Multi-protocol conversion Internet of Things terminal communication method

Abstract

The invention relates to the technical field of Internet of Things, in particular to a multi-protocol conversion Internet of Things terminal communication method. Comprising the following steps: monitoring a communication data packet sent by Internet of Things terminal equipment in real time through a protocol sniffing module, and extracting a protocol feature field; matching the protocol feature field based on a dynamic protocol library, and identifying a native protocol type of the equipment; generating a protocol conversion strategy according to a target platform protocol demand, and calling a corresponding protocol analysis engine to perform semantic analysis on the data packet; the analyzed data content is reconstructed according to a target protocol format, and a unified communication interface packaging layer is added; and an optimal communication link is selected through an adaptive routing algorithm, and cross-protocol data transparent transmission is completed. The invention provides an integrated communication method capable of realizing efficient protocol dynamic identification, high-reliability semantic conversion, intelligent link optimization and safe packaging, so as to break through the technical barrier of multi-protocol Internet of Things terminal interconnection.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an Internet of Things terminal communication method for multi-protocol conversion. Background Art

[0002] With the rapid development of IoT technology, the number of terminal devices is growing exponentially, and their communication protocols are becoming increasingly diverse and fragmented. Currently, mainstream IoT protocols include MQTT, CoAP, LoRaWAN, and Zigbee. These protocols exhibit significant differences in data formats, transmission mechanisms, and semantic expressions, leading to significant challenges in interoperability between heterogeneous devices. Existing multi-protocol conversion methods often rely on static protocol library matching and fixed rule conversion mechanisms, which present numerous challenges.

[0003] Traditional protocol sniffing modules rely on predefined static signature libraries for matching, making them difficult to adapt to the dynamic updates of new protocols or protocol versions. In edge computing scenarios, cloud-based synchronization of protocol signature libraries often involves full updates, which wastes bandwidth resources. Furthermore, the lack of a tree-based index structure significantly increases latency when matching large-scale protocols.

[0004] Traditional routing algorithms are often based on fixed weight allocation strategies that fail to comprehensively consider real-time network status, making it difficult to dynamically avoid link congestion during cross-protocol transmission. In industrial IoT scenarios, in particular, the lack of redundant transmission mechanisms for critical instructions can cause control delays or data loss. Existing protocol conversion systems have weak device authentication mechanisms, the encapsulation layer lacks anti-replay attack designs, and protocol instruction mapping table expansion relies on manual intervention. This inability to automatically capture undefined instruction features and feed them back into protocol library updates limits the system's applicability in open environments. Summary of the Invention

[0005] The present invention provides an integrated communication method that can achieve efficient dynamic protocol identification, highly reliable semantic conversion, intelligent link optimization and secure encapsulation, so as to break through the technical barriers of multi-protocol Internet of Things terminal interconnection.

[0006] The technical solution adopted by the present invention is: a multi-protocol conversion Internet of Things terminal communication method, comprising the following steps:

[0007] Step 1: Use the protocol sniffing module to monitor the communication data packets sent by the IoT terminal device in real time and extract the protocol feature fields;

[0008] Step 2: Match the protocol feature field based on the dynamic protocol library to identify the native protocol type of the device;

[0009] Step 3: Generate a protocol conversion strategy based on the target platform protocol requirements and call the corresponding protocol parsing engine to perform semantic parsing on the data packet;

[0010] Step 4: Reconstruct the parsed data content according to the target protocol format and add a unified communication interface encapsulation layer;

[0011] Step 5: Select the optimal communication link through the adaptive routing algorithm to complete cross-protocol data transmission.

[0012] As a further improvement of the present invention, the dynamic protocol library adopts a tree index structure, contains feature templates of at least three IoT protocols, and supports updating protocol feature templates through the cloud. The update process adopts a differential synchronization mechanism to reduce bandwidth consumption.

[0013] As a further improvement of the present invention, the tree index structure adopts B+ tree to realize multi-level protocol feature matching, and integrates the least recently used (LRU) cache mechanism to accelerate the feature matching process. The protocol feature template stored in the leaf node is attached with a version timestamp for differential synchronization verification.

[0014] As a further improvement of the present invention, the protocol parsing engine includes a syntax separation submodule, a semantic mapping submodule, and a data verification submodule.

[0015] As a further improvement of the present invention, the syntax separation submodule extracts the payload based on the protocol specification; the semantic mapping submodule converts the native protocol instruction set into the target protocol instruction set; and the data check submodule ensures the data integrity of the conversion process through the CRC redundant check code.

