A method, switch and transmission system for transmitting data information in the Internet of Things (IoT)

Abstract

This invention relates to the field of Internet of Things (IoT) communication technology, and discloses an IoT data transmission method, a switch, and a transmission system. The method includes acquiring multi-source data streams from IoT terminal devices, dynamically fragmenting them according to type to generate fragmented data packets, matching them with target transmission protocols, and generating dynamic routing paths based on priority marking before transmission. It also includes functions such as link quality assessment and route adjustment, redundant coding and packet loss recovery, transmission latency and integrity monitoring and processing, security level-based encrypted transmission, resource optimization, and transmission strategy adjustment. The switch integrates modules for data fragmentation and protocol matching. The transmission system includes a terminal device cluster, an edge node network, a cloud server, and a protocol conversion gateway. This invention improves data transmission efficiency, reliability, and security, optimizes resource utilization, and effectively solves the challenges of IoT data transmission.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) communication technology, specifically to an IoT data information transmission method, switch, and transmission system. Background Technology

[0002] In today's digital age, the Internet of Things (IoT) technology is booming and widely used in many fields such as smart homes, industrial monitoring, and intelligent transportation. With the rapid increase in the number of IoT devices and the increasing complexity of application scenarios, data transmission faces many serious challenges.

[0003] Different types of IoT terminal devices generate diverse data, such as sensor data, video streams, and control commands. Sensor data is typically characterized by small data volumes and strong periodicity; video streams are massive in volume and have extremely high requirements for real-time performance and continuity; control commands are highly sensitive to transmission latency and need to be delivered to the target device promptly and accurately. However, existing transmission methods often employ a uniform processing approach, failing to optimize for the characteristics of different data types, resulting in low transmission efficiency and inability to meet practical needs. For example, in smart home systems, temperature and humidity data collected by sensors may experience transmission delays due to using the same transmission strategy as large amounts of video stream data, failing to provide timely feedback on changes in the indoor environment and impacting user experience.

[0004] The Internet of Things (IoT) network environment is complex and dynamic, with network status parameters such as bandwidth utilization, packet loss rate, and jitter constantly changing. Traditional static routing methods are ill-suited to this complex environment and cannot adjust data transmission paths based on real-time network conditions. When a link experiences congestion or failure, data is easily lost or transmission delays increase significantly, severely impacting data transmission reliability. In the Industrial Internet of Things (IIoT), network failures causing delays in control command transmission can trigger abnormal operation of production equipment, resulting in substantial economic losses.

[0005] Data security and privacy protection are crucial in the Internet of Things (IoT) field. IoT data contains a large amount of sensitive information, such as personal identification information and enterprise production data. Existing data encryption methods mostly use fixed keys or simple encryption algorithms, which are insufficient to defend against increasingly sophisticated cyberattacks. Moreover, the lack of effective key management mechanisms during data transmission makes it impossible to guarantee the secure distribution and updating of keys, increasing the risk of data leakage. For example, in smart healthcare systems, if patient medical records are stolen during transmission, it will seriously infringe on patient privacy.

[0006] Furthermore, edge nodes and terminal devices in IoT systems have limited resources. The computing resources and storage capacity of edge nodes often cannot meet all data processing and storage needs, while the power of terminal devices is also extremely valuable. Existing transmission methods rarely consider resource optimization, leading to performance degradation of some edge nodes due to excessive load, and frequent battery replacements or charging of terminal devices due to rapid power consumption, increasing maintenance costs and inconvenience. In smart agriculture, a large number of sensor nodes are distributed in farmland; if transmission strategies are not optimized to reduce energy consumption, frequent battery replacements will be necessary, causing significant problems for practical applications. Summary of the Invention

[0007] The purpose of this invention is to provide an Internet of Things (IoT) data transmission method, switch, and transmission system to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an Internet of Things (IoT) data information transmission method, the method comprising:

[0009] Real-time acquisition of multi-source data streams from IoT terminal devices, including sensor data, video streams, and control commands;

[0010] Dynamically fragmenting multi-source data streams based on data stream type to generate fragmented data packets; the dynamic fragmentation includes: fixed-length fragmentation for sensor data, inter-frame correlation-based fragmentation for video streams, and priority-marking fragmentation for control commands.

