Power internet of things agent system for dynamic resource management

Abstract

The invention discloses a power internet of things agent system for dynamic resource management, which is connected between an internet of things terminal and an internet of things management platform and comprises an equipment atomic layer, an equipment arrangement layer and a network communication layer, the equipment atomic layer, the equipment arrangement layer and the network communication layer are sequentially connected in series; the equipment atomic layer is connected with the Internet of Things terminal and realizes data acquisition of the Internet of Things terminal through protocol analysis and data preprocessing; the equipment arrangement layer is connected with the equipment atomic layer and the network communication layer through a software defined network interface, and resource-task matching is realized through construction of a virtual resource pool; the network communication layer is connected with an Internet of Things management platform through an encryption channel, and hierarchical transmission of Internet of Things terminal data is achieved through a main communication link, a standby communication link and a queue buffering mode. The agent data transmission between the Internet of Things terminal and the Internet of Things management platform is realized, the reliability is higher, and the real-time performance is better.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and in particular relates to an electric power Internet of Things agent system for dynamic resource management. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Currently, power systems are rapidly developing towards the interconnection and intelligence of power systems. The Power Internet of Things (PoI) has gradually become the core support for the intelligentization of power systems. The goal of the PoI is to interconnect massive amounts of terminal devices (such as smart meters, sensors, and relay protection devices) to achieve real-time device status perception, remote control, and data analysis. However, the large-scale deployment of the power Internet of Things faces the following technical bottlenecks: (1) Device heterogeneity: Power terminal devices involve multiple communication protocols (such as RS-485, Modbus-TCP, DL / T645-2007, etc.) and interface types, resulting in high system integration complexity; (2) Resource constraints: Edge devices have limited computing power and are unable to cope with high-concurrency data processing requirements, and the traditional centralized resource scheduling model cannot adapt to dynamic business loads; (3) Unreliable communication: Power control services (such as relay protection) have strict requirements on latency and reliability (millisecond-level response), but existing communication links are susceptible to environmental interference and lack intelligent redundancy mechanisms; (4) Conflict between data quality and real-time performance: The original data on the edge side often contains noise, the traditional cloud cleaning solution has high latency, and the local processing algorithm lacks dynamic adaptability.

[0004] Currently, power systems use two transmission solutions to address these issues: single-link redundancy and priority queue scheduling. The single-link redundancy solution uses dual network cards or SIM cards to implement primary / backup link switching, while the priority queue scheduling solution divides transmission queues based on service type (e.g., QoS tiers in the CoAP protocol). However, the single-link redundancy solution uses traditional heartbeat detection mechanisms (such as TCP retransmission) for link switching, resulting in switching times typically in seconds, which is difficult to meet the millisecond-level switching requirements of power control services. Furthermore, the priority queue scheduling solution has fixed priorities and bandwidth allocations and cannot dynamically adjust based on network conditions. Summary of the Invention

[0005] The object of the present invention is to provide a power Internet of Things agent system for dynamic resource management with high reliability and good real-time performance.

[0006] The power IoT agent system for dynamic resource management provided by the present invention is connected between IoT terminals and an IoT management platform and includes a device atomic layer, a device orchestration layer, and a network communication layer. The device atomic layer, device orchestration layer, and network communication layer are connected in series in sequence. The device atomic layer is used to connect to IoT terminals and implement data collection from IoT terminals through protocol parsing and data preprocessing. The device orchestration layer connects to the device atomic layer and the network communication layer through a software-defined network interface and implements resource-task matching by building a virtual resource pool. The network communication layer connects to the IoT management platform through an encrypted channel and implements hierarchical transmission of IoT terminal data through primary and backup communication links and a queue buffer mode.

[0007] Among them, the device orchestration layer includes a virtual resource pool module and a dynamic scheduling module connected in series; the virtual resource pool module constructs a digital twin model of the Internet of Things device based on graph database technology, and then calculates the performance consumption value of each Internet of Things device according to the constructed digital twin model, and sets the resource portrait label of each Internet of Things device according to the performance consumption value; the dynamic scheduling module sets the dynamic scheduling strategy of the Internet of Things device based on the data information uploaded by the virtual resource pool module, and generates the collection strategy and transmission strategy based on the Nash equilibrium scheme.

[0008] The device atomic layer includes a protocol parsing module and an edge preprocessing module connected in series; the protocol parsing module is used to realize the power system protocol parsing and data structure conversion of various types of IoT terminals; the edge preprocessing module is used to perform standardized processing and noise warning on the received data.

