Internet of Things gateway and operation method thereof

Abstract

The invention relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things gateways, and in particular to an Internet of Things gateway and an operation method thereof. Background Art

[0002] The means of power grid monitoring are difficult to meet the needs of modern power grids for real-time, intelligent and multi-node collaborative management. The stable operation of the power grid system depends on the precise management of power transmission, distribution and use, and the introduction of Internet of Things technology provides a new solution for this process. As the core equipment in the power grid system, the Internet of Things gateway can realize real-time monitoring and data collection of each node in the power transmission network. Its main function is to collect key parameters such as voltage, current, load, temperature, etc. in the power grid by integrating multiple sensors, and transmit the data to the central monitoring system through wireless communication technology to realize real-time monitoring and abnormal detection of the power grid operation status. Through the Internet of Things gateway, each node in the power grid can be interconnected to build a highly intelligent power grid monitoring network, which improves the overall operation efficiency and stability of the power grid system. The Internet of Things gateway also plays an important role in the analysis of abnormal load status of the power grid. By comprehensively analyzing the power grid overload data and equipment efficiency attenuation data, the potential risk points in the power grid operation can be effectively identified. However, the traditional operation method of the Internet of Things gateway has the problem of inaccurate prediction of power grid transmission demand and inaccurate prediction of power grid attenuation and damage. Summary of the invention

[0003] Based on this, it is necessary to provide an operation method of an Internet of Things gateway to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for operating an Internet of Things gateway includes the following steps:

[0005] Step S1: Acquire power system data; construct a power grid node topology diagram based on the power system data to obtain a power grid node distribution topology diagram; construct an Internet of Things gateway based on the power system data and the power grid node distribution topology diagram to generate a power system Internet of Things gateway;

[0006] Step S2: forecasting the transmission demand of the power grid according to the Internet of Things gateway of the power system to obtain the transmission demand forecast data of the power grid; analyzing the abnormal load state of the power grid according to the transmission demand forecast data of the power grid and the distribution topology diagram of the power grid nodes to obtain the abnormal load state data of the power grid; detecting the abnormal voltage fluctuation according to the abnormal load state data of the power grid to obtain the abnormal voltage fluctuation data of the power grid; detecting the abnormal frequency of the power grid according to the abnormal voltage fluctuation data of the power grid to obtain the abnormal frequency data of the power grid;

[0007] Step S3: estimating the aging trend of power grid equipment according to the abnormal power grid frequency data and the abnormal power grid voltage fluctuation data to obtain the power grid equipment aging trend data; estimating the power resource loss according to the power system Internet of Things gateway on the power grid equipment aging trend data to obtain the power resource loss estimation data;

[0008] Step S4: Use the power system Internet of Things gateway to conduct abnormal warning on the power resource loss estimation data and the equipment damage prediction data, obtain the power grid abnormal warning data, and return the power grid abnormal warning data to the terminal.

[0009] In the power system, the present invention can clearly display the node distribution and connection relationship of the power grid by acquiring power system data and constructing a topological structure diagram of power grid nodes, which provides an intuitive basis for subsequent analysis and optimization. Through the construction of the Internet of Things gateway, real-time monitoring and data collection of the power system can be realized, and the system's responsiveness to external changes can be improved. This process combines the power system with modern information technology and provides support for data-driven decision-making. Forecasting the transmission demand of the power grid based on the Internet of Things gateway helps to reasonably allocate power resources, reduce the load pressure of the power grid, and ensure the stability of power supply. At the same time, by analyzing the abnormal load state of the power grid, potential load abnormality problems can be discovered in time, the risk of system failure can be reduced, and the reliability of the power grid can be improved. Further voltage and frequency anomaly detection helps to timely identify and solve unstable factors in the operation of the power grid, thereby ensuring the safe operation of the power system. The prediction of the aging trend of power grid equipment can provide a basis for equipment maintenance and replacement, reduce the probability of sudden equipment failure, and extend the service life of the equipment. In addition, combined with the power resource loss estimation data, the losses caused by equipment aging or failure can be accurately assessed, helping decision makers to formulate more effective management strategies and optimize resource allocation. Finally, by using the abnormal warning system, potential risks can be identified and intervened in advance, the intelligence level of the power system can be improved, and the continuity and safety of power supply can be ensured. By effectively feeding back these data to the terminal, it can promote managers at all levels to have a comprehensive grasp of the power system and provide a scientific basis for decision-making. Therefore, the present invention is an optimization of a traditional operation method of an Internet of Things gateway, which solves the problem of inaccurate prediction of power grid transmission demand and inaccurate prediction of power grid attenuation and damage in the operation method of a traditional Internet of Things gateway, and improves the accuracy of power grid transmission demand prediction and power grid attenuation and damage prediction.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: acquiring power system data;

[0012] Step S12: collecting grid node location distribution data based on the power system data to obtain grid node location distribution data;

[0013] Step S13: constructing a grid node topology structure diagram according to the grid node location distribution data to obtain a grid node distribution topology diagram;

[0014] Step S14: using a preset sensor to perform grid node sensing collection on the grid node distribution topology map to obtain grid node sensing data;

[0015] Step S15: construct an Internet of Things gateway according to the grid node sensor data and the grid node distribution topology map to generate a power system Internet of Things gateway.

[0016] The acquisition of power system data by the present invention is the basis of the entire power management and monitoring process, and provides necessary information support for subsequent analysis. By processing the power system data, the location distribution of the power grid nodes can be effectively collected. This process not only improves the accuracy of the power grid node information, but also lays a solid foundation for the subsequent topological structure diagram construction. The construction of the power grid node distribution topological diagram can intuitively display the structure of the power grid and the relationship between the nodes, helping engineers to better understand the power grid operation status, so as to formulate more reasonable management and maintenance strategies. Using preset sensors to sense and collect the power grid nodes can not only obtain the operation data of the power grid nodes in real time, but also monitor the health status of each node, thereby enhancing the monitoring ability of the system and the ability to respond to emergencies. These sensor data are combined with the power grid node distribution topological diagram to further promote the effective construction of the Internet of Things gateway, so that the power system can achieve intelligent management. Through the Internet of Things gateway, the transmission and processing of data become more efficient, thereby providing important support for the real-time monitoring and management of the power system. Overall, the implementation of this series of steps not only improves the intelligence level of the power system, but also provides a scientific basis for the safe operation, fault warning and management decision-making of the power grid, and ultimately promotes the efficient use of power resources and the stability of power supply. By achieving comprehensive monitoring and data collection of grid nodes, the power system can better cope with various operational challenges.

[0017] Preferably, step S15 comprises the following steps:

[0018] Step S151: Calculate the amount of sensor data based on the sensor data of the power grid nodes to obtain the total amount of sensor data of the power grid nodes;

[0019] Step S152: performing grid node operation status detection on the grid node sensor data and the grid node distribution topology map to obtain grid node operation status data;

[0020] Step S153: defining a gateway communication protocol according to the grid node operation status data and the amount of grid node sensor data, and obtaining the Internet of Things gateway communication protocol data;

[0021] Step S154: construct an Internet of Things gateway based on the Internet of Things gateway communication protocol data and the power grid node operation status data to generate an Internet of Things gateway for the power system.

[0022] The present invention can clearly understand the scale and complexity of each node data by calculating the amount of grid node sensor data, which provides an important basis for subsequent data processing and analysis. This process helps to evaluate the system's load capacity and data transmission requirements, and ensures that the Internet of Things gateway can efficiently process a large amount of data from each node. When performing grid node operation status detection, combined with grid node sensor data and topology diagram, the health status of the grid can be monitored in real time, and potential faults or abnormal conditions can be discovered in time, thereby improving the safety and reliability of the grid. The definition of the gateway communication protocol based on the grid node operation status data and sensor data volume ensures the efficiency and accuracy of information transmission during the operation of the system, optimizes the data interaction method, and promotes seamless connection between different devices and systems. The formulation of this protocol provides necessary specifications for the construction of the Internet of Things gateway, ensuring the compatibility between devices and the security of data transmission. Finally, by combining the Internet of Things gateway communication protocol data with the operation status data of the grid node, the successfully constructed power system Internet of Things gateway can not only realize efficient data collection and transmission, but also enhance the system's real-time monitoring and response capabilities to the grid status. The implementation of this series of steps improves the intelligence level of the power system, promotes data-driven decision support, and ultimately ensures the stability and security of power supply.

