Data transmission system and method

Abstract

This application provides a data transmission system and method, relating to the field of smart home technology. The system includes: a perception layer device, a network layer device, and an application layer device. The perception layer device acquires user data information and extracts data feature information from the user data information based on convolutional layers and a first preset number of pooling layers in a neural network model, and sends the data feature information to the network layer device. The network layer device transmits the data feature information based on a second preset number of pooling layers in the neural network model and transmits it to the application layer device. The application layer device processes the data feature information based on convolutional layers and a third preset number of pooling layers in the neural network model and obtains a first processing result. This technical solution can ensure the security of user data and avoid the risk of malicious use by third parties while guaranteeing the functional parameters of IoT devices.

Description

Technical Field

[0001] This application relates to the field of smart home technology, and in particular to a data transmission system and method. Background Technology

[0002] Currently, with the development and large-scale application of IoT technology, issues of personal privacy and data protection have become prominent, especially in the process of user authentication and voice and image recognition, where user data leakage may occur.

[0003] To ensure user data and information security, user data needs to be protected from three ends: cloud, edge, and terminal. Currently, the method adopted is to encrypt user data. However, once the password is cracked, user data can still be leaked.

[0004] Therefore, there is an urgent need for a data transmission system and method that can ensure the security of user data and avoid the risk of malicious use by third parties while guaranteeing the functional parameters of IoT devices. Summary of the Invention

[0005] This application provides a data transmission system and method that can ensure the security of user data and avoid the risk of malicious use by third parties while guaranteeing the functional parameters of IoT devices.

[0006] In a first aspect, this application provides a data transmission system, the system comprising: a perception layer device, a network layer device, and an application layer device;

[0007] The perception layer device is used to acquire user data information, extract data feature information from the user data information based on the convolutional layer and a first preset number of pooling layers in the neural network model, and send the data feature information to the network layer device;

[0008] The network layer device is used to transmit the data feature information based on a second preset number of pooling layers in the neural network model, and then transmit it to the application layer device.

[0009] The application layer device is used to process the data feature information based on the convolutional layer and a third preset number of pooling layers in the neural network model, and obtain a first processing result.

[0010] In one example, the perception layer device is further configured to encrypt the data feature information after extracting it, to obtain encrypted data feature information.

[0011] In one example, the application layer device is used to decrypt the encrypted data feature information before processing the data feature information based on the convolutional layers and a third preset number of pooling layers in the neural network model, to obtain the decrypted data feature information.

[0012] In one example, the application layer device is used to send the first processing result to the user terminal device or the perception layer device, so that the user terminal device or the perception layer device can execute corresponding instructions.

[0013] In one example, the network layer device is used to determine whether the data feature information should be sent to the application layer device based on the type of the data feature information.

[0014] In one example, the network layer device is configured to process the data feature information to obtain a second processing result if it determines that the data feature information will not be sent to the application layer device.

[0015] In one example, the network layer device is used to send the second processing result to the user terminal device or the perception layer device, so that the user terminal device or the perception layer device can execute corresponding instructions.

[0016] Secondly, this application provides a data transmission method, which is applied to a data transmission system, the system comprising: a perception layer device, a network layer device, and an application layer device; the method comprising:

[0017] The user's data information is acquired through the perception layer device, and data feature information is extracted from the user's data information based on the convolutional layer and a first preset number of pooling layers in the neural network model, and the data feature information is sent to the network layer device.

[0018] The data feature information is transmitted to the application layer device through the network layer device based on a second preset number of pooling layers in the neural network model;

[0019] The application layer device processes the data feature information based on the convolutional layers and a third preset number of pooling layers in the neural network model, and obtains a first processing result.

[0020] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0021] The memory stores computer-executed instructions;

[0022] The processor executes computer execution instructions stored in the memory to implement the method as described in the second aspect.

[0023] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method described in the second aspect.

[0024] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the second aspect.

[0025] This application provides a data transmission system and method. The system includes a perception layer device, a network layer device, and an application layer device. The perception layer device acquires user data information and extracts data feature information from the user data information based on convolutional layers and a first preset number of pooling layers in a neural network model, and sends the data feature information to the network layer device. The network layer device transmits the data feature information based on a second preset number of pooling layers in the neural network model to the application layer device. The application layer device processes the data feature information based on convolutional layers and a third preset number of pooling layers in the neural network model to obtain a first processing result. This technical solution can ensure the security of user data and avoid the risk of malicious use by third parties while guaranteeing the functional parameters of IoT devices. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0027] Figure 1 This is a schematic diagram of a data transmission system according to Embodiment 1 of this application;

[0028] Figure 2 This is a schematic diagram of a data transmission system according to Embodiment 2 of this application;

[0029] Figure 3 This is a flowchart illustrating a data transmission method according to Embodiment 3 of this application;

[0030] Figure 4 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0031] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0032] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0033] This application provides a data transmission system designed to solve the aforementioned technical problems of the prior art.

