Internet of Things data intelligent sharing method and system

Abstract

The invention relates to the technical field of Internet of Things data sharing, and discloses an Internet of Things data intelligent sharing method and system, and the method comprises the steps: obtaining perception data in a home network through an edge device, and carrying out the privacy data and non-privacy data classification of the data; calculating a privacy level by adopting an information entropy quantification method, and selecting a proper transmission protocol according to network state evaluation and equipment energy consumption; if the performance does not reach the standard, automatically switching to a low-power-consumption protocol; and when the transmission performance is good, multi-protocol parallel transmission is started. Compared with the prior art that a traditional data sharing method usually depends on a static protocol selection and encryption mechanism, and especially in a home network scene, the technical problem that balance among equipment energy consumption, network quality change and privacy protection requirements is ignored is solved. Due to the fact that a dynamic protocol selection mechanism is introduced, balance between privacy protection and data transmission efficiency is achieved, and the efficiency, safety and adaptability of data sharing of the Internet of Things are improved.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) data sharing technology, and in particular to an IoT data intelligent sharing method and system. Background Technology

[0002] Currently, IoT data sharing methods mainly rely on static protocol selection and encryption mechanisms, but these methods are poorly adapted to home network scenarios. Home networks contain a wide variety of devices, and the network environment is complex and ever-changing, making it difficult for existing technologies to fully function effectively in such dynamic environments. Most data sharing systems use fixed protocols, which cannot be flexibly adjusted according to the actual conditions of the home network (such as bandwidth changes, increases or decreases in the number of devices), resulting in compromised data transmission efficiency and privacy protection in certain situations. For example, smart home devices (such as cameras, sensors, and voice assistants) often face problems such as unstable network signals and bandwidth fluctuations when transmitting data, especially when multiple devices are working simultaneously, leading to excessive network load and impacting data transmission efficiency. While WiFi protocols offer high bandwidth, they are susceptible to interference and consume significant power. NB-IoT and LoRa protocols are relatively stable under low bandwidth conditions but are unsuitable for applications requiring large data volumes and low latency. Furthermore, home networks often contain battery-powered devices, such as smart sensors, making energy management a critical issue; existing technologies have failed to adequately address the conflict between privacy and low power consumption requirements. Therefore, there is an urgent need for an intelligent IoT data sharing method that can flexibly select the most suitable protocol and dynamically adjust transmission strategies based on network conditions, device privacy requirements, and energy management within a home network environment. This method can ensure data transmission efficiency while guaranteeing data privacy, optimizing resource utilization in the home network, and improving the overall performance and user experience of home IoT applications. Summary of the Invention

[0003] To address the aforementioned technical shortcomings, the present invention aims to propose an intelligent data sharing method for the Internet of Things (IoT). This method addresses the technical problem that traditional data sharing methods in the prior art typically rely on static protocol selection and encryption mechanisms, especially in home network scenarios, neglecting the balance between device energy consumption, network quality variations, and privacy protection requirements.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an intelligent data sharing method for the Internet of Things.

[0005] The IoT data intelligent sharing method includes:

[0006] Step S10: Obtain the first sensing data from the edge devices in the home network, classify and label the attributes of the first sensing data using a preset sensing data classification model, and obtain the second sensing data with attributes labeled as privacy data and the third sensing data with attributes labeled as non-privacy data.

[0007] Step S20: First, use predefined rules to determine whether the second sensing data contains sensitive information. Sensitive information includes facial image information, biometric information, and voice information. When it is determined that the second sensing data contains sensitive information, use the information entropy quantization method to calculate the information entropy value of the second sensing data, and output the corresponding privacy level of the second sensing data according to the magnitude of the information entropy value. The privacy level includes the first privacy level, the second privacy level, and the third privacy level.

[0008] Step S30: Detect the current home network signal data and use Shannon's theorem to evaluate the maximum capacity of the channel. Based on the different attribute tags and combined with the energy consumption parameters of the edge device, a dynamic allocation protocol mechanism is first used to transmit the second and third sensing data respectively.

