System and method for automatically acquiring network equipment information based on Internet of Things technology

Abstract

The invention discloses a system and a method for automatically acquiring network equipment information based on an internet of things technology, and relates to the technical field of network equipment monitoring, current equipment information and network environment information are acquired through an equipment end, a semantic network driven by AI and a knowledge graph are converted into standardized equipment semantic description, and after Transform cross-protocol conversion, the standardized equipment semantic description is sent to the equipment end. The method comprises the following steps: determining a transmission strategy according to a preset corresponding relation table, transmitting information to a management end through a terahertz frequency band of a photon-edge computing fusion architecture, receiving and storing equipment information to a distributed account book of a dynamic trust block chain by the management end, performing identity verification by using a Spartan protocol, receiving a natural language query instruction and feeding back a retrieval result, the system monitors equipment information update in real time, dynamically adjusts a transmission strategy and a corresponding relation table, ensures efficient and automatic acquisition, transmission and secure storage of equipment information, and improves the management efficiency of the Internet of Things equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of network device monitoring, and more specifically, to an automatic network device information acquisition system and method based on Internet of Things technology. Background Art

[0002] With the rapid development of the Internet of Things, the number of network devices has increased exponentially, and the types of devices have become increasingly complex and diverse, covering from simple sensors to complex intelligent control systems. These devices are usually widely distributed and deployed in different physical locations and network environments. The methods for obtaining device information mainly rely on manual configuration and periodic network scanning. This method is not only inefficient but also difficult to cope with the dynamic changes of devices and real-time information requirements. Manual configuration is prone to errors, and periodic scanning can often only obtain limited basic device information, such as IP addresses, port status, etc., and cannot deeply explore the detailed capability parameters and real-time operating status of devices; the complexity of the network environment also exacerbates the difficulty of obtaining device information. The dynamic changes of the network topology structure, the fluctuations of available bandwidth, the uncertainty of network latency and signal strength, and the imbalance of network load all pose challenges to the efficiency and reliability of information transmission. Existing technologies have significant deficiencies in information integration. Device information and network environment information usually exist in a scattered and unstructured manner, lacking a unified semantic description framework and being difficult to effectively integrate and analyze; in terms of communication links, traditional methods for obtaining device information mainly rely on in-band communication. Once a network failure occurs, information acquisition is interrupted. Although out-of-band communication can be used as a supplement, it has problems such as high cost and poor compatibility. In terms of data management and security guarantee, existing technologies lack a trustworthy and secure storage mechanism, as well as an efficient identity authentication and transmission strategy trustworthy decision-making means, and it is difficult to ensure the integrity and credibility of device information during the entire acquisition, transmission, and storage process.

[0003] Therefore, existing Internet of Things device information acquisition technologies have problems such as difficult information integration, poor transmission strategy matching, low communication link reliability, and lack of data management security mechanisms. Summary of the Invention

[0004] In order to overcome the above problems existing in the prior art, the present invention discloses an automatic network device information acquisition system and method based on Internet of Things technology, which can effectively solve the above technical problems.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows: An automatic network device information acquisition method based on Internet of Things technology, applied to the device side, the method includes: Obtain the current device information and the current network environment information; wherein, the device information at least includes device identification, device type, hardware configuration, operating status, communication protocol, and device capability parameters, and the network environment information at least includes network topology structure, available bandwidth, network latency, signal strength, and network load; Based on the AI-driven semantic network, use the knowledge graph to parse the current device information and the current network environment information into a device capability description model defined by RDF triples, and perform cross-protocol semantic conversion through the Transformer model to generate a standardized device semantic description; According to the pre-constructed correspondence table of standardized device semantic descriptions, network environment information, and information transmission strategies, determine the target transmission strategy corresponding to the current standardized device semantic description and the current network environment information; Through the photon-edge computing fusion architecture, use the terahertz band to send the target transmission strategy and device information to the management end, so that the management end can obtain the device information according to the target transmission strategy.

