresponding to the audio and video data to be transmitted is 2, and the level values ​​corresponding to the pre-set medium sensitivity include: 2 and 3, so that the sensitivity level corresponding to the audio and video data to be transmitted is medium sensitivity. Of course, in addition to the above-mentioned example of determining the sensitivity level corresponding to the audio and video data to be transmitted by summing, the sensitivity level can also be determined according to weighted sum, average value, maximum value or other calculation methods.

[0070] In some further exemplary embodiments, step 201 may include the following steps 201-1 to 201-8: Step 201-1: The AI ​​Agent obtains a data set corresponding to the audio and video data based on the audio and video data to be transmitted; The data set corresponding to the audio and video data may include: video information and audio information. For example, the video information may include: an image frame set and video attribute information, the image frame set may include multiple image frames, the video attribute information may include: video size (such as video width, video height), video resolution, video content bytes, video encoding length, and video character encoding string at least one or more, and the audio information may include: audio features, audio encoding length, audio character encoding string, audio sampling rate, and audio channel number at least one or more.

[0071] Step 201-2: the AI ​​Agent obtains the audio information in the data set corresponding to the audio and video data, and uses the pre-trained speech recognition model to convert the audio information in the data set corresponding to the audio and video data into text data; The text data refers to a combination of one or more data forms in characters, words, phrases, sentences, paragraphs, and chapters. For example, the text data may be one or more sentences, or one or more paragraphs, or one or more chapters.

[0072] For example, the pre-trained speech recognition model may include, but is not limited to, models in the AI ​​field such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), or transformer models based on deep learning; here, the pre-trained speech recognition model may be decoded according to the context and the language model to generate the most likely speech-to-text result corresponding to the audio information in the audio and video data; Step 201-3: The AI ​​Agent obtains the summary and key points of the text data based on the natural language processing (NLP) model; Among them, both the abstract and the key points contain part of the data in the text data. The abstract, also known as the summary or the abstract of the content, can be a brief summary of the important information in the text data, which can be a short sentence or a long sentence; the key point can refer to the keywords, key words or key sentences in the text data, which can be a character, a word or a short sentence, etc.

[0073] Step 201-4: The AI ​​Agent generates the video type to which the audio and video data belongs based on the summary and key points of the text data; For example, video types may include: educational videos, entertainment videos, news videos, etc.

[0074] Step 201-5: The AI ​​Agent generates an audio content analysis result corresponding to the audio and video data based on at least a summary and key points of the text data converted from the audio information in the audio and video data using at least one NLP model of sentiment analysis, topic modeling, and named entity recognition (NER); For example, the audio content analysis results may include: financial videos (such as videos containing account information and financial information in the audio), entertainment videos (such as videos containing entertainment information in the audio), educational videos (such as videos containing various subject knowledge points in the audio), ordinary videos (such as videos containing common information in the audio), privacy videos (such as videos containing identity information and account information in the audio), etc. Here, the audio content analysis results refer to the deep understanding information of audio content such as voice content recognition and text understanding obtained based on AI technology.

[0075] In some exemplary embodiments, step 201-5 may include: the AI ​​Agent uses at least one NLP model of sentiment analysis, topic modeling and NER to perform a comprehensive analysis based on the video type to which the audio and video data belongs and the summary and key points of the text data converted from the audio information in the audio and video data to generate an audio content analysis result corresponding to the audio and video data.

[0076] For example, when the AI ​​Agent recognizes that the text data corresponding to the audio and video data contains identity information in the format of an ID card number, it can be determined that the audio and video data involves personal privacy data, and the audio content analysis result can include: private video. Subsequently, based on the audio content analysis result, it can be determined that the sensitivity level corresponding to the audio and video data is high.

[0077] Among them, named entity recognition (NER), also known as "proper name recognition", can identify named entities of specified entity types from text data. Named entities are entities identified by names. Common entity types include names of people, places, time, numbers, festivals, organization names, product names, proper nouns, etc. Entity refers to something with a specific meaning, for example, it can include at least one of the names of people, places, organizations or proper nouns. The target entity is the entity in the target text, and a target text can contain one or more target entities. For example, assuming the target text is "Zhang San's ID number is xxxxxxxxx", the target entity can include "Zhang San" and "ID number".

[0078] Among them, sentiment analysis, also known as emotion analysis, can use AI technologies such as natural language processing and machine learning to analyze and process data such as text, voice and images, and extract data such as emotions, moods and psychological states from text data. For example, sentiment categories and sentiment words, sentiment categories can include positive, negative or neutral. For example, deep learning models such as RNN, convolutional neural network (CNN), or Transformer models in the AI ​​field can be used to extract text features from raw text and perform sentiment analysis.

[0079] Step 201-6: Using AI Agent and computer vision technology, based on the image frame set corresponding to the audio and video data, analyze the video content of the audio and video data to obtain the video content analysis result corresponding to the audio and video data; For example, the video content analysis results include: financial videos (such as videos containing account information and financial information in the screen), entertainment videos (such as videos containing entertainment information in the screen), educational videos (such as videos containing various subject knowledge points in the screen), ordinary videos (such as videos containing common information in the screen), privacy videos (such as videos containing identity information and account information in the screen), confidential videos (such as videos containing confidential information in the screen), etc. Here, the video content analysis results refer to the deep understanding information of video content such as screen recognition and scene understanding obtained based on AI technology.

[0080] For example, when the AI ​​Agent recognizes that the picture contains an ID card picture, it can be determined that the audio and video data involves personal privacy data, then the video content analysis result can include: private video, and then, based on the video content analysis result, it can be determined that the sensitivity level corresponding to the audio and video data is high. For another example, when the AI ​​Agent recognizes that the picture contains the keyword "internal information", it can be determined that the audio and video material is an internal confidential video, then the video content analysis result can include: confidential video, and then, based on the video content analysis result, it can be determined that the sensitivity level corresponding to the audio and video data is high.

[0081] Step 201-7: Through AI Agent, the data analysis results (such as video type, audio content analysis results, video content analysis results, etc.) are output and stored in the form of structured data (such as text (.txt), tag type, metadata), so that they can be analyzed in subsequent processes.

[0082] For example, the data analysis results may include video type and video content analysis results, or the data analysis results may include audio content analysis results and video content analysis results, or the data analysis results may include video type, audio content analysis results, video content analysis results, etc., or the data analysis results may include: speech to text, summary, key points, voice content analysis results, video content analysis results, etc.

[0083] For example, structured data (text (.txt), tag type, metadata) can be exemplified as a text type. For example, a tag type can be a text type.

[0084] Step 201-8: The AI ​​Agent determines the sensitivity level corresponding to the audio and video data based on the data analysis results (such as video type, audio content analysis results, video content analysis results, etc.).

[0085] The following describes how to determine the transmission strategy that matches the audio and video data.