[0009] Step S40: A preset period T is used to continuously collect the latency, throughput and packet loss rate under the dynamic allocation protocol mechanism within the period T, and calculate the current protocol transmission performance score. If the current protocol transmission performance score is less than the preset first performance score threshold, the protocol will automatically switch to the NB-IoT protocol or the LoRa protocol.

[0010] Step S50: After the protocol automatically switches to NB-IoT protocol or LoRa protocol, the transmission performance score of NB-IoT protocol or LoRa protocol is automatically evaluated. When the transmission performance score is lower than the preset transmission performance score threshold, the network adaptive mechanism is triggered.

[0011] Preferably, in step S10, the preset perception data classification model is constructed using a decision tree. The training set of the perception data classification model consists of a first set of labeled perception data. Each data point of the first perception data is explicitly labeled with second perception data containing privacy data and third perception data with attributes marked as non-privacy data.

[0012] Preferably, step S20, which involves first determining whether the second perceived data contains sensitive information using predefined rules, specifically includes:

[0013] Face image information judgment: The OpenCV library in C++ is used to read and analyze the second perception data. The CascadeClassifier library function is used to determine whether the second perception data contains face image information, and the result returned by the detectMultiScale library function is used to determine whether the second perception data contains sensitive information.

[0014] Biometric information judgment: The OpenCV library in C++ is used to read and analyze the second sensing data. By analyzing whether its format conforms to the preset standard library of fingerprint or iris, it is determined whether the second sensing data contains sensitive information.

[0015] Voice information judgment: The audio file is parsed using the PortAudio audio library in C++. The parsing results are then compared with a preset sensitive voice library for similarity analysis to determine whether the second perception data contains sensitive information.

[0016] Preferably, step S30, which involves transmitting the second and third sensing data separately using a dynamic allocation protocol mechanism based on the different attribute tags and the energy consumption parameters of the edge device, specifically includes:

[0017] First, obtain the power consumption parameters of the edge device, including "battery powered" and "non-battery powered";

[0018] Secondly, separate data transmission mechanisms are established for the second and third sensed data:

[0019] Regarding the transmission mechanism of the second sensing data: if the power consumption parameter of the edge device is "battery powered", the low power protocol LoRa or NB-IoT protocol is selected first, and AES-256 encryption is used to encrypt the data on the low power protocol LoRa or NB-IoT protocol; if the power consumption parameter of the edge device is "non-battery powered", the WiFi protocol is selected first for transmission, and WPA3 encryption is used to encrypt the data on the WiFi protocol.

[0020] Regarding the transmission mechanism for third-party sensing data: if the power consumption parameter of the edge device is "battery powered", the low-power protocol LoRa or NB-IoT protocol will still be preferred, and AES-128 will be used for data encryption on the LoRa or NB-IoT protocol; if the power consumption parameter of the edge device is "non-battery powered", the WiFi protocol or Ethernet protocol will be preferred for transmission, and AES-128 will be used for data encryption.

[0021] Preferably, in step S50, the network adaptive mechanism is to switch some traffic to another protocol or increase redundant transmission to ensure data integrity.

[0022] Preferably, in step S30, in the transmission mechanism of the second sensing data, if the privacy level of the second sensing data is the first privacy level, then after encrypting the data with AES-256 encryption, it is further encrypted with RSA or ECC.

[0023] Preferably, in step S30, the maximum capacity It is determined based on the home network bandwidth and home network signal-to-noise ratio, where the home network bandwidth and home network signal-to-noise ratio are determined by the home network signal quality.

[0024] This invention also provides an Internet of Things (IoT) data intelligent sharing system comprising:

[0025] The data classification module is used to acquire the first sensing data from edge devices in the home network. The first sensing data is classified and labeled using a preset sensing data classification model to obtain the second sensing data labeled as privacy data and the third sensing data labeled as non-privacy data.