[0006] Preferably, the obtaining of the current device information includes: Establish an in-band communication link and an out-of-band communication link with the internal modules of the device; When it is detected that the in-band communication link is communicating normally, receive the current device information transmitted by the internal modules of the device through the in-band communication link; When it is detected that the in-band communication link is communicating abnormally, receive the current device information transmitted by the internal modules of the device through the out-of-band communication link.

[0007] Preferably, after sending the target transmission strategy and device information to the management end, it further includes: Obtain the information transmission path status between the device end and the management end within the first preset duration; Upload the information transmission path status and device operation behavior data to the DAG-based distributed ledger of the dynamic trust blockchain; Based on the information recorded in the distributed ledger, use the consensus algorithm with dynamically adjusted weights based on device behavior, combine the current network environment information and device status, and determine a new transmission strategy; Use the Spartan protocol to perform zero-knowledge proof authentication on the new transmission strategy, use the verified new transmission strategy as the new target transmission strategy, and re-execute the step of sending the target transmission strategy and device information to the management end.

[0008] Preferably, the obtaining of the information transmission path status between the device end and the management end within the first preset duration includes: Obtain the information sent by the management end for characterizing the information transmission path status within the first preset duration; wherein, the information includes path quantity change information and path transmission quality information; Determine the information transmission path status between the device side and the management side within the first preset duration according to the information; When the information indicates that the number of paths decreases or the transmission quality is lower than the threshold, determine the faulty path according to the information; shield the faulty path, and determine a new transmission strategy from the remaining information transmission paths; When the information indicates that the number of paths increases, determine a new transmission strategy from all the information transmission paths according to the newly added paths, device load information, and network load information.

[0009] Preferably, a method for automatically obtaining network device information based on Internet of Things technology is applied to the management side, and the method includes: Through the photon-edge computing fusion architecture, receive the target transmission strategy and device information sent by the device side using the terahertz frequency band; wherein, the target transmission strategy is determined by the device side obtaining the current device information and the current network environment information, parsing based on the AI-driven semantic network, and according to the corresponding relationship. Obtain detailed device information from the device side according to the target transmission strategy; Store the obtained device information in the DAG-based distributed ledger of the dynamic trust blockchain, and use the Spartan protocol to anonymously verify and record the device identity; Receive the natural language device query instruction input by the user; Based on the AI-driven semantic network, use the knowledge graph to parse the natural language device query instruction into a query model defined by RDF triples, and perform semantic conversion through the Transformer model to generate a standardized query semantics; Retrieve the device information matching the standardized query semantics in the distributed ledger and feedback the retrieval result to the user.

[0010] Preferably, after storing the obtained device information in the distributed ledger, it further includes: Real-time monitor the update situation of the device information in the distributed ledger; When detecting the update of the device information, obtain the updated device information; According to the updated device information, update the correspondence table of the standardized device semantic description, network environment information, and information transmission strategy.

[0011] Preferably, a system for automatically obtaining network device information based on Internet of Things technology includes a device side and a management side: The device side is used to obtain the current device information and the current network environment information, parse the information based on the AI-driven semantic network and determine the target transmission strategy, and send the strategy and device information to the management side through the photon-edge computing fusion architecture; it is also used to update the target transmission strategy according to the information transmission path status; The management terminal is used to receive the target transmission policy and device information sent by the device terminal, obtain the device information according to the policy and store it in the distributed ledger of the dynamic trust blockchain; it is used to process the natural language device query instructions of users, retrieve the matching device information in the distributed ledger and feedback it to the users; it is also used to monitor the update situation of the device information in the distributed ledger and update the corresponding relationship table. The device terminal and the management terminal perform ultra-high-speed communication through the terahertz band of the photon-edge computing fusion architecture. The system realizes the secure storage, verification of device information and the trusted decision-making of transmission policies through the dynamic trust blockchain.

[0012] An electronic device includes: a memory and at least one processor. Instructions are stored in the memory, and at least one of the processors calls the instructions in the memory so that the device executes each step of the acquisition method as described above.