[0034] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0035] Figure 1 This is a schematic diagram of a data transmission system according to Embodiment 1 of this application. Embodiment 1 includes: the system 10 includes: a perception layer device 101, a network layer device 102, and an application layer device 103; the perception layer device 101 is used to acquire user data information, extract data feature information from the user data information based on the convolutional layer and a first preset number of pooling layers in the neural network model, and send the data feature information to the network layer device;

[0036] The network layer device 102 is used to transmit data feature information based on a second preset number of pooling layers in the neural network model and transmit it to the application layer device; the application layer device 103 is used to process the data feature information based on the convolutional layers and a third preset number of pooling layers in the neural network model and obtain a first processing result.

[0037] In this embodiment, the perception layer device 101 is a device capable of acquiring user data information. For example, in a smart home scenario, the perception layer device 101 could be a refrigerator, which could then acquire user data information, such as the user's body temperature. In this embodiment, the perception layer device 101 is equipped with a first computing unit, which can perform calculations using a neural network model. The neural network model includes convolutional layers and a first preset number of pooling layers. The value of the first preset number can be adjusted according to actual conditions. The perception layer device 101 extracts data feature information through the neural network model. Since the data feature information is processed data information, if this data feature information is not in the neural network model, it loses its specific physical meaning.

[0038] Network layer device 102 is a device for transmitting data feature information. It mainly consists of a transmission device and a second computing unit. The second computing unit includes a neural network model. The neural network model of network layer device 102 is the same model as the neural network model in perception layer device 101. The input of the neural network model in network layer device 102 is the output of the neural network model in perception layer device 101. After being processed by the neural network model in perception layer device 101, the data feature information is further processed by the neural network model in network layer device 102 before being transmitted to application layer device 103.

[0039] In this embodiment, the application layer device 103 includes a third computing unit, which includes convolutional layers and a third preset number of pooling layers in the neural network model. The network layer device 102 further processes the data feature information to obtain a first processing result. The first processing result can be the restoration of the data feature information to the user's data information.

[0040] This application provides a data transmission system comprising: a perception layer device, a network layer device, and an application layer device. The perception layer device acquires user data information and extracts data feature information from the user data information based on convolutional layers and a first preset number of pooling layers in a neural network model, and sends the data feature information to the network layer device. The network layer device transmits the data feature information based on a second preset number of pooling layers in the neural network model to the application layer device. The application layer device processes the data feature information based on convolutional layers and a third preset number of pooling layers in the neural network model and obtains a first processing result. This technical solution can ensure the security of user data and avoid the risk of malicious use by third parties while guaranteeing the functional parameters of IoT devices.

[0041] Figure 2 This is a schematic diagram of a data transmission system according to Embodiment 2 of this application. System 10 in Embodiment 2 includes: a perception layer device 101, a network layer device 102, and an application layer device 103. The perception layer device 101 acquires user data information and extracts data feature information from the user data information based on convolutional layers and a first preset number of pooling layers in a neural network model, and sends the data feature information to the network layer device 102. The network layer device 102 transmits the data feature information based on a second preset number of pooling layers in the neural network model and transmits it to the application layer device 103. The application layer device 103 processes the data feature information based on convolutional layers and a third preset number of pooling layers in the neural network model and obtains a first processing result.

[0042] In one example, the perception layer device 101 is also used to encrypt the data feature information after extracting it, so as to obtain encrypted data feature information.

[0043] In this embodiment, the encryption algorithms include DES, 3DES, AES, and Blowfish. DES is a data encryption standard, which is fast and suitable for encrypting large amounts of data. 3DES is based on DES and encrypts the data three times using three different keys, providing higher strength. AES is an advanced encryption standard, a next-generation encryption algorithm standard that is fast, has a high security level, and supports encryption with 128, 192, and 256-bit keys.

[0044] In this embodiment, the sensing layer device 101 encrypts the data feature information, thereby increasing the confidentiality of the data feature information.

[0045] In one example, the application layer device 103 is used to decrypt the encrypted data feature information before processing the data feature information through convolutional layers and a third preset number of pooling layers in a neural network model, thereby obtaining the decrypted data feature information.

[0046] In this embodiment, after receiving the encrypted data feature information, the application layer device 103 decrypts the encrypted data feature information, finally determines the decrypted data feature information, and inputs the decrypted data feature information into the neural network model for processing based on the convolutional layers and a third preset number of pooling layers in the neural network model.

[0047] In one example, network layer device 102 transmits via any of the following transport protocols: MQTT, HTTP, TCP, or NB-IoT.

