A circuit breaker digital monitoring method based on the Internet of Things

Abstract

The application relates to the technical field of digital data monitoring, in particular to a circuit breaker digital monitoring method based on the Internet of Things. The method comprises the following steps: monitoring a circuit breaker connected with a communication device to obtain circuit breaker monitoring data, and establishing and updating a circuit breaker state attribute matrix through the circuit breaker monitoring data; performing global abnormal state analysis on the circuit breaker based on the updated circuit breaker state attribute matrix to generate circuit breaker global abnormal state data; performing abnormal detection information mapping and circuit breaker connection state feedback control processing on the updated circuit breaker state attribute matrix based on the circuit breaker global abnormal state data to generate a circuit breaker intelligent control attribute matrix; and transmitting the circuit breaker intelligent control attribute matrix to a communication device multi-level space topology for circuit breaker multi-level monitoring mapping to generate circuit breaker multi-level monitoring topology data. The application realizes more comprehensive and accurate digital monitoring and intelligent control of the circuit breaker.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital data monitoring, and in particular to a circuit breaker digital monitoring method based on the Internet of Things. BACKGROUND

[0002] At present, the power system is continuously expanding in scale and becoming more complex in structure, which puts extremely strict requirements on the reliability and stability of power equipment. As a key device in the power system, the circuit breaker bears the important responsibility of controlling and protecting the circuit, and its operating state is directly related to the safe and stable operation of the power system. The Internet of Things technology has been widely applied in various fields due to its strong sensing, transmission and processing capabilities. The circuit breaker digital monitoring based on the Internet of Things has emerged as the times require. It can collect multi-dimensional data such as voltage, current, temperature and action frequency in real time by deploying various sensors on the circuit breaker, and transmit these data to the cloud or local monitoring platform through the Internet of Things communication technology. However, the existing circuit breaker digital monitoring method usually only monitors basic electrical parameters such as current and voltage, ignoring key indicators such as temperature distribution and wafer pressure that reflect the mechanical wear and thermal stability of the equipment, and is difficult to reveal potential fault hazards through multi-source data fusion. SUMMARY

[0003] Therefore, the present application provides a circuit breaker digital monitoring method based on the Internet of Things to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a circuit breaker digital monitoring method based on the Internet of Things comprises the following steps:

[0005] Step S1: using the Internet of Things cluster monitoring device to monitor the circuit breaker connected with the communication device to obtain circuit breaker monitoring data; and extracting circuit breaker state attributes based on the circuit breaker monitoring data to construct a circuit breaker state attribute matrix;

[0006] Step S2: performing matrix node dynamic update processing of circuit breaker current soft adjustment on the circuit breaker state attribute matrix to generate an updated circuit breaker state attribute matrix;

[0007] Step S3: performing circuit breaker global abnormal state analysis based on the updated circuit breaker state attribute matrix to generate circuit breaker global abnormal state data; and performing circuit breaker global abnormal state causal feature analysis according to the circuit breaker global abnormal state data to generate circuit breaker global abnormal state causal feature data;

[0008] Step S4: performing circuit breaker state abnormality detection information mapping and circuit breaker connectivity state feedback control processing on the updated circuit breaker state attribute matrix based on the circuit breaker global abnormal state causal feature data to generate a circuit breaker intelligent control attribute matrix;

[0009] Step S5: Establishing a multi-level spatial topology structure of the communication device; transmitting the intelligent control attribute matrix of the circuit breaker to the multi-level spatial topology structure of the communication device for multi-level monitoring mapping of the circuit breaker, generating multi-level monitoring topology data of the circuit breaker, and transmitting the multi-level monitoring topology data of the circuit breaker to the terminal for multi-level monitoring feedback operation of the circuit breaker.

[0010] Further, step S1 includes the following steps:

[0011] Step S11: monitoring the circuit breaker connected to the communication device by using the Internet of Things cluster monitoring device to obtain circuit breaker monitoring data, wherein the circuit breaker monitoring data includes circuit breaker current data, circuit breaker voltage data, circuit breaker temperature distribution data, circuit breaker wafer pressure distribution data, and circuit breaker connection state data;

[0012] Step S12: performing time sequence synchronization processing on the circuit breaker monitoring data to generate synchronized circuit breaker monitoring data; and performing spatial node identification on the synchronized circuit breaker monitoring data to generate circuit breaker spatial node data;

[0013] Step S13: establishing a circuit breaker state attribute matrix architecture by using the circuit breaker connection state data in the synchronized circuit breaker monitoring data and the circuit breaker spatial node data, and performing circuit breaker attribute state mapping on the circuit breaker current data, the circuit breaker voltage data, and the circuit breaker temperature distribution data in the synchronized circuit breaker monitoring data to obtain the circuit breaker state attribute matrix.

[0014] Further, step S2 includes the following steps:

[0015] Step S21: obtaining circuit breaker current soft adjustment parameters;

[0016] Step S22: setting a circuit breaker current short-time limiting threshold and a circuit breaker current long-time limiting threshold according to the circuit breaker current soft adjustment parameters, wherein the circuit breaker current short-time limiting threshold is greater than the circuit breaker current long-time limiting threshold;

[0017] Step S23: when the circuit breaker current data in the circuit breaker state attribute matrix is not less than the circuit breaker current short-time limiting threshold and the duration is not less than a preset short-time load time sequence parameter, or when the circuit breaker current data in the circuit breaker state attribute matrix is not less than the circuit breaker current short-time limiting threshold and the duration is not less than a preset short-time load time sequence parameter, performing circuit breaker soft adjustment flow expansion update processing on the circuit breaker current soft adjustment parameters to generate updated circuit breaker current soft adjustment parameters, and performing circuit breaker current upper limit update operation through the updated circuit breaker current soft adjustment parameters;

[0018] Step S24: dynamically updating the circuit breaker state attribute matrix based on the circuit breaker current upper limit update operation, to generate an updated circuit breaker state attribute matrix.