[0023] Preferably, step S2 comprises the following steps:

[0024] Step S21: extracting historical power grid transmission according to the power system IoT gateway to obtain historical power grid transmission data;

[0025] Step S22: forecasting the power grid transmission demand based on historical power grid transmission data to obtain power grid transmission demand forecast data;

[0026] Step S23: Analyze the abnormal load state of the power grid according to the power grid transmission demand forecast data and the power grid node distribution topology map to obtain abnormal load state data of the power grid;

[0027] Step S24: performing voltage abnormal fluctuation detection according to the abnormal load state data of the power grid to obtain abnormal voltage fluctuation data of the power grid;

[0028] Step S25: Perform grid frequency anomaly detection based on the grid voltage fluctuation data and the grid abnormal load state data to obtain grid frequency anomaly data.

[0029] The present invention extracts historical power grid transmission data from the power system Internet of Things gateway, laying an important foundation for subsequent analysis. This process not only provides rich historical data support, but also makes the understanding of the power grid operation mode and change trend more in-depth. By predicting the transmission demand of these historical data, the load change trend of the future power grid can be effectively identified, providing a scientific basis for power dispatching and resource allocation, and ensuring the stability and continuity of power supply. When analyzing the abnormal load state of the power grid, combined with the power grid transmission demand prediction data and the node distribution topology diagram, potential load abnormalities can be identified in time, which is crucial for preventing power grid overload and the failures caused by it. Monitoring and analyzing the abnormal load state of the power grid helps to improve the safety of the power grid, reduce the operating risk, and provide guarantee for the stable operation of the power grid. Further, by detecting abnormal fluctuations in the power grid voltage, the voltage instability problem of the power grid during operation can be discovered in time, which is crucial to maintaining the normal operation of the power system. Voltage fluctuations cause equipment damage or affect the quality of user electricity consumption. Therefore, timely detection and processing of abnormal voltage fluctuations help to ensure the safety of the power grid and the stability of equipment. Finally, frequency anomaly detection is performed in combination with power grid voltage fluctuation data and abnormal load state data, and the frequency stability of the power grid under specific load conditions can be deeply analyzed. This not only helps to promptly identify and respond to potential problems caused by frequency fluctuations, but also provides data support for the optimization of power grid operations.

[0030] Preferably, step S22 includes the following steps:

[0031] Step S221: performing time series analysis based on historical power grid transmission data to obtain historical power grid transmission time series data;

[0032] Step S222: performing a transmission power periodicity analysis on the historical power grid transmission time series data to obtain historical transmission power periodicity data;

[0033] Step S223: Calculate the transmission power fluctuation rate of the historical power grid transmission time series data to obtain the historical power grid transmission power fluctuation rate data;

[0034] Step S224: forecasting instantaneous high demand based on historical power grid transmission power fluctuation rate data to obtain instantaneous high demand data of power grid transmission power;

[0035] Step S225: forecasting the historical power grid transmission demand based on the historical transmission power periodicity data and the instantaneous high power grid transmission power demand data to obtain power grid transmission demand forecast data.

[0036] The present invention can reveal the time dependence and trend of power grid transmission by performing time series analysis on historical power grid transmission data, and provide clear dynamic characteristics for subsequent analysis. This process helps to identify the change pattern of transmission power, and then provides important data support for optimizing power dispatching and planning. Periodic analysis of historical power grid transmission time series data can effectively identify the seasonal changes and periodic laws of power transmission, thereby helping power system managers to better understand the load characteristics of the power grid in different time periods. Further, the volatility calculation of historical power grid transmission time series data can quantify the degree of fluctuation of power grid transmission power, thereby identifying the unstable factors in the operation of the power grid. The analysis of this volatility data helps to evaluate the stability and reliability of the power grid under different load conditions, and provides an important reference for the safe operation of the power system. Based on the historical power grid transmission power volatility data, instantaneous high demand forecasting can accurately identify the peak of power demand, take measures in advance to cope with the sudden peak of power consumption, and ensure the safety and stability of the power grid. Finally, combining the historical transmission power periodicity data and instantaneous high demand data to forecast the historical power grid transmission demand can provide a more accurate basis for the dispatching and resource allocation of the power grid. This comprehensive analysis not only improves the power system's ability to respond to demand fluctuations, but also helps power suppliers better meet user needs and optimize the efficiency of power resource use.

[0037] Preferably, step S23 includes the following steps:

[0038] Step S231: dividing the power grid transmission demand forecast data and the power grid node distribution topology map into multiple-node power demand, and obtaining multiple-node power high demand data and multiple-node power low demand data;

[0039] Step S232: performing a power grid operation overload analysis according to the multi-node power high demand data and the multi-node power low demand data to obtain power grid operation overload data;

[0040] Step S233: performing power generation equipment efficiency attenuation detection according to the low demand data transmitted by the power grid to obtain power generation equipment efficiency attenuation data;

[0041] Step S234: Analyze the abnormal load state of the power grid according to the power generation equipment efficiency decay data and the power grid overload data to obtain abnormal load state data of the power grid.

[0042] The present invention can clarify the power demand characteristics of different nodes by dividing the power grid transmission demand forecast data and the power grid node distribution topology map, so as to divide the power demand into two categories: high demand and low demand. This division helps to identify the load distribution of the power grid, provides an important basis for subsequent power grid management and scheduling, and effectively optimizes the allocation and use of power resources. At the same time, understanding the differences in power demand at multiple nodes helps to improve the responsiveness of the power system and ensure that the power demand of users can be met in time during high demand periods. When performing overload analysis on power grid operation, the overload risk of the power grid under specific load conditions can be discovered in time by analyzing the high and low demand data of power at multiple nodes. This step helps to evaluate the current operating status of the power grid, identify potential safety hazards, and then take corresponding measures to prevent power grid failures and accidents. This forward-looking analysis can effectively improve the safety and stability of the power system and ensure the reliable operation of the power grid. According to the power grid transmission low demand data, the efficiency attenuation detection of power generation equipment can quantify the performance changes of power generation equipment under different operating conditions. This process can help managers identify potential problems of equipment and take maintenance and optimization measures in time to improve the efficiency and reliability of power generation equipment. Combining the power generation equipment efficiency decay data with the power grid overload data to analyze the abnormal load status of the power grid can comprehensively evaluate the operation status of the power grid, timely discover and solve the abnormal load problems, and ensure the safety and stability of the power system. In summary, the implementation of this series of steps not only improves the adaptability of the power system to different load conditions, but also enhances the monitoring and analysis capabilities of the power grid operation status, ensuring the safety and stability of power supply.

[0043] Preferably, step S232 includes the following steps:

[0044] Obtaining grid security capacity data;

[0045] A demand heat map is constructed based on multi-node power high demand data and multi-node power low demand data to obtain power demand heat map data;

[0046] Conduct unbalanced load analysis on power grid nodes on the power demand heat map data to obtain unbalanced load data on power grid nodes;

[0047] Perform transmission capacity overload detection on power grid safety capacity data according to multi-node power high demand data to obtain power grid transmission capacity overload data;

[0048] Perform grid stability anomaly detection based on grid node unbalanced load data and grid transmission capacity overload data to obtain grid stability anomaly data;

[0049] The grid operation overload analysis is performed based on the grid stability abnormal data and the grid transmission capacity overload data to obtain the grid operation overload data.

[0050] The implementation of each step of the present invention has significant beneficial effects in the management and optimization of the power system. Acquiring the safe capacity data of the power grid lays a solid foundation for subsequent load analysis and transmission capacity detection. By understanding the safe capacity of the power grid, its carrying capacity can be effectively evaluated to ensure the stability and reliability of power supply. Constructing a demand heat map provides a spatial distribution view of power demand, so that the intuitive display of high and low demand data can help decision makers quickly identify hot spots of power demand, which is convenient for optimizing resource allocation and scheduling strategies. Conducting unbalanced load analysis helps to identify the imbalance of load distribution in the power grid, thereby providing data support for power dispatching and load balancing, reducing power waste and equipment overload risks. In the transmission capacity overload detection, by comparing high demand data with safe capacity, potential overload conditions can be identified in time, so that corresponding measures can be taken to adjust and ensure the safe operation of the power grid. Power grid stability anomaly detection can timely discover potential risks in power grid operation by analyzing the unbalanced load and transmission capacity overload data of nodes, and improve the stability and anti-interference ability of the power grid. Finally, the implementation of operational overload analysis has further deepened the understanding of the operating status of the power grid. By comprehensively considering stability and transmission capacity, it can more comprehensively evaluate the operational safety of the power grid, provide accurate data support for operational decisions, and ensure the safe and reliable operation of the power grid under high load conditions.