[0013] A computer-readable storage medium stores instructions thereon, and when the instructions are executed by a processor, each step of the acquisition method as described above is implemented.

[0014] A computer program product includes instructions, and when the instructions are run, the steps of the acquisition method as described above are executed.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: By integrating device information and network environment information and converting them into a unified semantic description, the present invention solves the problem of information integration. The device information obtained by the device side includes device identification, type, hardware configuration, etc., and the network environment information covers network topology, bandwidth, latency, etc. These information are processed by an AI-driven semantic network and knowledge graph, and then converted into a device capability description model defined by RDF triples. Through a Transformer model, cross-protocol semantic conversion is performed to generate a standardized device semantic description, which unifies and integrates the originally scattered and differently formatted information, improves the manageability of information, and provides a basis for analysis and decision-making. In terms of transmission strategies, the prior art adopts fixed strategies and is difficult to adapt to the dynamic changes of the network. The present invention constructs a correspondence table between standardized device semantic descriptions, network environment information, and information transmission strategies, and dynamically determines the optimal transmission strategy in combination with the current network conditions. It uses the terahertz band of the photon-edge computing fusion architecture for ultra-high-speed communication, improves transmission efficiency and reliability, and ensures that the transmission strategy matches the network conditions in real time, optimizing the information transmission process. The problem of low reliability of communication links is solved by establishing a redundancy mechanism for in-band and out-of-band communication links. The system monitors the status of communication links in real time. When the in-band communication link is abnormal, it automatically switches to the out-of-band communication link, ensuring the continuous and stable transmission of device information, avoiding information acquisition interruption caused by network failures, and improving the reliability of communication. In terms of data management and security mechanisms, the present invention introduces a distributed ledger of a dynamic trust blockchain to record the status of information transmission paths and device operation behavior data, uses a consensus algorithm based on dynamically adjusting weights according to device behavior to determine transmission strategies, and combines the Spartan protocol for zero-knowledge proof identity verification, ensuring the security and credibility of device information during acquisition, transmission, and storage, preventing data tampering and unauthorized access, and solving the problem of insufficient data management security mechanisms in the prior art. The management end receives user query instructions through natural language processing technology and uses an AI semantic network to parse them into standardized query semantics, retrieves and feedbacks matching device information in the distributed ledger, which not only improves the user experience but also enhances the accuracy and efficiency of retrieval. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can be obtained according to the provided drawings.

[0017] Figure 1 It is a step diagram of the device side of the present invention; Figure 2 It is a step diagram of the management side of the present invention; Figure 3 This is the system structure diagram of the present invention. Specific implementation manners

[0018] The accompanying drawings are only for illustrative purposes and should not be construed as a limitation to this patent; To better illustrate this embodiment, some components in the accompanying drawings are omitted, enlarged or reduced, which do not represent the dimensions of the actual product; For those skilled in the art, it is understandable that some well-known structures and their descriptions in the accompanying drawings may be omitted.

[0019] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Embodiment 1

[0021] Please refer to Figure 1-2 , a method for automatically obtaining network device information based on the Internet of Things technology, which is applied to the device side. The method includes: Obtain the current device information and the current network environment information; wherein, the device information at least includes the device identifier, device type, hardware configuration, operating status, communication protocol, and device capability parameters, and the network environment information at least includes the network topology structure, available bandwidth, network latency, signal strength, and network load; Based on the AI-driven semantic network, use the knowledge graph to parse the current device information and the current network environment information into a device capability description model defined by RDF triples, and perform cross-protocol semantic conversion through the Transformer model to generate a standardized device semantic description; According to the pre-constructed correspondence table between the standardized device semantic description, network environment information, and information transmission strategy, determine the target transmission strategy corresponding to the current standardized device semantic description and the current network environment information; Through the photon-edge computing fusion architecture, use the terahertz band to send the target transmission strategy and device information to the management end, so that the management end can obtain the device information according to the target transmission strategy.

