Management method and device of intelligent shadow device, equipment and storage medium

Abstract

The embodiment of the invention provides a management method and device of intelligent shadow equipment, equipment and a storage medium. The method comprises the steps of performing state prediction on first terminal equipment according to initial data information of the first terminal equipment and a state prediction model, and obtaining an operation state of the first terminal equipment at a next moment. And adaptively determining the communication protocol of the first terminal device according to the initial data information of the first terminal device. And the intelligent shadow device establishes a communication link with the first terminal device according to the operation state of the first terminal device at the next moment, the communication protocol and the reliable data transmission mechanism, and encrypts and sends the instruction data to the first terminal device. And performing distributed intelligent allocation of CPU resources and network bandwidth according to the running state of the first terminal device at the next moment, the priority of the instruction data and the system resource utilization condition. In this way, the efficiency and safety of intelligent home equipment management can be improved, and the resource utilization rate and ecological construction of an intelligent home system are improved.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of Internet of Things device management, and in particular, to a management method, device, equipment, and storage medium for intelligent shadow devices. Background Art

[0002] With the rapid development of Internet of Things technology, intelligent shadow devices, as a key bridge connecting physical devices and cloud applications, have shown great application potential in various fields. For example, in the smart home field, intelligent shadow devices, as middleware in the smart home system, achieve the interconnection and interoperability of smart devices of different brands and different protocols, significantly improving the flexibility of device management and the efficiency of application development. However, despite the broad technical prospects and application values of intelligent shadow devices, there are still some deficiencies. First, the development of intelligent shadow devices involves the integration of multiple technologies such as the Internet of Things, cloud computing, big data, and artificial intelligence, with a high technical complexity, increasing the R & D cost and maintenance difficulty of the devices, and to a certain extent, affecting the popularization and application of intelligent shadow devices. Second, although intelligent shadow devices usually have certain security functions, due to their need to connect to the network, they still face potential security risks such as being hacked and data leakage. Moreover, once the network fails or is unstable, it will affect its remote management and control functions, thereby affecting the user experience. In addition, when abnormalities occur during the data synchronization process between smart devices and intelligent shadow devices, it may also lead to data inconsistency, thus affecting the normal operation of the system and the accuracy of data.

[0003] Therefore, there is an urgent need for a more efficient, secure, and reliable intelligent shadow device management solution to improve the user's smart home experience. Summary of the Invention

[0004] According to the embodiments of the present application, there are provided a management method, device, equipment, and storage medium for intelligent shadow devices, which can effectively improve the management efficiency and security of smart devices and greatly improve the user's smart home experience.

[0005] In the first aspect of the present application, there is provided a management method for intelligent shadow devices. The method includes: Performing state prediction on the first terminal device according to the initial data information and state prediction model of the first terminal device to obtain the operating state of the first terminal device at the next moment; Adaptive determining the communication protocol of the first terminal device according to the initial data information of the first terminal device; The intelligent shadow device establishes a communication link with the first terminal device according to the operating state, communication protocol, and reliable data transmission mechanism of the first terminal device at the next moment, and encrypts and sends the instruction data to the first terminal device; Distributively and intelligently allocate CPU resources and network bandwidth according to the operating state of the first terminal device at the next moment, the priority of the instruction data, and the system resource utilization.

[0006] In a possible implementation, state prediction of the first terminal device is performed according to the initial data information and state prediction model of the first terminal device, including: Match the initial data information of the first terminal device with the feature information in the device database to determine the device type of the first terminal device; Input the device type and initial data information of the first terminal device into the state prediction model to obtain the operating state of the first terminal device at the next moment; The state prediction model is an autoregressive model pre-trained from historical terminal device information.