[0009] The processing of the protocol analysis module includes the following steps:

[0010] Collect data information from each IoT terminal through a protocol supported by the power system; the protocol supported by the power system includes RS-485 protocol, Modbus-TCP protocol and DL / T645-2007 protocol;

[0011] The data information received from each IoT terminal is converted into a JSON structure that complies with the IEC 61850 standard; the JSON structure includes the device ID, timestamp, measurement value and extended field check code.

[0012] The processing process of the edge preprocessing module includes the following steps:

[0013] Data standardization: The acquired data information is normalized using the following formula using a sliding window:

[0014] In the formula It is the standardized data information; This is the data information before standardization; is the mean of the data in the sliding window; is the standard deviation of the data in the sliding window; the window length is set according to the IoT device corresponding to the acquired data; the sliding step length The value of , is the set proportional coefficient, w is the length of the sliding window;

[0015] Noise processing: The value of noise level L is defined as ; When L is less than the first noise threshold, it is marked as no noise; when L is greater than or equal to the first noise threshold and less than the second noise threshold, it is marked as mild noise, at which time the data is retained and an abnormal identifier is added to the data; when L is greater than or equal to the second noise threshold, it is marked as severe noise, at which time the data is discarded and an alarm is issued; wherein, the first noise threshold is less than the second noise threshold.

[0016] The processing process of the virtual resource pool module includes the following steps:

[0017] The digital twin model of IoT devices is constructed using Neo4j graph database; the device attributes in the digital twin model of the i-th IoT device include the real-time state vector ,in is the CPU usage of interacting with the i-th IoT device, is the memory usage of interacting with the i-th IoT device, is the network delay of interacting with the i-th IoT device;

[0018] The real-time state vector of the i-th IoT device Normalize to the set scale, expressed as ,in is the normalized real-time state vector of the ith IoT device, After normalization , After normalization , After normalization ;

[0019] Calculate the performance consumption value of the i-th IoT device for ,in is the first weight value set, is the second weight value set, is the set third weight value;

[0020] For all IoT devices, calculate the average performance consumption value and variance ;

[0021] Resource profile tags for set IoT devices:

[0022] like , then the i-th IoT device is determined to be a high-performance consumer device;

[0023] like , then the i-th IoT device is determined to be a central energy consuming device;

[0024] like , then the i-th IoT device is determined to be a low-performance consumption device.

[0025] The processing of the dynamic scheduling module includes the following steps:

[0026] A. Generate a scheduling plan based on the data information of the virtual resource pool module:

[0027] If the service type is a control instruction, the priority is set to the highest level and the transmission strategy is preemptive real-time queue;

[0028] If the service type is real-time data upload, set the priority to high and the transmission strategy to elastic real-time queue;

[0029] If the service type is periodic data upload, set the priority to medium and the transmission strategy to the first buffer queue;

[0030] If the service type is batch data transmission, the priority is set to low and the transmission strategy is the second buffer queue;

[0031] The preemptive real-time queue is defined as being able to preempt the resources of low-priority queues and always giving priority to transmission. The elastic real-time queue is defined as not preempting the resources of low-priority queues and giving priority to transmission when the bandwidth is above a set threshold, and being downgraded to a cache queue when the bandwidth is below or equal to the set threshold. The first buffer queue is defined as having the highest priority in the cache queue, but lower than the downgraded elastic real-time queue, and having a bandwidth allocation ratio greater than a set threshold. The second buffer queue is defined as allowing data to be transmitted according to a set delay value and packet loss rate, and having a bandwidth allocation ratio lower than a set threshold.

[0032] B. Generate collection and transmission strategies based on the data information of the virtual resource pool module:

[0033] The resource competition process is set as a non-cooperative game process, and Nash equilibrium is used to implement resource allocation to ensure that the CPU usage and memory occupancy of each IoT device do not exceed the set threshold. At the same time, the difference in the performance consumption value of each IoT device is ensured to be within the set range, avoiding a single IoT device from monopolizing resources and ensuring fairness.

[0034] The network communication layer includes a dual-channel routing module and a dual-queue buffer module; the dual-channel routing module is used to switch the sending communication channel according to the set primary and secondary channel switching mechanism; the dual-queue buffer module is used to perform data communication according to the queue content in the set dual-queue mode.