[0022] The obtaining of the current device information includes: Establish an in-band communication link and an out-of-band communication link with the internal modules of the device; When it is detected that the in-band communication link is communicating normally, receive the current device information transmitted by the internal modules of the device through the in-band communication link; When it is detected that the in-band communication link is communicating abnormally, receive the current device information transmitted by the internal modules of the device through the out-of-band communication link.

[0023] After sending the target transmission strategy and device information to the management end, it further includes: Obtain the information transmission path status between the device side and the management side within the first preset duration; Upload the information transmission path status and device operation behavior data to the DAG-based distributed ledger of the dynamic trust blockchain; Based on the information recorded in the distributed ledger, use a consensus algorithm with dynamically adjusted weights based on device behavior, combined with the current network environment information and device status, to determine a new transmission strategy; Use the Spartan protocol to perform zero-knowledge proof authentication on the new transmission strategy, take the new transmission strategy that passes the verification as the new target transmission strategy, and re-execute the step of sending the target transmission strategy and device information to the management side.

[0024] The obtaining of the information transmission path status between the device side and the management side within the first preset duration includes: Obtain the information sent by the management side for characterizing the information transmission path status within the first preset duration; wherein, the information includes path quantity change information and path transmission quality information; Determine the information transmission path status between the device side and the management side within the first preset duration according to the information; When the information indicates that the path quantity decreases or the transmission quality is lower than the threshold, determine the faulty path according to the information; shield the faulty path, and determine a new transmission strategy from the remaining information transmission paths; When the information indicates that the path quantity increases, determine a new transmission strategy from all the information transmission paths according to the newly added paths, device load information, and network load information.

[0025] A method for automatically obtaining network device information based on Internet of Things technology, applied to the management side, the method includes: Through the photon-edge computing fusion architecture, use the terahertz frequency band to receive the target transmission strategy and device information sent by the device side; wherein, the target transmission strategy is obtained by the device side by acquiring the current device information and current network environment information, parsing based on the AI-driven semantic network, and determining according to the corresponding relationship; Obtain detailed device information from the device side according to the target transmission strategy; Store the obtained device information in the DAG-based distributed ledger of the dynamic trust blockchain, and use the Spartan protocol to perform anonymous authentication on the device identity and record it; Receive the natural language device query instruction input by the user; Based on the AI-driven semantic network, use the knowledge graph to parse the natural language device query instruction into a query model defined by RDF triples, and perform semantic conversion through the Transformer model to generate a standardized query semantics; Retrieve device information that matches the standardized query semantics in the distributed ledger and feedback the retrieval results to the user.

[0026] After storing the obtained device information in the distributed ledger, it further includes: Real-time monitor the update situation of the device information in the distributed ledger; When it is detected that the device information is updated, obtain the updated device information; According to the updated device information, update the correspondence table of the standardized device semantics description, network environment information, and information transmission strategy.

[0027] An electronic device, including: a memory and at least one processor. Instructions are stored in the memory, and at least one of the processors calls the instructions in the memory so that the device executes each step of the acquisition method as described above.

[0028] A computer-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, each step of the acquisition method as described above is implemented.

[0029] A computer program product, which includes instructions. When the instructions are run, the steps of the acquisition method as described above are executed.

[0030] In a specific implementation, a factory workshop environment based on Internet of Things technology deploys different types of network devices such as sensors, controllers, and actuators.

[0031] During operation, the intelligent sensor device at the device end, such as a temperature sensor, will first start its internal monitoring module. This module conducts real-time data interaction with the core processing module of the device through a pre-configured in-band communication link, such as the device internal bus, and continuously obtains device information including its own device identifier, such as the serial number sensor_001, device type (temperature sensor), hardware configuration, such as memory size, processor model, operating status, such as current power, data acquisition frequency, etc., communication protocol, such as the supported Modbus TCP protocol, and device capability parameters, such as measurement accuracy, range, etc. At the same time, the sensor also obtains the current network environment information through communication and interaction with network devices such as routers and switches in the workshop internal network, covering the workshop network topology, such as star topology, available bandwidth, such as 100Mbps, network latency, such as average latency 50ms, signal strength, such as WiFi signal strength of -60dBm, and network load, such as the current network traffic ratio of 70%.