[0007] In a possible implementation, the reliable data transmission mechanism includes: Before sending the instruction data, group the instruction data and add redundant check information to obtain reliable instruction data; After receiving the reliable instruction data, the first terminal device checks and corrects the reliable instruction data according to the error correction coding algorithm; When the communication link is interrupted, retransmit the lost reliable instruction data according to the timestamps and identification information in the already transmitted part of the reliable instruction data.

[0008] In a possible implementation, encrypting and sending the instruction data to the first terminal device includes: Use a multi-layer encryption structure to encrypt and send the instruction data to the first terminal device. The multi-layer encryption structure includes link layer encryption, application layer encryption, and data block encryption; The link layer encryption encrypts the communication link according to the SSL / TLS protocol; The application layer encryption adaptively selects a data encryption algorithm according to the type of the instruction data; The data block encryption cuts the instruction data into data blocks, performs encryption processing on each data block respectively, and encrypts and decrypts the data blocks according to the encryption index table.

[0009] In a possible implementation, the priority of the instruction data is determined according to the first priority of the instruction data, the urgency of the instruction data, and the state change frequency of the first terminal device.

[0010] In a possible implementation, the method further includes: Perform intrusion behavior detection on the first terminal device according to the operating state and historical operating data of the first terminal device; Input the operating status and historical operating data of the first terminal device into the intrusion detection model. When the behavior data of the first terminal device predicted by the intrusion detection model deviates from the behavior baseline, that is, when an intrusion behavior is detected, the intelligent shadow device immediately activates the defense mode, cuts off the connection with the first terminal device, and closes the attacked port. Automatically generate and save intrusion information, where the intrusion information includes the intrusion time, intrusion method, and information about the terminal device under attack.

[0011] In a possible implementation, the method further includes: Input the operating status of the first terminal device at the next moment and the device historical operating information into the ecological prediction model, and perform combined prediction analysis by the ecological prediction model to obtain the second terminal device that operates in coordination with the first terminal device. The intelligent shadow device makes pre - adaptation preparations for the connection with the second terminal device.

[0012] In the second aspect of this application, a management device for an intelligent shadow device is provided. The device includes: A status prediction module, configured to perform status prediction on the first terminal device according to the initial data information of the first terminal device and the status prediction model, and obtain the operating status of the first terminal device at the next moment. A communication determination module, configured to adaptively determine the communication protocol of the first terminal device according to the initial data information of the first terminal device. A communication connection module, where the intelligent shadow device establishes a connection with the first terminal device according to the operating status, communication protocol, and reliable data transmission mechanism of the first terminal device at the next moment, and encrypts and sends the instruction data to the first terminal device. An intelligent allocation module, configured to perform distributed intelligent allocation of CPU resources and network bandwidth according to the operating status of the first terminal device at the next moment, the priority of the instruction data, and the system resource utilization situation.

[0013] In the third aspect of this application, an electronic device is provided. The electronic device includes: a memory and a processor, and a computer program is stored on the memory. When the processor executes the program, the method described above is implemented.

[0014] In the fourth aspect of this application, a computer - readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect of this application is implemented.

[0015] A management method for an intelligent shadow device provided by an embodiment of the present application predicts the state of a first terminal device according to the initial data information and the state prediction model of the first terminal device, and obtains the operating state of the first terminal device at the next moment. The communication protocol of the first terminal device is adaptively determined according to the initial data information of the first terminal device. The intelligent shadow device establishes a communication link with the first terminal device according to the operating state, communication protocol, and reliable data transmission mechanism of the first terminal device at the next moment, and encrypts and sends instruction data to the first terminal device. The CPU resources and network bandwidth are distributed and intelligently allocated according to the operating state of the first terminal device at the next moment, the priority of the instruction data, and the system resource utilization. Rational system resource allocation is achieved, the security of instruction sending is ensured, the resource utilization rate and response speed are improved. At the same time, the second terminal device is predicted and pre-adapted through the ecological prediction model, and a more efficient and intelligent collaborative smart home ecosystem is established.