[0016] As a further improvement of the present invention, the semantic mapping submodule has a built-in extensible protocol instruction mapping table, and adopts a deep learning model based on the attention mechanism to achieve cross-protocol semantic alignment. When an undefined instruction is detected, the fuzzy matching mode is enabled and the instruction features are recorded for dynamic protocol library updates.

[0017] As a further improvement of the present invention, the data check submodule adds a double verification mechanism of parity bits on the basis of CRC redundant check. When the check fails, the backtracking mechanism of the protocol parsing engine is triggered to re-extract the data packet, and a check exception report is generated and pushed to the protocol sniffing module.

[0018] As a further improvement of the present invention, the unified communication interface encapsulation layer includes a dynamic QoS tag, allocates transmission resources according to data priority, and the encapsulation header includes a protocol version identifier and a device fingerprint hash value.

[0019] As a further improvement of the present invention, the dynamic QoS tag implements a bandwidth reservation strategy, enables dual-link redundant transmission for key control instructions, the device fingerprint hash value is generated using the SHA-256 algorithm, and a timestamp is added to the encapsulation header to prevent replay attacks.

[0020] As a further improvement of the present invention, the adaptive routing algorithm is based on a link quality evaluation model, and generates a routing decision matrix by integrating signal strength, historical packet loss rate and real-time delay data, with a decision cycle of no more than 200ms.

[0021] The beneficial effects of the present invention are as follows: the present invention significantly improves the protocol recognition efficiency and reduces the cloud update bandwidth consumption through the tree index structure and differential synchronization mechanism of the dynamic protocol library; adopts the semantic mapping and double verification mechanism based on the attention mechanism to ensure the high precision and data integrity of cross-protocol semantic conversion; combines the adaptive routing algorithm with the dynamic QoS label to realize intelligent link optimization and redundant transmission of key instructions, effectively reducing the packet loss rate and improving the real-time transmission; at the same time, through the extensible instruction mapping table and the unified communication interface encapsulation layer, the system compatibility and security are enhanced, and the device fingerprint encryption and anti-replay attack are supported, which significantly reduces the heterogeneous system docking cost, and is suitable for the large-scale deployment needs of complex scenarios such as industrial Internet of Things and smart cities. DETAILED DESCRIPTION

[0022] In order to make the technical problems, technical solutions and beneficial effects to be solved by this application more clearly understood, this application is further described in detail below in conjunction with the embodiments. It should be understood that the embodiments described herein are only used to explain this application and are not intended to limit this application.

[0023] The present invention provides an Internet of Things terminal communication method for multi-protocol conversion, comprising the following steps:

[0024] Step 1: Use the protocol sniffing module to monitor the communication data packets sent by the IoT terminal device in real time and extract the protocol feature fields;

[0025] Step 2: Match the protocol feature fields based on the dynamic protocol library to identify the native protocol type of the device;

[0026] Step 3: Generate a protocol conversion strategy based on the target platform protocol requirements and call the corresponding protocol parsing engine to perform semantic parsing on the data packet;

[0027] Step 4: Reconstruct the parsed data content according to the target protocol format and add a unified communication interface encapsulation layer;

[0028] Step 5: Select the optimal communication link through the adaptive routing algorithm to complete cross-protocol data transmission.

[0029] The dynamic protocol library in this invention utilizes a tree-based index structure, encompassing feature templates for at least three IoT protocols. It supports cloud-based updates of these templates, employing a differential synchronization mechanism to reduce bandwidth consumption. This tree-based index structure employs a B+ tree for multi-level protocol feature matching and integrates a least recently used (LRU) cache mechanism to accelerate the feature matching process. The protocol feature templates stored in leaf nodes are appended with version timestamps for differential synchronization verification.

[0030] The protocol parsing engine in the present invention includes a syntax separation submodule, a semantic mapping submodule, and a data verification submodule. The syntax separation submodule extracts the payload based on the protocol specification; the semantic mapping submodule converts the native protocol instruction set into the target protocol instruction set; and the data verification submodule ensures data integrity during the conversion process through CRC redundancy check code.

[0031] The semantic mapping submodule of this invention incorporates a scalable protocol instruction mapping table and employs a deep learning model based on an attention mechanism to achieve cross-protocol semantic alignment. When undefined instructions are detected, fuzzy matching mode is enabled and instruction features are recorded for dynamic protocol library updates. The data validation submodule adds a parity bit double verification mechanism based on CRC redundancy checks. When validation fails, the protocol parsing engine's backtracking mechanism is triggered to re-extract the data packet and generate a validation anomaly report that is pushed to the protocol sniffing module.