[0026] The Sensitive Information Judgment and Privacy Level Module is used to first determine whether the second perceived data contains sensitive information using predefined rules. Sensitive information includes facial image information, biometric information, and voice information. When it is determined that the second perceived data contains sensitive information, the information entropy quantification method is used to calculate the information entropy value of the second perceived data, and the corresponding privacy level of the second perceived data is output according to the magnitude of the information entropy value. The privacy level includes a first privacy level, a second privacy level, and a third privacy level.

[0027] The protocol selection and allocation module is used to detect the current home network signal data and evaluate the maximum capacity of the channel using Shannon's theorem. Based on the different attribute tags and combined with the energy consumption parameters of the edge device, a dynamic allocation protocol mechanism is first used to transmit the second and third sensing data respectively.

[0028] The protocol performance monitoring and switching module is used to preset a period T, continuously collect latency, throughput and packet loss rate under the dynamic allocation protocol mechanism within the period T, and calculate the current protocol transmission performance score. If the current protocol transmission performance score is less than the preset first performance score threshold, the protocol will automatically switch to the NB-IoT protocol or the LoRa protocol.

[0029] The multi-protocol parallel transmission module is used to automatically evaluate the transmission performance score of the NB-IoT protocol or LoRa protocol after the protocol is automatically switched to NB-IoT protocol or LoRa protocol. When the transmission performance score is lower than the preset transmission performance score threshold, the network adaptive mechanism is triggered.

[0030] The present invention also provides an IoT data intelligent sharing device, comprising: a memory, a processor, and an IoT data intelligent sharing program stored in the memory and executable on the processor, wherein the IoT data intelligent sharing program implements an IoT data intelligent sharing method when executed by the processor.

[0031] The present invention also provides a computer program product, including an Internet of Things (IoT) data intelligent sharing program, which implements the IoT data intelligent sharing method when executed by a processor.

[0032] The beneficial effects of this invention are as follows: Compared to traditional data sharing methods in the prior art, which typically rely on static protocol selection and encryption mechanisms, especially in home network scenarios, neglecting the technical challenge of balancing device power consumption, network quality variations, and privacy protection requirements, this application achieves a balance between privacy protection and data transmission efficiency by introducing a dynamic protocol selection mechanism, thereby improving the efficiency, security, and adaptability of IoT data sharing. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating an intelligent data sharing method for the Internet of Things (IoT) according to the present invention.

[0035] Figure 2 This is a schematic diagram of the structure of an IoT data intelligent sharing device according to the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1: As Figure 1 The diagram shown is a flowchart of the IoT data intelligent sharing method of the present invention, and an embodiment of the IoT data intelligent sharing method of the present invention is presented.

[0038] In Embodiment 1, the IoT data intelligent sharing method includes:

[0039] Step S10: Obtain the first sensing data from the edge devices in the home network, classify and label the attributes of the first sensing data using a preset sensing data classification model, and obtain the second sensing data with attributes labeled as privacy data and the third sensing data with attributes labeled as non-privacy data.

[0040] Understandably, through this process, the first-level perceived data is accurately classified as either private or non-private data, providing a basis for subsequent privacy protection measures (such as encryption selection) and protocol selection (such as low-power protocols or high-bandwidth protocols). Private data is then labeled as second-level perceived data, while non-private data is labeled as third-level perceived data, ensuring that different protection strategies are adopted based on the sensitivity of the data during subsequent transmission.

[0041] Step S20: First, use predefined rules to determine whether the second sensing data contains sensitive information. Sensitive information includes facial image information, biometric information, and voice information. When it is determined that the second sensing data contains sensitive information, use the information entropy quantization method to calculate the information entropy value of the second sensing data, and output the corresponding privacy level of the second sensing data according to the magnitude of the information entropy value. The privacy level includes the first privacy level, the second privacy level, and the third privacy level.

[0042] It should be noted that if the second-sensor data contains sensitive information, the information entropy quantification method will then be used to calculate the information entropy of the data. The information entropy quantification method quantifies the privacy level of the data by assessing its uncertainty; the higher the information entropy, the higher the privacy requirements. The formula for calculating the information entropy value of the second-sensor data is as follows: ,in, The data set containing the second perceived data i Information entropy For the second perceived data i in the data set The probability of occurrence in the dataset, where n is the number of data points. The total number of second-sensory data items included.