[0016] It should be understood that the content described in the summary of the invention section is not intended to limit the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Brief Description of the Drawings

[0017] In combination with the drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present application will become more apparent. In the drawings, the same or similar reference numerals represent the same or similar elements, where: Figure 1 is a flowchart of a management method for an intelligent shadow device according to an embodiment of the present application; Figure 2 is a schematic structural diagram of a state prediction model according to an embodiment of the present application; Figure 3 is an operating schematic diagram of a traditional intelligent shadow device connecting to a terminal device according to an embodiment of the present application; Figure 4 is a block diagram of a management device for an intelligent shadow device according to an embodiment of the present application; Figure 5 is a schematic structural diagram of a terminal device or a server suitable for implementing the embodiments of the present application. Detailed Description of the Embodiments

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some but not all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0019] In addition, the term "and / or" in this document is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after.

[0020] Figure 1 The flowchart of the management method of the intelligent shadow device according to the embodiment of the present disclosure is shown, as Figure 1 shown.

[0021] S101, perform state prediction on the first terminal device according to the initial data information and the state prediction model of the first terminal device, and obtain the operating state of the first terminal device at the next moment.

[0022] In this embodiment, through the state prediction model, the operating state of the first terminal device can be effectively predicted, providing a basis for subsequent establishment of communication with the first terminal device and sending instruction data, and improving the management efficiency of the intelligent shadow device.

[0023] Optionally, performing state prediction on the first terminal device according to the initial data information and the state prediction model of the first terminal device includes: Match the initial data information of the first terminal device with the feature information in the device database to determine the device type of the first terminal device; Input the device type and the initial data information of the first terminal device into the state prediction model to obtain the operating state of the first terminal device at the next moment; The state prediction model is an autoregressive model pre-trained by historical terminal device information.

[0024] Among them, the initial data information includes but is not limited to the brand, device model, device description, MAC address, communication protocol, historical operation data, and historical switch status of the first terminal device. The historical operation data includes but is not limited to the CPU usage rate, memory occupancy rate, and network traffic of the first terminal device in the past period of time.

[0025] The device database in the intelligent shadow device is updated in real time. The intelligent shadow device can perform image recognition and feature extraction techniques on the device brand, Logo, and model in the photo by obtaining the appearance photo of the terminal device being photographed. Then, it combines multi-dimensional information such as the electromagnetic signal characteristics and communication protocol characteristics sent by the device for comprehensive analysis to determine the type, function, and communication method of the device, and further classifies and manages the newly added terminal devices. In addition, for each device type, the intelligent shadow device can also establish separate management strategies and parameter configuration templates to facilitate the subsequent unified management and configuration of terminal devices. In addition, an example of the device database is shown in the following table:

[0026] In the present invention, the intelligent shadow device compares and matches the initial data information received from the first terminal device with the feature information in the device database one by one to determine the device type of the first terminal device. For example, when the feature information in the first terminal device includes: the brand is "Xiaomi" and the device model is "LS2000", it can be determined that the device type of the first terminal device is "intelligent light bulb".

[0027] Figure 2 Schematic diagram of the structure of the state prediction model according to an embodiment of the present application, as Figure 2 shown: The state prediction model may include an input layer, an input layer, a time series prediction layer, a fully connected layer, and an output layer. The input layer is responsible for performing standardized preprocessing on the device type and initial data information of the first terminal device. The time series prediction layer is responsible for capturing and learning the device type and initial data information of the first terminal device after preprocessing to obtain the feature data therein. The fully connected layer is responsible for mapping the feature data output by the time series prediction layer to a lower-dimensional space and performing a non-linear transformation to prepare for the final prediction result. The output layer uses an activation function to predict the mapped data output by the fully connected layer to obtain the health prediction value of the final first storage node. The model adopted by the time series prediction layer is the Prophet model. The Prophet model is a time prediction sequence model released by Facebook in 2017, which decomposes time series data into trend and seasonal time series data, automatically learns and fits the trend, and has a relatively flexible implementation method and supports non-linear data. In addition, in the present invention, the batch size batch_size in the training process of the state prediction model is set to 8.