[0011] According to a preset set of transmission protocols, a target transmission protocol is matched for each fragmented data packet; the set of transmission protocols includes: MQTT, CoAP, and custom low-latency protocols;

[0012] Fragmented data packets are bound to a matching target transport protocol, and dynamic routing paths are generated based on the priority marking of the fragmented data packets; the dynamic routing paths are determined by calculating the latency sensitivity, link load rate, and hop count constraints of the fragmented data packets.

[0013] Fragmented data packets bound to the target transport protocol are transmitted to the target edge node or cloud server along a dynamic routing path.

[0014] Preferably, the IoT data transmission method further includes:

[0015] Real-time monitoring of network status parameters for each transmission link, including bandwidth utilization, packet loss rate, and jitter value;

[0016] A link quality assessment model is constructed, which is generated by training the relationship between historical network state parameters and transmission success rate.

[0017] Input the current network status parameters into the link quality assessment model, and output the dynamic weight values ​​of each transmission link;

[0018] The dynamic routing path of fragmented data packets is adjusted according to the dynamic weight value, and links with weight values ​​higher than the preset threshold are selected first.

[0019] Preferably, the IoT data transmission method further includes:

[0020] When the target transmission protocol of the fragmented data packet is a custom low-latency protocol, the fragmented data packet is redundantly encoded to generate a redundancy check block.

[0021] Insert at least one backup node into the dynamic routing path and distribute the redundant check block to the backup node;

[0022] If the packet loss rate of the main transmission path exceeds a preset threshold, the backup node is triggered to recover the lost fragmented data packets based on the redundant check block.

[0023] Preferably, the IoT data transmission method further includes:

[0024] During transmission, the transmission delay and integrity metrics of each data packet are statistically analyzed in real time.

[0025] If the transmission delay difference of fragmented data packets in the same data stream exceeds the preset delay tolerance, the undelivered fragmented data packets are locally cached and reassembled, and a retransmission request is triggered.

[0026] If the integrity index of the fragmented data packet is lower than the preset threshold, data repair is performed based on the redundancy check block or historical fragmented data.

[0027] Preferably, the IoT data transmission method further includes:

[0028] The security level of data flow is classified according to the type of IoT terminal device and business scenario;

[0029] Dynamic encryption is performed on fragmented data packets with a security level higher than a preset threshold. The dynamic encryption includes generating a temporary key based on the generation timestamp of the fragmented data packet and the device's unique identifier.

[0030] Specify at least one encrypted relay node in the dynamic routing path to perform the distribution and updating of temporary keys.

[0031] Preferably, the IoT data transmission method further includes:

[0032] Calculate the computing resource utilization and remaining storage capacity of each edge node;

[0033] If the target transmission protocol of the fragmented data packet is a video streaming protocol and the computing resource utilization of the edge node is lower than a preset threshold, the fragmented data packet will be routed to the edge node for real-time video analysis.

[0034] Otherwise, the fragmented data packets will be routed to the cloud server.

[0035] Preferably, the IoT data transmission method further includes:

[0036] During transmission, the number of protocol conversions and routing hops of fragmented data packets are collected;

[0037] If the number of protocol conversions exceeds the preset number or the number of routing hops exceeds the preset number, the fragmented data packet aggregation operation is triggered, merging multiple fragmented data packets into an aggregated data block, and switching to a high-bandwidth transmission protocol for transmission.

[0038] Preferably, the IoT data transmission method further includes:

[0039] An energy consumption model is constructed for each IoT terminal device. The energy consumption model is generated by training the model based on the relationship between historical power consumption and data volume.

[0040] Predict transmission energy consumption based on the current size of the fragmented data packets and the target transmission protocol;

[0041] If the predicted energy consumption exceeds the remaining power threshold of the terminal device, the system switches to a low-power transmission mode, which includes reducing the transmission frequency or enabling compression encoding.

[0042] Preferably, the present invention further includes a switch, comprising:

[0043] The data fragmentation module is used to dynamically fragment multi-source data streams based on data stream type and generate fragmented data packets.