[0048] In this embodiment, the wireless transmission of the network layer device 102 can be implemented by Wi-Fi, BT / BLE, ZigBee modules, or by LPWAN network modules such as LoRa, NB-IoT, 5G (NR), and other LTE network modules. These communication modules implement one-way / two-way certificate authentication and handshake, such as SSL / TLS digital certificates, through transport layer protocols such as MQTT, HTTP, TCP, NB-IoT, etc.

[0049] In one example, the application layer device 103 is used to send the first processing result to the user terminal device 104 or the perception layer device 101 so that the user terminal device 104 or the perception layer device 101 can execute the corresponding instructions.

[0050] In this embodiment, the user terminal device 104 can be a mobile phone, tablet, or laptop. In this embodiment, the application layer device 103 sends the first processing result to the user terminal device 104, so that the user terminal device 104 controls the perception layer device 101 to execute the corresponding instructions in the perception layer device 101. The corresponding instructions may be turning on the air conditioner to the optimal body temperature or turning on the lights to the optimal brightness value, which is not limited here.

[0051] In one example, network layer device 102 is used to determine whether data feature information should be sent to application layer device 103 based on the type of data feature information.

[0052] In this embodiment, the data feature information can be either a calculation type or a verification type. If it is a calculation type, it is sent to the application layer device 103; if it is a verification type, it is not sent to the application layer device 103. The advantage of this setting is that data feature information of the calculation type is relatively complex, and therefore requires a relatively high computing power from the device. In this case, sending it to the application layer device 103 facilitates the calculation of the data feature information.

[0053] In one example, network layer device 102 is used to process the data feature information to obtain a second processing result if it is determined that the data feature information will not be sent to application layer device 103.

[0054] In this embodiment, if the data feature information is not sent to the application layer device 103, it is processed in the network layer device 102. The advantage of this setting is that even when the network signal is poor or the application layer device 103 cannot be connected, the data feature information can still be processed to obtain the second processing result.

[0055] In one example, network layer device 102 is used to send the second processing result to user terminal device 104 or perception layer device 101 so that the user terminal device 104 or perception layer device 101 can execute the corresponding instructions.

[0056] In this embodiment, the user terminal device 104 or the perception layer device 101 executes the corresponding instructions.

[0057] This application provides a data transmission system in which a network layer device 102 processes data feature information to obtain a second processing result if it is determined that the data feature information will not be sent to the application layer device 103. By adopting this technical solution, the computational load of the neural network can be flexibly allocated according to the inference computing capabilities of devices at different layers, achieving the goals of effectively utilizing device resources, reducing power consumption, and optimizing the configuration of multi-party computing power.

[0058] Figure 3This is a flowchart illustrating a data transmission method according to Embodiment 3 of this application. The method is applied to a data transmission system, which includes: a perception layer device, a network layer device, and an application layer device; the method includes:

[0059] S301. Obtain user data information through the perception layer device, extract data feature information from the user data information based on the convolutional layer and the first preset number of pooling layers in the neural network model, and send the data feature information to the network layer device.

[0060] S302. Data feature information is transmitted to the application layer device through the network layer device based on the second preset number of pooling layers in the neural network model.

[0061] S303. The data feature information is processed by the application layer device based on the convolutional layer and the third preset number of pooling layers in the neural network model, and the first processing result is obtained.

[0062] In one example, after the perception layer device extracts the data feature information, it encrypts the data feature information to obtain encrypted data feature information.

[0063] In one example, the encrypted data feature information is decrypted by the application layer device before the data feature information is processed by the convolutional layer and the third preset number of pooling layers in the neural network model, so as to obtain the decrypted data feature information.

[0064] In one example, the network layer device transmits data via any of the following transport protocols: MQTT, HTTP, TCP, or NB-IoT.

[0065] In one example, the first processing result is sent to the user terminal device or the perception layer device through the application layer device, so that the corresponding instructions can be executed through the user terminal device or the perception layer device.

[0066] In one example, the network layer device determines whether to send the data feature information to the application layer device based on the type of data feature information.

[0067] In one example, if the network layer device determines that the data feature information will not be sent to the application layer device, it processes the data feature information to obtain a second processing result.

[0068] In one example, the second processing result is sent to a user terminal device or a perception layer device via a network layer device, so that the user terminal device or perception layer device can execute the corresponding instructions.

[0069] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the above-described method can be referred to the corresponding process in the aforementioned system embodiments, and will not be repeated here.

[0070] Figure 4 This is a block diagram illustrating an electronic device according to an exemplary embodiment. The device may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc.

[0071] The device 400 may include one or more of the following components: a processing component 402, a memory 404, a power supply component 406, a multimedia component 408, an audio component 410, an input / output (I / O) interface 412, a sensor component 414, and a communication component 416.

[0072] Processing component 402 typically controls the overall operation of device 400, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 402 may include one or more modules to facilitate interaction between processing component 402 and other components. For example, processing component 402 may include a multimedia module to facilitate interaction between multimedia component 408 and processing component 402.