[0086] In some exemplary embodiments, Figure 3As shown, step 202 may include: step 301: obtaining at least one of the network security level of the network environment where the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data; step 302: configuring a matching transmission strategy for the audio and video data according to the network security level of the network environment where the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data, as well as the sensitivity level corresponding to the audio and video data. In this way, the matching transmission strategy can be adaptively and dynamically determined according to multiple factors such as the sensitivity level corresponding to the audio and video data, the network security level of the network environment, the user behavior pattern of the user, and the resource status of the system resources of the client, so that the security and transmission performance of the audio and video data can be more reasonably guaranteed. Compared with the method of using a fixed encryption algorithm based on experience in some technologies, it has certain flexibility in practical applications and has higher practical application value.

[0087] In some exemplary embodiments, step 301 may include: step 3011: obtaining at least one of the network environment data of the network environment in which the audio and video data is located, the user behavior data of the target user corresponding to the audio and video data, and the system resource data of the system resources of the target client corresponding to the audio and video data; step 3012: using an artificial intelligence big model to analyze at least one of the network environment data of the network environment in which the audio and video data is located, the user behavior data of the target user corresponding to the audio and video data, and the system resource data of the system resources of the target client corresponding to the audio and video data, and obtain at least one of the network security level of the network environment in which the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data.

[0088] In some exemplary embodiments, step 301 may include: step 3013: the AI ​​Agent obtains the network environment data of the network environment where the audio and video data is located; step 3014: the AI ​​Agent analyzes the network environment data of the network environment where the audio and video data is located by using the AI ​​big model to obtain the network security level of the network environment where the audio and video data is located.

[0089] In other exemplary embodiments, step 301 may include: step 3015: AI Agent obtains user behavior data of the target user corresponding to the audio and video data; step 3016: AI Agent analyzes the user behavior data of the target user corresponding to the audio and video data by using the AI ​​big model to obtain the user behavior pattern of the target user corresponding to the audio and video data.

[0090] For example, user behavior data may include account login data and key usage data. Account login data may include, but is not limited to, any one or more of: user ID, login time, operation time, operation frequency, operation type, commonly used devices, and geographic location, etc. Key usage data may include, but is not limited to, any one or more of: key ID, usage time, usage frequency, and usage location, etc. Among them, the user ID is used to uniquely identify the user who uses the audio and video data service, and the key ID is used to uniquely identify the key of the encryption or decryption process.

[0091] For example, user behavior patterns may include normal behavior patterns and abnormal behavior patterns. Abnormal behavior patterns may include but are not limited to: abnormal logins, abnormal use of keys, etc. Abnormal logins may refer to logging into an account more than a preset number of times within a preset time or the account login location is not a commonly used address, etc. Abnormal use of keys may refer to using a key more than a preset number of times within a preset time or the key use location is not a commonly used location, etc. Normal behavior patterns may include but are not limited to normal logins, normal use of keys, etc.

[0092] In some further exemplary embodiments, step 301 may include: step 3017: the AI ​​Agent obtains the system resource data of the system resources of the target client corresponding to the audio and video data; step 3018: the AI ​​Agent analyzes the system resource data of the system resources of the target client corresponding to the audio and video data by using the AI ​​big model, and obtains the resource status of the system resources of the target client corresponding to the audio and video data.

[0093] In some exemplary embodiments, Figure 4 As shown, step 302 may include: step 401: based on the sensitivity level corresponding to the audio and video data, determining an initial transmission strategy that matches the sensitivity level corresponding to the audio and video data; step 402: based on at least one of the network security level of the network environment in which the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data, determining whether to adjust the initial transmission strategy.

[0094] In some exemplary embodiments, step 402 may include: Step 4021: When the network security level of the network environment in which the audio and video data is located is a preset first network security level for characterizing the existence of security risks, the user behavior pattern of the target user corresponding to the audio and video data is a preset first behavior pattern for characterizing the existence of abnormal behavior, and the resource status of the system resources of the target client corresponding to the audio and video data is at least one of a preset first resource status for indicating insufficient client system resources, determine to adjust the initial transmission strategy and obtain the adjusted transmission strategy.

[0095] In other exemplary embodiments, step 402 may include: Step 4022: When the network security level of the network environment in which the audio and video data is located is a preset second network security level for characterizing the absence of security risks, the user behavior pattern of the target user corresponding to the audio and video data is a preset second behavior pattern for characterizing the absence of abnormal behavior, and the resource status of the system resources of the target client corresponding to the audio and video data is at least one of a preset second resource status for indicating that the client system resources are sufficient, determine that the initial transmission strategy is not adjusted.

[0096] When it is determined that the initial transmission strategy is to be adjusted, step 403 is executed; when it is determined that the initial transmission strategy is not to be adjusted, step 404 is executed.

[0097] Step 403: when it is determined that the initial transmission policy is to be adjusted, the initial transmission policy is adjusted and the adjusted transmission policy is determined as the matching transmission policy; or, Step 404: when it is determined that the initial transmission policy is not to be adjusted, the initial transmission policy is determined as the matching transmission policy.

[0098] In some exemplary embodiments, the adjustment of the initial transmission strategy in step 403 may include at least one of: adjusting from an encrypted transmission strategy to a non-encrypted transmission strategy, adjusting from a non-encrypted transmission strategy to an encrypted transmission strategy, and adjusting parameters of the encrypted transmission strategy.

[0099] In some exemplary embodiments, step 202 may include: pre-setting a plurality of transmission strategies that match sensitivity levels, and obtaining a correspondence between the pre-set sensitivity levels and the transmission strategies, wherein the transmission strategies may include encrypted transmission strategies and non-encrypted transmission strategies; after obtaining the sensitivity level corresponding to the audio and video data to be transmitted, a transmission strategy that matches the sensitivity level corresponding to the audio and video data to be transmitted may be selected from a plurality of preset transmission strategies based on the encryption level and the correspondence between the pre-set sensitivity level and the transmission strategy; and determining a transmission strategy that matches the audio and video data to be transmitted, at least based on the transmission strategy that matches the sensitivity level corresponding to the audio and video data to be transmitted.

[0100] In other exemplary embodiments, step 202 may include: generating a transmission strategy matching the audio and video data to be transmitted based on the AI ​​big model at least according to the sensitivity level corresponding to the audio and video data to be transmitted.

[0101] In some exemplary embodiments, the transmission strategy may include: a preset first transmission strategy, a preset second transmission strategy, and a preset third transmission strategy. The preset first transmission strategy, the preset second transmission strategy, and the preset third transmission strategy are different. For example, the preset first transmission strategy may refer to encryption processing using an encryption algorithm with a higher security level, such as encryption using the AES-256 algorithm; the preset second transmission strategy may refer to encryption processing using an encryption algorithm with a lower security level, such as encryption using the AES-128 algorithm; the preset third transmission strategy may refer to no encryption processing.