[0044] The protocol matching module is used to match the target transmission protocol for fragmented data packets based on the set of transmission protocols;

[0045] The routing calculation module is used to generate dynamic routing paths based on the priority marking of fragmented data packets and network status parameters;

[0046] The transmission execution module is used to transmit fragmented data packets bound to the target transmission protocol along a dynamic routing path;

[0047] The redundancy processing unit is used to generate redundant check blocks for fragmented data packets of a custom low-latency protocol and manage backup nodes.

[0048] Preferably, the present invention further includes an Internet of Things (IoT) data transmission system, the system comprising:

[0049] A cluster of terminal devices is used to generate multi-source data streams and execute a method for transmitting Internet of Things (IoT) data information.

[0050] Edge node network, used to receive fragmented data packets and perform real-time analysis or forwarding;

[0051] Cloud servers are used to store aggregated data blocks and provide global resource scheduling;

[0052] Protocol conversion gateways are used to adaptively convert between different transport protocols and unify data formats.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] In terms of data transmission efficiency, dynamic fragmentation is performed for different types of multi-source data streams. For example, sensor data is fragmented with fixed lengths, video streams are fragmented based on inter-frame correlation, and control commands are fragmented using priority marking. This fully utilizes the characteristics of various data types, improving the efficiency of data processing and transmission. Simultaneously, target transmission protocols are matched to fragmented data packets according to a preset set of transmission protocols. Combined with the generation of dynamic routing paths, factors such as latency sensitivity, link load rate, and hop count constraints are comprehensively considered to ensure that data can be transmitted quickly and efficiently to the target node. For instance, in intelligent transportation systems, vehicle driving status data (sensor data) can be transmitted quickly through appropriate protocols and routes, while traffic monitoring video streams can also be processed and transmitted reasonably, ensuring timely acquisition and processing of traffic information and effectively improving the data transmission efficiency of the entire system.

[0055] Regarding transmission reliability, network status parameters of each transmission link are monitored in real time, and a link quality assessment model is constructed. Routing paths are adjusted based on the assessment results, prioritizing links with high quality, significantly reducing data loss and latency caused by link failures or congestion. For fragmented data packets using a custom low-latency protocol, redundant coding is performed and backup nodes are set up. When a high packet loss rate occurs on the main transmission path, lost data can be recovered through the backup nodes, ensuring data integrity and transmission reliability. On industrial automated production lines, critical control commands and equipment operation data transmission are guaranteed, reducing production accidents and losses caused by data transmission problems.

[0056] In terms of data security, data flow security levels are categorized based on the type of IoT terminal device and business scenario. High-security-level fragmented data packets are dynamically encrypted. Temporary keys are generated based on the generation timestamp and the device's unique identifier, and key distribution and updates are performed through encrypted relay nodes, effectively preventing data from being stolen or tampered with during transmission. In the field of financial IoT, when transmitting user account information and transaction data, this encryption method greatly enhances data security and protects users' property and privacy.

[0057] From a resource optimization perspective, by statistically analyzing the computing resource utilization and remaining storage capacity of edge nodes, and rationally allocating processing tasks based on the video stream protocol data packets, real-time video analysis is performed when edge node resources are sufficient; otherwise, data is transmitted to the cloud server, achieving efficient utilization of computing resources. Simultaneously, transmission energy consumption is predicted based on terminal device energy consumption models. When the predicted energy consumption exceeds the remaining power threshold, a low-power transmission mode is switched, extending the lifespan of terminal devices and reducing maintenance costs. In intelligent security monitoring systems, where numerous surveillance cameras serve as terminal devices, this approach reduces equipment energy consumption while ensuring monitoring effectiveness, thereby improving the overall operational efficiency of the system.

[0058] Furthermore, the number of protocol conversions and routing hops are monitored during transmission. When these exceed preset values, the aggregation of fragmented data packets is triggered, and a switch to a high-bandwidth transmission protocol is initiated, reducing transmission overhead and improving transmission performance. Through these multifaceted optimizations, this invention comprehensively enhances the quality and efficiency of IoT data transmission, effectively solving the problems existing in current technologies, and possesses broad application prospects and significant economic value. Attached Figure Description

[0059] Figure 1 This is a schematic diagram illustrating the working principle of the IoT data information transmission method described in this invention.