[0019] Further, step S3 includes the following steps:

[0020] Step S31: performing circuit breaker pressure-uniform current abnormal node analysis on the circuit breaker current data and the circuit wafer pressure distribution data corresponding to the updated circuit breaker state attribute matrix, to generate circuit breaker pressure-uniform current abnormal distribution data;

[0021] Step S32: performing circuit breaker first abnormal state recognition based on the circuit breaker pressure-uniform current abnormal distribution data, to generate circuit breaker first abnormal state data;

[0022] Step S33: screening circuit breaker state data to be detected that is not related to the circuit breaker first abnormal state data in the updated circuit breaker state attribute matrix;

[0023] Step S34: performing circuit breaker second abnormal state data analysis on the circuit breaker state data to be detected based on the updated circuit breaker state attribute matrix, to generate circuit breaker second abnormal state data;

[0024] Step S35: performing circuit breaker global abnormal state analysis according to the circuit breaker first abnormal state data and the circuit breaker second abnormal state data, to generate circuit breaker global abnormal state data;

[0025] Step S36: performing circuit breaker global abnormal state causal feature analysis according to the circuit breaker global abnormal state data, to generate circuit breaker global abnormal state causal feature data.

[0026] Further, step S31 includes the following steps:

[0027] Performing branch current waveform analysis on the circuit breaker current data corresponding to the updated circuit breaker state attribute matrix, to generate circuit breaker branch current waveform data;

[0028] Performing circuit breaker branch current analysis through the circuit breaker branch current waveform data, to generate circuit breaker branch current data;

[0029] Performing circuit breaker pressure-uniform current abnormal node analysis according to the circuit wafer pressure distribution data and the circuit breaker branch current data, to generate circuit breaker pressure-uniform current abnormal distribution data.

[0030] Further, step S34 includes the following steps:

[0031] Step S341: performing circuit breaker current effective value time sequence feature analysis according to the circuit breaker current data corresponding to the updated circuit breaker state attribute matrix, to generate circuit breaker current effective value time sequence feature data;

[0032] Step S342: Perform circuit breaker temperature time sequence feature analysis on the circuit breaker temperature data corresponding to the updated circuit breaker state attribute matrix, and generate circuit breaker temperature time sequence feature data;

[0033] Step S343: Perform circuit breaker voltage harmonic time sequence feature analysis on the circuit breaker current data corresponding to the updated circuit breaker state attribute matrix, and generate circuit breaker voltage harmonic time sequence feature data;

[0034] Step S344: Perform circuit breaker current-temperature anomaly analysis based on the circuit breaker current effective value time sequence feature data and the circuit breaker temperature time sequence feature data, and generate circuit breaker current-temperature anomaly data;

[0035] Step S345: Perform circuit breaker voltage-temperature rise anomaly data analysis based on the circuit breaker voltage harmonic time sequence feature data and the circuit breaker temperature time sequence feature data, and generate circuit breaker voltage-temperature rise anomaly data;

[0036] Step S346: Perform circuit breaker second abnormal state data analysis on the to-be-detected circuit breaker state data based on the circuit breaker current-temperature anomaly data and the circuit breaker voltage-temperature rise anomaly data, and generate circuit breaker second abnormal state data.

[0037] Further, step S344 includes the following steps:

[0038] Perform circuit breaker current-temperature time sequence feature analysis based on the circuit breaker current effective value time sequence feature data and the circuit breaker temperature time sequence feature data;

[0039] Perform circuit breaker current-temperature correlation analysis according to the circuit breaker current-temperature time sequence feature data, and generate circuit breaker current-temperature correlation data; and perform circuit breaker current-temperature anomaly analysis through the circuit breaker current-temperature correlation data, and generate circuit breaker current-temperature anomaly data.

[0040] Further, step S345 includes the following steps:

[0041] Perform circuit breaker voltage harmonic-temperature rise mapping relationship analysis according to the circuit breaker voltage harmonic distortion time sequence feature data and the circuit breaker temperature time sequence feature data, and generate circuit breaker voltage harmonic-temperature rise mapping relationship data; and perform circuit breaker voltage-temperature rise anomaly data analysis through the circuit breaker voltage harmonic-temperature rise mapping relationship data, and generate circuit breaker voltage-temperature rise anomaly data.

[0042] Further, step S4 includes the following steps:

[0043] Step S41: based on the circuit breaker global abnormal state causal characteristic data, circuit breaker digital intelligent control analysis is performed to generate circuit breaker digital intelligent control data;

[0044] Step S42: the circuit breaker global abnormal state causal characteristic data and the circuit breaker digital intelligent control data are transmitted to the matrix node corresponding to the circuit breaker connection state data in the updated circuit breaker state attribute matrix to perform circuit breaker state abnormality detection information mapping and circuit breaker connection state feedback control processing, and a circuit breaker intelligent control attribute matrix is generated.

[0045] Further, step S5 includes the following steps:

[0046] Step S51: obtain the communication equipment space parameters, the base station belonging association data of the communication equipment, and the region belonging association data of the communication equipment;

[0047] Step S52: establish a communication equipment topology node according to the communication equipment space parameters, establish a base station sub-topological structure according to the communication equipment topology node and the base station belonging association data of the communication equipment, and establish a communication equipment multi-level space topological structure according to the region belonging association data of the communication equipment and the base station sub-topological structure;

[0048] Step S53: transmit the circuit breaker intelligent control attribute matrix to the communication equipment topology node corresponding to the communication equipment multi-level space topological structure to perform circuit breaker multi-level monitoring mapping, generate circuit breaker multi-level monitoring topological data, and transmit the circuit breaker multi-level monitoring topological data to the terminal to perform circuit breaker multi-level monitoring feedback work.