[0028] In this embodiment, the state prediction model is used to predict the operating state of the first terminal device at the next moment, so as to make corresponding resource configurations and connection preparations for the subsequent connection with the first terminal device, improving the efficiency and stability of the intelligent shadow device.

[0029] S102. Adaptively determine the communication protocol of the first terminal device according to the initial data information of the first terminal device.

[0030] Among them, the intelligent shadow device continuously monitors the network environment parameters and the communication requirements of the first terminal device. The network environment parameters include but are not limited to network bandwidth, network latency, and signal strength. The intelligent shadow device preferentially determines the communication protocol according to the communication requirements of the first terminal device. The communication protocols include but are not limited to the MATTER protocol, Wi-Fi, ZigBee, and Bluetooth. However, due to the poor network condition, the intelligent shadow device adjusts the communication protocol according to the network state. For example, when the network bandwidth is low and the data volume of the instruction data is small, the Bluetooth protocol with low power consumption and high stability is preferentially selected; when the network bandwidth is sufficient and the instruction data contains high-definition video data, the Wi-Fi protocol with higher quality is switched to. In addition, the intelligent shadow device also uses a real-time learning mechanism to continuously optimize the communication protocol strategy with the first terminal device according to the feedback of the effects of using different communication protocols by the first terminal device in different scenarios.

[0031] For example, when the first terminal device is a medical monitoring device, the required network bandwidth is 20 Mbps, and the instruction data is to upload health data in real time. Among the above-mentioned centralized communication protocols, the MATTER protocol has high transmission efficiency and good stability, but is not suitable for the medical environment. Wi-Fi is suitable for data transmission with high bandwidth requirements and has high power consumption. ZigBee is suitable for low-power and short-distance communication with high stability, but the bandwidth is insufficient. Bluetooth is suitable for low-power and short-distance communication with high stability and is suitable for the upload of real-time health data. Then, at this time, the intelligent shadow device will determine the communication protocol with the first terminal device as Bluetooth communication.

[0032] In this embodiment, adaptively determining the communication protocol according to the initial data information can meet the communication requirements of different first terminal devices and optimize the communication efficiency.

[0033] S103. The intelligent shadow device establishes a communication link with the first terminal device according to the operating state, communication protocol, and reliable data transmission mechanism of the first terminal device at the next moment, and encrypts and sends the instruction data to the first terminal device.

[0034] Figure 3 The operating schematic diagram of the traditional intelligent shadow device connecting the terminal device according to the embodiment of the present application is as Figure 3 shown: The intelligent shadow device obtains the instruction requirements of cloud applications and determines the corresponding first terminal device according to the instruction data of the cloud applications. When the first terminal device is online, the intelligent shadow device establishes a communication connection with the first terminal device, then directly sends the instruction data to the first terminal device, and then determines the instruction reception status of the first terminal device according to the instruction response of the first terminal device; when the first terminal device is not online, the intelligent shadow device temporarily stores the instruction data, waits for the first terminal device to go online, then establishes a connection with the first terminal device, updates the running status of the first terminal device, and sends the instruction data to the first terminal device.

[0035] In this embodiment, through the reliable data transmission mechanism and the encryption mechanism, the security of the instruction data transmission is ensured, and the delay of the instruction data transmission is reduced.

[0036] Optionally, the reliable data transmission mechanism includes: Before sending the instruction data, the instruction data is grouped and redundant check information is added to obtain reliable instruction data; After receiving the reliable instruction data, the first terminal device checks and corrects the reliable instruction data according to the error correction coding algorithm; When the communication link is interrupted, according to the timestamps and identification information in the already transmitted part of the reliable instruction data, the lost reliable instruction data is retransmitted.