[0035] The processing of the dual-channel routing module includes the following steps:

[0036] The configured dual channels include optical fiber links and 5G wireless links;

[0037] During normal communication, optical fiber links are used; when the UDP probe detects continuous Probe packets are lost or delayed for more than ms, link switching is initiated and the communication link is switched to a 5G wireless link.

[0038] The processing of the dual-queue buffer module includes the following steps:

[0039] The configured dual queues include a real-time queue and a cache queue;

[0040] For real-time queues: use priority scheduling to transmit data according to the generated scheduling plan;

[0041] For cache queues: When the bandwidth utilization is detected to be less than the set value, batch transmission is triggered and the packet survival time is set to Second.

[0042] The power IoT agent system for dynamic resource management provided by the present invention is connected between the IoT terminal and the IoT management platform, adopts a layered connection method, takes business interaction as the goal, and through data collection, resource-task matching, and main and backup communication links and queue buffering mode, not only realizes the agent data transmission between the IoT terminal and the IoT management platform, but also has higher reliability and better real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0044] like Figure 1The figure shows a schematic diagram of the functional modules of the system of the present invention: the power IoT agent system for dynamic resource management disclosed in the present invention is connected between the IoT terminal and the IoT management platform, and includes a device atomic layer, a device orchestration layer, and a network communication layer; the device atomic layer, the device orchestration layer, and the network communication layer are connected in series in sequence; the device atomic layer is used to connect to the IoT terminal and realize data collection from the IoT terminal through protocol parsing and data preprocessing; the device orchestration layer realizes connection with the device atomic layer and the network communication layer through a software-defined network interface, and realizes resource-task matching by constructing a virtual resource pool; the network communication layer is connected to the IoT management platform through an encrypted channel, and realizes hierarchical transmission of IoT terminal data through primary and backup communication links and queue buffer mode;

[0045] Among them, the device orchestration layer includes a virtual resource pool module and a dynamic scheduling module connected in series; the virtual resource pool module constructs a digital twin model of the Internet of Things device based on graph database technology, and then calculates the performance consumption value of each Internet of Things device according to the constructed digital twin model, and sets the resource portrait label of each Internet of Things device according to the performance consumption value; the dynamic scheduling module sets the dynamic scheduling strategy of the Internet of Things device based on the data information uploaded by the virtual resource pool module, and generates the collection strategy and transmission strategy based on the Nash equilibrium scheme.

[0046] In specific implementation, the device atomic layer includes a protocol parsing module and an edge preprocessing module connected in series; the protocol parsing module is used to realize the power system protocol parsing and data structure conversion of various types of IoT terminals; the edge preprocessing module is used to perform standardized processing and noise warning on the received data.

[0047] The processing of the protocol analysis module includes the following steps:

[0048] Data information from each IoT terminal is collected through protocols supported by the power system; the protocols supported by the power system include RS-485, Modbus-TCP, and DL / T645-2007. Through the protocol parsing process, plug-and-play access of multi-protocol power equipment can be achieved.

[0049] The data information received from each IoT terminal is converted into a JSON structure that complies with the IEC 61850 standard; the JSON structure includes the device ID, timestamp, measurement value and extended field check code.

[0050] The processing process of the edge preprocessing module includes the following steps:

[0051] Data standardization: The acquired data information is normalized using the following formula using a sliding window:

[0052] In the formula It is the standardized data information; This is the data information before standardization; is the mean of the data in the sliding window; is the standard deviation of the data in the sliding window; the window length is set according to the IoT device corresponding to the acquired data; the sliding step length The value of , is the set proportional coefficient (preferably 0.1), w is the length of the sliding window (preferably );

[0053] Noise processing: The value of noise level L is defined as When L is less than the first noise threshold, it is marked as no noise; when L is greater than or equal to the first noise threshold and less than the second noise threshold, it is marked as mild noise. At this time, the data is retained and an abnormal identifier is added to the data (for example, an abnormal identifier 0xEE is added); when When it is greater than or equal to the second noise threshold, it is marked as severe noise, and the data is discarded and an alarm is issued; wherein, the first noise threshold is less than the second noise threshold, the first noise threshold is preferably 3, and the second noise threshold is preferably 5.