[0102] In some exemplary embodiments, the sensitivity levels can be divided into several levels in advance, and transmission strategies can be set accordingly to generate and store the correspondence between the sensitivity levels and the transmission strategies. In this way, after obtaining the audio and video data to be transmitted, when the audio and video data to be transmitted is analyzed by the AI ​​Agent and the sensitivity level corresponding to the audio and video data to be transmitted is determined, the transmission strategy corresponding to the sensitivity level corresponding to the audio and video data can be determined according to the correspondence.

[0103] For example, the correspondence between the sensitivity level and the transmission strategy can be stored in the form of a table. Then, as shown in Table 1, when it is determined that the sensitivity level corresponding to the audio and video data to be transmitted is a higher sensitivity, the matching transmission strategy can be determined to be the first transmission strategy, that is, an encryption algorithm with a higher security level is used for encryption processing.

[0104] Table 1 Correspondence between sensitivity levels and transmission strategies

[0105] Of course, the correspondence between the sensitivity level and the transmission strategy can also be in other forms and with other contents, which can be set by those skilled in the art, and the present disclosure does not make any specific limitation on this.

[0106] The following describes how to transmit audio and video data based on a matching transmission strategy.

[0107] In some exemplary embodiments, step 203 may include: step 2031: determining whether the sensitivity level corresponding to the audio and video data is a preset sensitivity level; step 2032: when the sensitivity level corresponding to the audio and video data is the preset sensitivity level, sending an identity authentication message for instructing the user to perform an identity authentication operation to the target client corresponding to the audio and video data to determine whether the target user corresponding to the audio and video data is an authorized user; step 2033: when it is determined that the target user is an authorized user, based on the matching transmission strategy, sending the audio and video data to the target client corresponding to the audio and video data.

[0108] The identity authentication message is used to instruct the target user to perform an identity authentication operation, and the identity authentication operation includes: any one or more of a password verification operation, a fingerprint recognition operation, a facial recognition operation, a voiceprint recognition operation, and an iris recognition operation.

[0109] In some exemplary embodiments, step 2032 may include: when the sensitivity level corresponding to the audio and video data is a preset sensitivity level, sending an authentication message to the target client corresponding to the audio and video data to instruct the user to perform an authentication operation; receiving authentication information sent by the client; determining whether the target user corresponding to the audio and video data is an authorized user based on whether the authentication information matches the pre-stored authentication information; when the authentication information matches the pre-stored authentication information, determining that the target user corresponding to the audio and video data is an authorized user.

[0110] In some exemplary embodiments, taking the matched transmission strategy as an encrypted transmission strategy matching the audio and video data as an example, step 2033 may include: when it is determined that the target user is an authorized user, based on the encrypted transmission strategy matching the audio and video data, encrypting the audio and video data, and transmitting the processed audio and video data to the target client corresponding to the audio and video data.

[0111] In this way, after the AI ​​agent uses the AI ​​data perception model to perform content analysis on the audio and video data and determines the sensitivity level corresponding to the audio and video data, the AI ​​agent can instruct the user to perform additional identity verification, such as password verification, fingerprint recognition, or facial recognition, for the audio and video data that meets the preset sensitivity level after content analysis, to ensure that only authorized users can access the encrypted data. In this way, the security of audio and video data can be better guaranteed.

[0112] Figure 5 is another flow chart of the data management method in the exemplary embodiment of the present disclosure, such as Figure 5 As shown, the data management method may include: Step 501: Acquire audio and video data to be transmitted; Step 502: Use the AI ​​big model to analyze the audio and video data to determine the sensitivity level corresponding to the audio and video data; For example, AI agents can use AI data perception big models to analyze audio and video data and determine the sensitivity level corresponding to the audio and video data.

[0113] Step 503: Based on the sensitivity level corresponding to the audio and video data, determine an initial transmission strategy that matches the sensitivity level corresponding to the audio and video data; Step 504: Acquire network environment data of the network environment where the audio and video data is located; Among them, the network environment data can characterize the network security of the transmission path where the audio and video data is located. For example, the network environment data can include but is not limited to: network traffic, system logs, user behavior data, sensor data, etc. The data is divided into static data (i.e. local static data) and dynamic data (i.e. network-related data); for example, sensor data can include static data and dynamic data, among which static data can include local data analyzed by AI agents, video content data, etc., and dynamic data can include network connection parameters, such as service set identifier (SSID), password, encryption, received signal strength indication (RSI), and Internet Protocol (IP) address, etc.

[0114] Step 505: Use an AI big model (such as a data perception big model) to analyze the network environment data of the network environment where the audio and video data is located, and obtain the network security level of the network environment where the audio and video data is located; Step 506: Determine whether to adjust the initial transmission strategy based on the network security level of the network environment where the audio and video data is located; Step 507a: when it is determined that the network security level of the network environment where the audio and video data is located is a preset first network security level for characterizing the presence of potential security risks, at least based on the network security level of the network environment where the audio and video data is located (such as the first network security level), the initial transmission strategy is adjusted to obtain an adjusted transmission strategy, and the adjusted transmission strategy is determined as a matching transmission strategy; or, step 507b: when it is determined that the network security level of the network environment where the audio and video data is located is a preset second network security level for characterizing the absence of potential security risks, the initial transmission strategy is not adjusted, and the initial transmission strategy is determined as a matching transmission strategy; Step 508: Transmit the audio and video data based on the matched transmission strategy.

[0115] In some exemplary embodiments, step 505 may include: Step 5051: the AI ​​Agent extracts characteristic data of the network environment data from the network environment data; Step 5052: the AI ​​Agent inputs the characteristic data of the network environment data into an AI big model (such as a data perception big model) to determine the network security level of the network environment in which the audio and video data is located.

[0116] For example, step 5052 may include: using a trained AI big model (such as a data-aware big model) to perform real-time or offline analysis on the characteristic data of the network environment data to obtain a network analysis result; based on the network analysis result, determining whether there is a network security threat in the current network environment to determine the network security level of the network environment where the audio and video data is located. For example, network security threats may include: network attacks, malware, vulnerabilities, etc.

[0117] For example, the AI ​​big model (such as the data-aware big model) can output a probability value (such as between 0 and 1) for indicating the degree of threat, or can directly output a classification result for indicating threat or non-threat, and the classification result includes: a first result (such as 1) for indicating the existence of a threat and a first result (such as 0) for indicating the absence of a threat. For example, when the AI ​​big model (such as the data-aware big model) outputs a probability value (such as between 0 and 1) for indicating the degree of threat, the network security level of the network environment in which the audio and video data is located can be determined based on a pre-set probability threshold.

[0118] Before step 5052, the model may be trained using sample data in advance to obtain a trained AI big model. The sample data may include: model data with label data and monitoring data representing abnormal data, and the model may include: deep data learning models such as convolutional neural networks, recursive neural networks, and Transformers. For example, the AI ​​big model may be a data-aware big model, in which case the sample data may be network environment data.