[0060] Figure 2 Workflow diagram for adjusting routes for link quality assessment;

[0061] Figure 3 Workflow diagram for transmission latency and integrity monitoring;

[0062] Figure 4 This is a flowchart illustrating the workflow of encrypted transmission based on security levels. Detailed Implementation

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] Please see Figure 1-4 This invention proposes a method for transmitting Internet of Things (IoT) data information, the method comprising:

[0065] Real-time acquisition of multi-source data streams: Through IoT terminal devices, various types of data are collected in real time, forming multi-source data streams. This data includes various environmental data collected by sensors, such as temperature, humidity, and light intensity; video streams generated by surveillance cameras; and control commands issued by users or the system. Taking a smart home scenario as an example, a smart temperature sensor continuously collects indoor temperature data; a smart camera captures real-time indoor images to form a video stream; and users send commands to adjust the indoor temperature via a mobile app as control commands.

[0066] Dynamic data packet generation: Different fragmentation strategies are adopted based on the different types of data streams. For sensor data, due to its relatively small and stable data volume, a fixed-length fragmentation method is used. For example, a fixed length of 1024 bytes is set, and sensor data is cut into segments according to this length for easier subsequent unified processing and transmission. For video streams, given their large data volume and inter-frame correlation, fragmentation is based on inter-frame correlation. Several consecutive frames with minimal changes can be divided into a single fragment based on the degree of variation in video frames, reducing transmission redundancy. For control commands, due to their extremely high timeliness requirements, priority-marking fragmentation is used to ensure that important commands are transmitted first.

[0067] Matching the Target Transport Protocol: From a pre-defined set of transport protocols, the most suitable target transport protocol is selected for each data packet fragment. This set includes MQTT, CoAP, and custom low-latency protocols. MQTT is suitable for scenarios with less stringent real-time requirements, smaller data volumes, and relatively stable network environments, such as periodic reporting of sensor data. CoAP is more suitable for resource-constrained IoT devices, enabling lightweight data transmission. Custom low-latency protocols are specifically designed for latency-sensitive control commands and critical data. In practical applications, if the data packet fragment is sensor data and the network environment is stable, the MQTT protocol can be selected; if it is a control command, a custom low-latency protocol should be chosen.

[0068] Dynamic routing path generation: Fragmented data packets are bound to matching target transport protocols, and dynamic routing paths are generated based on the priority marking of the fragmented data packets. Simultaneously, factors such as packet latency sensitivity, link load rate, and hop count constraints are comprehensively calculated to generate dynamic routing paths. For packets with high latency sensitivity, links with low latency are prioritized. Regarding link load rate, the load status of each link is monitored in real time to avoid selecting excessively loaded links, ensuring transmission efficiency. For example, if a control command fragmented data packet has high latency sensitivity, and link A in the current network has low load rate, few hops, and low latency, while link B has high load rate, many hops, and high latency, then link A is prioritized as the routing path.

[0069] Data transmission to the target node: Following the generated dynamic routing path, the fragmented data packets bound to the target transmission protocol are transmitted to the target edge node or cloud server. In smart home scenarios, sensor data fragmented data packets may first be transmitted to nearby edge nodes for preliminary processing, while video stream fragmented data packets may be directly transmitted to the cloud server for storage and analysis. Control command fragmented data packets are quickly transmitted to the edge node where the corresponding execution device is located to achieve timely control of the device.

[0070] The present invention will be further described below with reference to Examples 1 to 5:

[0071] Example 1:

[0072] This embodiment improves the reliability and stability of data transmission by monitoring network status in real time, constructing a link quality assessment model, and adjusting routing paths based on the assessment results, effectively responding to dynamic changes in the network environment.

[0073] In practical applications, taking an industrial IoT production line as an example, numerous IoT terminal devices such as sensors and actuators transmit data via a network. To achieve routing adjustments based on link quality assessment, the specific steps are as follows:

[0074] Real-time monitoring of network status parameters: Using network monitoring tools, network status parameters for each transmission link are acquired in real time, including bandwidth utilization, packet loss rate, and jitter value. Data is collected at regular time intervals (e.g., 5 seconds) to form a real-time monitoring data sequence. For example, at a certain moment, the bandwidth utilization of link 1 is monitored to be 60%, the packet loss rate is 2%, and the jitter value is 5ms; the bandwidth utilization of link 2 is monitored to be 30%, the packet loss rate is 1%, and the jitter value is 3ms.