[0043] It should be noted that in step S20, the step of first determining whether the second sensing data contains sensitive information using predefined rules specifically includes: Face image information determination: The second sensing data is read and analyzed using the OpenCV library in C++. The CascadeClassifier library function is used to determine whether the second sensing data contains face image information, and the result returned by the detectMultiScale library function is used to determine whether the second sensing data contains sensitive information; Biometric information determination: The second sensing data is read and analyzed using the OpenCV library in C++. By analyzing whether its format conforms to the preset fingerprint or iris standard library, it is determined whether the second sensing data contains sensitive information; Voice information determination: The audio file is parsed using the PortAudio library in C++. The parsing result is compared with the preset sensitive voice library for similarity analysis, thereby determining whether the second sensing data contains sensitive information.

[0044] Using C++ for data processing, especially in image and audio analysis, effectively improves program efficiency and real-time performance. Leveraging powerful libraries such as OpenCV and PortAudio, it enables rapid parsing of different data formats and sensitive information identification. Furthermore, C++'s memory management and hardware compatibility ensure that the system can run efficiently on resource-constrained devices, meeting the stringent performance and power consumption requirements of IoT devices.

[0045] Understandably, this step quantifies the privacy sensitivity of data using the information entropy quantification method and categorizes the data based on privacy levels. High-privacy data (such as facial images and biometrics) will be assigned a higher level of protection to ensure data security and privacy. This privacy level classification provides a basis for subsequent decision-making regarding protocol selection and encryption methods, ensuring a balance between privacy protection and efficiency during data sharing.

[0046] It should be understood that during the transmission of privacy-sensitive data, the entropy value of the data is calculated using the information entropy quantification method, and the encryption algorithm is automatically selected based on the data's sensitivity. For example, highly sensitive data at the first privacy level is encrypted using AES-256; for data at a lower privacy level, a lighter encryption method (such as AES-128) is used. This adaptive encryption mechanism not only ensures data privacy but also optimizes the balance between encryption strength and transmission efficiency. Compared to traditional fixed encryption schemes, it can intelligently adjust the encryption strategy according to the privacy sensitivity of different data, improving the accuracy and efficiency of privacy protection.

[0047] Step S30: Detect the current home network signal data and use Shannon's theorem to evaluate the maximum capacity of the channel. Based on the different attribute tags and combined with the energy consumption parameters of the edge device, a dynamic allocation protocol mechanism is first used to transmit the second and third sensing data respectively.

[0048] It's important to note that this step first assesses the signal quality of the current home network by acquiring signal data such as WiFi RSSI and NB-IoT RSRP. This signal data reflects the actual condition of the home network, including network bandwidth and signal-to-noise ratio (SNR). This data will be used to calculate the maximum channel capacity. Channel capacity is the maximum rate at which the network can carry data streams, and its calculation method is based on Shannon's theorem: maximum capacity... ,in, For home network bandwidth, The home network signal-to-noise ratio (SNR), home network bandwidth, and home network SNR are all determined based on home network signal data. Then, the applicable protocol is determined based on the attribute labels of the data (i.e., the privacy levels of the second and third sensed data).

[0049] Understandably, this mechanism ensures that the most suitable transmission protocol is selected under different privacy data and device power consumption conditions, thus avoiding situations where privacy protection needs are not met.

[0050] Step S40: A preset period T is used to continuously collect the latency, throughput and packet loss rate under the dynamic allocation protocol mechanism within the period T, and calculate the current protocol transmission performance score. If the current protocol transmission performance score is less than the preset first performance score threshold, the protocol will automatically switch to the NB-IoT protocol or the LoRa protocol.