[0119] In some exemplary embodiments, step 507a may include: adjusting the initial transmission strategy based on the network security level of the network environment in which the audio and video data is located, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy; or, adjusting the initial transmission strategy based on the network security level of the network environment in which the audio and video data is located and the resource status of the system resources of the target client corresponding to the audio and video data, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy; or, adjusting the initial transmission strategy based on the network security level of the network environment in which the audio and video data is located and the user behavior pattern of the target user corresponding to the audio and video data, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy; or, adjusting the initial transmission strategy based on the network security level of the network environment in which the audio and video data is located, the resource status of the system resources of the target client corresponding to the audio and video data, and the user behavior pattern of the target user corresponding to the audio and video data, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy.

[0120] In some exemplary embodiments, adjusting the initial transmission strategy includes at least one of: adjusting from an encrypted transmission strategy to a non-encrypted transmission strategy, adjusting from a non-encrypted transmission strategy to an encrypted transmission strategy, and adjusting parameters of the encrypted transmission strategy. For example, adjusting parameters of the encrypted transmission strategy may include at least one of adjusting an encryption algorithm, adjusting a key length, and adjusting key complexity.

[0121] Among them, the AES algorithm supports different key lengths, including 128 bits, 256 bits and 512 bits, etc. According to the length of the key used by the AES algorithm, the AES algorithm can be divided into the AES-128 algorithm, the AES-256 algorithm, etc., wherein the key length in AES-256 is 256 bits, and the key length in AES-128 is 128 bits. For example, taking the adjustment of the encryption transmission strategy including the adjustment of the complexity of the encryption algorithm as an example, when the encryption algorithm determined according to the sensitivity level corresponding to the audio and video data is a level 1 encryption algorithm (such as using AES-128), then, when it is determined that there is a network security threat in the network environment (such as detecting a network attack attempt during the audio and video transmission process), the corresponding encryption algorithm level can be upgraded, and the encryption algorithm in the transmission strategy can be adjusted to a level 2 encryption algorithm (such as using AES-256 encryption) or a level 3 encryption algorithm (using AES-512 encryption).

[0122] For example, taking the adjustment of the encrypted transmission strategy including the adjustment of the key length as an example, when it is determined to use a 12-bit key length for encryption based on the sensitivity level corresponding to the audio and video data, then when a network security threat is detected in the network environment based on the network environment data (such as a network attack is detected during the audio and video transmission process), the key length in the corresponding encryption algorithm can be increased, and the key in the transmission strategy can be adjusted to: use a random 28-bit key length for encryption.

[0123] For example, taking the adjustment of the encryption transmission strategy including the adjustment of the key complexity as an example, when it is determined to use a pure digital key with a fixed 12-bit key length for encryption based on the sensitivity level of the audio and video data, then when a network security threat is detected in the network environment based on the network environment data (such as a network attack is detected during the audio and video transmission process), the key complexity in the corresponding encryption algorithm can be increased, and the key in the transmission strategy can be adjusted to: encrypt with a random 28-bit key length, or encrypt with a complex key that combines characters and numbers.

[0124] In this way, the data management method provided by the exemplary embodiment of the present disclosure can, after determining the initial transmission strategy according to the sensitivity level corresponding to the audio and video data, at least adaptively and flexibly determine the transmission strategy that matches the audio and video data according to the network security level of the network environment in which the audio and video data is located. In this way, by comprehensively considering multiple factors such as video content and network security, the security control of the data can be adaptively adjusted according to changes in the security level of the network environment, the matching degree of the transmission strategy can be improved, which is conducive to preventing the leakage of audio and video data, and further improving the security of data transmission, and can effectively avoid the occurrence of audio and video data tampering, thereby effectively ensuring the security of data transmission and the smoothness of audio and video playback, and having higher practical application value.

[0125] Figure 6 is another flow chart of the data management method in the exemplary embodiment of the present disclosure, such as Figure 6 As shown, the data management method may include: Step 601: Acquire audio and video data to be transmitted; Step 602: Use the AI ​​big model to analyze the audio and video data to determine the sensitivity level corresponding to the audio and video data; Step 603: Based on the sensitivity level corresponding to the audio and video data, determine an initial transmission strategy that matches the sensitivity level corresponding to the audio and video data; Step 604: Obtain the resource status of the system resources of the target client corresponding to the audio and video data; The system resource data refers to the data representing the target client receiving the audio and video data. For example, the system resource data includes system logs, local system data, etc. The system logs may include basic information of the Framework system layer and application layer LOG, etc. The local system data may include local CPU information, system memory usage data, etc.

[0126] Step 605: using the AI ​​big model, analyzing the system resource data of the system resources of the target client corresponding to the audio and video data, and obtaining the resource status of the system resources of the target client corresponding to the audio and video data; Step 606: Determine whether to adjust the initial transmission strategy based on the resource status of the system resources of the target client corresponding to the audio and video data; Step 607a: When it is determined that the resource status of the system resources of the target client corresponding to the audio and video data is the preset first resource status used to indicate that the client system resources are insufficient, the initial transmission strategy is adjusted at least based on the resource status of the system resources of the target client corresponding to the audio and video data (i.e., the preset first resource status), the adjusted transmission strategy is obtained, and the adjusted transmission strategy is determined as the matching transmission strategy; or, Step 607b: When it is determined that the resource status of the system resources of the target client corresponding to the audio and video data is the preset second resource status used to indicate that the client system resources are sufficient, it is determined not to adjust the initial transmission strategy based on the resource status of the system resources of the target client corresponding to the audio and video data, and the initial transmission strategy is determined as the matching transmission strategy.

[0127] Step 608: Transmit the audio and video data based on the matched transmission strategy.

[0128] In some exemplary embodiments, step 605 may include: step 6051: the AI ​​Agent extracts feature data of the system resource data from the system resource data of the target client; step 6052: the AI ​​Agent inputs the feature data of the system resource data into an AI big model (such as a data perception big model) to determine the resource status of the system resources of the target client corresponding to the audio and video data.

[0129] In some exemplary embodiments, the resource status of the system resources may be preset to include sufficient or insufficient, or the resource status of the system resources may be preset to include tight GPU resources, surplus GPU resources, normal GPU resources, etc.

[0130] For example, an AI big model (such as a data-aware big model) can output a probability value (such as between 0 and 1) for indicating the status of resources, or an AI big model (such as a data-aware big model) can directly output a classification result for indicating whether resources are sufficient or insufficient, and the classification results include: a first result (such as 1) for indicating that resources are sufficient and a first result (such as 0) for indicating that resources are insufficient.

[0131] For example, when an AI big model (such as a data-aware big model) outputs a probability value (such as between 0 and 1) for indicating a resource status, the resource status of the system resources of the target client corresponding to the audio and video data can be determined based on a pre-set probability threshold.

[0132] In some exemplary embodiments, step 607a may include: adjusting the initial transmission strategy based on the resource status of the system resources of the target client corresponding to the audio and video data (such as a preset first resource status), obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy; or, adjusting the initial transmission strategy based on the resource status of the system resources of the target client corresponding to the audio and video data and the network security level of the network environment in which the audio and video data is located, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy; or, adjusting the initial transmission strategy based on the resource status of the system resources of the target client corresponding to the audio and video data and the user behavior pattern of the target user corresponding to the audio and video data, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy; or, adjusting the initial transmission strategy based on the resource status of the system resources of the target client corresponding to the audio and video data, the network security level of the network environment in which the audio and video data is located, and the user behavior pattern of the target user corresponding to the audio and video data, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy.