[0049] The application has the advantages that the application comprehensively monitors the circuit breaker connected with the communication equipment through the Internet of Things cluster monitoring device, realizes multi-dimensional data acquisition of the circuit breaker current, voltage, temperature, wafer pressure and communication state, and performs high-precision time sequence synchronization processing. Based on these synchronized monitoring data, a circuit breaker state attribute matrix architecture is constructed by space node identification, the various data are mapped to attribute states, a multi-level state attribute matrix is formed, and comprehensive digital description of the running state of the circuit breaker is realized. This processing process can significantly improve the real-time and accuracy of monitoring, which is helpful for rapid evaluation and judgment of the dynamic running state of the circuit breaker, and ensures the stable and efficient operation of the circuit breaker in a complex power grid environment. Based on the constructed circuit breaker state attribute matrix, a current soft regulation mechanism is further introduced to dynamically update the matrix nodes. By obtaining the current soft regulation parameters and setting short-time and long-time threshold values, the current load change in the running of the circuit breaker can be quickly responded. When the current exceeds the short-time or long-time threshold value, the current expansion update processing is automatically performed, and the current upper limit is adjusted, so that the circuit breaker state attribute matrix is dynamically refreshed. This mechanism effectively avoids the impact of instantaneous overload or long-time overload on the circuit breaker, prolongs the service life of the equipment, improves the fault tolerance and self-adaptive ability of the system, significantly enhances the safety and reliability of the circuit breaker operation, and observes the temperature reaction of the internal components of the circuit breaker under large current through current expansion. Based on the circuit breaker current data and wafer pressure distribution data, pressure-uniform current abnormal node analysis is performed, which can quickly capture the problem of uneven current distribution or local pressure abnormality in the circuit breaker. Through the coupling processing of branch current waveform analysis and wafer pressure distribution data, the pressure-uniform current abnormal node caused by poor contact of the contact can be quickly located. For example, by identifying the correlation between the current waveform distortion rate and the pressure drop, the physical position of the increased contact resistance can be accurately locked, which is earlier than the traditional single current monitoring in discovering contact fault hidden dangers. Through the cross analysis of branch current waveform, uniform current data and pressure distribution, the first abnormal state is accurately identified, and a hierarchical filtering mechanism of abnormal state is established through the first abnormal state identification and the to-be-detected data identification. On this basis, the time sequence characteristics of current, temperature and voltage harmonics of the to-be-detected circuit breaker state data are analyzed, the correlation modeling of temperature and current, voltage and temperature rise is performed, and more fine-grained second abnormal state data can be effectively identified. The time sequence characteristic cross analysis of current effective value, temperature and voltage harmonics can effectively distinguish the temperature rise anomalies caused by different causes such as load fluctuation and harmonic pollution. For example, when the current-temperature correlation coefficient is greater than a certain baseline threshold, it is determined that the temperature rise is caused by contact resistance abnormality. Through multi-level abnormal state cross analysis, the global abnormal state of the circuit breaker is comprehensively identified, and the abnormal reason is accurately located based on the causal characteristics.This series of analysis processes not only improves the comprehensiveness and sensitivity of circuit breaker state monitoring, but also significantly improves the prediction ability of abnormal faults, providing scientific basis and data support for subsequent intelligent control and maintenance. Based on the global abnormal state causal feature data of the circuit breaker, using digital analysis methods, considering the causes of abnormality, development trend and equipment operating parameters, digital intelligent control analysis of the circuit breaker is carried out, and digital intelligent control data of the circuit breaker containing control strategy, adjustment parameter, execution priority, etc. are generated. For example, when the causal characteristics show that the abnormal temperature rise of the circuit breaker is caused by insufficient contact pressure, the intelligent control data generates contact pressure adjustment instructions and current adjustment amplitude. The global abnormal state causal feature data of the circuit breaker and the digital intelligent control data of the circuit breaker are transmitted to the matrix node corresponding to the interconnection state data of the circuit breaker in the updated circuit breaker state attribute matrix, and the abnormal information and control instructions are accurately associated to the specific circuit breaker equipment through the mapping mechanism, and based on the feedback control principle, the control strategy is dynamically adjusted to realize the whole process intelligentization from abnormal diagnosis to precise control. According to the space parameters of the communication equipment, the communication equipment topology node is established, and based on the node, the base station sub-topological structure is constructed combining with the associated data of the base station, so as to realize the data aggregation and preliminary processing of the equipment in the local area; then according to the associated data of the region and the base station sub-topological structure, the multi-level space topological structure of the communication equipment covering multiple levels is established. The intelligent control attribute matrix of the circuit breaker is transmitted to the communication equipment topology node corresponding to the communication equipment multi-level space topological structure, and through the multi-level monitoring mapping mechanism, the data is orderly transmitted and processed between different levels of topologies, the multi-level monitoring topological data of the circuit breaker is generated, and it is transmitted to the terminal, so that the operation and maintenance personnel can clearly and intuitively obtain the running state and control information of the circuit breaker from the global to the local, and realize efficient monitoring feedback operation across regions and levels.

[0050] Therefore, the Internet of Things-based circuit breaker digital monitoring method of the present application is to obtain current soft regulation parameters and set short-time and long-time limit thresholds for circuit breaker current regulation problems, update the current soft regulation parameters when the current data meets the threshold trigger condition, dynamically adjust the upper limit of the current, and synchronously update the circuit breaker state attribute matrix, improve the dynamic adaptability of current regulation and observe the abnormal problems of each component of the circuit breaker under high-performance operation. The updated circuit breaker state attribute matrix is subjected to multi-dimensional cross analysis, and the abnormal causal characteristics are mined to realize accurate identification and root cause tracing of abnormalities. Not only basic electrical parameters such as current and voltage are monitored, but also key indicators such as temperature distribution and wafer pressure reflecting equipment mechanical wear and thermal stability are used to reveal potential equipment hidden dangers. According to the identified circuit breaker abnormal results, self-adjusting feedback control and feedback of the corresponding abnormal information are carried out, and through different devices and corresponding base stations and corresponding regions, cross-regional and cross-level efficient monitoring feedback of the circuit breaker operating state is realized, which significantly improves the intelligent level of circuit breaker monitoring and operation. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A step flowchart of the Internet of Things-based circuit breaker digital monitoring method of the present application is shown in the figure.

[0052] Figure 2 A detailed implementation step flowchart of step S3 in the present application is shown in the figure. Figure 1 A detailed implementation step flowchart of step S3 in the present application is shown in the figure.

[0053] Figure 3 A detailed implementation step flowchart of step S34 in the present application is shown in the figure. Figure 2 A detailed implementation step flowchart of step S34 in the present application is shown in the figure.