[0054] In specific implementation, the processing process of the virtual resource pool module includes the following steps:

[0055] The digital twin model of IoT devices is constructed using Neo4j graph database; the device attributes in the digital twin model of the i-th IoT device include the real-time state vector ,in is the CPU usage of interacting with the i-th IoT device, is the memory usage of interacting with the i-th IoT device, is the network delay of interacting with the i-th IoT device;

[0056] The real-time state vector of the i-th IoT device Normalize to the set scale (preferably to the range of 0 to 1), expressed as ,in is the normalized real-time state vector of the ith IoT device, After normalization , After normalization , After normalization ;

[0057] Calculate the performance consumption value of the i-th IoT device for ,in is the first weight value set, is the second weight value set, is the third weight value set; in specific implementation, 、 and You can set it according to the specific situation. For example, if you pay attention to CPU usage, you can set The value of is increased;

[0058] For all IoT devices, calculate the average performance consumption value and variance ;

[0059] Resource profile tags for set IoT devices:

[0060] like , then the i-th IoT device is determined to be a high-performance consumer device;

[0061] like , then the i-th IoT device is determined to be a central energy consuming device;

[0062] like , then the i-th IoT device is determined to be a low-performance consumption device.

[0063] In specific implementation, the processing process of the dynamic scheduling module includes the following steps:

[0064] A. Generate a scheduling plan based on the data information of the virtual resource pool module:

[0065] If the service type is a control instruction (such as relay protection data), the priority is set to the highest level and the transmission strategy is preemptive real-time queue;

[0066] If the service type is real-time data upload (such as fault recording data), set the priority to high and the transmission strategy to elastic real-time queue;

[0067] If the service type is periodic data upload (such as equipment status monitoring data), set the priority to medium and the transmission strategy to the first buffer queue;

[0068] If the service type is batch data transmission (such as historical data return data), the priority is set to low and the transmission strategy is the second buffer queue;

[0069] The preemptive real-time queue is defined as being able to preempt the resources of low-priority queues and always giving priority to transmission. The elastic real-time queue is defined as not preempting the resources of low-priority queues, giving priority to transmission when the bandwidth is above a set threshold, and being downgraded to a cache queue when the bandwidth is below or equal to the set threshold. The first buffer queue is defined as having the highest priority in the cache queue, but lower than the downgraded elastic real-time queue, and having a bandwidth allocation ratio greater than a set threshold (preferably 20%). The second buffer queue is defined as allowing data to be transmitted according to a set delay value and packet loss rate (generally allowing higher delays and packet loss rates), and having a bandwidth allocation ratio lower than a set threshold (preferably 10%).

[0070] B. Generate collection and transmission strategies based on the data information of the virtual resource pool module:

[0071] The resource competition process is set as a non-cooperative game process, and Nash equilibrium is used to implement resource allocation to ensure that the CPU usage and memory occupancy of each IoT device do not exceed the set threshold. At the same time, the difference in the performance consumption value of each IoT device is ensured to be within the set range, avoiding a single IoT device from monopolizing resources and ensuring fairness.

[0072] In specific implementation, the network communication layer includes a dual-channel routing module and a dual-queue buffer module; the dual-channel routing module is used to switch the sending communication channel according to the set main and secondary channel switching mechanism; the dual-queue buffer module is used to communicate data according to the queue content according to the set dual-queue mode.

[0073] The processing of the dual-channel routing module includes the following steps:

[0074] The configured dual channels include optical fiber links and 5G wireless links;

[0075] During normal communication, optical fiber links are used; when the UDP probe detects continuous Probe packets (preferably 3) are lost or delayed for more than ms (preferably 100ms), link switching is initiated to switch the communication link to a 5G wireless link.

[0076] The processing process of the dual-queue buffer module includes the following steps:

[0077] The configured dual queues include a real-time queue and a cache queue;

[0078] For real-time queues: use priority scheduling to transmit data according to the generated scheduling plan;

[0079] For cache queues: When the bandwidth utilization is detected to be less than the set value (preferably 70%), batch transmission is triggered and the packet survival time is set to seconds (preferably 300s).