[0037] Among them, the error correction coding algorithm is Cyclic Redundancy Check (CRC). By adding redundant bits at the end of the instruction data, errors in the instruction data during transmission are detected. First, the intelligent shadow device regards the instruction data as a binary polynomial, performs modulo operation on the binary polynomial according to the preset generating polynomial to obtain reliable instruction data, and sends it to the first terminal device. Then, after receiving the reliable instruction data, the first terminal device performs modulo operation on the received instruction data using the same generating polynomial to detect whether there are errors and correct the error bits.

[0038] In this embodiment, through the reliable data transmission mechanism, the reliability and integrity of data transmission are effectively improved. Even in the presence of noise or communication interruption, it can ensure that the instruction data can be correctly transmitted to the first terminal device.

[0039] Optionally, encrypting and sending the instruction data to the first terminal device includes: The instruction data is encrypted and sent to the first terminal device using a multi-layer encryption structure. The multi-layer encryption structure includes link layer encryption, application layer encryption, and data block encryption; The link layer encryption encrypts the communication link according to the SSL / TLS protocol; The application layer encryption adaptively selects a data encryption algorithm according to the type of instruction data; The data block encryption splits the instruction data into data blocks and performs encryption processing on each block, and encrypts and decrypts the data blocks according to the encryption index table.

[0040] Among them, the application layer encryption adaptively selects a data encryption algorithm according to the type of instruction data. For different types of instruction data (such as obtaining device status data, sending control instructions, etc.), the intelligent shadow device can adopt different encryption algorithms. For example, when the instruction data contains sensitive user privacy data, the AES (Advanced Encryption Standard) can be used for encryption. In addition, in the data block encryption part, the intelligent shadow device will build a dynamic key management system and regularly update the data block encryption key according to factors such as the communication frequency and data interaction volume of the first terminal device to ensure the security of data block encryption.

[0041] In this embodiment, a multi-layer encryption structure is adopted to encrypt the instruction data, which significantly improves the reliability of data transmission in the management process of the intelligent shadow device.

[0042] S104, perform distributed intelligent allocation of CPU resources and network bandwidth according to the operating state of the first terminal device at the next moment, the priority of the instruction data, and the system resource utilization.

[0043] For example, in a smart home system, there are multiple instruction data that need to be transmitted to the first terminal device simultaneously, including a security monitoring camera, a smart sweeping robot, and a smart air conditioner. At this time, the intelligent shadow device will detect that the overall resource utilization rate of the smart home system is relatively high and the CPU load is relatively high. Among them, the operating state of the security monitoring camera at the next moment is on, the priority of the instruction data is high, the operating state of the smart sweeping robot at the next moment is off, the priority of the instruction data is low, and the operating state of the smart air conditioner at the next moment is on, the priority of the instruction data is medium. Then the intelligent shadow device will allocate more CPU resources and network bandwidth to the security monitoring camera to ensure that it can receive the instruction data in time and transmit the monitoring video data in real time. Reduce the data transmission frequency of the smart sweeping robot and reduce its occupancy of system resources, while ensuring the accurate transmission of instruction data, reducing system energy consumption and improving communication efficiency.

[0044] In addition, the intelligent shadow device can also use a resource prediction algorithm to predict the peak resource demand time period in advance according to the historical resource usage rules of the overall smart home system and the current system load trend, and make resource allocation preparations to avoid system jams or terminal device failures caused by insufficient resources. Among them, the resource prediction algorithm includes but is not limited to the LSTM model (Long Short-term Memory Networks).

[0045] In this embodiment, by distributing and intelligently allocating CPU resources and network bandwidth, the resource utilization rate of the overall smart home system is improved, key tasks are ensured to be processed first, and the user experience is enhanced.

[0046] Optionally, the priority of the instruction data is determined according to the first priority of the instruction data, the urgency of the instruction data, and the state change frequency of the first terminal device.