[0032] Then, with the help of the embedded AI-driven semantic network software module, the device side uses the pre-constructed knowledge graph library to deeply semantically analyze the obtained device information and network environment information. In the knowledge graph, semantic concepts of various devices and their related attributes, network parameters, and the association relationships between them have been predefined. By matching these concepts and relationships, the device information and network environment information are converted into a device capability description model based on RDF triples (Resource Description Framework triples, consisting of a subject, a predicate, and an object). For example, taking the device identifier sensor_001 of the temperature sensor as the subject, and the power information, measurement range, etc. in its operating state as the predicate and object, multiple RDF triples are formed to describe the device capabilities in detail. Using the trained Transformer model on the device side, cross-protocol semantic conversion is performed on the semantic content corresponding to different communication protocols, such as Modbus TCP, OPC UA, etc., to generate a standardized device semantic description, so that devices with different protocols can accurately understand and interact with their capability information.

[0033] The system constructs a correspondence table based on the standardized semantic descriptions of different devices, network environment information, and various information transmission strategies. Based on this correspondence table, the device side combines its current standardized device semantic description, such as the specific measurement range, accuracy, etc. of the temperature sensor, and the current network environment information, such as the bandwidth and latency of the workshop network, to accurately determine the corresponding target transmission strategy. For example, it selects to use the TCP protocol with low latency and high reliability for data transmission, and determines the appropriate data transmission period and data format, etc.

[0034] Then, the device side uses its equipped photon-edge computing fusion architecture module, especially the built-in terahertz band communication chip. Through the edge computing part, the transmitted data is preliminarily compressed, encrypted, etc. Then, relying on the high-speed transmission characteristics of the terahertz band, the target transmission strategy and device information (including various detailed information obtained previously) are packaged and sent to the management server of the workshop. After receiving it, the management side can smoothly obtain the detailed device information from the device side according to this target transmission strategy to achieve centralized management and monitoring of the devices in the entire workshop.

[0035] After sending the target transmission strategy and device information to the management end, the device end will also obtain the information transmission path status between the device end and the management end through continuous interaction with network nodes such as routers in the workshop network within a first preset time period, such as 10 minutes. The status information comes from the feedback data returned by the management end, which represents the changes in the number of paths of the information transmission path within the first preset time period, such as adding or reducing several transmission paths, and the path transmission quality, such as the packet loss rate and delay changes of each path. The device end determines the current transmission path status based on this information. If it is found that the transmission quality of a path is lower than the set threshold, such as the packet loss rate exceeds 10 %, the path is determined to be faulty and blocked immediately. The remaining normal information transmission paths are combined with device load information, such as the amount of tasks currently being processed by the device and network load information, to re-determine a new transmission strategy, such as switching to a path with lower latency and more bandwidth. The Spartan protocol is then used to perform zero-knowledge proof authentication on the new transmission strategy to ensure the legitimacy and security of the policy change. After the verification is passed, the new transmission strategy is used as the new target transmission strategy, and the steps of sending the target transmission strategy and device information to the management end are re-executed to ensure the stability and reliability of communication between the device and the management end.

[0036] At the same time, for the management side, after receiving the target transmission strategy and device information sent by the device side, it will further obtain more detailed device information from the device side according to the target transmission strategy, such as real-time temperature data collected by the temperature sensor in different time periods, calibration records, etc., and store the acquired device information in the DAG (directed acyclic graph)-based distributed ledger of the dynamic trust blockchain. The distributed ledger with DAG structure has the characteristics of high performance and high scalability, and records these device information safely and efficiently. Then, the Spartan protocol is used to anonymously verify the device identity to ensure the security of the device identity and the credibility of the device information, and the verification results are recorded together with the device information in the distributed ledger.