[0054] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0055] The technical method of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0056] In addition, the accompanying drawings are included to provide a further understanding of the application, and are incorporated herein and constitute part of this application. The application can be better understood with reference to the drawings together with the description, of which:

[0057] To achieve the above object, the present application provides a circuit breaker digital monitoring method based on Internet of Things, in the embodiment of the present application, please refer to Figures 1 to 3 , the present application provides a circuit breaker digital monitoring method based on Internet of Things, in the embodiment of the present application, please refer to Figure 1 The flow chart of the steps of the circuit breaker digital monitoring method based on Internet of Things, the circuit breaker digital monitoring method based on Internet of Things comprises the following steps:

[0058] Step S1: using Internet of Things cluster monitoring equipment to monitor the circuit breaker connected with the communication equipment, so as to obtain the circuit breaker monitoring data; based on the circuit breaker monitoring data, the circuit breaker state attribute is extracted to construct the circuit breaker state attribute matrix;

[0059] In the embodiment of the application, in the 5G energy-saving system scenario, taking a power distribution system composed of 20 circuit breakers in a certain large 5G base station as an example, an Internet of Things cluster monitoring device integrating high-precision current transformers, high-impedance voltage sensors, thermocouple temperature sensors, and piezoresistive pressure sensors is used to monitor the circuit breakers connected to the communication equipment. The current transformer with an accuracy of plus or minus 0.5% is installed at the inlet and outlet positions of the circuit breaker to collect current data through electromagnetic induction principle; the voltage sensor with a measurement error of plus or minus 1% is connected in parallel at both ends of the circuit breaker to obtain voltage data by using resistance voltage division principle; the thermocouple sensor with a temperature resolution of 0.1℃ is arranged at the key heating positions of the circuit breaker contacts and connection parts to collect temperature distribution data in real time; the piezoresistive pressure sensor with a pressure measurement accuracy of plus or minus 2% FS is embedded in the wafer contact surface to obtain wafer pressure distribution data; the auxiliary contact connected to the main contact of the circuit breaker is used to detect the opening and closing state to determine the connection state data. After collection, the high-precision microsecond clock module is used to add time stamps to the sensor data, and the data is sorted and aligned in time sequence to generate synchronous circuit breaker monitoring data. Then, according to the base station area and power supply line rules, the unique spatial node number of the 20 circuit breakers is allocated (for example, the number of the circuit breaker in area A is A-01), and the synchronous data is associated to form the circuit breaker spatial node data. Finally, according to the circuit breaker connection state data and the spatial node number, the matrix architecture is constructed, the connection state of the circuit breakers 1-10 is filled in the corresponding matrix position in the form of 1 (connected) and 0 (disconnected), and the attribute data such as current, voltage, and temperature of each circuit breaker is mapped to the corresponding row and column of the matrix, for example, the current of the circuit breaker 3 is 5A, the voltage is 220V, and the temperature of a certain point is 30℃, which are filled in the corresponding position of the matrix in turn, so as to construct a complete circuit breaker state attribute matrix.

[0060] Step S2: performing matrix node dynamic update processing on the circuit breaker state attribute matrix by circuit breaker current soft adjustment to generate an updated circuit breaker state attribute matrix;

[0061] In the embodiment of the application, the Internet of Things cluster monitoring device is connected with each circuit breaker control unit in Modbus communication protocol, and current soft adjustment parameters such as short-time threshold proportion coefficient 1.5 and long-time threshold proportion coefficient 1.2 are read from the device parameter configuration file. Taking a CB-05 circuit breaker with a rated current of 200 A as an example, the short-time limited threshold is calculated as 300 A and the long-time limited threshold is calculated as 240 A. The system continuously monitors the current data in the circuit breaker state attribute matrix. When the current of CB-05 reaches 320 A and lasts for 6 seconds (exceeding the preset short-time load time sequence parameter 5 seconds), the soft adjustment expansion flow update is triggered, the current upper limit adjustment coefficient is increased by 0.1, and the original 250 A current upper limit is increased by 10% to 275 A. If the current of a certain circuit breaker lasts for 12 minutes and is lower than 100 A (i.e. lower than 50% of the rated current 200 A in the current soft adjustment parameter), the current limiting adjustment is started, and the current upper limit is reduced. After the adjustment is completed, the system locates the row corresponding to CB-05 in the matrix, updates the current upper limit data from the original value to 275 A, and synchronously corrects the current overload early warning threshold and other related parameters, thereby generating an updated circuit breaker state attribute matrix containing the latest adjustment information.

[0062] Step S3: performing circuit breaker global abnormal state analysis based on the updated circuit breaker state attribute matrix to generate circuit breaker global abnormal state data; performing circuit breaker global abnormal state causal feature analysis according to the circuit breaker global abnormal state data to generate circuit breaker global abnormal state causal feature data;

[0063] In the embodiment of the present application, for updating the circuit breaker state attribute matrix, the branch current waveform analysis is performed on the current data of each circuit breaker. For the CB-07 circuit breaker, the A, B and C three-phase current waveform curves are obtained through the signal conditioning module and the 10 kHz sampling frequency, the C-phase current deviation rate 6.85% is calculated to be higher than the threshold value 5%, and the left upper corner of the C-phase wafer is determined as the pressure-uniform current abnormal node in combination with the wafer pressure data. The abnormal node information of all circuit breakers is summarized to generate the pressure-uniform current abnormal distribution data. Then, based on the data, when the wafer position pressure of the CB-03 circuit breaker main contact point decreases by 50% and the corresponding branch current imbalance degree reaches 35%, it is determined as the first abnormal state, and the related data is generated. The unassociated data in the matrix is selected as the to-be-detected data. Then, the to-be-detected data is analyzed. Taking the CB-05 circuit breaker as an example, the time sequence characteristic data of the current effective value, temperature and voltage harmonic are calculated. It is found through comparison that when the current effective value rises, the temperature rising rate is 2 times higher than the normal value, and when the voltage harmonic content increases, the temperature rise is 15% higher than the model prediction value, which are respectively determined as the current-temperature abnormality and the voltage-temperature rise abnormality, and the second abnormal state data is generated. Subsequently, the first and second abnormal state data are integrated, the potential influence between the CB-03 contact poor contact and the CB-05 voltage harmonic temperature rise abnormality is analyzed, and the global abnormal state data is generated. Finally, the past 1 hour data of the CB-03 circuit breaker is traced back, the causal relationship between the current fluctuation causing the contact wear and tear, the pressure drop and the poor contact is found, all abnormal conditions are analyzed one by one, and the complete global abnormal state causal characteristic data set is generated.