[0051] It should be noted that in this step, the period T represents a fixed time period (e.g., a few minutes or hours). During this period, the data transmission performance under the current protocol is continuously monitored and collected, mainly including key performance indicators such as latency, throughput, and packet loss rate. To quantify the transmission effect of the protocol, a transmission performance score is calculated based on these indicators, which helps to assess whether the current protocol is suitable for continued use. Latency: reflects the delay in data transmission; Throughput: refers to the amount of data transmitted per unit time; the higher the throughput, the higher the transmission efficiency; Packet loss rate: reflects the proportion of data lost. During the evaluation process, a current protocol transmission performance score is calculated and compared with a preset first performance score threshold. If the current protocol's transmission performance score is lower than this threshold, the protocol will be automatically switched, for example, to the NB-IoT protocol or the LoRa protocol. These two protocols perform well in low-bandwidth, low-latency, and low-power scenarios. The calculation expression for the current protocol transmission performance score is as follows: ,in, Rate the current protocol's transmission performance. For the current protocol's delay, This is the preset maximum tolerance value for latency. The throughput of the current protocol, This is the preset ideal maximum throughput. The packet loss rate of the current protocol. This is the preset maximum tolerance value for packet loss rate. , , The weighting coefficients for different performance indicators satisfy... .

[0052] Understandably, this step involves real-time monitoring of protocol transmission performance and dynamic protocol selection based on transmission quality feedback. This ensures automatic protocol switching when network quality is unstable or transmission performance is substandard. This intelligent protocol switching mechanism improves data transmission reliability and avoids inefficient transmission or packet loss caused by incompatible protocols.

[0053] Step S50: After the protocol automatically switches to NB-IoT protocol or LoRa protocol, the transmission performance score of NB-IoT protocol or LoRa protocol is automatically evaluated. When the transmission performance score is lower than the preset transmission performance score threshold, the network adaptive mechanism is triggered.

[0054] It should be noted that the transmission performance score is a comprehensive evaluation metric that reflects the actual performance of the current protocol in data transmission. It considers several key factors, including: Latency: the time delay from data transmission to reception, usually measured in milliseconds (ms). Protocols with excessively high latency will lead to low data transmission efficiency, especially for real-time data streams (such as video and audio); Throughput: the amount of data transmitted per unit of time, usually measured in bits per second (bps). Protocols with low throughput may not be able to meet the transmission needs of large data volumes, resulting in reduced transmission efficiency; Packet loss rate: the proportion of data packets lost during data transmission. A high packet loss rate means unstable data transmission, which may lead to loss of data integrity; Signal quality: such as signal-to-noise ratio (SNR), the ratio of received signal to noise. Poor signal quality will affect the accurate reception and transmission stability of data. Factors such as latency, throughput, packet loss rate, and signal quality together constitute the transmission performance score. The actual values ​​of these indicators are compared with preset target values ​​to generate a comprehensive score: the transmission performance score. This score is used to determine whether the current protocol is suitable for continued use. For example, if the latency is too high, the protocol's transmission performance score will be lower; if the throughput is insufficient, it will affect the transmission efficiency, and the score will also decrease.

[0055] Understandably, the purpose of transmission performance scoring is to quantify protocol performance and provide a basis for subsequent network adaptation mechanisms. A higher score indicates better transmission performance in the current network environment, while a lower score suggests problems with transmission performance and the protocol may not meet data transmission requirements.

[0056] For example, suppose data transmission in a home network is performed via the NB-IoT protocol. When evaluating transmission performance, if latency is too high and throughput is low, causing video streams to fail to play smoothly, a low transmission performance score will be assigned based on these data. For instance, if latency exceeds 500ms and throughput is below 100kbps, resulting in a score below a preset threshold, a network adaptive mechanism will be triggered to switch to the LoRa protocol or another more suitable data transmission protocol. In another home scenario, sensor data is transmitted via the LoRa protocol. The signal quality is good and the packet loss rate is low, thus a high transmission performance score is assigned, indicating that the current protocol can efficiently transmit low-bandwidth, low-latency sensor data. Due to its stable transmission, the system continues to use the LoRa protocol.