[0037] When a user, such as a workshop manager, inputs a natural language device query command on the management side, for example, to query the information of all sensor devices with a temperature exceeding 50 degrees, the management side uses its own AI-driven semantic network software module and the knowledge graph to semantically parse the natural language command and convert it into a query model defined by RDF triples. It then performs semantic conversion through the Transformer model to generate standardized query semantics. It then retrieves device information that matches the standardized query semantics in the distributed ledger, and feeds back detailed information such as the location of the device and the production line it belongs to to the user, helping managers quickly locate and understand the status of related equipment.

[0038] In addition, after storing the obtained device information in the distributed ledger, the management end will monitor the update of the device information in the distributed ledger in real time. Once it detects the update of the device information, such as the range parameter of a certain sensor has changed after calibration, it will promptly obtain the updated device information, and accordingly update the correspondence table of the pre-constructed standardized device semantic description, network environment information, and information transmission strategy based on the updated device information, so as to ensure the accuracy and adaptability of the subsequent transmission strategy determination and realize the dynamic management and efficient acquisition of device information.

[0039] Embodiment 2

[0040] An automatic acquisition system for network device information based on the Internet of Things technology includes a device end and a management end: The device end is used to obtain the current device information and the current network environment information, parse the information based on the AI-driven semantic network and determine the target transmission strategy, and send the strategy and device information to the management end through the photon-edge computing fusion architecture; it is also used to update the target transmission strategy according to the information transmission path status. The management end is used to receive the target transmission strategy and device information sent by the device end, obtain the device information according to the strategy and store it in the distributed ledger of the dynamic trust blockchain; it is used to process the user's natural language device query instruction, retrieve the matching device information in the distributed ledger and feedback it to the user; it is also used to monitor the update of the device information in the distributed ledger and update the correspondence table. The device end and the management end conduct ultra-high-speed communication through the terahertz band of the photon-edge computing fusion architecture, and the system realizes the secure storage, verification of device information, and trusted decision-making of the transmission strategy through the dynamic trust blockchain.

[0041] Please refer to Figure 3 , the device end covers various intelligent devices deployed inside the workshop, including but not limited to intelligent temperature sensors, pressure sensors, intelligent controllers, and actuators, etc. These devices are all equipped with an information acquisition module, an AI parsing and conversion module, a transmission strategy determination module, and a photon-edge computing fusion communication module.

[0042] Taking an intelligent temperature sensor as an example, its internal monitoring module interacts with the core processing module through an in-band communication link, such as the internal bus of the device, to obtain device information in real time, including device identification (serial number sensor_001), device type (temperature sensor), hardware configuration (memory size, processor model), operating status (power level, data acquisition frequency), communication protocol (Modbus TCP), and device capability parameters (measurement accuracy, range). At the same time, the sensor obtains network environment information, such as network topology (star topology), available bandwidth (100Mbps), network latency (average 50ms), signal strength (WiFi signal strength - 60dBm), and network load (current traffic occupancy 70%), by communicating with routers and switches in the workshop network.

[0043] The AI-driven semantic network software module embedded in the device end uses a pre-built knowledge graph library to deeply analyze the collected information into a device capability description model based on RDF triples. For example, taking the device identification sensor_001 of the temperature sensor as the subject, the power level information in the operating status as the predicate has power level, and the specific power level value as the object, to form an RDF triple. Subsequently, the trained Transformer model is used to convert the semantics of different communication protocols to generate a standardized device semantic description, realizing information interoperability between devices with different protocols.

[0044] Based on the pre-built correspondence table between the standardized device semantic description, network environment information, and transmission strategy, the device end determines the target transmission strategy, such as selecting the TCP protocol with low latency and high reliability, setting the data transmission period and format. Through the photon-edge computing fusion architecture module, after the edge computing part compresses and encrypts the transmission data, the terahertz band communication chip uses its high-speed characteristics to package and send the target transmission strategy and device information to the management end.