[0073] Memory 404 is configured to store various types of data to support the operation of device 400. Examples of such data include instructions for any application or method operating on device 400, contact data, phonebook data, messages, pictures, videos, etc. Memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0074] Power supply component 406 provides power to various components of device 400. Power supply component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 400.

[0075] Multimedia component 408 includes a screen that provides an output interface between device 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 408 includes a front-facing camera and / or a rear-facing camera. When device 400 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0076] Audio component 410 is configured to output and / or input audio signals. For example, audio component 410 includes a microphone (MIC) configured to receive external audio signals when device 400 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 404 or transmitted via communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.

[0077] I / O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0078] Sensor assembly 414 includes one or more sensors for providing state assessments of various aspects of device 400. For example, sensor assembly 414 may detect the on / off state of device 400, the relative positioning of components such as the display and keypad of device 400, changes in the position of device 400 or a component of device 400, the presence or absence of user contact with device 400, the orientation or acceleration / deceleration of device 400, and temperature changes of device 400. Sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 414 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0079] Communication component 416 is configured to facilitate wired or wireless communication between device 400 and other devices. Device 400 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 416 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 416 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0080] In an exemplary embodiment, the apparatus 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0081] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, which can be executed by a processor 420 of the device 400 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0082] A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform a data transmission method thereof.

[0083] This application also discloses a computer program product, including a computer program that, when executed by a processor, implements the method described in this embodiment.

[0084] Various embodiments of the systems and technologies described above in this application can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0085] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or electronic device.

[0086] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0087] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0088] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as data electronic devices), or computing systems that include middleware components (e.g., application electronic devices), or computing systems that include front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0089] Computer systems can include client and electronic devices. Clients and electronic devices are generally geographically separated and typically interact via communication networks. The client-electronic device relationship is created by computer programs running on the respective computers and having a client-electronic device relationship with each other. The electronic device can be a cloud electronic device, also known as a cloud computing electronic device or cloud host, a host product within the cloud computing service system, addressing the shortcomings of traditional physical hosts and VPS services ("Virtual Private Server," or simply "VPS") in terms of management difficulty and weak business scalability. The electronic device can also be an electronic device in a distributed system or an electronic device incorporating blockchain technology. It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application is achieved, and this is not limited herein.

[0090] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0091] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A data transmission system, characterized by The system comprises a perception layer device, a network layer device and an application layer device; The perception layer device is configured to acquire data information of a user, extract data feature information from the data information of the user based on a convolution layer and a first preset number of pooling layers in a neural network model, and send the data feature information to the network layer device; The network layer device is configured to transmit the data feature information based on a second preset number of pooling layers in the neural network model, determine whether the data feature information is sent to the application layer device according to a type of the data feature information, transmit the data feature information to the application layer device if it is determined that the data feature information is sent to the application layer device, process the data feature information to obtain a second processing result if it is determined that the data feature information is not sent to the application layer device, and send the second processing result to a user terminal device or the perception layer device; The application layer device is configured to process the data feature information based on a convolution layer and a third preset number of pooling layers in the neural network model, and obtain a first processing result.

2. The system of claim 1, wherein, The perception layer device is further configured to encrypt the data feature information after the data feature information is extracted to obtain encrypted data feature information.

3. The system of claim 2, wherein, The application layer device is configured to decrypt the encrypted data feature information to obtain decrypted data feature information before the data feature information is processed based on the convolution layer and the third preset number of pooling layers in the neural network model.

4. The system of claim 1, wherein, The application layer device is configured to send the first processing result to the user terminal device or the perception layer device to execute corresponding instructions through the user terminal device or the perception layer device.

5. A data transmission method, characterized by, The method is applied to a data transmission system, and the system comprises a perception layer device, a network layer device and an application layer device. The perception layer device is configured to acquire data information of a user, extract data feature information from the data information of the user based on a convolution layer and a first preset number of pooling layers in a neural network model, and send the data feature information to the network layer device; The network layer device is configured to transmit the data feature information based on a second preset number of pooling layers in the neural network model, determine whether the data feature information is sent to the application layer device according to a type of the data feature information, transmit the data feature information to the application layer device if it is determined that the data feature information is sent to the application layer device, process the data feature information to obtain a second processing result if it is determined that the data feature information is not sent to the application layer device, and send the second processing result to a user terminal device or the perception layer device; The application layer device is configured to process the data feature information based on a convolution layer and a third preset number of pooling layers in the neural network model, and obtain a first processing result.

6. An electronic device, comprising: The method comprises: a processor, and a memory connected to the processor in communication; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method of claim 5.

7. A computer readable storage medium characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method of claim 5.

8. A computer program product, characterised in that, A computer program is included, which, when executed by a processor, implements the method of claim 5.

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