[0133] In some exemplary embodiments, adjusting the initial transmission strategy includes at least one of: adjusting from an encrypted transmission strategy to a non-encrypted transmission strategy, adjusting from a non-encrypted transmission strategy to an encrypted transmission strategy, and adjusting parameters of the encrypted transmission strategy. For example, adjusting parameters of the encrypted transmission strategy may include at least one of adjusting an encryption algorithm, adjusting a key length, and adjusting key complexity.

[0134] For example, taking the adjustment of the encryption transmission strategy including the adjustment of the complexity of the encryption algorithm as an example, when the encryption algorithm determined according to the sensitivity level corresponding to the audio and video data is a level 1 encryption algorithm (such as using AES-128), then, when it is determined that the resource status of the system resources of the target client corresponding to the audio and video data is that the GPU resources are abundant, the corresponding encryption algorithm level can be increased, and the encryption algorithm in the transmission strategy can be adjusted to a level 2 encryption algorithm (such as using AES-256 encryption) or a level 3 encryption algorithm (using AES-521 encryption). For another example, when the encryption algorithm determined according to the sensitivity level corresponding to the audio and video data is a level 3 encryption algorithm (using AES-521 encryption), then, when it is determined that the resource status of the system resources of the target client corresponding to the audio and video data is that the GPU resources are scarce, the corresponding encryption algorithm level can be lowered, and the encryption algorithm in the transmission strategy can be adjusted to a level 2 encryption algorithm (such as using AES-256 encryption) or a level 1 encryption algorithm (such as using AES-128).

[0135] In this way, the data management method provided by the exemplary embodiment of the present invention can, after determining the initial transmission strategy according to the sensitivity level corresponding to the audio and video data, at least according to the resource status of the target client, adaptively and flexibly determine the transmission strategy that matches the audio and video data, and comprehensively consider multiple factors such as video content and client performance to find a balance between security and transmission performance. It can ensure that the computer resources (such as CPU performance, battery life) required for the encryption process match the resource status of the target client, avoid delays, freezes or increased power consumption, and thus avoid users' instant access to and smooth playback of audio and video data. In this way, the security of data transmission and the smoothness of audio and video playback can be effectively guaranteed, and a good user experience can be ensured, which has higher practical application value.

[0136] Figure 7 is another flow chart of the data management method in the exemplary embodiment of the present disclosure, such as Figure 7 As shown, the data management method may include: Step 701: Acquire audio and video data to be transmitted; Step 702: Use the AI ​​big model to analyze the audio and video data to determine the sensitivity level corresponding to the audio and video data; Step 703: Based on the sensitivity level corresponding to the audio and video data, determine an initial transmission strategy that matches the sensitivity level corresponding to the audio and video data; Step 704: Obtain user behavior data of the target user corresponding to the audio and video data; The target user refers to the user who requests to log in, that is, the user who requests to obtain audio and video data.

[0137] For example, user behavior data may include account login data and key usage data. Account login data may include but is not limited to: user ID, login time, operation time, operation frequency, operation type, commonly used devices, geographic location, etc. Key usage data may include but is not limited to: key ID, usage time, usage frequency, usage location, etc.

[0138] In some exemplary embodiments, step 704 may include: the AI ​​Agent may obtain user behavior data of the target user corresponding to the audio and video data through log management, application programming interface (API) or other means.

[0139] Step 705: Determine the user behavior pattern of the target user corresponding to the audio and video data based on the user behavior data of the target user corresponding to the audio and video data through an AI big model (such as a risk control model, an abnormal behavior detection model, etc.); The user behavior pattern includes: user login pattern and user key usage pattern, etc.

[0140] Step 706: Determine whether to adjust the initial transmission strategy based on the user behavior pattern of the target user corresponding to the audio and video data; Step 707a: When the user behavior pattern of the target user corresponding to the audio and video data is a preset first behavior pattern for characterizing the presence of abnormal behavior, determine to adjust the initial transmission strategy, at least based on the user behavior pattern of the target user corresponding to the audio and video data, adjust the initial transmission strategy to obtain the adjusted transmission strategy, and determine the adjusted transmission strategy as the matching transmission strategy; or, step 707b: When the user behavior pattern of the target user corresponding to the audio and video data is a preset second behavior pattern for characterizing the absence of abnormal behavior, determine not to adjust the initial transmission strategy, and determine the initial transmission strategy as the matching transmission strategy.

[0141] Step 708: Transmit the audio and video data based on the matched transmission strategy.

[0142] In some exemplary embodiments, step 707a may include: adjusting the initial transmission strategy based on the user behavior pattern of the target user corresponding to the audio and video data, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy; or, adjusting the initial transmission strategy based on the user behavior pattern of the target user corresponding to the audio and video data and the network security level of the network environment in which the audio and video data is located, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy; or, adjusting the initial transmission strategy based on the user behavior pattern of the target user corresponding to the audio and video data and the resource status of the system resources of the target client corresponding to the audio and video data, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy; or, adjusting the initial transmission strategy based on the user behavior pattern of the target user corresponding to the audio and video data, the network security level of the network environment in which the audio and video data is located, and the resource status of the system resources of the target client corresponding to the audio and video data, obtaining the adjusted transmission strategy, and determining the adjusted transmission strategy as the matching transmission strategy.

[0143] In this way, the data management method provided by the exemplary embodiment of the present disclosure can, after determining the initial transmission strategy according to the sensitivity level corresponding to the audio and video data, at least according to the user behavior pattern of the target user corresponding to the audio and video data, adaptively and flexibly determine the transmission strategy that matches the audio and video data, comprehensively considering multiple factors such as video content and user behavior pattern, and can avoid improper use of keys or potential key leakage events, which is conducive to preventing key data leakage, effectively avoiding the occurrence of audio and video data tampering, and further improving the security of data transmission. In this way, the security of data transmission and the smoothness of audio and video playback can be more effectively guaranteed, and it has higher practical application value.

[0144] Figure 8 is another flow chart of the data management method in the exemplary embodiment of the present disclosure, such as Figure 8 As shown, in some exemplary embodiments, the data management method may further include: Step 801: Obtain user behavior data of a target user corresponding to the audio and video data; The target user refers to the user who requests to log in, that is, the user who requests to obtain audio and video data.

[0145] For example, user behavior data includes account login data and key usage data. Account login data includes: user ID, login time, operation time, operation frequency, operation type, commonly used devices, and geographic location. Key usage data includes key ID, usage time, usage frequency, and usage location.