[0064] Step S4: performing circuit breaker state abnormality detection information mapping and circuit breaker communication state feedback control processing on the circuit breaker based on the circuit breaker global abnormal state causal characteristic data to update the circuit breaker state attribute matrix, and generating a circuit breaker intelligent control attribute matrix;

[0065] In the embodiment of the application, taking a circuit breaker numbered CB-03 in a certain 5G base station as an example, when the global abnormal state causal feature data of the CB-03 circuit breaker indicates that the contact wear is caused by long-term current fluctuation, which further causes the contact pressure to drop and the contact resistance to increase, and finally causes the effective value of the current to abnormally rise and the temperature to exceed the standard, the system starts the digital intelligent control analysis according to this causal chain. The system first adjusts the built-in pressure regulating device of the circuit breaker to increase the contact pressure from 800 Pa to 1000 Pa; if the abnormality is still not eliminated after pressure regulation, load shedding control is started to gradually reduce the load current from 200 A to 160 A, or the control switch of the circuit breaker is disconnected, and these control strategies, adjustment parameters and execution sequence are integrated into the digital intelligent control data of the circuit breaker. Then, the digital intelligent control data of the circuit breaker and the global abnormal state causal feature data are transmitted to the matrix node corresponding to the CB-03 circuit breaker communication state data in the updated circuit breaker state attribute matrix, and the mapping relationship between the abnormal causal information and the control strategy is marked in the corresponding row in the matrix. At the same time, feedback control is started, the contact pressure is collected in real time after pressure regulation, and if it does not reach 1000 Pa, the parameters are adjusted again; the current and temperature are continuously monitored after load shedding, and if the abnormality still exists, the load shedding amplitude is further adjusted, and after multiple feedback adjustments, the abnormality detection, control execution and real-time monitoring information are integrated to generate the intelligent control attribute matrix of the circuit breaker.

[0066] Step S5: Establish a multi-level spatial topology structure of the communication equipment; transmit the intelligent control attribute matrix of the circuit breaker to the multi-level spatial topology structure of the communication equipment for multi-level monitoring mapping of the circuit breaker, generate multi-level monitoring topology data of the circuit breaker, and transmit the multi-level monitoring topology data of the circuit breaker to the terminal for multi-level monitoring feedback operation of the circuit breaker.

[0067] In the embodiment of the application, the longitude and latitude coordinates, altitude and other spatial parameters of all 5G base station communication devices in a certain area range are obtained through an Internet of Things positioning system, the homing relationship of the devices and the base stations is extracted from a base station management database, and the communication device association data set is integrated according to the association data of the communication devices in each base station in a certain area. Based on the spatial parameters, a topology node containing the position and function attributes of each communication device is created, such as the topology node of device X. Then, according to the association data of the base stations, the device nodes in the same base station are associated, and the sub-topology structure of devices X, Y and Z in base station A is established. Finally, according to the association data of the area, the sub-topology of the same area base station is aggregated to form an area-level topology layer, and a multi-level spatial topology structure of communication devices at the device level, base station level and area level is constructed. The data in the intelligent control attribute matrix of the circuit breaker, such as the control attributes of the CB-03 circuit breaker pressure regulation to 1000 Pa and load reduction to 160 A, are mapped to the corresponding communication device topology node. When the temperature of CB-03 exceeds the standard, the system marks the abnormality in the topology structure, generates multi-level monitoring topology data covering device, base station and area-level abnormal information, and transmits the data to the operation and maintenance terminal through a special communication channel, thereby realizing multi-level monitoring feedback operation.

[0068] Further, step S1 comprises the following steps:

[0069] Step S11: using the Internet of Things cluster monitoring device to perform circuit breaker monitoring processing on the circuit breaker connected to the communication device, to obtain circuit breaker monitoring data, wherein the circuit breaker monitoring data includes circuit breaker current data, circuit breaker voltage data, circuit breaker temperature distribution data, circuit breaker wafer pressure distribution data and circuit breaker connection state data;

[0070] Step S12: performing time sequence synchronization processing on the circuit breaker monitoring data to generate synchronized circuit breaker monitoring data; performing spatial node identification on the synchronized circuit breaker monitoring data to generate circuit breaker spatial node data;

[0071] Step S13: establishing a circuit breaker state attribute matrix architecture through the circuit breaker connection state data in the synchronized circuit breaker monitoring data and the circuit breaker spatial node data, and performing circuit breaker attribute state mapping on the circuit breaker current data, circuit breaker voltage data and circuit breaker temperature distribution data in the synchronized circuit breaker monitoring data, to obtain a circuit breaker state attribute matrix.