[0051] Preferably, step S3 comprises the following steps:

[0052] Step S31: predicting the aging trend of power grid equipment according to the power grid frequency abnormality data and the power grid voltage fluctuation data to obtain power grid equipment aging trend data;

[0053] Step S32: performing equipment damage prediction according to the power grid equipment aging trend data to obtain equipment damage prediction data;

[0054] Step S33: estimating the power resource loss based on the equipment damage prediction data of the power system Internet of Things gateway to obtain power resource loss estimation data.

[0055] The present invention has significant beneficial effects on the implementation of equipment aging trend estimation, damage prediction and resource loss estimation in the management and maintenance of the power system. First, by analyzing the abnormal frequency data and voltage fluctuation data of the power grid, the aging trend of the power grid equipment can be effectively identified. The prediction of this trend provides a forward-looking perspective for the operation and maintenance team, enabling it to take preventive maintenance measures before the equipment actually fails, thereby extending the service life of the equipment and reducing the losses caused by unexpected downtime. In addition, accurate aging trend data can help optimize equipment replacement and maintenance plans, improve resource utilization efficiency, and reduce maintenance costs. Next, based on the aging trend data, equipment damage prediction can be carried out to identify the key equipment with problems in advance. This prediction not only helps to reduce the impact of equipment failure on power supply, but also effectively reduces maintenance costs. By reasonably arranging maintenance work, the risk of power outages caused by sudden equipment failures can be reduced, ensuring the stable operation of the power grid. At the same time, the reliability of damage prediction data improves the overall safety of the power system and can more effectively optimize maintenance resources and time arrangements. Finally, using the Internet of Things gateway to estimate the power resource loss of equipment damage prediction data can quantify potential losses and help management make more scientific investment and operation decisions. This kind of forecast can not only provide power companies with an accurate basis for financial planning, but also enable risk management and optimize power resource allocation before equipment damage occurs.

[0056] Preferably, step S31 includes the following steps:

[0057] Step S311: Calculate the probability of electrical shock to the power grid according to the power grid voltage fluctuation data to obtain the probability data of electrical shock to the power grid;

[0058] Step S312: performing cumulative calculation of thermal effects of power grid equipment according to power grid frequency abnormality data to obtain cumulative data of thermal effects of power grid equipment;

[0059] Step S313: Performing equipment insulation layer damage detection according to the power grid electrical shock probability data and the accumulated data of thermal effects of power grid equipment to obtain power grid equipment insulation layer damage data;

[0060] Step S314: Performing insulation failure detection on the power grid equipment according to the insulation layer damage data of the power grid equipment to obtain insulation failure data on the power grid equipment;

[0061] Step S315: predicting the aging trend of the power grid equipment according to the insulation failure data of the power grid equipment to obtain the aging trend data of the power grid equipment.

[0062] In the safety and stability management of the power system, the present invention conducts a comprehensive analysis of voltage fluctuation and frequency anomaly data, which can significantly improve the monitoring and maintenance efficiency of power grid equipment. First, by calculating the probability of electrical shock through the power grid voltage fluctuation data, the risk of electrical shock to the power grid under different conditions can be quantified, which provides an important basis for formulating reasonable protective measures and emergency plans, reduces the probability of equipment being damaged by electrical shock, and thus improves the overall safety of the power grid. Secondly, based on the power grid frequency anomaly data, the cumulative calculation of the thermal effect of the equipment can effectively monitor the potential damage caused by the thermal effect of the equipment during operation. The accumulation of this data provides an important reference for evaluating the health status of the equipment, helping the operation and maintenance team to adjust the equipment operating parameters in time, avoid equipment failures caused by overheating, and ensure the stable operation of the power grid. Subsequently, the insulation layer damage detection is carried out in combination with the electrical shock probability data and the cumulative data of the thermal effect of the equipment, which can realize a comprehensive evaluation of the insulation status of the power grid equipment. The implementation of this process helps to discover the potential damage of the insulation layer in advance, so as to carry out targeted maintenance and replacement, prevent short circuits or safety accidents caused by insulation failure, and ensure the safe operation of the power grid. Insulation failure detection based on insulation damage data can effectively identify equipment that is about to fail and arrange maintenance and replacement work in advance. This forward-looking maintenance strategy can not only reduce the impact of failures, but also save maintenance costs and improve the efficiency of equipment use. Finally, aging trend prediction based on insulation failure data can provide a scientific basis for the long-term management of power grid equipment. Accurate prediction of equipment aging trends enables the operation and maintenance team to more effectively plan equipment maintenance and replacement cycles, avoid sudden failures caused by equipment aging, and improve the overall reliability of the power grid.

[0063] This specification also provides an Internet of Things gateway, which is used to execute the operation method of the Internet of Things gateway as described above, and the Internet of Things gateway includes:

[0064] IoT gateway construction module: obtain power system data; construct a grid node topology diagram based on the power system data to obtain a grid node distribution topology diagram; construct an IoT gateway based on the power system data and the grid node distribution topology diagram to obtain a power system IoT gateway;

[0065] Grid frequency anomaly detection module: Grid transmission demand prediction is performed based on the power system IoT gateway to obtain grid transmission demand prediction data; grid abnormal load state analysis is performed based on the grid transmission demand prediction data and the grid node distribution topology diagram to obtain grid abnormal load state data; abnormal voltage fluctuation detection is performed based on the grid abnormal load state data to obtain grid abnormal voltage fluctuation data; grid frequency anomaly detection is performed based on the grid abnormal voltage fluctuation data to obtain grid frequency anomaly data;

[0066] Power resource loss estimation module: The power grid equipment aging trend is estimated based on the power grid frequency abnormality data and the power grid voltage abnormal fluctuation data to obtain the power grid equipment aging trend data; the power resource loss is estimated based on the power system Internet of Things gateway for the power grid equipment aging trend data to obtain the power resource loss estimation data.

[0067] Abnormal warning module: Use the power system IoT gateway to issue abnormal warnings on the power resource loss estimation data and equipment damage prediction data, obtain the power grid abnormal warning data, and return the power grid abnormal warning data to the terminal.

[0068] The invention has the beneficial effect that, in the power system, by acquiring the power system data and constructing the topological structure diagram of the power grid node, the node distribution and connection relationship of the power grid can be clearly displayed, which provides an intuitive basis for subsequent analysis and optimization. Through the construction of the Internet of Things gateway, real-time monitoring and data collection of the power system can be realized, and the system's responsiveness to external changes can be improved. This process combines the power system with modern information technology and provides support for data-driven decision-making. Forecasting the transmission demand of the power grid based on the Internet of Things gateway helps to reasonably allocate power resources, reduce the load pressure of the power grid, and ensure the stability of power supply. At the same time, by analyzing the abnormal load state of the power grid, potential load abnormality problems can be discovered in time, the risk of system failure can be reduced, and the reliability of the power grid can be improved. Further voltage and frequency anomaly detection helps to timely identify and solve unstable factors in the operation of the power grid, thereby ensuring the safe operation of the power system. The prediction of the aging trend of power grid equipment can provide a basis for equipment maintenance and replacement, reduce the probability of sudden equipment failure, and extend the service life of the equipment. In addition, combined with the power resource loss estimation data, the losses caused by equipment aging or failure can be accurately assessed, helping decision makers to formulate more effective management strategies and optimize resource allocation. Finally, by using the abnormal warning system, potential risks can be identified and intervened in advance, the intelligence level of the power system can be improved, and the continuity and safety of power supply can be ensured. By effectively feeding back these data to the terminal, it can promote managers at all levels to have a comprehensive grasp of the power system and provide a scientific basis for decision-making. Therefore, the present invention is an optimization of a traditional operation method of an Internet of Things gateway, which solves the problem of inaccurate prediction of power grid transmission demand and inaccurate prediction of power grid attenuation and damage in the operation method of a traditional Internet of Things gateway, and improves the accuracy of power grid transmission demand prediction and power grid attenuation and damage prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of a step flow of an operation method of an Internet of Things gateway;