[0146] For example, step 801 may include: collecting user behavior data of target users corresponding to audio and video data through an application program interface (API) and a software development kit (SDK). API refers to a function that implements a specific function, and is a data interface. For example, the user behavior data of target users corresponding to audio and video data is collected through an Android exposure interface and a data statistics interface. SDK is a collection of many functional functions, and is a development integration toolkit containing functions and function interfaces. For example, the user behavior data of target users corresponding to audio and video data is collected through a buried data SDK.

[0147] Step 802: Determine whether the target user has abnormal behavior based on the user behavior data; In some exemplary embodiments, step 802 may include: the AI ​​Agent extracts corresponding feature data from the user behavior data of the target user; the AI ​​Agent determines whether the target user has abnormal behavior based on the feature data in the user behavior data and based on the AI ​​big model (such as the big model for anomaly detection, etc.) according to the user behavior data. For example, the feature data may include operation time, operation frequency, operation type, use location, etc. For example, the big model for anomaly detection may use anomaly detection algorithms such as principal component analysis (PCA) to determine whether the target user has abnormal behavior based on the user behavior data.

[0148] Step 803: When it is determined that the target user has abnormal behavior, an identity authentication message for instructing the user to perform an identity authentication operation is sent to the target client corresponding to the audio and video data to determine whether the target user corresponding to the audio and video data is an authorized user; The identity verification operation includes any one or more of a password verification operation, a fingerprint recognition operation, a facial recognition operation, a voiceprint recognition operation, and an iris recognition operation.

[0149] Step 804: When it is determined that the target user corresponding to the audio and video data is an authorized user, keys are distributed to the target client corresponding to the audio and video data.

[0150] In some exemplary embodiments, step 803 may include: when it is determined that the target user has abnormal behavior, sending an authentication message to the target client; receiving authentication information sent by the target client; determining whether the target user is an authorized user based on whether the authentication information sent by the target client matches the pre-stored authentication information; when it is determined that the authentication information sent by the target client matches the pre-stored authentication information, determining that the target user is an authorized user, at this time, distributing keys to the target client.

[0151] In this way, the data management method provided by the exemplary embodiment of the present disclosure can not only more reasonably determine the transmission strategy that matches the audio and video data to be transmitted, but also intelligently manage the key distribution process. By detecting abnormal behavior and identity authentication, it can ensure that only the client used by authorized users can receive the key, thereby improving the security of key access and further ensuring the security of data transmission.

[0152] Fig. 9 is another flow chart of the data management method in the exemplary embodiment of the present disclosure, such as Fig. 9 As shown, in some exemplary embodiments, the data management method may further include: Step 901: Obtain key usage data corresponding to each of a plurality of pre-stored keys; The key usage data of each key includes key ID, usage time, usage frequency, and usage location; Step 902: Analyze the key usage data corresponding to each of the multiple keys to determine the key usage mode corresponding to each of the multiple keys; In some exemplary embodiments, step 902 may include: the AI ​​Agent uses an AI model (such as a large anomaly detection model such as a clustering algorithm, time series analysis, and a deep learning model) to analyze the key usage data corresponding to each of the multiple keys, and determine the key usage mode corresponding to each of the multiple keys. The key usage mode may include: a normal usage mode and an abnormal usage mode. In other exemplary embodiments, step 902 may include: the AI ​​Agent pre-sets a feature vector; based on: the AI ​​Agent uses the pre-set feature vector to determine the key usage mode corresponding to each of the multiple keys. Among them, the feature vector is a data standard, which may include a normal standard and an abnormal standard, and can be set to determine whether the key usage mode is a normal usage mode or an abnormal usage mode based on the key usage data.

[0153] Step 903: Determine whether there is an abnormal usage pattern in the key usage patterns corresponding to the multiple keys; Step 904: When there are abnormal usage patterns in the key usage patterns corresponding to the multiple keys, based on the key usage data corresponding to the multiple keys, the abnormal keys are identified from the multiple keys and the abnormal keys are deleted; in this way, improper use of keys or potential key leakage incidents can be avoided, which is conducive to preventing the leakage of key data, further improving the security of data transmission, and effectively avoiding the tampering of audio and video data.

[0154] For example, abnormal keys may include keys that have not been used for a long time and can be marked as pending destruction. Once it is determined that certain keys are no longer needed, their use rights can be revoked immediately, thereby avoiding security risks caused by abnormal keys.

[0155] Step 904 may include: when there is an abnormal usage pattern in the key usage patterns corresponding to the multiple keys, using an abnormality detection algorithm, based on the key usage data corresponding to the multiple keys, identifying the abnormal key from the multiple keys, and deleting the abnormal key. For example, the abnormality detection algorithm may be an isolation forest, a one-class support vector machine (One-Class SVM), etc.

[0156] Step 905: when the key usage mode corresponding to each of the multiple keys is the normal usage mode, obtaining the usage time corresponding to each of the multiple keys, and determining the update time corresponding to each of the multiple keys based on the usage time corresponding to each of the multiple keys and a preset key life cycle; Step 906: Based on the update time corresponding to each of the multiple keys, the multiple keys are updated respectively. In this way, by assigning different key life cycle management to different keys, intelligent and fully automated key management can be achieved, which can avoid the security risks caused by expired keys, help prevent key data leakage, and further improve the security of data transmission.

[0157] In this way, the data management method provided by the exemplary embodiment of the present disclosure can avoid improper use of keys, potential key leakage incidents or security risks caused by expired keys in the process of more reasonably determining the transmission strategy that matches the audio and video data to be transmitted, which is beneficial to preventing the leakage of key data, further improving the security of data transmission, and effectively avoiding the tampering of audio and video data.

[0158] Fig.10 is another flow chart of the data management method in the exemplary embodiment of the present disclosure, such as Fig.10 As shown, in some exemplary embodiments, the data management method may also include: Step 1001: monitoring the life cycles of multiple pre-stored keys, wherein the life cycles of at least two of the multiple keys are different; Step 1002: when it is determined that there is a target key whose life cycle has expired among the multiple keys, generating a new key corresponding to the target key; wherein the new key is different from the target key; Step 1003: updating the target key to the new key.

[0159] In some exemplary embodiments, step 1003 may include: deleting the target key, and storing a new key.

[0160] In this way, the data management method provided by the exemplary embodiment of the present disclosure can more reasonably determine the transmission strategy that matches the audio and video data to be transmitted. It can also specify different key lifecycle management through different key management, so as to achieve intelligent and fully automated management. It can avoid the security risks brought by expired keys, prevent the leakage of key data, improve the security of data transmission, and effectively avoid the tampering of audio and video data.

[0161] The following is a description of the process of implementing data management based on AI Agent, with reference to the accompanying figures. Fig.11 As shown, the data management method may include: Step 1101: The AI ​​Agent starts running (while) and determines whether it is the first iteration; Step 1102: When it is determined to be the first iteration, the perceptron in the AI ​​Agent obtains a data set corresponding to the audio and video data based on the obtained audio and video data; for example, the perceptron in the AI ​​Agent uses the get_perception_infos() interface to obtain a data set corresponding to the audio and video data based on the obtained audio and video data.