[0057] Example 2: Furthermore, the present invention provides an IoT data intelligent sharing system that employs an IoT data intelligent sharing method from the above embodiments, thereby solving a technical problem related to IoT data intelligent sharing. Compared with the prior art, the beneficial effects of the IoT data intelligent sharing system provided by the present invention are the same as those of the IoT data intelligent sharing method provided in the above embodiments, and other technical features of the IoT data intelligent sharing system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0058] Example 3: This invention provides an IoT data intelligent sharing device. Please refer to... Figure 2An IoT data intelligent sharing device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform an IoT data intelligent sharing method as described in Embodiment 1 above. An IoT data intelligent sharing device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. An IoT data intelligent sharing device is merely an example and should not impose any limitations on the functionality and scope of use of this embodiment. An IoT data intelligent sharing device may include a processor 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of an IoT data intelligent sharing device. Processor 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows an IoT data intelligent sharing device to communicate wirelessly or wiredly with other devices to exchange data. Although an IoT data intelligent sharing device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0059] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the IoT data intelligent sharing method described above. The computer program product provided by this invention can solve a technical problem related to IoT data intelligent sharing. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the IoT data intelligent sharing method provided in the above embodiments, and will not be repeated here.

[0060] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processor 1001, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0061] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0062] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for intelligent data sharing in the Internet of Things, characterized in that, The methods include: Step S10: Obtain the first sensing data from the edge devices in the home network, classify and label the attributes of the first sensing data using a preset sensing data classification model, and obtain the second sensing data with attributes labeled as privacy data and the third sensing data with attributes labeled as non-privacy data. Step S20: First, use predefined rules to determine whether the second sensing data contains sensitive information. Sensitive information includes facial image information, biometric information, and voice information. When it is determined that the second sensing data contains sensitive information, use the information entropy quantization method to calculate the information entropy value of the second sensing data, and output the corresponding privacy level of the second sensing data according to the magnitude of the information entropy value. The privacy level includes the first privacy level, the second privacy level, and the third privacy level. Step S30: Detect the current home network signal data and use Shannon's theorem to evaluate the maximum capacity of the channel. Based on the different attribute tags and combined with the energy consumption parameters of the edge device, a dynamic allocation protocol mechanism is first used to transmit the second and third sensing data respectively. Step S40: A preset period T is used to continuously collect the latency, throughput and packet loss rate under the dynamic allocation protocol mechanism within the period T, and calculate the current protocol transmission performance score. If the current protocol transmission performance score is less than the preset first performance score threshold, the protocol will automatically switch to the NB-IoT protocol or the LoRa protocol. Step S50: After the protocol automatically switches to NB-IoT protocol or LoRa protocol, the transmission performance score of NB-IoT protocol or LoRa protocol is automatically evaluated. When the transmission performance score is lower than the preset transmission performance score threshold, the network adaptive mechanism is triggered.

2. The IoT data intelligent sharing method as described in claim 1, characterized in that, In step S10, the preset perception data classification model is constructed using a decision tree. The training set of the perception data classification model consists of a set of labeled first perception data. Each data point of the first perception data is explicitly labeled with second perception data containing privacy data and third perception data with attribute markings as non-privacy data.

3. The IoT data intelligent sharing method as described in claim 1, characterized in that, Step S20 involves first determining whether the second perceived data contains sensitive information using predefined rules, specifically including: Face image information judgment: The OpenCV library in C++ is used to read and analyze the second perception data. The CascadeClassifier library function is used to determine whether the second perception data contains face image information, and the result returned by the detectMultiScale library function is used to determine whether the second perception data contains sensitive information. Biometric information judgment: The OpenCV library in C++ is used to read and analyze the second sensing data. By analyzing whether its format conforms to the preset standard library of fingerprint or iris, it is determined whether the second sensing data contains sensitive information. Voice information judgment: The audio file is parsed using the PortAudio audio library in C++. The parsing results are then compared with a preset sensitive voice library for similarity analysis to determine whether the second perception data contains sensitive information.