[0070] Figure 2 for Figure 1Detailed implementation steps of step S2 in the flowchart;

[0071] Figure 3 for Figure 1 Detailed implementation steps of step S22;

[0072] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0073] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0074] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0075] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0076] To achieve this, please refer to Figures 1 to 3 , an operation method of an Internet of Things gateway, comprising the following steps:

[0077] Step S1: Acquire power system data; construct a power grid node topology diagram based on the power system data to obtain a power grid node distribution topology diagram; construct an Internet of Things gateway based on the power system data and the power grid node distribution topology diagram to generate a power system Internet of Things gateway;

[0078] Step S2: forecasting the transmission demand of the power grid according to the Internet of Things gateway of the power system to obtain the transmission demand forecast data of the power grid; analyzing the abnormal load state of the power grid according to the transmission demand forecast data of the power grid and the distribution topology diagram of the power grid nodes to obtain the abnormal load state data of the power grid; detecting the abnormal voltage fluctuation according to the abnormal load state data of the power grid to obtain the abnormal voltage fluctuation data of the power grid; detecting the abnormal frequency of the power grid according to the abnormal voltage fluctuation data of the power grid to obtain the abnormal frequency data of the power grid;

[0079] Step S3: estimating the aging trend of power grid equipment according to the abnormal power grid frequency data and the abnormal power grid voltage fluctuation data to obtain the power grid equipment aging trend data; estimating the power resource loss according to the power system Internet of Things gateway on the power grid equipment aging trend data to obtain the power resource loss estimation data;

[0080] Step S4: Use the power system Internet of Things gateway to conduct abnormal warning on the power resource loss estimation data and the equipment damage prediction data, obtain the power grid abnormal warning data, and return the power grid abnormal warning data to the terminal.

[0081] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of an operation method of an Internet of Things gateway of the present invention. In this example, the operation method of the Internet of Things gateway includes the following steps:

[0082] Step S1: Acquire power system data; construct a power grid node topology diagram based on the power system data to obtain a power grid node distribution topology diagram; construct an Internet of Things gateway based on the power system data and the power grid node distribution topology diagram to generate a power system Internet of Things gateway;

[0083] In an embodiment of the present invention, firstly, the power system data is obtained, and the data sources include real-time monitoring equipment, sensors and historical operation records. These data include multiple parameters such as voltage, current, power, load, etc. A data acquisition module is used to transmit real-time data to a centralized control system through industrial communication protocols such as 485 communication protocols or Modbus. Subsequently, based on the acquired data, the minimum spanning tree algorithm in graph theory is applied to construct a topological structure diagram of the power grid nodes, identify the connection relationship between each node, and record the attribute information of the node to form a topological diagram of the power grid node distribution. On this basis, an Internet of Things gateway is constructed. A hardware platform with powerful computing power and stable network connection is selected, such as a Raspberry Pi or an industrial gateway, and a corresponding communication module (such as Wi-Fi, LoRa) is configured to realize data collection and transmission functions. Finally, the constructed Internet of Things gateway is docked with the data interface of the power system to form a complete power system Internet of Things gateway.

[0084] Step S2: forecasting the transmission demand of the power grid according to the Internet of Things gateway of the power system to obtain the transmission demand forecast data of the power grid; analyzing the abnormal load state of the power grid according to the transmission demand forecast data of the power grid and the distribution topology diagram of the power grid nodes to obtain the abnormal load state data of the power grid; detecting the abnormal voltage fluctuation according to the abnormal load state data of the power grid to obtain the abnormal voltage fluctuation data of the power grid; detecting the abnormal frequency of the power grid according to the abnormal voltage fluctuation data of the power grid to obtain the abnormal frequency data of the power grid;

[0085] In an embodiment of the present invention, the power system Internet of Things gateway is used to predict the transmission demand of the power grid, and the real-time and historical load data are collected through the gateway. The sliding window method is used to perform time series analysis to calculate the load demand in the future. After obtaining the power grid transmission demand forecast data, the load status of each node is analyzed abnormally in combination with the power grid node distribution topology diagram. By setting the load threshold, it is determined whether there is a load abnormality, and the abnormal status data is recorded. Then, according to the abnormal load status data of the power grid, the voltage abnormal fluctuation detection is performed, and the voltage signal is processed using a digital filter to detect whether the voltage change exceeds the set tolerance range. Finally, based on the abnormal voltage fluctuation data, the spectrum analysis method is used to monitor the power grid frequency and obtain the abnormal power grid frequency data.

[0086] Step S3: Estimate the aging trend of power grid equipment based on the abnormal power grid frequency data and the abnormal power grid voltage fluctuation data to obtain the power grid equipment aging trend data; estimate the power resource loss based on the power system Internet of Things gateway for the power grid equipment aging trend data to obtain the power resource loss estimation data.

[0087] In an embodiment of the present invention, the aging trend of power grid equipment is estimated based on the abnormal power grid frequency data and the abnormal power grid voltage fluctuation data. First, the operating parameters of each device in the power grid are collected, and the aging trend model is established by combining historical data and using a linear regression analysis method to predict the aging status of the equipment. The aging trend data of the power grid equipment is obtained by quantitatively analyzing the voltage and frequency fluctuations of the equipment. Then, the power system Internet of Things gateway is used to estimate the power resource loss based on the aging trend data. This process includes evaluating the equipment efficiency, calculating the power loss caused by equipment aging, and generating power resource loss estimation data.

[0088] Step S4: Use the power system Internet of Things gateway to conduct abnormal warning on the power resource loss estimation data and the equipment damage prediction data, obtain the power grid abnormal warning data, and return the power grid abnormal warning data to the terminal.

[0089] In the embodiment of the present invention, the power system Internet of Things gateway is used to perform abnormal warning on the power resource loss estimation data and the equipment damage prediction data. By setting the warning threshold, when the loss estimation data and the equipment damage prediction data exceed this threshold, the abnormal warning mechanism is triggered. In the specific implementation, a message queue or event-driven architecture is used to realize real-time processing and analysis of data and generate abnormal power grid warning data.

[0090] Preferably, step S1 comprises the following steps:

[0091] Step S11: acquiring power system data;

[0092] Step S12: collecting grid node location distribution data based on the power system data to obtain grid node location distribution data;

[0093] Step S13: constructing a grid node topology structure diagram according to the grid node location distribution data to obtain a grid node distribution topology diagram;

[0094] Step S14: using a preset sensor to perform grid node sensing collection on the grid node distribution topology map to obtain grid node sensing data;

[0095] Step S15: construct an Internet of Things gateway according to the grid node sensor data and the grid node distribution topology map to generate a power system Internet of Things gateway.

[0096] In the embodiment of the present invention, the acquisition of power system data depends on a series of real-time monitoring devices and historical databases. First, a variety of sensors, such as voltage sensors, current sensors and power meters, are configured and deployed at key nodes of the power grid. These sensors transmit data to the data acquisition system in real time through industrial standard communication protocols such as RS-485 or Modbus. In addition, network crawling technology is used in combination with relevant power system management software to obtain historical operation records and load data from the power dispatching center. Ultimately, all collected data will be integrated into a centralized database to form a comprehensive power system data foundation. The distribution of power grid node locations is collected based on the power system data. Through GIS (geographic information system) technology, the physical location of the power grid is associated with the collected power system data, and the precise location of each power grid node is marked using GPS positioning equipment. Then, GIS software is used to integrate the node location data with power parameters (such as voltage and load) to form power grid node location distribution data. In this process, the accuracy and real-time nature of the data are ensured, and the node location data is regularly updated by setting the data acquisition frequency to reflect the dynamic changes of the power grid. A power grid node topology diagram is constructed based on the power grid node location distribution data. The topological analysis method in graph theory is used to analyze the connection relationship between nodes. By performing geometric calculations on the node positions, the connection lines between adjacent nodes are determined to form the topological structure of the power grid. In this process, professional power system simulation software is used to input node data and its connection information to automatically generate a topological map of the power grid node distribution. The preset sensors are used to collect the power grid node sensing data from the power grid node distribution topological map. Appropriate types of sensors, such as temperature sensors, humidity sensors, and current sensors, are selected and installed at each node of the power grid. The sensor configuration should comply with industrial standards and have real-time data collection capabilities. By setting up a timed sampling mechanism, the operating status data of each node is regularly collected and transmitted to the data processing center via a wireless or wired network. Finally, comprehensive power grid node sensing data is formed, and the Internet of Things gateway is constructed based on the power grid node sensing data and the power grid node distribution topological map. A suitable hardware platform, such as an industrial router or embedded device, is selected as the basis of the Internet of Things gateway. The hardware needs to support multiple communication protocols (such as MQTT, CoAP) to achieve compatibility with different types of sensors. Then, combined with the power grid node sensing data, the communication interface of the gateway is configured to ensure that it can receive and process the data of each node in real time. Finally, the data formatting and storage functions are realized through software programming to form a complete power system Internet of Things gateway.