[0162] The data set corresponding to the audio and video data may include: video information and audio information. For example, the video information may include: an image frame set and video attribute information, the image frame set may include multiple image frames, the video attribute information may include: video size (such as video width, video height), video resolution, video content bytes, video encoding length, and video character encoding string at least one or more, and the audio information may include: audio features, audio encoding length, audio character encoding string, audio sampling rate, and audio channel number at least one or more.

[0163] Step 1103: The perceptron in the perception module of the AI ​​Agent calls the AI ​​model, analyzes the audio and video data based on the data set corresponding to the audio and video data, and obtains a data analysis result; For example, the AI ​​model may include at least one or more of an OCR model, an anomaly detection model, a data perception model, and a speech model.

[0164] For example, the data analysis results may include video type and video content analysis results, or the data analysis results may include audio content analysis results and video content analysis results, or the data analysis results may include video type, audio content analysis results, video content analysis results, etc., or the data analysis results may include: speech to text, summary, key points, voice content analysis results, video content analysis results, etc.

[0165] Among them, the video content analysis results may refer to the deep understanding information of the video content such as picture recognition and scene understanding obtained based on AI technology, the video content context understanding results, video dialogue tracking and recognition results, video type, video intent, etc.

[0166] Step 1104: The decision maker in the action module of the AI ​​Agent determines the sensitivity level corresponding to the audio and video data based on the data analysis result; Step 1105: The executor in the action module of the AI ​​Agent configures a matching transmission strategy for the audio and video data according to the sensitivity level corresponding to the audio and video data; Step 1106: The learning module (Learning Mechanisms) in the AI ​​Agent calls the AI ​​language model (such as GPT-4O) to determine whether the transmission strategy configured to match the audio and video data based on the sensitivity level is appropriate; The transmission strategy includes: an encrypted transmission strategy or a non-encrypted transmission strategy. The parameters of the encrypted transmission strategy include: at least one of the encryption algorithm type, encryption algorithm complexity, encryption key length and encryption key complexity.

[0167] For example, determining whether the transmission strategy configured to match the audio and video data is appropriate may include: determining whether the selected encryption algorithm is appropriate, whether the selected number of encryption layers is appropriate, and the like.

[0168] For example, the learning module in the AI ​​Agent determines whether the transmission strategy configured to match the audio and video data is appropriate based on at least one of the network security level of the network environment in which the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data.

[0169] Step 1107: If it is determined to be appropriate, execute the corresponding transmission strategy.

[0170] Step 1108: If it is determined to be inappropriate, adjust the transmission strategy.

[0171] The description of the above application scenario embodiment is similar to the description of the above data management method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the application scenario embodiment of the present disclosure, those skilled in the art should refer to the description in the method embodiment of the present disclosure for understanding, and no further description is given here.

[0172] It should be understood that in the various embodiments of the present disclosure, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

[0173] Based on the same inventive concept, an embodiment of the present disclosure also provides a data management device, including: an artificial intelligence agent, wherein the agent is configured to execute the steps of the data management method in any of the above embodiments.

[0174] Based on the same inventive concept, an embodiment of the present disclosure also provides a computer device, including: a processor, and a memory storing a computer program that can be run on the processor, wherein the processor is configured to implement the steps of the data management method in any of the above embodiments when executing the program.

[0175] In an exemplary embodiment, Fig.12 FIG. 1 is a schematic diagram of the structure of a computer device in an exemplary embodiment of the present disclosure. Fig.12 As shown, the computer device 1200 may include: at least one processor 1201; and at least one memory 1202 and a bus 1203 connected to the processor 1201; wherein the processor 1201 and the memory 1202 communicate with each other through the bus 1203; the processor 1201 is configured to call the program instructions in the memory 1202 to execute the steps of the data management method in one or more of the above exemplary embodiments. It can be understood that Fig.12 The structure of the computer device 1200 shown in the figure does not constitute a limitation on the computer device. The actual computer device may include more or fewer components than those shown in the figure, or combine certain components, or arrange the components differently.

[0176] In an exemplary embodiment, the processor may be, for example, a central processing unit (CPU), other general-purpose processors, a digital signal processor (DSP), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, application-specific integrated circuits, and other chips with computing functions. A general-purpose processor may be a microprocessor (MPU) or the processor may be any conventional processor, etc. Here, the embodiments of the present disclosure do not limit this.

[0177] In an exemplary embodiment, the memory may include a non-permanent memory and / or a non-volatile memory in a computer-readable storage medium, for example, the memory may be a random access memory (RAM), a read-only memory (ROM), or a flash memory (Flash RAM), or any combination thereof, which is configured to store a program executed by a processor, and the memory may include at least one storage chip. Here, the embodiments of the present disclosure do not limit this.

[0178] In an exemplary embodiment, the bus may include a power bus, a control bus, a status signal bus, etc. in addition to the data bus. Fig.12 In the figure, multiple buses are all labeled as bus 1203. Here, the embodiment of the present disclosure does not limit this.

[0179] In the implementation process, the processing performed by the computer device can be completed by the hardware integrated logic circuit in the processor or the instructions in the form of software. That is, the method steps of the embodiment of the present disclosure can be embodied as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0180] Based on the same inventive concept, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are set as steps of the data management method in one or more of the above exemplary embodiments.

[0181] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, ROM / RAM, a magnetic disk or an optical disk, etc. Here, the embodiment of the present disclosure does not limit this.

[0182] The description of the above computer device or computer-readable storage medium embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the computer device or computer-readable storage medium embodiment of the present disclosure, those skilled in the art refer to the description of the method embodiment of the present disclosure for understanding. No further description is given here.

[0183] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / modules in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / modules mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0184] Although the embodiments disclosed in the present disclosure are as above, the above contents are only embodiments adopted for facilitating the understanding of the present disclosure and are not intended to limit the present disclosure. Any technician in the field to which the present disclosure belongs can make any modifications and changes in the form and details of implementation without departing from the spirit and scope disclosed in the present disclosure, but the scope of patent protection of the present disclosure shall still be subject to the scope defined in the attached claims.

Claims

1. A data management method, characterized in that: include: Obtaining audio and video data to be transmitted and a sensitivity level corresponding to the audio and video data; At least according to the sensitivity level corresponding to the audio and video data, configure a matching transmission strategy for the audio and video data, wherein the matching transmission strategy includes: an encrypted transmission strategy or a non-encrypted transmission strategy, and the parameters of the encrypted transmission strategy include: at least one of an encryption algorithm type, an encryption algorithm complexity, an encryption key length, and an encryption key complexity; Based on the matched transmission strategy, the audio and video data are transmitted.

2. The method according to claim 1, characterized in that The configuring a matching transmission strategy for the audio and video data at least according to the sensitivity level corresponding to the audio and video data includes: Obtaining at least one of a network security level of a network environment in which the audio and video data is located, a user behavior pattern of a target user corresponding to the audio and video data, and a resource status of a system resource of a target client corresponding to the audio and video data; The matching transmission strategy is determined based on the network security level of the network environment in which the audio and video data is located, at least one of the user behavior pattern of the target user corresponding to the audio and video data and the resource status of the system resources of the target client corresponding to the audio and video data, and the sensitivity level corresponding to the audio and video data.