[0075] Constructing a link quality assessment model: Collect a large amount of historical network state parameters and corresponding transmission success rate data. Assume the historical data includes network state parameters collected every 5 seconds over the past week, along with records of whether data transmission was successful. Use machine learning algorithms, such as linear regression, to train and construct the link quality assessment model. Let the network state parameters be x1 (bandwidth utilization), x2 (packet loss rate), and x3 (jitter value), and the transmission success rate be y. The training model formula is y = a1x1 + a2x2 + a3x3 + b, where a1, a2, and a3 are coefficients obtained from model training, and b is a constant term.

[0076] Calculate dynamic weight values: Input the currently collected network status parameters into the link quality assessment model to calculate the dynamic weight values ​​for each transmission link. Taking the parameters of link 1 and link 2 that were just monitored as an example, x... 11 =60%, x 21 =2%, x 31=5ms Substitute into the model to calculate the weight value y1 of link 1; then x 12 =30%, x 22 =1%, x 32 =3ms Substitute into the model to calculate the weight value y2 of link 2.

[0077] Adjusting the routing path: Based on the calculated dynamic weight values, the dynamic routing path of fragmented data packets is adjusted. A weight threshold is preset, such as 0.6. If the weight value y1 of link 1 is lower than the threshold, while the weight value y2 of link 2 is higher than the threshold, then link 2 is selected as the transmission link for subsequent fragmented data packets to ensure more reliable data transmission and reduce data loss or transmission delays caused by poor link quality.

[0078] Example 2:

[0079] This embodiment targets fragmented data packets using a custom low-latency protocol. Through redundant coding and backup node settings, it can quickly recover lost data in the event of network packet loss, ensuring data integrity and transmission reliability. It is particularly suitable for application scenarios with extremely high requirements for data integrity.

[0080] Taking a smart grid power monitoring system as an example, the real-time transmitted power data requires extremely high accuracy and timeliness. In this scenario, packet loss recovery based on redundancy coding is implemented, and the specific steps are as follows:

[0081] Redundancy coding generates redundancy check blocks: When the target transmission protocol of the fragmented data packets is a custom low-latency protocol, redundancy coding techniques such as erasure coding are used to process the fragmented data packets. Assuming the fragmented data packet size is N bytes, (n,k) erasure coding is used, where n is the total number of blocks generated after encoding, and k is the original number of data blocks, where n>k. For example, choosing (5,3) erasure coding will encode the original 3 fragmented data packets into 5 blocks, of which 3 are original data blocks and 2 are redundancy check blocks.

[0082] Insert backup nodes and distribute redundant check blocks: Select at least one suitable node as a backup node in the dynamic routing path. For example, select a stable intermediate node as a backup node on the path from the data source to the target node. Distribute the generated redundant check blocks to the backup node for storage. For example, send the two redundant check blocks generated above to the selected backup node for storage.

[0083] Packet Loss Detection and Recovery: The system monitors packet loss on the main transmission path in real time, with a preset packet loss rate threshold, such as 5%. If the packet loss rate on the main transmission path exceeds this threshold, for example, if the actual packet loss rate reaches 8%, a backup node is triggered to recover lost fragmented data packets based on a redundancy check block. The backup node uses the received redundancy check block and the remaining correct fragmented data packets, employing an erasure coding decoding algorithm to calculate and recover the lost fragmented data packets. The recovered data is then sent to the target node, ensuring data integrity and preventing power monitoring system malfunctions or misjudgments due to data loss.

[0084] Example 3:

[0085] The unique role of this embodiment is that by monitoring the transmission latency and integrity indicators of fragmented data packets in real time, this embodiment can promptly detect and handle problems that occur during transmission, ensuring that fragmented data packets of the same data stream can arrive at the target node in an orderly and complete manner, thereby improving the quality and availability of data transmission.