[0097] Preferably, step S15 comprises the following steps:

[0098] Step S151: Calculate the amount of sensor data based on the sensor data of the power grid nodes to obtain the total amount of sensor data of the power grid nodes;

[0099] Step S152: performing grid node operation status detection on the grid node sensor data and the grid node distribution topology map to obtain grid node operation status data;

[0100] Step S153: defining a gateway communication protocol according to the grid node operation status data and the amount of grid node sensor data, and obtaining the Internet of Things gateway communication protocol data;

[0101] Step S154: construct an Internet of Things gateway based on the Internet of Things gateway communication protocol data and the power grid node operation status data to generate an Internet of Things gateway for the power system.

[0102] In an embodiment of the present invention, the amount of grid node sensor data is calculated, and the sensor data of each grid node is first extracted from the power system Internet of Things gateway. These data include multiple parameters such as voltage, current, power, temperature, etc. In order to accurately calculate the total amount of sensor data, a data processing script is used to summarize and count the sensor data of all nodes. In the specific implementation process, the structure and format of the sensor data are defined, for example, each data packet contains a timestamp, a node ID, and a corresponding sensor reading. Then, the total amount of data is calculated by looping through the data of each node. Finally, the value of the total amount of grid node sensor data is generated, and the grid node operation status is detected based on the grid node sensor data and the grid node distribution topology map. The collected node sensor data is combined with the topology map to clarify the connection relationship between each node. Use the set operating status threshold, such as the maximum and minimum values ​​of voltage and current, to determine whether each node is in a normal operating state. Use a state monitoring algorithm to detect abnormal states and record them by comparing real-time data with historical data. In this process, data analysis tools can be used, such as Python combined with the NumPy library for efficient calculation, and finally the grid node operation status data is generated. The IoT gateway communication protocol is defined based on the grid node operation status data and the amount of sensor data, and the operation status data of each grid node is analyzed to determine the frequency and priority of data transmission. In combination with the total amount of sensor data of the grid nodes, the communication protocol is designed to ensure efficient and stable data transmission. Standardized communication protocols, such as MQTT or CoAP, are used to define the format of the data packet, including header information, load information, and checksums. At the same time, the interaction rules between the gateway and the node are clarified, including the data reporting method and the error retransmission mechanism. Finally, a complete IoT gateway communication protocol data is formed, and the IoT gateway is constructed by combining the IoT gateway communication protocol data and the grid node operation status data. A hardware platform with powerful computing power and rich interfaces is selected as the core part of the gateway. The hardware is configured to support the defined communication protocol. Next, the grid node operation status data is integrated with the communication protocol, and the corresponding firmware is written so that the gateway can receive the node data in real time and process it according to the protocol. In this process, an embedded development environment (such as Arduino or Raspberry Pi) is used to realize the data reception, parsing and storage functions. Finally, the construction of the power system IoT gateway is completed.

[0103] Preferably, step S2 comprises the following steps:

[0104] Step S21: extracting historical power grid transmission according to the power system IoT gateway to obtain historical power grid transmission data;

[0105] Step S22: forecasting the power grid transmission demand based on historical power grid transmission data to obtain power grid transmission demand forecast data;

[0106] Step S23: Analyze the abnormal load state of the power grid according to the power grid transmission demand forecast data and the power grid node distribution topology map to obtain abnormal load state data of the power grid;

[0107] Step S24: performing voltage abnormal fluctuation detection according to the abnormal load state data of the power grid to obtain abnormal voltage fluctuation data of the power grid;

[0108] Step S25: Perform grid frequency anomaly detection based on the grid voltage fluctuation data and the grid abnormal load state data to obtain grid frequency anomaly data.

[0109] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0110] Step S21: extracting historical power grid transmission according to the power system IoT gateway to obtain historical power grid transmission data;

[0111] In an embodiment of the present invention, historical power grid transmission extraction is performed based on the power system Internet of Things gateway. First, the power grid transmission data in the past period of time is obtained by connecting to the centralized database through the gateway. These data include information such as current, voltage, power, etc. of each node, and are marked with timestamps for subsequent analysis. Use data extraction tools, such as SQL query language, to write query statements and select the required fields and time ranges. After the extraction is completed, the historical power grid transmission data is organized into a structured format and stored in a local data file.

[0112] Step S22: forecasting the power grid transmission demand based on historical power grid transmission data to obtain power grid transmission demand forecast data;

[0113] In an embodiment of the present invention, a grid transmission demand forecast is performed based on the extracted historical grid transmission data. First, the historical data is processed using a statistical analysis method, such as time series analysis or regression analysis. The data is divided into a training set and a test set, and a linear regression model or an ARIMA model is applied for training to identify trends and periodic changes in the data. After the model is generated, the model is used to forecast future grid transmission demand, and grid transmission demand forecast data is output, including the estimated load and power demand of each node in the future period.

[0114] Step S23: Analyze the abnormal load state of the power grid according to the power grid transmission demand forecast data and the power grid node distribution topology map to obtain abnormal load state data of the power grid;

[0115] In an embodiment of the present invention, an abnormal load state analysis of the power grid is performed based on the power grid transmission demand forecast data and the power grid node distribution topology map. First, the power grid transmission demand forecast data is combined with the power grid node distribution topology map to identify the normal load range of each node. Using the set load threshold, the predicted load of each node is evaluated to detect whether there is an overload or underload situation. An anomaly detection algorithm, such as a Z-score or a rule-based method, is applied to mark nodes with abnormal loads. Finally, abnormal load state data of the power grid is generated, including the ID of the abnormal node, the load value, and the degree to which it deviates from the normal load range.

[0116] Step S24: performing voltage abnormal fluctuation detection according to the abnormal load state data of the power grid to obtain abnormal voltage fluctuation data of the power grid;

[0117] In an embodiment of the present invention, abnormal voltage fluctuation detection is performed based on abnormal load state data of the power grid. First, voltage data related to the abnormal load state is collected, and a normal voltage range is set. The standard deviation and average value of the voltage fluctuation are calculated by real-time monitoring of the voltage data of each node. A threshold judgment method is applied, and when the voltage fluctuation exceeds the set standard range, it is recorded as a voltage abnormality. Signal processing tools, such as fast Fourier transform (FFT), are used to perform spectrum analysis on the voltage signal to further identify the frequency characteristics of the voltage fluctuation. Finally, abnormal power grid voltage fluctuation data is formed, indicating the affected nodes and their fluctuation amplitudes.

[0118] Step S25: Perform grid frequency anomaly detection based on the grid voltage fluctuation data and the grid abnormal load state data to obtain grid frequency anomaly data.

[0119] In an embodiment of the present invention, grid frequency anomaly detection is performed based on grid voltage fluctuation data and grid abnormal load status data. First, the frequency information of the corresponding node is extracted using the voltage fluctuation data and the load status data. The normal range of frequency is set, and the frequency deviation is identified by comparing the real-time monitoring with the historical data. Frequency analysis tools, such as sliding window technology, are applied to calculate the short-term and long-term average values ​​of the frequency to detect the trend of frequency change. If the frequency exceeds the normal range, it is recorded as a frequency anomaly. Finally, grid frequency anomaly data is generated, including the ID of the abnormal node, the frequency value and the degree of its deviation from the normal range.

[0120] Preferably, step S22 includes the following steps:

[0121] Step S221: performing time series analysis based on historical power grid transmission data to obtain historical power grid transmission time series data;

[0122] Step S222: performing a transmission power periodicity analysis on the historical power grid transmission time series data to obtain historical transmission power periodicity data;

[0123] Step S223: Calculate the transmission power fluctuation rate of the historical power grid transmission time series data to obtain the historical power grid transmission power fluctuation rate data;

[0124] Step S224: forecasting instantaneous high demand based on historical power grid transmission power fluctuation rate data to obtain instantaneous high demand data of power grid transmission power;

[0125] Step S225: forecasting the historical power grid transmission demand based on the historical transmission power periodicity data and the instantaneous high power grid transmission power demand data to obtain power grid transmission demand forecast data.