[0072] In the embodiment of the present application, a thing networking cluster monitoring device with multiple sensing functions is adopted, for example, a high-precision current transformer is installed at the inlet and outlet positions of the circuit breaker, and a large current is converted into a small current in proportion by using the principle of electromagnetic induction, so as to accurately collect the circuit breaker current data, and the accuracy can reach plus or minus 0.5%. A high-impedance voltage sensor is connected in parallel at both ends of the circuit breaker, and the circuit breaker voltage data is measured according to the resistance voltage division principle, and the measurement error is controlled within plus or minus 1%. A plurality of high-precision temperature sensors, such as thermocouple sensors, are uniformly arranged at the key heat generating positions inside the circuit breaker, such as the contact and the connection position, and the temperature distribution data of the circuit breaker is obtained in real time by using the thermoelectric effect, and the temperature resolution reaches 0.1℃. For the wafer pressure distribution data of the circuit breaker, a pressure sensing element, such as a piezoresistive pressure sensor, is embedded on the contact surface between the wafer and the related components, and the wafer pressure distribution data of the circuit breaker is obtained by detecting the resistance change caused by the pressure change, and the pressure measurement accuracy can reach plus or minus 2% FS. The circuit breaker communication state data is obtained by detecting the opening and closing state of the auxiliary contact of the circuit breaker, and the auxiliary contact is mechanically linked with the main contact of the circuit breaker. When the main contact is closed, the auxiliary contact is closed, and the circuit is turned on, and vice versa. The circuit breaker communication state data is determined. The circuit breaker monitoring data is processed in time sequence, and since there is a difference in the time of collecting data by different types of sensors, a time stamp is first added to each sensor data. The time stamp is obtained by using a high-precision clock module, and the accuracy can reach microseconds. For example, for current data, the time of the clock module is recorded as a time stamp at each current sampling time. According to the time stamp, all circuit breaker monitoring data is sorted and aligned in chronological order to generate synchronized circuit breaker monitoring data. Then, the spatial node identification of the synchronized circuit breaker monitoring data is performed. Each circuit breaker is assigned a unique spatial node number, such as according to different regions, different power supply lines and other rules in the base station. The number is associated with the synchronized circuit breaker monitoring data to generate circuit breaker spatial node data. For example, in a 5G base station with multiple regions, different number prefixes are assigned to the circuit breakers in each region, and then combined with the sequential number in the region to complete the spatial node identification, and finally the circuit breaker spatial node data is obtained. According to the circuit breaker communication state data in the synchronized circuit breaker monitoring data and the circuit breaker spatial node data, a circuit breaker state attribute matrix architecture is constructed. The on or off state in the circuit breaker communication state data is arranged in the rows and columns of the matrix according to the order of the circuit breaker spatial node number. For example, if there are 10 circuit breakers, numbered 1-10, when circuit breaker 1 is connected and circuit breaker 2 is disconnected, the corresponding positions in the matrix are marked as 1 and 0 respectively. At the same time, the circuit breaker current data, circuit breaker voltage data and circuit breaker temperature distribution data in the synchronized circuit breaker monitoring data are mapped to the circuit breaker attribute state. The current value, voltage value and temperature distribution value of each circuit breaker are filled into the corresponding circuit breaker position in the above matrix.For example, the current of the circuit breaker 3 is 5A, the voltage is 220V, and the temperature at a certain point is 30℃. These values are filled in the position corresponding to the circuit breaker 3 in the matrix in turn, so as to obtain the circuit breaker state attribute matrix.

[0073] Further, the step S2 comprises the following steps:

[0074] Step S21: acquiring a circuit breaker current soft adjustment parameter;

[0075] Step S22: setting a circuit breaker current short-time limiting threshold and a circuit breaker current long-time limiting threshold according to the circuit breaker current soft adjustment parameter, wherein the circuit breaker current short-time limiting threshold is greater than the circuit breaker current long-time limiting threshold;

[0076] Step S23: when the circuit breaker current data in the circuit breaker state attribute matrix is not less than the circuit breaker current short-time limiting threshold and the duration is not less than a preset short-time load timing parameter, or when the circuit breaker current data in the circuit breaker state attribute matrix is not less than the circuit breaker current short-time limiting threshold and the duration is not less than a preset short-time load timing parameter, performing a circuit breaker soft adjustment expansion flow updating processing on the circuit breaker current soft adjustment parameter, generating an updated circuit breaker current soft adjustment parameter, and performing a circuit breaker current upper limit updating operation through the updated circuit breaker current soft adjustment parameter;

[0077] Step S24: dynamically updating the circuit breaker state attribute matrix based on the circuit breaker current upper limit updating operation to generate an updated circuit breaker state attribute matrix.

[0078] In the embodiment of the present application, the Internet of Things cluster monitoring device establishes a communication connection with the circuit breaker control unit, and directly reads the circuit breaker current soft regulation parameters from the device parameter configuration file of the circuit breaker. For example, in a certain 5G base station room, the base station is configured with 10 circuit breakers for power distribution, and each circuit breaker control unit stores a parameter file containing current regulation characteristics. The Internet of Things cluster monitoring device directly obtains the current soft regulation parameters of the corresponding circuit breaker from the register of the circuit breaker control unit through the Modbus communication protocol, and these parameters include basic values and regulation rules related to current regulation. According to the obtained circuit breaker current soft regulation parameters, the circuit breaker current short-time limit threshold and the circuit breaker current long-time limit threshold are set according to the fixed numerical proportion relationship. Specifically, taking the rated current value of the circuit breaker as the basis, if the short-time threshold proportion coefficient in the circuit breaker current soft regulation parameters is 1.5 and the long-time threshold proportion coefficient is 1.2, when the rated current of a certain 5G base station circuit breaker is 200A, the circuit breaker current short-time limit threshold is calculated as 200A x 1.5 = 300A, and the circuit breaker current long-time limit threshold is calculated as 200A x 1.2 = 240A, and 300A is greater than 240A. The circuit breaker current data in the circuit breaker state attribute matrix is continuously monitored. Taking the circuit breaker numbered CB-05 in a certain 5G base station as an example, the preset short-time load time sequence parameter is 5 seconds, when the circuit breaker current data of CB-05 reaches 320A (greater than the short-time limit threshold 300A), and the current value duration reaches 6 seconds (not less than the preset 5 seconds), the circuit breaker soft regulation expansion flow update processing of the circuit breaker current soft regulation parameters is triggered. The current upper limit regulation coefficient in the circuit breaker current soft regulation parameters is increased by 0.1, the updated circuit breaker current soft regulation parameters are generated, and the circuit breaker current upper limit is increased by 10% according to the updated parameters. For example, the original current upper limit is 250A, and after updating, it is increased to 250A x (1+10%) = 275A, and the circuit breaker current upper limit update is completed. When the circuit breaker current data in the circuit breaker state attribute matrix exceeds the low current running time parameter (such as 10 minutes) and maintains low current running (such as less than 50% of the circuit breaker current soft regulation parameters), the circuit breaker current soft regulation parameters are adjusted, and the circuit breaker current limit update is performed accordingly, and the circuit breaker state attribute matrix is dynamically updated by the circuit breaker current limit update. After the circuit breaker current upper limit update, the circuit breaker state attribute matrix is dynamically updated. Taking the state attribute matrix of all circuit breakers in the 5G base station as an example, the matrix row where the circuit breaker corresponding to the completed current upper limit update is located is found, and the current upper limit data of the circuit breaker in the matrix is modified to the updated value.If the updated current upper limit of CB-05 breaker is 275A, the current upper limit data of the corresponding row of CB-05 in the breaker state attribute matrix is modified from the original value to 275A, and other associated parameters related to the current upper limit, such as the current overload warning threshold, are updated, and finally the updated breaker state attribute matrix is generated.