[0032] The unified communication interface encapsulation layer in this invention includes dynamic QoS tags, allocating transmission resources based on data priority. The encapsulation header contains a protocol version identifier and a device fingerprint hash value. The dynamic QoS tags implement bandwidth reservation strategies and enable dual-link redundant transmission for critical control instructions. The device fingerprint hash value is generated using the SHA-256 algorithm, and a timestamp is added to the encapsulation header to prevent replay attacks.

[0033] The adaptive routing algorithm in the present invention is based on a link quality assessment model, and integrates signal strength, historical packet loss rate and real-time delay data to generate a routing decision matrix, with a decision cycle of no more than 200ms.

[0034] Example 1: Multi-protocol conversion and redundant transmission in the industrial IoT scenario

[0035] In an industrial IoT environment, a manufacturing production line deployed sensor nodes using the LoRaWAN protocol and a central control system supporting MQTT. A protocol sniffing module monitored LoRaWAN data packets sent by sensors through an edge gateway, extracting the preamble, frame control field, and device address as protocol signature fields. A dynamic protocol library used a B+ tree index to quickly match signature fields, identify the LoRaWAN protocol type, and then generate an MQTT conversion strategy based on the target system's requirements. The protocol parsing engine's syntax separation submodule stripped the LoRaWAN physical layer header and extracted payload data such as temperature and vibration. The semantic mapping submodule used an attention mechanism model to map LoRaWAN binary alarm codes into MQTT JSON-formatted event tags. The data validation submodule performed dual CRC and parity checks on the converted data to ensure numerical accuracy. A unified encapsulation layer added dynamic QoS tags to the data packets, marking device anomaly alarms as the highest priority and triggering dual-link redundant transmission: the primary link transmitted directly over the 5G network, while the backup link transmitted via a wired ring network. An adaptive routing algorithm monitors 5G base station signal fluctuations in real time. If a packet loss rate exceeds 5%, it switches to a backup link within 150ms, ensuring critical command transmission latency is under 300ms. The system also encapsulates each data packet with a device fingerprint hash and nanosecond-level timestamp to prevent malicious replay attacks on production line control commands.

[0036] Example 2: Semantic alignment and dynamic update of heterogeneous protocols in smart cities

[0037] A smart streetlight system requires protocol interoperability between a Zigbee dimming controller and a CoAP cloud management platform. After the protocol sniffing module captures Zigbee data frames, the dynamic protocol library quickly matches the 16-bit network identifier and cluster ID features using the LRU cache, identifying the protocol version as Zigbee 3.0. The semantic mapping submodule detects that the target platform's newly added "brightness gradient rate" command is not defined in the mapping table and immediately initiates fuzzy matching. Using a pre-trained BERT model, it analyzes the command context, semantically correlates it with the CoAP "Dimming-Step" parameter, and generates a temporary mapping rule. After the converted CoAP message is verified as correct by the data validation submodule, the encapsulation layer adds a dynamic QoS tag, allocating bandwidth resources by streetlight zone to ensure that trunk road equipment receives at least 80% of the transmission bandwidth. Furthermore, undefined command features are recorded and a differential update package is generated. Only the newly added 200-byte feature template is uploaded to the cloud, saving 95% of the data traffic compared to a full update. After the cloud verifies the timestamp, the new template is inserted into the third-layer node of the dynamic protocol library B+ tree and distributed to edge nodes across the city in the next synchronization cycle, realizing seamless iterative upgrades of the protocol library.

[0038] Example 3: Low-latency routing optimization in a medical IoT environment

[0039] In an intensive care unit (ICU) medical device interconnection scenario, vital sign monitors supporting the Bluetooth HDP protocol need to interact with the HL7 system on the hospital's Wi-Fi network. An adaptive routing algorithm establishes a link quality assessment model, continuously collecting 2.4GHz channel occupancy, Wi-Fi AP RSSI values, and TCP retransmission rates. When transparent transmission of ventilator alarm data is required, the routing decision matrix calculates that the direct AP path has a latency of 120ms but is subject to co-channel interference, while the hybrid path via a Zigbee relay gateway has a latency of 180ms but offers greater stability. The system identifies this data as a critical alarm based on the dynamic QoS tag at the encapsulation layer, prioritizing the direct path while reserving 10% of the bandwidth for a parallel transmission channel. If an initial transmission fails to receive an ACK, the protocol parsing engine triggers a backtracking mechanism within 80ms, re-extracting the original data packet, re-encapsulating it, and retransmitting it via an alternate channel, ultimately achieving a stable end-to-end latency of less than 250ms. The device fingerprint hash value in the encapsulation header is generated by combining the patient ID and the device MAC address to ensure that data traceability complies with HIPAA medical privacy regulations. The timestamp is accurate to the millisecond level, effectively defending against data replay attacks.