[0126] As an example of the present invention, refer to Figure 3 As shown, in this example, step S22 includes:

[0127] Step S221: performing time series analysis based on historical power grid transmission data to obtain historical power grid transmission time series data;

[0128] In an embodiment of the present invention, time series analysis is performed based on historical power grid transmission data. First, the transmission power data of each node of the power grid in different time periods is extracted to form a one-dimensional time series. Data analysis tools, such as Python's Pandas library, are used to organize the data in chronological order. The autocorrelation function (ACF) and partial autocorrelation function (PACF) are used to analyze the correlation of the data and identify trends and seasonal patterns in the time series. The seasonal fluctuations are removed by linear regression or moving average method to extract the historical power grid transmission time series data.

[0129] Step S222: performing a transmission power periodicity analysis on the historical power grid transmission time series data to obtain historical transmission power periodicity data;

[0130] In an embodiment of the present invention, the periodicity analysis of the transmission power is performed on the historical power grid transmission time series data. First, the periodic characteristics in the time series data are analyzed by applying Fourier transform or seasonal decomposition method. By calculating the periodic components, the seasonal variation law of the power grid transmission power is determined. For example, different cycles such as days, weeks, and months can be analyzed to identify the peaks and valleys of power in a specific time period. Finally, the historical transmission power periodicity data is generated.

[0131] Step S223: Calculate the transmission power fluctuation rate of the historical power grid transmission time series data to obtain the historical power grid transmission power fluctuation rate data;

[0132] In the embodiment of the present invention, the historical power grid transmission time series data is used to calculate the transmission power fluctuation rate. First, based on the time series data, the power change in each time period is calculated to obtain the absolute value of the power increase or decrease. Then, the volatility formula is used to calculate the ratio of the standard deviation of these changes to the average value to obtain the transmission power fluctuation rate data. This process can be efficiently calculated using the NumPy library in Python to form a volatility data set that identifies the intensity of power fluctuations in different time periods.

[0133] Step S224: forecasting instantaneous high demand based on historical power grid transmission power fluctuation rate data to obtain instantaneous high demand data of power grid transmission power;

[0134] In the embodiment of the present invention, instantaneous high demand is predicted based on historical power grid transmission power fluctuation rate data. First, the historical fluctuation rate data is analyzed, combined with the previous data trend, and the instantaneous demand peak is identified using a time series prediction method, such as the exponential smoothing method. The threshold of high demand is defined, the historical fluctuation rate is combined with the average power value, and the conditions are set. When the fluctuation rate exceeds a certain critical value, it is predicted that an instantaneous high demand will occur in this period. Finally, the instantaneous high demand data of power grid transmission power is generated.

[0135] Step S225: forecasting the historical power grid transmission demand based on the historical transmission power periodicity data and the instantaneous high power grid transmission power demand data to obtain power grid transmission demand forecast data.

[0136] In an embodiment of the present invention, historical power grid transmission demand prediction is performed based on historical transmission power periodicity data and instantaneous high demand data of power grid transmission power. First, the periodicity data is combined with the instantaneous high demand data, and their intersection is analyzed to determine the overlapping interval of the high demand period. A weighted average method or a linear regression model is used to comprehensively consider the periodicity of historical data and instantaneous high demand to predict the overall transmission demand of the future power grid. The result will form the power grid transmission demand prediction data.

[0137] Preferably, step S1 comprises the following steps:

[0138] Step S231: dividing the power grid transmission demand forecast data and the power grid node distribution topology map into multiple-node power demand, and obtaining multiple-node power high demand data and multiple-node power low demand data;

[0139] Step S232: performing a power grid operation overload analysis according to the multi-node power high demand data and the multi-node power low demand data to obtain power grid operation overload data;

[0140] Step S233: performing power generation equipment efficiency attenuation detection according to the low demand data transmitted by the power grid to obtain power generation equipment efficiency attenuation data;

[0141] Step S234: Analyze the abnormal load state of the power grid according to the power generation equipment efficiency decay data and the power grid overload data to obtain abnormal load state data of the power grid.

[0142] In an embodiment of the present invention, the power grid transmission demand forecast data and the power grid node distribution topology map are divided into multi-node power demand. First, the connection relationship of each node is identified using the power grid node distribution topology map, and the nodes are divided into two categories of high demand and low demand in combination with the predicted power demand data. The standard of the high-demand node can be set as a load exceeding a certain threshold, while the low-demand node is a load below the threshold. Data processing tools, such as Python's Pandas library, are used to write data screening scripts to automate the classification process. Finally, the high-demand nodes and low-demand nodes are recorded as multi-node power high demand data and multi-node power low demand data, respectively. The power grid operation overload analysis is performed based on the multi-node power high demand data and the multi-node power low demand data. According to the rated capacity and actual load of the node, the load rate of each node, that is, the ratio of the actual load to the rated load, is calculated. An overload threshold is set, and when the load rate exceeds the set value (for example, 90%), it is marked as an overload state. Use power system simulation software or calculation tools to batch process the load data of each node, generate power grid operation overload data, perform power generation equipment efficiency attenuation detection based on power grid transmission low demand data, and collect power generation equipment operating parameters at low demand, such as power generation, fuel consumption, and equipment operating hours. By setting the standard efficiency value, calculate the actual efficiency and compare it with the standard efficiency. Use linear regression or data mining methods to analyze historical operation data and identify efficiency attenuation trends. Finally, generate power generation equipment efficiency attenuation data, analyze power grid abnormal load status based on power generation equipment efficiency attenuation data and power grid overload data, combine power generation equipment efficiency attenuation data with power grid overload data, and analyze their mutual influence. By setting the normal range of load status, determine which equipment fails to meet the needs of the power grid due to efficiency attenuation. Use logistic regression analysis methods to evaluate the probability of load abnormality and generate power grid abnormal load status data.

[0143] Preferably, step S232 includes the following steps:

[0144] Obtaining grid security capacity data;

[0145] A demand heat map is constructed based on multi-node power high demand data and multi-node power low demand data to obtain power demand heat map data;

[0146] Conduct unbalanced load analysis on power grid nodes on the power demand heat map data to obtain unbalanced load data on power grid nodes;

[0147] Perform transmission capacity overload detection on power grid safety capacity data according to multi-node power high demand data to obtain power grid transmission capacity overload data;

[0148] Perform grid stability anomaly detection based on grid node unbalanced load data and grid transmission capacity overload data to obtain grid stability anomaly data;

[0149] The grid operation overload analysis is performed based on the grid stability abnormal data and the grid transmission capacity overload data to obtain the grid operation overload data.

[0150] In an embodiment of the present invention, in the process of obtaining the safety capacity data of the power grid, it is first necessary to extract the safety capacity data of each node of the power grid from the power dispatching center or the relevant monitoring system. These data generally include the rated load capacity and critical load value of each node. Through the data interface call, a standardized API (such as a RESTful API) is used to obtain real-time and historical node capacity information. Then, data analysis tools such as the Pandas library in Excel or Python are used to organize these data and classify them according to the node ID. Finally, a detailed power grid safety capacity data table is formed to record the safety capacity value of each node. In the process of constructing a demand heat map based on multi-node power high demand data and low demand data, the high demand and low demand data of each node of the power grid are extracted. These data need to be summarized by time and node to generate the input data set of the heat map. Then, a data visualization tool such as Matplotlib or Seaborn library is used to construct a heat map. The node demand data is mapped to the color intensity of the heat map, and the color depth represents the high and low power demand. Finally, the power demand heat map data is generated, and the graphics clearly show the power demand in different areas. When analyzing the unbalanced load of power grid nodes on the power demand heat map data, it is necessary to calculate the load distribution of each node. The power demand heat map data is segmented to identify the concentrated and sparse areas of load distribution. The standard deviation and mean methods are used to evaluate the balance of the load, and the deviation of each node from the mean load of the entire network is calculated. If the load of a node is significantly higher or lower than the mean, it is marked as unbalanced load. Using data analysis tools, unbalanced load data of power grid nodes are generated, including the load value, mean and deviation of each node. In the process of overload detection of transmission capacity of power grid safety capacity based on multi-node power high demand data, the high demand data is compared with the safety capacity data to determine the ratio of the load of each node to its safety capacity. Set a threshold (for example, 95%), and if the load of a node exceeds this ratio, it is marked as overloaded. Use computing tools, such as Python, to write automated scripts to batch calculate the load rate of each node and generate power grid transmission capacity overload data, including the ID, load value and capacity value of the overloaded node. When detecting grid stability anomalies based on grid node unbalanced load data and transmission capacity overload data, the two types of data are combined to analyze the impact of load imbalance on the overall stability of the grid. A stability evaluation standard is set to combine load imbalance and overload data. If a node has high load and significant deviation at the same time, it will lead to stability problems. Logistic regression analysis method is used to identify abnormal patterns, generate grid stability abnormal data, and record abnormal nodes and their load characteristics. When analyzing grid operation overload based on grid stability abnormal data and transmission capacity overload data, the two sets of data are combined to analyze the impact of stability anomalies on the operating status.Comprehensively evaluate the load and stability status of each node, set the comprehensive overload standard, for example, when the comprehensive load rate exceeds the set threshold, it is judged as operation overload. Use data processing software for batch analysis to generate power grid operation overload data, including the operation status, load conditions and corresponding stability indicators of each node. Finally, a detailed operation overload report is formed.