[0086] In intelligent transportation systems, vehicles frequently exchange data with roadside infrastructure, such as vehicle location information and speed. In this scenario, monitoring and processing of transmission latency and integrity are implemented as follows:

[0087] Real-time statistics on transmission latency and integrity metrics: During data transmission, a timestamp is added to each data packet fragment, recording its transmission time t. send and receiving time t receive By calculating t delay =t receive -t send The transmission delay is obtained. Simultaneously, the received fragmented data packets are verified, such as using a CRC check algorithm, to calculate the integrity index. For example, a data stream containing vehicle location information might contain 10 fragmented data packets; at the receiving end, the transmission delay and integrity verification result of each data packet are recorded sequentially.

[0088] To handle excessive transmission latency differences: A latency tolerance, such as 50ms, is preset. If the transmission latency difference of fragmented data packets within the same data stream exceeds this tolerance—for example, one fragment has a latency of 30ms and another of 100ms, exceeding the 50ms tolerance—then the unarrived fragmented data packets are locally buffered and reassembled. Arrived fragmented data packets are temporarily stored in the local buffer, awaiting reassembly upon the arrival of other fragmented data packets. Simultaneously, a retransmission request is triggered, sending a retransmission command to the sender to resend the excessively delayed or lost fragmented data packets, ensuring the orderly arrival of data.

[0089] Handling integrity metrics below a threshold: A preset integrity threshold, such as 95% (indicating that a certain percentage of verification errors are allowed), is used. If the integrity metric of a fragmented data packet falls below this threshold—for example, if the integrity verification result shows that only 90% of the data packets are correctly verified—data repair is performed based on redundant check blocks or historical fragmented data. If redundant check blocks were previously generated using redundant encoding, the redundant check blocks and correctly received fragmented data packets are used to repair erroneous data through the appropriate decoding algorithm. If no redundant check blocks are available, fragmented data of the same data stream in historical transmission records are searched to retrieve the correct data for repair, ensuring data integrity and providing reliable data support for accurate decision-making in intelligent transportation systems.

[0090] Example 4:

[0091] This embodiment classifies the security level of data streams according to the type of IoT terminal device and business scenario, dynamically encrypts high-security-level data, and ensures the secure distribution and updating of keys through encryption relay nodes, effectively protecting the security and privacy of data during transmission.

[0092] In financial IoT scenarios, such as data transmission between bank ATMs and bank servers, a large amount of sensitive user account information and transaction data is involved. To address this scenario, encrypted transmission based on security levels is implemented, with the following specific steps:

[0093] Data flow security levels are categorized: Based on the ATM device's business needs and data sensitivity, data flows are classified into different security levels. For example, user account passwords and transaction amounts are classified as high-security data; device operating status information and general transaction records are classified as medium-security data; and device software update notifications are classified as low-security data. A security level threshold is preset, such as setting high-security data above the threshold and medium / low-security data below the threshold.

[0094] Dynamic encryption processing: For fragmented data packets with a security level higher than a preset threshold, i.e., high-security data, dynamic encryption is performed. A temporary key is generated based on the generation timestamp t of the fragmented data packet and the device's unique identifier ID. A hash function, such as the SHA-256 algorithm, is used to calculate the temporary key K = SHA256(t + ID). Using this temporary key, a symmetric encryption algorithm, such as the AES algorithm, is used to encrypt the fragmented data packets, ensuring data security during transmission.

[0095] Configure an encrypted relay node: Specify at least one encrypted relay node in the dynamic routing path. For example, select a secure gateway located at the boundary between the bank's internal and external networks as the encrypted relay node. This node is responsible for distributing and updating the ephemeral key. Before data transmission, the encrypted relay node securely distributes the generated ephemeral key to both the sender and receiver. As data transmission proceeds, to enhance security, the ephemeral key is periodically updated, recalculated, and redistributed, such as every 10 minutes, ensuring the encrypted security of the data throughout the transmission process and preventing sensitive data from being stolen or tampered with.

[0096] Example 5:

[0097] This embodiment comprehensively considers the computing and storage resources of edge nodes, as well as the number of protocol conversions and routing hops during data transmission. It also takes into account the energy consumption of terminal devices to optimize data transmission strategies, improve resource utilization, reduce transmission costs, and ensure the efficient operation of the system.