3. The method according to claim 2, characterized in that The determining of the matching transmission strategy according to the network security level of the network environment in which the audio and video data is located, at least one of the user behavior pattern of the target user corresponding to the audio and video data and the resource state of the system resources of the target client corresponding to the audio and video data, and the sensitivity level corresponding to the audio and video data, comprises: Based on the sensitivity level corresponding to the audio and video data, determining an initial transmission strategy that matches the sensitivity level corresponding to the audio and video data; Determining whether to adjust the initial transmission strategy based on at least one of a network security level of a network environment in which the audio and video data is located, a user behavior pattern of a target user corresponding to the audio and video data, and a resource state of a system resource of a target client corresponding to the audio and video data; When it is determined that the initial transmission policy is to be adjusted, the initial transmission policy is adjusted to obtain the adjusted transmission policy, and the adjusted transmission policy is determined as the matching transmission policy; or, when it is determined that the initial transmission policy is not to be adjusted, the initial transmission policy is determined as the matching transmission policy.

4. The method according to claim 3, characterized in that The adjusting of the initial transmission strategy includes at least one of adjusting from the encrypted transmission strategy to the non-encrypted transmission strategy, adjusting from the non-encrypted transmission strategy to the encrypted transmission strategy, and adjusting parameters of the encrypted transmission strategy.

5. The method according to claim 3, characterized in that: The obtaining of at least one of a network security level of a network environment in which the audio and video data is located, a user behavior pattern of a target user corresponding to the audio and video data, and a resource status of a system resource of a target client corresponding to the audio and video data comprises: Acquire at least one of network environment data of a network environment where the audio and video data is located, user behavior data of a target user corresponding to the audio and video data, and system resource data of a system resource of a target client corresponding to the audio and video data; Using a large artificial intelligence model, at least one of the network environment data of the network environment in which the audio and video data is located, the user behavior data of the target user corresponding to the audio and video data, and the system resource data of the system resources of the target client corresponding to the audio and video data is analyzed to obtain at least one of the network security level of the network environment in which the audio and video data is located, the user behavior pattern of the target user corresponding to the audio and video data, and the resource status of the system resources of the target client corresponding to the audio and video data.

6. The method according to claim 3, characterized in that The determining whether to adjust the initial transmission strategy based on at least one of a network security level of a network environment in which the audio and video data is located, a user behavior pattern of a target user corresponding to the audio and video data, and a resource state of a system resource of a target client corresponding to the audio and video data comprises: When the network security level of the network environment in which the audio and video data is located is a preset first network security level for characterizing the existence of potential security risks, the user behavior pattern of the target user corresponding to the audio and video data is a preset first behavior pattern for characterizing the existence of abnormal behavior, and the resource state of the system resources of the target client corresponding to the audio and video data is a preset first resource state for characterizing the existence of system resource shortage, determine to adjust the initial transmission strategy to obtain the adjusted transmission strategy; Alternatively, when the network security level of the network environment in which the audio and video data is located is a preset second network security level for characterizing the absence of security risks, the user behavior pattern of the target user corresponding to the audio and video data is a preset second behavior pattern for characterizing the absence of abnormal behavior, and the resource status of the system resources of the target client corresponding to the audio and video data is at least one of a preset second resource status for characterizing the absence of system resource shortages, it is determined that the initial transmission strategy will not be adjusted.

7. The method according to claim 1, characterized in that The method further comprises: Obtaining user behavior data of the target user corresponding to the audio and video data, wherein the user behavior data includes account login data and key usage data, the account login data includes: any one or more of user ID, login time, operation time, operation frequency, operation type, commonly used device and geographical location, and the key usage data includes any one or more of key ID, usage time, usage frequency and usage location; Determining whether the target user has abnormal behavior based on the user behavior data; When it is determined that the target user has abnormal behavior, an identity authentication message for instructing the user to perform an identity authentication operation is sent to the target client corresponding to the audio and video data to determine whether the target user is an authorized user; When it is determined that the target user is an authorized user, distributing a key to a target client corresponding to the audio and video data; The identity verification operation includes any one or more of a password verification operation, a fingerprint recognition operation, a facial recognition operation, a voiceprint recognition operation and an iris recognition operation.

8. The method according to claim 1, characterized in that The transmitting the audio and video data based on the matched transmission strategy includes: Determining whether the sensitivity level corresponding to the audio and video data is a preset sensitivity level; When the sensitivity level corresponding to the audio and video data is a preset sensitivity level, sending an identity authentication message for instructing the user to perform an identity authentication operation to a target client corresponding to the audio and video data, so as to determine whether the target user corresponding to the audio and video data is an authorized user; When it is determined that the target user is an authorized user, based on the matched transmission strategy, sending the audio and video data to the target client corresponding to the audio and video data; The identity verification operation includes any one or more of a password verification operation, a fingerprint recognition operation, a facial recognition operation, a voiceprint recognition operation and an iris recognition operation.

9. The method according to any one of claims 1 to 8, characterized in that The method further comprises: Obtaining key usage data corresponding to each of the pre-stored multiple keys, wherein the key usage data of each key includes any one or more of a key ID, a usage time, a usage frequency, and a usage location; Analyze the key usage data corresponding to each of the multiple keys to determine the key usage mode corresponding to each of the multiple keys; When the key usage mode corresponding to at least one of the multiple keys is an abnormal usage mode, based on the key usage data corresponding to each of the multiple keys, identifying the abnormal key from the multiple keys, and deleting the abnormal key; Alternatively, when the key usage modes corresponding to each of the multiple keys are all normal usage modes, the usage time corresponding to each of the multiple keys is obtained, and based on the usage time corresponding to each of the multiple keys and a preset key life cycle, the update time corresponding to each of the multiple keys is determined; based on the update time corresponding to each of the multiple keys, the multiple keys are updated respectively.

10. The method according to any one of claims 1 to 8, characterized in that The method further comprises: Monitoring life cycles of a plurality of pre-stored keys, wherein life cycles of at least two keys of the plurality of keys are different; When it is determined that there is a target key whose life cycle has ended among the multiple keys, generating a new key corresponding to the target key, wherein the new key is different from the target key; The target key is updated to the new key.

11. A data management device, characterized in that: include: An artificial intelligence agent, the agent being configured to execute the steps of the data management method as described in any one of claims 1 to 10.

12. A computer device, characterized in that: include: A processor, and a memory storing a computer program executable on the processor, wherein the processor is configured to execute the steps of the data management method according to any one of claims 1 to 10.

13. A non-transitory computer-readable storage medium storing computer-executable instructions, characterized in that: The computer executable instructions are configured to execute the steps of the data management method according to any one of claims 1 to 10.

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