[0047] Among them, the first priority of the instruction data is the preset priority in the instruction data. The urgency of the instruction data is determined according to the running status of the current smart home system. For example, when the security monitoring camera detects abnormal data or an alarm is triggered, the urgency of the instruction data communication between it and the smart shadow device will be increased. The higher the state change frequency of the first terminal device, the higher the priority of the instruction data communication between it and the smart shadow device, so as to ensure the capture of rapidly changing information.

[0048] In this embodiment, by dynamically adjusting the priority of the instruction data, the timely transmission of key instruction data is ensured, and the operation efficiency of the smart home system is improved.

[0049] Optionally, the method further includes: Performing intrusion behavior detection on the first terminal device according to the running status and historical running data of the first terminal device; Inputting the running status and historical running data of the first terminal device into the intrusion detection model. When the behavior data of the first terminal device predicted by the intrusion detection model deviates from the behavior baseline, that is, when an intrusion behavior is detected, the smart shadow device immediately activates the defense mode, cuts off the connection with the first terminal device, and closes the attacked port; Automatically generating and saving intrusion information, where the intrusion information includes the intrusion time, the intrusion method, and the information of the terminal device under attack.

[0050] The historical running data of the first terminal device includes, but is not limited to, network traffic data, log data, and instruction transmission data. The network traffic data includes, but is not limited to, the source IP address, the destination IP address, the port number, and the network bandwidth in the communication transmission. The log data includes, but is not limited to, file access information and user information.

[0051] Among them, the intrusion detection model may include an input layer, a feature extraction layer, a K-means clustering layer, and a prediction layer. The input layer is used to perform standardized preprocessing on the operating status and historical operation data of the first terminal device to make it conform to the input format of the model. The feature extraction layer uses the Bert (Pre-trained Language Model, Bidirectional Encoder Representations from Transformers) model to obtain valuable feature information from the preprocessed data. The Bert model is a pre-trained language model proposed by Google in 2018, which generates deep bidirectional language representations through two stages of pre-training and fine-tuning, helping the model better understand the features of the input data. The K-means clustering algorithm is an unsupervised learning algorithm used to cluster feature data points into K categories. By randomly selecting K data points as the initial category centers, for each feature data point, calculate its distance from each category center and assign it to the category center with the minimum distance, and then continuously update and iterate until the category centers no longer change or reach the maximum number of iterations. Based on this, the K-means clustering layer performs clustering analysis on the feature data extracted by the Bert model to identify the normal behavior clusters and abnormal behavior deviation values in the feature data. Then, the prediction layer predicts whether the behavior of the first terminal device deviates from the behavior baseline, generates a prediction value, and obtains the final prediction result.

[0052] In this embodiment, through the intrusion detection model, a real-time intrusion detection and defense mechanism is implemented, improving the security and reliability of the overall system.

[0053] Optionally, the method further includes: Input the operating status of the first terminal device at the next moment and the device historical operation information into the ecological prediction model, and perform combined prediction analysis by the ecological prediction model to obtain the second terminal device that operates in coordination with the first terminal device; The intelligent shadow device makes pre-adaptation preparations for the connection with the second terminal device.

[0054] The device historical operation information includes, but is not limited to, the operating time of the terminal device and the switch sequence between different terminal devices.

[0055] Among them, the ecological prediction model may include an input layer, a graph neural network layer, a fully connected layer, and a prediction layer. The input layer is used to perform standardized preprocessing on the operating state of the first terminal device at the next moment and the device historical operating information to make it conform to the input format of the model. The graph neural network layer (GNN, Graph Neural Networks) is used to capture the collaborative operating relationship between terminal devices and obtain feature information. The fully connected layer is used to map the feature information output by the graph neural network layer to a lower-dimensional space and perform a non-linear transformation to prepare for the final prediction result. The output layer uses an activation function to predict the mapped data output by the fully connected layer to obtain the final second terminal device that operates in collaboration with the first terminal device.