[0098] Taking an intelligent security monitoring system as an example, it includes a large number of surveillance cameras as IoT terminal devices, as well as multiple edge nodes and cloud servers. Resource optimization and transmission strategy adjustment are implemented in this system, with the following specific steps:

[0099] Edge Node Resource Assessment and Video Analytics Routing: Real-time statistics are provided on the computing resource utilization and remaining storage capacity of each edge node. Assume edge node 1 has a computing resource utilization of 40% and a remaining storage capacity of 50GB; edge node 2 has a computing resource utilization of 70% and a remaining storage capacity of 20GB. When the target transmission protocol for fragmented data packets is a video streaming protocol, a preset computing resource utilization threshold, such as 50%, is established. If the computing resource utilization of edge node 1 is lower than this threshold, the video stream fragmented data packets are routed to edge node 1 for real-time video analytics, utilizing its idle computing resources for target detection, behavior analysis, and other operations, reducing data transmission pressure and the computational burden on the cloud server. If the computing resource utilization of an edge node is higher than the threshold, such as edge node 2, the fragmented data packets are routed to the cloud server, ensuring the smooth completion of the video analytics task.

[0100] Fragmented Data Packet Aggregation and Protocol Switching: During data transmission, the number of protocol transitions and routing hops for fragmented data packets are collected. The preset thresholds for protocol transitions are 3 and routing hops are 5. If a video stream fragmented data packet undergoes 4 protocol transitions (exceeding the preset thresholds) or 6 routing hops (exceeding the preset thresholds), fragmented data packet aggregation is triggered. Multiple fragmented data packets are merged into an aggregated data block, for example, 10 fragmented data packets are merged into one aggregated data block. Simultaneously, a high-bandwidth transmission protocol is switched, such as from MQTT to TCP, to improve transmission efficiency, reduce transmission latency, and mitigate the performance degradation caused by frequent protocol transitions and excessive routing hops.

[0101] Energy consumption prediction and low-power mode switching: An energy consumption model is built for each surveillance camera, trained by collecting historical data on the relationship between transmission power consumption and data volume. Assume the trained energy consumption model formula is P = c1D + c2, where P is the transmission power consumption, D is the data volume, and c1 and c2 are model coefficients. The model size is determined based on the current fragmented data packet size D. current With the target transmission protocol, predict the transmission energy consumption P predict =c1D current +c2. Preset a threshold for the remaining battery power of the terminal device, such as 20%. If the predicted energy consumption exceeds this threshold, for example, if the predicted energy consumption will cause the device's remaining battery power to fall below 20%, switch to low-power transmission mode. This can reduce the transmission frequency, such as changing from transmitting data once per second to transmitting once every 5 seconds; or enable compression encoding, such as using the H.265 video encoding standard to compress video data, reducing the data volume, thereby reducing transmission energy consumption, extending the lifespan of the terminal device, and ensuring the continuous and stable operation of the intelligent security monitoring system.

[0102] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0103] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for transmitting data information in the Internet of Things (IoT), characterized in that, include: Real-time acquisition of multi-source data streams from IoT terminal devices, including sensor data, video streams, and control commands; Dynamically fragment multi-source data streams based on data stream type to generate fragmented data packets; The dynamic segmentation includes: fixed-length segmentation for sensor data, segmentation based on inter-frame correlation for video streams, and priority-marked segmentation for control commands; According to a preset set of transmission protocols, a target transmission protocol is matched for each fragmented data packet; the set of transmission protocols includes: MQTT, CoAP, and custom low-latency protocols; Fragmented data packets are bound to a matching target transport protocol, and dynamic routing paths are generated based on the priority marking of the fragmented data packets; the dynamic routing paths are determined by calculating the latency sensitivity, link load rate, and hop count constraints of the fragmented data packets. Fragmented data packets bound to the target transport protocol are transmitted to the target edge node or cloud server along a dynamic routing path.

2. The IoT data information transmission method according to claim 1, characterized in that, Also includes: Real-time monitoring of network status parameters for each transmission link, including bandwidth utilization, packet loss rate, and jitter value; A link quality assessment model is constructed, which is generated by training the relationship between historical network state parameters and transmission success rate. Input the current network status parameters into the link quality assessment model, and output the dynamic weight values ​​of each transmission link; The dynamic routing path of fragmented data packets is adjusted according to the dynamic weight value, and links with weight values ​​higher than the preset threshold are selected first.