[0045] After sending the target transmission strategy and device information, within the preset duration of 10 minutes, the device end obtains the information transmission path status information by interacting with the workshop network nodes, including the change in the number of paths returned by the management end, transmission quality (packet loss rate, latency change), etc. If it is found that the packet loss rate of a certain path exceeds 10%, it is determined as a fault and blocked. Combining the device load and network load information, a new transmission strategy is re-determined from the remaining normal paths. After passing the zero-knowledge proof identity verification of the Spartan protocol, it is re-sent to the management end as a new target transmission strategy to ensure stable and reliable communication.

[0046] The management end receives the target transmission strategy and device information sent by the device end through the terahertz band of the photon-edge computing fusion architecture. After processing the received data, such as decoding and decompressing, it obtains detailed device information from the device end according to the target transmission strategy, such as the real-time temperature data and calibration records of the temperature sensor.

[0047] The acquired device information is stored in the DAG distributed ledger of the dynamic trust blockchain, and its high performance and high scalability are used to record data safely and efficiently. The Spartan protocol is used to anonymously verify the device identity to ensure the security of the device identity and the credibility of the information, and the verification results are recorded together with the device information.

[0048] When workshop managers input natural language instructions such as querying sensor equipment information with a temperature exceeding 50 degrees, the management end uses the AI-driven semantic network software module and the knowledge graph to parse the instructions into a query model defined by RDF triples. After semantic transformation through the Transformer model, standardized query semantics are generated, and matching equipment information is retrieved from the distributed ledger. Detailed information on sensors that meet the temperature conditions (equipment location, production line, etc.) is fed back to the user.

[0049] Real-time monitoring of device information updates in the distributed ledger, such as changes in sensor range parameters, timely acquisition of update information and update of the standardized device semantic description, network environment information and transmission strategy correspondence table to ensure the accuracy and adaptability of subsequent transmission strategies and achieve dynamic and efficient management of device information.

[0050] A photonic-edge computing fusion architecture is deployed in the workshop. The photonic communication link (terahertz frequency band) realizes ultra-high-speed data transmission between the device side and the management side. Edge computing nodes are distributed in various areas of the workshop to pre-process the device-side data (aggregation, filtering, etc.), improve transmission efficiency and reliability, and reduce the computing burden on the management side.

[0051] The dynamic trust blockchain based on DAG realizes the secure storage, verification and trusted decision-making of transmission strategy of device information. Each device and management end corresponds to a blockchain node. The consensus algorithm that dynamically adjusts weights based on device behavior ensures data consistency and security, prevents data tampering and illegal access, and ensures stable and reliable operation of the system.

[0052] The same or similar reference numerals correspond to the same or similar components; The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent; Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made on the basis of the above description. It is not necessary and impossible to list all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. An automatic information acquisition method for network devices based on Internet of Things technology, applied to the device side, characterized in that, The method includes: Obtaining current device information and current network environment information; wherein, the device information at least includes device identifier, device type, hardware configuration, operating status, communication protocol, and device capability parameters, and the network environment information at least includes network topology structure, available bandwidth, network latency, signal strength, and network load; Based on the AI-driven semantic network, using the knowledge graph to parse the current device information and current network environment information into a device capability description model defined by RDF triples, and performing cross-protocol semantic conversion through the Transformer model to generate a standardized device semantic description; According to the pre-constructed correspondence table between the standardized device semantic description, network environment information, and information transmission strategy, determining the target transmission strategy corresponding to the current standardized device semantic description and current network environment information; Through the photon-edge computing fusion architecture, using the terahertz band to send the target transmission strategy and device information to the management end, so that the management end can obtain the device information according to the target transmission strategy.

2. The acquisition method according to claim 1, wherein The obtaining of the current device information includes: Establishing an in-band communication link and an out-of-band communication link with the internal modules of the device; When it is detected that the in-band communication link is communicating normally, receiving the current device information transmitted by the internal modules of the device through the in-band communication link; When it is detected that the in-band communication link has abnormal communication, receiving the current device information transmitted by the internal modules of the device through the out-of-band communication link.