[0056] For example, when the instruction data received by the intelligent shadow device is to close the intelligent curtain, it can be known that the operating state of the intelligent curtain at the next moment is closed. Then, after closing the intelligent curtain, the instruction data is usually to turn on the intelligent light bulb. If the ecological prediction model obtains the second terminal device that operates in collaboration with the intelligent curtain as the intelligent light bulb after analyzing the operating state of the intelligent curtain device at the next moment and the device historical operating information, the intelligent shadow device will prepare in advance the communication connection protocol with the intelligent light bulb device, efficiently realize the collaborative operation of the intelligent curtain and the intelligent light bulb, and improve the overall user experience and the response speed of the terminal device.

[0057] In this embodiment, through the combined prediction analysis of the ecological prediction model, the collaboration and efficiency between terminal devices are improved, more intelligent device operation and management are realized, and the user experience is improved.

[0058] According to the embodiments of the present disclosure, the following technical effects are achieved: 1. An efficient, safe, reliable, and scalable management system for intelligent shadow devices is formed, realizing the intelligent management of terminal devices, improving system efficiency, reducing maintenance costs, enhancing system security, and having good adaptability and scalability.

[0059] 2. The management method of the intelligent shadow device proposed in the present invention improves the flexibility and compatibility of the system while ensuring the security and stability of the smart home system, ensuring that the system can respond to and process tasks efficiently and in real time, and preparing in advance for possible tasks.

[0060] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0061] The above is the introduction of the method embodiments. The following further illustrates the solution of this application through device embodiments.

[0062] Figure 4 The block diagram of the management device of the intelligent shadow device according to the embodiment of the present application is shown. As Figure 4 shown, it includes: A state prediction module 401, configured to perform state prediction on the first terminal device according to the initial data information of the first terminal device and the state prediction model, and obtain the operating state of the first terminal device at the next moment; A communication determination module 402, configured to adaptively determine the communication protocol of the first terminal device according to the initial data information of the first terminal device; A communication connection module 403, where the intelligent shadow device establishes a connection with the first terminal device according to the operating state, communication protocol, and reliable data transmission mechanism of the first terminal device at the next moment, and encrypts and sends the instruction data to the first terminal device; An intelligent allocation module 404, configured to perform distributed intelligent allocation of CPU resources and network bandwidth according to the operating state of the first terminal device at the next moment, the priority of the instruction data, and the system resource utilization situation.

[0063] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the described modules can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0064] Figure 5 The structural schematic diagram of the terminal device or server suitable for implementing the embodiment of the present application is shown.

[0065] As Figure 5As shown, the terminal device or server includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage section 508 into the random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the terminal device or server are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0066] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read from it can be installed into the storage section 508 as needed.

[0067] Specifically, according to an embodiment of the present application, the above method flow steps can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program contains program codes for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above functions defined in the system of the present application are executed.

[0068] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0069] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the aforementioned module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0070] The units or modules involved in the embodiments described in this application can be implemented in software or in hardware. The described units or modules can also be provided in a processor. Among them, the names of these units or modules do not, in some cases, constitute a limitation on the units or modules themselves.

[0071] On the other hand, this application also provides a computer-readable storage medium, which can be included in the electronic device described in the foregoing embodiments; or can exist alone without being assembled into the electronic device. The foregoing computer-readable storage medium stores one or more programs, and when the foregoing programs are executed by one or more processors, the methods described in this application are implemented.

[0072] The above description is only for the preferred embodiments of this application and the description of the technical principles applied. Those skilled in the art should understand that the scope of the application involved in this application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions described in this application.