[0079] Further, as an embodiment of the present application, referring to Figure 2 as shown in Figure 1 The detailed step flow diagram of step S3 in the embodiment, step S3 in the embodiment includes the following steps:

[0080] Step S31: According to the breaker current data corresponding to the updated breaker state attribute matrix and the wafer pressure distribution data of the breaker, the breaker pressure-uniform current abnormal node analysis is performed to generate the breaker pressure-uniform current abnormal distribution data;

[0081] In the embodiment of the present application, taking the breaker group in a certain 5G base station room as an example, for the current data of each breaker in the updated breaker state attribute matrix, the real-time current data collected by the current transformer is used to analyze the waveform of each current, obtain the distortion degree, harmonic content and other characteristics of the current waveform, and generate the breaker branch current waveform data. By comparing the current peak value, effective value and other parameters in each branch current waveform data, the unbalance degree of each branch current is calculated, and the breaker branch current uniform data is generated. At the same time, combined with the wafer pressure distribution data collected by the pressure sensor array installed around the wafer of the breaker, the pressure values at each position are cross-compared with the current uniform data of the corresponding branch. If the pressure value at a certain wafer position of a certain breaker is lower than the standard value by 30%, and the current unbalance degree of the corresponding branch exceeds 20%, it is determined that the position is a pressure-uniform current abnormal node. The positions, pressure values, current unbalance degrees and other information of all abnormal nodes are summarized to generate the breaker pressure-uniform current abnormal distribution data.

[0082] Step S32: Based on the breaker pressure-uniform current abnormal distribution data, the first abnormal state of the breaker is identified to generate the first abnormal state data of the breaker;

[0083] In the embodiment of the present application, based on the abnormal distribution data of the circuit breaker pressure-current, the specific abnormal identification rule is set. For example, it is stipulated that when the number of abnormal nodes exceeds 10% of the total number of circuit breaker nodes, or the pressure drop at a certain key position (such as the wafer position corresponding to the main contact) exceeds 40% and the current imbalance degree of the corresponding branch exceeds 30%, it is determined that the first abnormal state of the circuit breaker occurs. Taking the circuit breaker numbered CB-03 in the 5G base station room as an example, the wafer position corresponding to the main contact of the circuit breaker is detected to have a pressure drop of 50%, and the current imbalance degree of the corresponding branch reaches 35%, which meets the above abnormal identification rule, and the first abnormal state data of the circuit breaker is generated, which includes the circuit breaker number, the abnormal type (pressure-current abnormality), the abnormal position (the wafer position of the main contact), and the abnormal severity (high).

[0084] Step S33: screening the circuit breaker state attribute matrix related to the first abnormal state data of the circuit breaker as the to-be-detected circuit breaker state data;

[0085] In the embodiment of the present application, according to the first abnormal state data of the circuit breaker, the circuit breaker state data not associated with the abnormality is identified in the updated circuit breaker state attribute matrix, and all related data in the matrix is marked, including current data, voltage data, temperature distribution data, wafer pressure distribution data and communication state data, as to-be-detected circuit breaker state data. At the same time, an identification tag is added to the group of to-be-detected data for subsequent targeted analysis.

[0086] Step S34: analyzing the to-be-detected circuit breaker state data based on the updated circuit breaker state attribute matrix to generate the second abnormal state data of the circuit breaker;

[0087] In the embodiment of the present application, based on the circuit breaker state data to be detected, the current data of the corresponding circuit breaker in the updated circuit breaker state attribute matrix is calculated, the current effective value of the circuit breaker in a certain time period (such as the past 10 minutes) is calculated, and the trend of the current effective value with time is analyzed to generate circuit breaker current effective value time sequence characteristic data. For temperature data, the temperature values at each time point are recorded, the temperature change curve with time is drawn, and circuit breaker temperature time sequence characteristic data is generated. For voltage data, the harmonic components and the content fluctuation with time are analyzed, and circuit breaker voltage harmonic time sequence characteristic data is generated. Then, the current effective value time sequence characteristic data and the temperature time sequence characteristic data are compared. If the temperature also presents a rapid rising trend in the time period when the current effective value continuously rises, and the temperature rising rate exceeds 2 times of the temperature rising rate corresponding to the current change under normal circumstances, it is determined that a circuit breaker current-temperature anomaly occurs, and circuit breaker current-temperature anomaly data containing information such as abnormal time and abnormal degree is generated. Similarly, the voltage harmonic time sequence characteristic data and the temperature time sequence characteristic data are compared. If the voltage harmonic content increases at the same time, and the temperature rising amplitude exceeds 15% of the predicted value of the voltage harmonic-temperature rise relationship model established based on historical data, it is determined that a circuit breaker voltage-temperature rise anomaly occurs, and circuit breaker voltage-temperature rise anomaly data is generated. Finally, the current-temperature anomaly data and the voltage-temperature rise anomaly data are comprehensively analyzed to determine whether there are other potential anomalies, and circuit breaker second abnormal state data containing all abnormal information is generated.