3. The acquisition method according to claim 1, wherein After sending the target transmission strategy and device information to the management end, it further includes: Obtaining the information transmission path status between the device end and the management end within the first preset duration; Uploading the information transmission path status and device operation behavior data to the DAG-based distributed ledger of the dynamic trust blockchain; Based on the information recorded in the distributed ledger, using the consensus algorithm with dynamically adjusted weights based on device behavior, combining the current network environment information and device status, to determine a new transmission strategy; Performing zero-knowledge proof authentication on the new transmission strategy using the Spartan protocol, taking the verified new transmission strategy as the new target transmission strategy, and re-executing the step of sending the target transmission strategy and device information to the management end.

4. The obtaining method according to claim 3, wherein The obtaining of the information transmission path status between the device end and the management end within the first preset duration includes: Obtaining the information sent by the management end for characterizing the information transmission path status within the first preset duration; wherein, the information includes path quantity change information and path transmission quality information; Determining the information transmission path status between the device end and the management end within the first preset duration according to the information; When the information indicates that the path quantity decreases or the transmission quality is lower than the threshold, determining the faulty path according to the information; shielding the faulty path, and determining a new transmission strategy from the remaining information transmission paths; When the information indicates that the path quantity increases, determining a new transmission strategy from all the information transmission paths according to the newly added paths, device load information, and network load information.

5. A method for automatically obtaining network device information based on Internet of Things technology, which is applied to the management side, is characterized in that, The method includes: Through the photon-edge computing fusion architecture, the target transmission strategy and device information sent by the device end are received in the terahertz frequency band; wherein, the target transmission strategy is determined by the device end obtaining the current device information and the current network environment information, parsing based on the AI-driven semantic network and according to the corresponding relationship; According to the target transmission strategy, detailed device information is obtained from the device end; The obtained device information is stored in the DAG-based distributed ledger of the dynamic trust blockchain, and the Spartan protocol is used to anonymously verify and record the device identity; Receive the natural language device query instruction input by the user; Based on the AI-driven semantic network, the knowledge graph is used to parse the natural language device query instruction into a query model defined by RDF triples, and semantic conversion is performed through the Transformer model to generate a standardized query semantics; Retrieve the device information matching the standardized query semantics in the distributed ledger and feedback the retrieval result to the user.

6. The acquisition method according to claim 5, characterized in that After storing the obtained device information in the distributed ledger, it further includes: Real-time monitor the update situation of the device information in the distributed ledger; When it is detected that the device information is updated, obtain the updated device information; According to the updated device information, update the correspondence table of the standardized device semantic description, network environment information and information transmission strategy.

7. An automatic network device information acquisition system based on Internet of Things technology, which is used to implement through the acquisition method described in any one of claims 1-6, characterized in that, It includes a device end and a management end: The device end is used to obtain the current device information and the current network environment information, parse the information based on the AI-driven semantic network and determine the target transmission strategy, and send the strategy and device information to the management end through the photon-edge computing fusion architecture; it is also used to update the target transmission strategy according to the information transmission path state; The management end is used to receive the target transmission strategy and device information sent by the device end, obtain the device information according to the strategy and store it in the distributed ledger of the dynamic trust blockchain; it is used to process the natural language device query instruction of the user, retrieve the matching device information in the distributed ledger and feedback it to the user; it is also used to monitor the update situation of the device information in the distributed ledger and update the correspondence table; The device end and the management end communicate at ultra-high speed through the terahertz frequency band of the photon-edge computing fusion architecture, and the system realizes the secure storage, verification of device information and the trusted decision-making of the transmission strategy through the dynamic trust blockchain.

8. An electronic device, characterized in that, It includes: A memory and at least one processor, wherein instructions are stored in the memory, and at least one of the processors calls the instructions in the memory so that the device executes each step of the acquisition method described in any one of claims 1-6.

9. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instructions are executed by the processor, each step of the acquisition method described in any one of claims 1-6 is realized.

10. A computer program product, characterized in that, The computer program product includes instructions, and when the instructions are run, the steps of the acquisition method described in any one of claims 1-6 are executed.

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