[0080] In the solution of the present invention, plug-and-play access of multi-protocol power equipment is realized, and mainstream power protocols (such as RS-485, Modbus-TCP, DL / T645-2007) and standardized data conversion (IEC 61850 JSON structure) are supported to improve system compatibility; by establishing a virtualized resource pool and dynamic resource orchestration for terminal devices, the paradigm shift of power edge agents from "device-oriented" to "business-oriented" is realized, which not only solves the pain points of tight coupling and low efficiency of traditional systems, but also provides support for flexible business of smart grids; through the virtualized resource pool and dynamic scheduling engine, the device resource status (CPU, memory, latency) is monitored in real time, and closed-loop control of resource-task matching is achieved based on resource portrait tags and non-cooperative game models (Nash equilibrium), thereby improving resource utilization and system dynamic adaptability; through a multi-factor decision-making algorithm and priority mapping mechanism, four levels of business priority and corresponding transmission strategies (such as preemptive Queues), combined with device resource profiles and communication layer status, dynamically adjust the collection frequency and transmission strategy to ensure resource exclusivity for high-priority tasks and overall system fairness; through dual-channel intelligent routing (fiber + 5G redundant links) and millisecond-level switching mechanism (UDP probe detection of packet loss or delay > 100ms trigger), combined with dual-queue hierarchical transmission (real-time queue preemptive scheduling + cache queue dynamic triggering), low latency and high reliability are guaranteed for high-priority services (such as relay protection); through sliding window standardization, dynamic threshold adjustment and noise classification processing (light noise identification is retained, heavy noise is discarded and alarms are issued), both data cleaning efficiency and quality are taken into account to adapt to dynamic data fluctuations caused by device status switching.

[0081] The following example further illustrates the present method: A typical existing solution, Solution A (based on static queues combined with a traditional centralized scheduling architecture), was selected as a comparison. Under the same data set conditions (30 terminal devices in a 110kV substation, collecting 450 data items per minute), the solution was tested in three typical application scenarios. The performance of this solution was compared with that of existing mainstream solutions in terms of average data transmission latency, high-priority service transmission success rate, resource scheduling response time, and data processing accuracy. The results are shown in Table 1 below: Table 1 Performance evaluation results

[0082] As can be seen from the table above, the method of the present invention effectively overcomes the problems of chaotic resource preemption and single-device resource monopoly in traditional methods through virtual resource pool modeling and non-cooperative game scheduling strategies; through dual-channel routing intelligent switching and UDP probe mechanism, it significantly enhances the low latency and high reliability of high-priority services; in terms of noise processing, a hierarchical identification mechanism is used to optimize data quality and avoid false alarms and data pollution.

Claims

1. A power IoT agent system for dynamic resource management, characterized in that Connected between IoT terminals and the IoT management platform, it includes the device atomic layer, device orchestration layer, and network communication layer. These layers are connected in series. The device atomic layer is used to connect to IoT terminals and collect data from them through protocol parsing and data preprocessing. The device orchestration layer connects to the device atomic layer and network communication layer through a software-defined network interface and achieves resource-task matching by building a virtual resource pool. The network communication layer connects to the IoT management platform through an encrypted channel and implements hierarchical transmission of IoT terminal data through primary and backup communication links and queue buffering mode. Among them, the device orchestration layer includes a virtual resource pool module and a dynamic scheduling module connected in series; the virtual resource pool module constructs a digital twin model of the Internet of Things device based on graph database technology, and then calculates the performance consumption value of each Internet of Things device according to the constructed digital twin model, and sets the resource portrait label of each Internet of Things device according to the performance consumption value; the dynamic scheduling module sets the dynamic scheduling strategy of the Internet of Things device based on the data information uploaded by the virtual resource pool module, and generates the collection strategy and transmission strategy based on the Nash equilibrium scheme.

2. The power IoT agent system for dynamic resource management according to claim 1 is characterized in that The device atomic layer includes a protocol parsing module and an edge preprocessing module connected in series; the protocol parsing module is used to realize the power system protocol parsing and data structure conversion of various types of IoT terminals; the edge preprocessing module is used to perform standardized processing and noise warning on the received data.

3. The power IoT agent system for dynamic resource management according to claim 2 is characterized in that The processing of the protocol analysis module includes the following steps: Collect data information from each IoT terminal through a protocol supported by the power system; the protocol supported by the power system includes RS-485 protocol, Modbus-TCP protocol and DL / T645-2007 protocol; The data information received from each IoT terminal is converted into a JSON structure that complies with the IEC 61850 standard; the JSON structure includes the device ID, timestamp, measurement value and extended field check code.