Claims

1. A management method for intelligent shadow devices, characterized in that: include: Performing state prediction on the first terminal device according to the initial data information of the first terminal device and the state prediction model to obtain the operating state of the first terminal device at the next moment; Adaptively determining a communication protocol of the first terminal device according to initial data information of the first terminal device; The intelligent shadow device establishes a communication link with the first terminal device according to the operating state of the first terminal device at the next moment, the communication protocol and the reliable data transmission mechanism, and encrypts the instruction data and sends it to the first terminal device; Distributed intelligent allocation of CPU resources and network bandwidth is performed according to the operating status of the first terminal device at the next moment, the priority of the instruction data and the utilization of system resources.

2. The management method of the intelligent shadow device according to claim 1, characterized in that: The performing state prediction on the first terminal device according to the initial data information of the first terminal device and the state prediction model includes: Matching the initial data information of the first terminal device with the characteristic information in the device database to determine the device type of the first terminal device; Inputting the device type of the first terminal device and the initial data information into the state prediction model to obtain the operating state of the first terminal device at the next moment; The state prediction model is an autoregressive model pre-trained by historical terminal device information.

3. The management method of the intelligent shadow device according to claim 1, characterized in that: The reliable data transmission mechanism includes: Before sending the instruction data, grouping the instruction data and adding redundant check information to obtain reliable instruction data; After receiving the reliable instruction data, the first terminal device verifies and corrects the reliable instruction data according to an error correction coding algorithm; When the communication link is interrupted, the lost reliable instruction data is reissued according to the timestamp and identification information in the transmitted part of the reliable instruction data.

4. The management method of the intelligent shadow device according to claim 1, characterized in that: The step of encrypting and sending the instruction data to the first terminal device comprises: The instruction data is encrypted and sent to the first terminal device using a multi-layer encryption structure, wherein the multi-layer encryption structure includes link layer encryption, application layer encryption and data block encryption; The link layer encryption encrypts the communication link according to the SSL / TLS protocol; The application layer encryption adaptively selects a data encryption algorithm according to the type of the instruction data; The data block encryption divides the instruction data into data blocks, performs encryption processing on each of the data blocks, and encrypts and decrypts the data blocks according to the encryption index table.

5. The management method of the intelligent shadow device according to claim 1, characterized in that: The priority of the instruction data is determined according to the first priority of the instruction data, the urgency of the instruction data, and the state change frequency of the first terminal device.

6. The management method of the intelligent shadow device according to claim 1, characterized in that: The method further comprises: Performing intrusion behavior detection on the first terminal device according to the operating status and historical operating data of the first terminal device; Inputting the operation status and historical operation data of the first terminal device into the intrusion detection model, when the behavior data of the first terminal device predicted by the intrusion detection model deviates from the behavior baseline, that is, when the intrusion behavior is detected, the smart shadow device immediately turns on the defense mode, cuts off the connection with the first terminal device, and closes the attacked port; Automatically generate and save intrusion information, including intrusion time, intrusion method and information of the terminal device that has been invaded.

7. The management method of the intelligent shadow device according to claim 1, characterized in that: The method further comprises: Inputting the operation status of the first terminal device at the next moment and the historical operation information of the device into the ecological prediction model, and performing combined prediction analysis by the ecological prediction model to obtain a second terminal device that operates in coordination with the first terminal device; The smart shadow device performs pre-adaptation preparation for connection with the second terminal device.

8. A management device for intelligent shadow devices, characterized in that: include: A state prediction module, used to predict the state of the first terminal device according to the initial data information of the first terminal device and the state prediction model, and obtain the operating state of the first terminal device at the next moment; a communication determination module, configured to adaptively determine a communication protocol of the first terminal device according to initial data information of the first terminal device; A communication connection module, wherein the smart shadow device establishes a connection with the first terminal device according to the operating state of the first terminal device at the next moment, the communication protocol and the reliable data transmission mechanism, and encrypts the instruction data and sends it to the first terminal device; The intelligent allocation module is used to perform distributed intelligent allocation of CPU resources and network bandwidth according to the operating status of the first terminal device at the next moment, the priority of the instruction data and the system resource utilization.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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