[0151] Preferably, step S3 comprises the following steps:

[0152] Step S31: predicting the aging trend of power grid equipment according to the power grid frequency abnormality data and the power grid voltage fluctuation data to obtain power grid equipment aging trend data;

[0153] Step S32: performing equipment damage prediction according to the power grid equipment aging trend data to obtain equipment damage prediction data;

[0154] Step S33: estimating the power resource loss based on the equipment damage prediction data of the power system Internet of Things gateway to obtain power resource loss estimation data.

[0155] In an embodiment of the present invention, the aging trend of power grid equipment is estimated based on the power grid frequency abnormality data and the power grid voltage fluctuation data. First, the historical abnormality data of power grid frequency and voltage are collected, which record the frequency fluctuation amplitude and voltage change in a specific time period. Use data analysis tools, such as Pandas and NumPy libraries in Python, to clean and organize these data. Then, a time series analysis method, such as a moving average method or an exponential smoothing method, is used to evaluate the impact of frequency and voltage anomalies on equipment aging. Combined with the technical parameters of the equipment, a linear regression is used to analyze the trend of equipment aging, and finally the power grid equipment aging trend data is generated. The equipment damage is predicted based on the power grid equipment aging trend data, and the aging trend data generated in step S31 is analyzed to identify severely aged equipment and components. Statistical methods, such as survival analysis and fault tree analysis, are applied to determine the relationship between aging and equipment damage in combination with the historical damage records of the equipment. Machine learning tools, such as the Scikit-learn library, can be used to train a prediction model to predict future damage risks through equipment aging indicators. Generate equipment damage prediction data, and estimate the power resource loss based on the equipment damage prediction data by the power system IoT gateway. The IoT gateway collects real-time operation data from each device, including load, fault status, and maintenance records. Combine these real-time data with the damage prediction data in step S32, and use the cost analysis model to evaluate the power resource loss caused by equipment damage. In the specific operation, define the loss calculation formula, quantify the impact of equipment damage on the grid operation efficiency as loss, and use Excel or Python to write calculation scripts for automated processing. Finally, generate power resource loss estimation data.

[0156] Preferably, step S31 includes the following steps:

[0157] Step S311: Calculate the probability of electrical shock to the power grid according to the power grid voltage fluctuation data to obtain the probability data of electrical shock to the power grid;

[0158] Step S312: performing cumulative calculation of thermal effects of power grid equipment according to power grid frequency abnormality data to obtain cumulative data of thermal effects of power grid equipment;

[0159] Step S313: Performing equipment insulation layer damage detection according to the power grid electrical shock probability data and the accumulated data of thermal effects of power grid equipment to obtain power grid equipment insulation layer damage data;

[0160] Step S314: Performing insulation failure detection on the power grid equipment according to the insulation layer damage data of the power grid equipment to obtain insulation failure data on the power grid equipment;

[0161] Step S315: predicting the aging trend of the power grid equipment according to the insulation failure data of the power grid equipment to obtain the aging trend data of the power grid equipment.

[0162] In an embodiment of the present invention, the probability of electrical shock of the power grid is calculated according to the power grid voltage fluctuation data. First, the historical voltage fluctuation data of the power grid is collected to ensure that the data covers the fluctuations under multiple time periods and different load conditions. Statistical analysis tools, such as the SciPy library in Python, are used to calculate the standard deviation and mean of the voltage fluctuation to evaluate the degree of fluctuation. By analyzing the relationship between the voltage fluctuation amplitude and the normal working range of the equipment, an occurrence model of electrical shock is established, and a probability model such as Poisson distribution is used to calculate the probability of electrical shock occurring within a specific time. Finally, the probability data of electrical shock of the power grid is generated, and the cumulative calculation of the thermal effect of the power grid equipment is performed according to the abnormal frequency data of the power grid, the abnormal frequency data of the power grid is extracted, and the amplitude and duration of the frequency fluctuation are recorded. The thermal effect model is used to associate the frequency fluctuation with the heat generated by the equipment. In the specific calculation, the law of conservation of energy is used to calculate the power change caused by the frequency abnormality, so as to estimate the thermal effect of the equipment caused by the frequency fluctuation. Data processing tools, such as MATLAB or Excel, are used to perform cumulative calculation of thermal effect, generate cumulative data of thermal effect of power grid equipment, record the cumulative value of thermal effect of each device in a specific time period, and provide basic data for subsequent insulation layer damage detection. According to the power grid electrical shock probability data and the cumulative data of thermal effect of power grid equipment, the insulation layer damage detection of equipment is carried out, and the generated electrical shock probability data and thermal effect cumulative data are integrated to analyze the impact of the two on the insulation layer of the equipment. The insulation layer damage threshold is set, and the impact of the shock probability and thermal effect is quantified through the risk assessment model to form an early warning indicator for insulation layer damage. The decision tree algorithm or fuzzy logic is used for analysis to determine whether there is a risk of insulation layer damage, and the insulation layer damage data of power grid equipment is generated, and the affected equipment and its damage risk level are marked. According to the insulation layer damage data of power grid equipment, the insulation failure detection of power grid equipment is carried out, and the damage degree of each equipment and its historical operation data are analyzed. The insulation failure standard is set, and the insulation resistance test is compared with the critical value to determine whether there is a failure risk. The insulation status of the equipment is monitored in real time using the power system monitoring software, and a comprehensive analysis is conducted in combination with historical data. Finally, the insulation failure data of power grid equipment is generated, and the type of failed equipment, the scope of impact and its potential risk to power grid operation are recorded. The aging trend of power grid equipment is estimated based on the insulation failure data of power grid equipment, and the generated insulation failure data is extracted, and a comprehensive analysis is conducted in combination with factors such as the service life and working environment of the equipment. Use time series analysis methods to identify equipment aging patterns and trends, and use regression analysis models to predict future aging conditions. Use data analysis tools, such as R or Python, to predict trends, generate grid equipment aging trend data, and record aging rates and expected failure times.

[0163] This specification also provides an Internet of Things gateway, which is used to execute the operation method of the Internet of Things gateway as described above, and the Internet of Things gateway includes:

[0164] IoT gateway construction module: obtain power system data; construct a grid node topology diagram based on the power system data to obtain a grid node distribution topology diagram; construct an IoT gateway based on the power system data and the grid node distribution topology diagram to obtain a power system IoT gateway;

[0165] Grid frequency anomaly detection module: Grid transmission demand prediction is performed based on the power system IoT gateway to obtain grid transmission demand prediction data; grid abnormal load state analysis is performed based on the grid transmission demand prediction data and the grid node distribution topology diagram to obtain grid abnormal load state data; abnormal voltage fluctuation detection is performed based on the grid abnormal load state data to obtain grid abnormal voltage fluctuation data; grid frequency anomaly detection is performed based on the grid abnormal voltage fluctuation data to obtain grid frequency anomaly data;

[0166] Power resource loss estimation module: The power grid equipment aging trend is estimated based on the power grid frequency abnormality data and the power grid voltage abnormal fluctuation data to obtain the power grid equipment aging trend data; the power resource loss is estimated based on the power system Internet of Things gateway for the power grid equipment aging trend data to obtain the power resource loss estimation data.