4. The power IoT agent system for dynamic resource management according to claim 2 is characterized in that The processing process of the edge preprocessing module includes the following steps: Data standardization: The acquired data information is normalized using the following formula using a sliding window: Where It is the standardized data information; This is the data information before standardization; is the mean of the data in the sliding window; is the standard deviation of the data in the sliding window; the sliding step size The value of , is the set proportional coefficient, w is the length of the sliding window; Noise processing: The value of noise level L is defined as ; When L is less than the first noise threshold, it is marked as no noise; when L is greater than or equal to the first noise threshold and less than the second noise threshold, it is marked as mild noise, at which time the data is retained and an abnormal identifier is added to the data; when L is greater than or equal to the second noise threshold, it is marked as severe noise, at which time the data is discarded and an alarm is issued; wherein, the first noise threshold is less than the second noise threshold.

5. The power IoT agent system for dynamic resource management according to claim 1 is characterized in that The processing process of the virtual resource pool module includes the following steps: The digital twin model of IoT devices is constructed using Neo4j graph database; the device attributes in the digital twin model of the i-th IoT device include the real-time state vector ,in is the CPU usage of interacting with the i-th IoT device, is the memory usage of interacting with the i-th IoT device, is the network delay of interacting with the i-th IoT device; The real-time state vector of the i-th IoT device Normalize to the set scale, expressed as ,in is the normalized real-time state vector of the ith IoT device, After normalization , After normalization , After normalization ; Calculate the performance consumption value of the i-th IoT device for ,in is the first weight value set, is the second weight value set, is the set third weight value; For all IoT devices, calculate the average performance consumption value and variance ; Resource profile tags for set IoT devices: like , then the i-th IoT device is determined to be a high-performance consumer device; like , then the i-th IoT device is determined to be a central energy consuming device; like , then the i-th IoT device is determined to be a low-performance consumption device.

6. The power IoT agent system for dynamic resource management according to claim 5, characterized in that The processing of the dynamic scheduling module includes the following steps: A. Generate a scheduling plan based on the data information of the virtual resource pool module: If the service type is a control instruction, the priority is set to the highest level and the transmission strategy is preemptive real-time queue; If the service type is real-time data upload, set the priority to high and the transmission strategy to elastic real-time queue; If the service type is periodic data upload, set the priority to medium and the transmission strategy to the first buffer queue; If the service type is batch data transmission, the priority is set to low and the transmission strategy is the second buffer queue; The preemptive real-time queue is defined as being able to preempt the resources of low-priority queues and always giving priority to transmission. The elastic real-time queue is defined as not preempting the resources of low-priority queues and giving priority to transmission when the bandwidth is above a set threshold, and being downgraded to a cache queue when the bandwidth is below or equal to the set threshold. The first buffer queue is defined as having the highest priority in the cache queue, but lower than the downgraded elastic real-time queue, and having a bandwidth allocation ratio greater than a set threshold. The second buffer queue is defined as allowing data to be transmitted according to a set delay value and packet loss rate, and having a bandwidth allocation ratio lower than a set threshold. B. Generate collection and transmission strategies based on the data information of the virtual resource pool module: The resource competition process is set as a non-cooperative game process, and Nash equilibrium is used to implement resource allocation to ensure that the CPU usage and memory occupancy of each IoT device do not exceed the set threshold. At the same time, the difference in the performance consumption value of each IoT device is ensured to be within the set range, avoiding a single IoT device from monopolizing resources and ensuring fairness.

7. The power IoT agent system for dynamic resource management according to claim 1, characterized in that The network communication layer includes a dual-channel routing module and a dual-queue buffer module; the dual-channel routing module is used to switch the sending communication channel according to the set primary and secondary channel switching mechanism; the dual-queue buffer module is used to perform data communication according to the queue content in the set dual-queue mode.

8. The power IoT agent system for dynamic resource management according to claim 7, characterized in that The processing of the dual-channel routing module includes the following steps: The configured dual channels include optical fiber links and 5G wireless links; During normal communication, optical fiber links are used; when the UDP probe detects continuous Probe packets are lost or delayed for more than ms, link switching is initiated and the communication link is switched to a 5G wireless link.

9. The power Internet of Things agent system for dynamic resource management according to claim 7, characterized in that The processing of the dual-queue buffer module includes the following steps: The configured dual queues include a real-time queue and a cache queue; For real-time queues: use priority scheduling to transmit data according to the generated scheduling plan; For cache queues: When the bandwidth utilization is detected to be less than the set value, batch transmission is triggered and the packet survival time is set to Second.

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