[0040] As can be seen from the above-mentioned Examples 1-3, the multi-protocol conversion IoT terminal communication method of the present invention achieves seamless interconnection and efficient communication between heterogeneous devices through real-time updating and intelligent matching of the dynamic protocol library, precise semantic conversion of the protocol parsing engine, safe and efficient design of the unified communication interface encapsulation layer, and intelligent link optimization of the adaptive routing algorithm. In the medical IoT environment, this method effectively solves the problem of low-latency routing optimization, ensures the real-time transmission and reliability of medical device data, and provides strong technical support for medical treatment. At the same time, the scalability and security design of this method enable it to be applicable to a wider range of IoT application scenarios and meet the diversified needs of future IoT development.

[0041] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-protocol conversion method for Internet of Things terminals, characterized in that: The following steps are involved: Step 1: Use the protocol sniffing module to monitor the communication data packets sent by the IoT terminal device in real time and extract the protocol feature fields; Step 2: Match the protocol feature field based on the dynamic protocol library to identify the native protocol type of the device; Step 3: Generate a protocol conversion strategy based on the target platform protocol requirements and call the corresponding protocol parsing engine to perform semantic parsing on the data packet; Step 4: Reconstruct the parsed data content according to the target protocol format and add a unified communication interface encapsulation layer; Step 5: Select the optimal communication link through the adaptive routing algorithm to complete cross-protocol data transmission.

2. The multi-protocol conversion method for Internet of Things terminals according to claim 1, characterized in that: The dynamic protocol library adopts a tree index structure, contains feature templates of at least three IoT protocols, and supports updating protocol feature templates through the cloud. The update process adopts a differential synchronization mechanism to reduce bandwidth consumption.

3. The multi-protocol conversion method for Internet of Things terminals according to claim 2, characterized in that: The tree index structure uses B+ tree to realize multi-level protocol feature matching and integrates the least recently used (LRU) cache mechanism to accelerate the feature matching process. The protocol feature template stored in the leaf node is attached with a version timestamp for differential synchronization verification.

4. The multi-protocol conversion method for Internet of Things terminals according to claim 1, characterized in that: The protocol parsing engine includes a syntax separation submodule, a semantic mapping submodule, and a data verification submodule.

5. The multi-protocol conversion Internet of Things terminal communication method according to claim 4, characterized in that: The syntax separation submodule extracts the payload based on the protocol specification; the semantic mapping submodule converts the native protocol instruction set into the target protocol instruction set; and the data check submodule ensures the data integrity of the conversion process through the CRC redundancy check code.

6. The multi-protocol conversion method for Internet of Things terminals according to claim 5, characterized in that: The semantic mapping submodule has a built-in extensible protocol instruction mapping table and adopts a deep learning model based on the attention mechanism to achieve cross-protocol semantic alignment. When an undefined instruction is detected, the fuzzy matching mode is enabled and the instruction features are recorded for dynamic protocol library updates.

7. The multi-protocol conversion Internet of Things terminal communication method according to claim 5, characterized in that: The data check submodule adds a parity check bit double verification mechanism based on the CRC redundant check. When the check fails, the backtracking mechanism of the protocol parsing engine is triggered to re-extract the data packet, and a check exception report is generated and pushed to the protocol sniffing module.

8. The multi-protocol conversion method for Internet of Things terminals according to claim 1, characterized in that: The unified communication interface encapsulation layer includes a dynamic QoS tag, allocates transmission resources according to data priority, and the encapsulation header includes a protocol version identifier and a device fingerprint hash value.

9. The multi-protocol conversion Internet of Things terminal communication method according to claim 8, characterized in that: The dynamic QoS tag implements a bandwidth reservation strategy and enables dual-link redundant transmission for key control instructions. The device fingerprint hash value is generated using the SHA-256 algorithm, and a timestamp is added to the encapsulation header to prevent replay attacks.

10. The multi-protocol conversion Internet of Things terminal communication method according to claim 1, characterized in that: The adaptive routing algorithm is based on a link quality assessment model and integrates signal strength, historical packet loss rate and real-time delay data to generate a routing decision matrix with a decision cycle of no more than 200ms.