3. The IoT data information transmission method according to claim 1, characterized in that, Also includes: When the target transmission protocol of the fragmented data packet is a custom low-latency protocol, the fragmented data packet is redundantly encoded to generate a redundancy check block. Insert at least one backup node into the dynamic routing path and distribute the redundant check block to the backup node; If the packet loss rate of the main transmission path exceeds a preset threshold, the backup node is triggered to recover the lost fragmented data packets based on the redundant check block.

4. The IoT data information transmission method according to claim 1, characterized in that, Also includes: During transmission, the transmission delay and integrity metrics of each data packet are statistically analyzed in real time. If the transmission delay difference of fragmented data packets in the same data stream exceeds the preset delay tolerance, the arrived fragmented data packets are temporarily stored in the local cache, waiting for other fragmented data packets to arrive before being reassembled and a retransmission request is triggered. A retransmission instruction is sent to the sender to request the retransmission of fragmented data packets with excessive delay or loss, ensuring the orderly arrival of data. If the integrity index of fragmented data packets is lower than the preset threshold, data repair is performed based on redundancy check blocks or historical fragmented data.

5. The IoT data information transmission method according to claim 1, characterized in that, Also includes: The security level of data flow is classified according to the type of IoT terminal device and business scenario; Dynamic encryption is performed on fragmented data packets with a security level higher than a preset threshold. The dynamic encryption includes generating a temporary key based on the generation timestamp of the fragmented data packet and the device's unique identifier. Specify at least one encrypted relay node in the dynamic routing path to perform the distribution and updating of temporary keys.

6. The IoT data information transmission method according to claim 1, characterized in that, Also includes: Calculate the computing resource utilization and remaining storage capacity of each edge node; If the target transmission protocol of the fragmented data packet is a video streaming protocol and the computing resource utilization of the edge node is lower than a preset threshold, the fragmented data packet will be routed to the edge node for real-time video analysis. Otherwise, the fragmented data packets will be routed to the cloud server.

7. The IoT data information transmission method according to claim 1, characterized in that, Also includes: During transmission, the number of protocol conversions and routing hops of fragmented data packets are collected; If the number of protocol conversions exceeds the preset number or the number of routing hops exceeds the preset number, the fragmented data packet aggregation operation is triggered, merging multiple fragmented data packets into an aggregated data block, and switching to a high-bandwidth transmission protocol for transmission.

8. The IoT data information transmission method according to claim 1, characterized in that, Also includes: An energy consumption model is constructed for each IoT terminal device. The energy consumption model is generated by training the model based on the relationship between historical power consumption and data volume. Predict transmission energy consumption based on the current size of the fragmented data packets and the target transmission protocol; If the predicted energy consumption exceeds the remaining power threshold of the terminal device, the system switches to a low-power transmission mode, which includes reducing the transmission frequency or enabling compression encoding.

9. A switch, characterized in that, include: The data fragmentation module uses differentiated fragmentation strategies based on different data stream types to generate fragmented data packets. For sensor data, a fixed-length fragmentation method is used. For video streams, segmentation is based on inter-frame correlation; while for control commands, segmentation is based on priority marking. The protocol matching module is used to match the target transmission protocol for fragmented data packets based on the set of transmission protocols; The routing calculation module binds fragmented data packets to the matching target transmission protocol, marks the priority of the fragmented data packets, and comprehensively calculates the latency sensitivity, link load rate and hop count constraints of the data packets to generate dynamic routing paths. The transmission execution module is used to transmit fragmented data packets bound to the target transmission protocol along a dynamic routing path; The redundancy processing unit is used to generate redundant check blocks for fragmented data packets of a custom low-latency protocol and manage backup nodes.

10. An Internet of Things (IoT) data transmission system, characterized in that, include: A cluster of terminal devices for generating multi-source data streams and executing the method of any one of claims 1-8; Edge node network, used to receive fragmented data packets and perform real-time analysis or forwarding; Cloud servers are used to store aggregated data blocks and provide global resource scheduling; Protocol conversion gateways are used to adaptively convert between different transport protocols and unify data formats.

Images