4. The IoT data intelligent sharing method as described in claim 1, characterized in that, Step S30 involves transmitting the second and third sensing data separately based on the different attribute tags and the energy consumption parameters of the edge device using a dynamic allocation protocol mechanism. This specifically includes: First, obtain the power consumption parameters of the edge device, including "battery powered" and "non-battery powered"; Secondly, separate data transmission mechanisms are established for the second and third sensed data: Regarding the transmission mechanism of the second sensing data: if the power consumption parameter of the edge device is "battery powered", the low power protocol LoRa or NB-IoT protocol is selected first, and AES-256 encryption is used to encrypt the data on the low power protocol LoRa or NB-IoT protocol; if the power consumption parameter of the edge device is "non-battery powered", the WiFi protocol is selected first for transmission, and WPA3 encryption is used to encrypt the data on the WiFi protocol. Regarding the transmission mechanism of third-party sensing data: if the power consumption parameter of the edge device is "battery powered", the low-power protocol LoRa or NB-IoT protocol will still be preferred, and AES-128 will be used for data encryption on the low-power protocol LoRa or NB-IoT protocol; if the power consumption parameter of the edge device is "non-battery powered", the WiFi protocol or Ethernet protocol will be preferred for transmission, and AES-128 will be used for data encryption.

5. The IoT data intelligent sharing method as described in claim 1, characterized in that, In step S50, the network adaptive mechanism involves switching some traffic to another protocol or adding redundant transmissions to ensure data integrity.

6. The IoT data intelligent sharing method as described in claim 4, characterized in that, In step S30, regarding the transmission mechanism of the second sensing data, if the privacy level of the second sensing data is the first privacy level, then after encrypting the data with AES-256 encryption, it continues to be encrypted with RSA or ECC.

7. The IoT data intelligent sharing method as described in claim 1, characterized in that, In step S30, the maximum capacity It is determined based on the home network bandwidth and home network signal-to-noise ratio, where the home network bandwidth and home network signal-to-noise ratio are determined by the home network signal quality.

8. An IoT data intelligent sharing system, applied to the IoT data intelligent sharing method according to any one of claims 1-7, characterized in that, The IoT data intelligent sharing system includes: The data classification module is used to acquire the first sensing data from edge devices in the home network. The first sensing data is classified and labeled using a preset sensing data classification model to obtain the second sensing data labeled as privacy data and the third sensing data labeled as non-privacy data. The Sensitive Information Judgment and Privacy Level Module is used to first determine whether the second perceived data contains sensitive information using predefined rules. Sensitive information includes facial image information, biometric information, and voice information. When it is determined that the second perceived data contains sensitive information, the information entropy quantification method is used to calculate the information entropy value of the second perceived data, and the corresponding privacy level of the second perceived data is output according to the magnitude of the information entropy value. The privacy level includes a first privacy level, a second privacy level, and a third privacy level. The protocol selection and allocation module is used to detect the current home network signal data and evaluate the maximum capacity of the channel using Shannon's theorem. Based on the different attribute tags and combined with the energy consumption parameters of the edge device, a dynamic allocation protocol mechanism is first used to transmit the second and third sensing data respectively. The protocol performance monitoring and switching module is used to preset a period T, continuously collect latency, throughput and packet loss rate under the dynamic allocation protocol mechanism within the period T, and calculate the current protocol transmission performance score. If the current protocol transmission performance score is less than the preset first performance score threshold, the protocol will automatically switch to the NB-IoT protocol or the LoRa protocol. The multi-protocol parallel transmission module is used to automatically evaluate the transmission performance score of the NB-IoT protocol or LoRa protocol after the protocol is automatically switched to NB-IoT protocol or LoRa protocol. When the transmission performance score is lower than the preset transmission performance score threshold, the network adaptive mechanism is triggered.

9. An Internet of Things (IoT) data intelligent sharing device, characterized in that, The IoT data intelligent sharing device includes: a memory, a processor, and an IoT data intelligent sharing program stored in the memory and executable on the processor. When the IoT data intelligent sharing program is executed by the processor, it implements an IoT data intelligent sharing method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes an Internet of Things (IoT) data intelligent sharing program, which, when executed by a processor, implements an IoT data intelligent sharing method according to any one of claims 1 to 7.

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