[0167] Abnormal warning module: Use the power system IoT gateway to issue abnormal warnings on the power resource loss estimation data and equipment damage prediction data, obtain the power grid abnormal warning data, and return the power grid abnormal warning data to the terminal.

[0168] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for operating an Internet of Things gateway, characterized in that: The following steps are involved: Step S1: Acquire power system data; construct a power grid node topology diagram based on the power system data to obtain a power grid node distribution topology diagram; construct an Internet of Things gateway based on the power system data and the power grid node distribution topology diagram to generate a power system Internet of Things gateway; Step S2: forecasting the transmission demand of the power grid according to the Internet of Things gateway of the power system to obtain the transmission demand forecast data of the power grid; analyzing the abnormal load state of the power grid according to the transmission demand forecast data of the power grid and the distribution topology diagram of the power grid nodes to obtain the abnormal load state data of the power grid; detecting the abnormal voltage fluctuation according to the abnormal load state data of the power grid to obtain the abnormal voltage fluctuation data of the power grid; detecting the abnormal frequency of the power grid according to the abnormal voltage fluctuation data of the power grid to obtain the abnormal frequency data of the power grid; Step S3: estimating the aging trend of power grid equipment according to the power grid frequency abnormality data and the power grid voltage abnormal fluctuation data to obtain the power grid equipment aging trend data; The power resource loss is estimated based on the power system IoT gateway's aging trend data of power grid equipment to obtain power resource loss estimation data; Step S4: Use the power system Internet of Things gateway to conduct abnormal warning on the power resource loss estimation data and the equipment damage prediction data, obtain the power grid abnormal warning data, and return the power grid abnormal warning data to the terminal.

2. The method for operating the Internet of Things gateway according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: acquiring power system data; Step S12: collecting grid node location distribution data based on the power system data to obtain grid node location distribution data; Step S13: constructing a grid node topology structure diagram according to the grid node location distribution data to obtain a grid node distribution topology diagram; Step S14: using a preset sensor to perform grid node sensing collection on the grid node distribution topology map to obtain grid node sensing data; Step S15: construct an Internet of Things gateway according to the grid node sensor data and the grid node distribution topology map to generate a power system Internet of Things gateway.

3. The operation method of the Internet of Things gateway according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: Calculate the amount of sensor data based on the sensor data of the power grid nodes to obtain the total amount of sensor data of the power grid nodes; Step S152: performing grid node operation status detection on the grid node sensor data and the grid node distribution topology map to obtain grid node operation status data; Step S153: defining a gateway communication protocol according to the grid node operation status data and the amount of grid node sensor data, and obtaining the Internet of Things gateway communication protocol data; Step S154: construct an Internet of Things gateway based on the Internet of Things gateway communication protocol data and the power grid node operation status data to generate an Internet of Things gateway for the power system.

4. The operation method of the Internet of Things gateway according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: extracting historical power grid transmission according to the power system IoT gateway to obtain historical power grid transmission data; Step S22: forecasting the power grid transmission demand based on historical power grid transmission data to obtain power grid transmission demand forecast data; Step S23: Analyze the abnormal load state of the power grid according to the power grid transmission demand forecast data and the power grid node distribution topology map to obtain abnormal load state data of the power grid; Step S24: performing voltage abnormal fluctuation detection according to the abnormal load state data of the power grid to obtain abnormal voltage fluctuation data of the power grid; Step S25: Perform grid frequency anomaly detection based on the grid voltage fluctuation data and the grid abnormal load state data to obtain grid frequency anomaly data.

5. The operation method of the Internet of Things gateway according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: performing time series analysis based on historical power grid transmission data to obtain historical power grid transmission time series data; Step S222: performing a transmission power periodicity analysis on the historical power grid transmission time series data to obtain historical transmission power periodicity data; Step S223: Calculate the transmission power fluctuation rate of the historical power grid transmission time series data to obtain the historical power grid transmission power fluctuation rate data; Step S224: forecasting instantaneous high demand based on historical power grid transmission power fluctuation rate data to obtain instantaneous high demand data of power grid transmission power; Step S225: forecasting the historical power grid transmission demand based on the historical transmission power periodicity data and the instantaneous high power grid transmission power demand data to obtain power grid transmission demand forecast data.

6. The method for operating the Internet of Things gateway according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: dividing the power grid transmission demand forecast data and the power grid node distribution topology map into multiple-node power demand, and obtaining multiple-node power high demand data and multiple-node power low demand data; Step S232: performing a power grid operation overload analysis according to the multi-node power high demand data and the multi-node power low demand data to obtain power grid operation overload data; Step S233: performing power generation equipment efficiency attenuation detection according to the low demand data transmitted by the power grid to obtain power generation equipment efficiency attenuation data; Step S234: Analyze the abnormal load state of the power grid according to the power generation equipment efficiency decay data and the power grid overload data to obtain abnormal load state data of the power grid.

7. The operation method of the Internet of Things gateway according to claim 6, characterized in that: Step S232 includes the following steps: Obtaining grid security capacity data; A demand heat map is constructed based on multi-node power high demand data and multi-node power low demand data to obtain power demand heat map data; Conduct unbalanced load analysis on power grid nodes on the power demand heat map data to obtain unbalanced load data on power grid nodes; Perform transmission capacity overload detection on power grid safety capacity data according to multi-node power high demand data to obtain power grid transmission capacity overload data; Perform grid stability anomaly detection based on grid node unbalanced load data and grid transmission capacity overload data to obtain grid stability anomaly data; The grid operation overload analysis is performed based on the grid stability abnormal data and the grid transmission capacity overload data to obtain the grid operation overload data.

8. The method for operating the Internet of Things gateway according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: predicting the aging trend of power grid equipment according to the power grid frequency abnormality data and the power grid voltage fluctuation data to obtain power grid equipment aging trend data; Step S32: performing equipment damage prediction according to the power grid equipment aging trend data to obtain equipment damage prediction data; Step S33: estimating the power resource loss based on the equipment damage prediction data of the power system Internet of Things gateway to obtain power resource loss estimation data.

9. The method for operating the Internet of Things gateway according to claim 8, characterized in that: Step S31 The following steps are involved: Step S311: Calculate the probability of electrical shock to the power grid according to the power grid voltage fluctuation data to obtain the probability data of electrical shock to the power grid; Step S312: performing cumulative calculation of thermal effects of power grid equipment according to power grid frequency abnormality data to obtain cumulative data of thermal effects of power grid equipment; Step S313: Performing equipment insulation layer damage detection according to the power grid electrical shock probability data and the accumulated data of thermal effects of power grid equipment to obtain power grid equipment insulation layer damage data; Step S314: Performing insulation failure detection on the power grid equipment according to the insulation layer damage data of the power grid equipment to obtain insulation failure data on the power grid equipment; Step S315: predicting the aging trend of the power grid equipment according to the insulation failure data of the power grid equipment to obtain the aging trend data of the power grid equipment.

10. An Internet of Things gateway, characterized in that: Used to execute the operation method of the Internet of Things gateway as claimed in claim 1, the Internet of Things gateway comprising: IoT gateway construction module: obtain power system data; construct a grid node topology diagram based on the power system data to obtain a grid node distribution topology diagram; construct an IoT gateway based on the power system data and the grid node distribution topology diagram to obtain a power system IoT gateway; Grid frequency anomaly detection module: Grid transmission demand prediction is performed based on the power system IoT gateway to obtain grid transmission demand prediction data; grid abnormal load state analysis is performed based on the grid transmission demand prediction data and the grid node distribution topology diagram to obtain grid abnormal load state data; abnormal voltage fluctuation detection is performed based on the grid abnormal load state data to obtain grid abnormal voltage fluctuation data; grid frequency anomaly detection is performed based on the grid abnormal voltage fluctuation data to obtain grid frequency anomaly data; Power resource loss estimation module: The power grid equipment aging trend is estimated based on the power grid frequency abnormality data and the power grid voltage abnormal fluctuation data to obtain the power grid equipment aging trend data; the power resource loss is estimated based on the power system Internet of Things gateway for the power grid equipment aging trend data to obtain the power resource loss estimation data. Abnormal warning module: Use the power system IoT gateway to issue abnormal warnings on the power resource loss estimation data and equipment damage prediction data, obtain the power grid abnormal warning data, and return the power grid abnormal warning data to the terminal.

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