[0088] Step S35: performing circuit breaker global abnormal state analysis according to the circuit breaker first abnormal state data and the circuit breaker second abnormal state data, and generating circuit breaker global abnormal state data;

[0089] In the embodiment of the present application, the circuit breaker first abnormal state data and the circuit breaker second abnormal state data are integrated and analyzed. In the circuit breaker system of a certain 5G base station, the first abnormal state data shows that the CB-03 circuit breaker A-phase contact is poor in the same device, and the second abnormal state data shows that the CB-05 circuit breaker has a voltage harmonic causing temperature rise anomaly. The two types of abnormal data are summarized, and the mutual influence relationship between the abnormalities is analyzed, such as whether the poor contact can exacerbate the voltage harmonic problem of other circuit breakers. Or, the circuit breaker first abnormal state data and the circuit breaker second abnormal state data existing in different 5G communication devices are summarized and recorded, and finally circuit breaker global abnormal state data containing information such as all abnormal types, involved circuit breakers, and correlation between abnormalities is generated.

[0090] Step S36: performing circuit breaker global abnormal state causal feature analysis according to the circuit breaker global abnormal state data, and generating circuit breaker global abnormal state causal feature data.

[0091] In the embodiments of the present application, according to the global abnormal state data of the circuit breaker, the cause-effect feature analysis is performed on each abnormal condition. For example, for the abnormality of the CB-03 circuit breaker A-phase contact poor contact, by backtracking the current, pressure, temperature and other data change curves of the circuit breaker in the past 1 hour, it is found that the current fluctuation increases before the poor contact occurs, the pressure gradually decreases, and the temperature subsequently rises, thereby determining the cause-effect relationship of “long-term current fluctuation leading to contact wear, and then causing pressure drop and poor contact”, the monitoring result is transmitted to the preset Transformer structure for intelligent abnormality identification, and the circuit breaker global abnormal state cause-effect feature data containing abnormal reason, development process, influence result and other information is generated. The cause-effect analysis is performed on all abnormal conditions in the global abnormal state data one by one, and a complete cause-effect feature data set is formed.

[0092] Further, step S31 comprises the following steps:

[0093] According to the updated circuit breaker state attribute matrix corresponding circuit breaker current data branch current waveform analysis, the circuit breaker branch current waveform data is generated;

[0094] Through the circuit breaker branch current waveform data, the circuit breaker branch current sharing analysis is performed, and the circuit breaker branch current sharing data is generated;

[0095] According to the circuit breaker wafer pressure distribution data and the circuit breaker branch current sharing data, the circuit breaker pressure-current sharing abnormal node analysis is performed, and the circuit breaker pressure-current sharing abnormal distribution data is generated.

[0096] In the embodiment of the present application, three-phase current data of each circuit breaker is extracted from the updated circuit breaker state attribute matrix, and the current data is filtered and amplified by a signal conditioning module in the hardware circuit. Taking the circuit breaker numbered CB-07 in the base station as an example, the original A, B and C three-phase current data are 120 A, 115 A and 130 A respectively, and after being processed by the signal conditioning module, the three-phase current signals are sampled at a sampling frequency of 10 kHz by using a high-speed data acquisition card, and the discrete current values obtained by sampling are arranged in time sequence to draw the current waveform curves of the A, B and C three-phase. For example, in a continuous 1-second sampling period, the A-phase current waveform appears 3 times of sharp pulse, the B-phase waveform is relatively smooth, and the C-phase exists periodic distortion. The three-phase current waveform data containing amplitude, phase and waveform distortion characteristics are integrated to generate the branch current waveform data of the CB-07 circuit breaker. For the circuit breaker branch current waveform data, the amplitudes of the three-phase current waveforms of each circuit breaker are compared and analyzed. Still taking the CB-07 circuit breaker as an example, the average value of the three-phase current amplitudes is calculated as (120A+115A+130A) / 3=121.67A, and the deviation rates of the respective phase currents from the average value are calculated as follows: the A-phase deviation rate is |120A-121.67A| / 121.67A×100%=1.37%, the B-phase is |115A-121.67A| / 121.67A×100%=5.48%, and the C-phase is

[0097] |130A-121.67A| / 121.67A x 100% = 6.85%. Set the current sharing deviation threshold value as 5%, since the C-phase deviation rate exceeds the threshold value, it is determined that CB-07 circuit breaker C-phase exists current sharing abnormality. Traverse all 20 circuit breakers, and arrange the three-phase current deviation rate, whether there is current sharing abnormality and other information of each circuit breaker into a table form to generate the circuit breaker branch current sharing data of the entire system. Cross compare the obtained circuit breaker branch current sharing data with the circuit breaker wafer pressure distribution data. In this 5G base station, 4 pressure sensors (in cross distribution) are installed on the surface of each circuit breaker wafer for collecting pressure data. For the CB-07 circuit breaker determined to exist current sharing abnormality, the C-phase wafer pressure sensor data shows that the pressure value of the upper left corner sensor is 800Pa, the upper right corner is 900Pa, the lower left corner is 1100Pa, and the lower right corner is 1000Pa, while the pressure value of each sensor should normally fluctuate in the interval of 1000-1100Pa. Combined with the C-phase current sharing abnormality, it is found that the left upper corner sensor with low pressure value corresponds to the position of current concentration, so it is determined that the left upper corner of the C-phase wafer of CB-07 circuit breaker is the pressure-current sharing abnormality node. According to the same method, the pressure data correlation analysis is carried out on all circuit breakers with current sharing abnormality, and the abnormal node position (such as the left upper corner of the C-phase wafer of CB-07 circuit breaker), current sharing deviation value (such as the C-phase current deviation rate 6.85%), pressure abnormal value (such as the upper left corner pressure 800Pa) and other information are summarized, and a plane distribution diagram containing all abnormal nodes is drawn, and detailed parameters are recorded in the form of data list, and finally the circuit breaker pressure-current sharing abnormality distribution data is generated.

[0098] Further, as an embodiment of the present application, referring to Figure 3 , it is Figure 2 the detailed step flowchart of step S34 in the embodiment, step S34 in the embodiment includes the following steps:

[0099] Step S341: performing circuit breaker current effective value time sequence feature analysis on the circuit breaker current data corresponding to the updated circuit breaker state attribute matrix, to generate circuit breaker current effective value time sequence feature data;

[0100] In the embodiment of the application, the three-phase current data of each circuit breaker is extracted from the updated circuit breaker state attribute matrix. Taking the circuit breaker numbered CB-05 as an example, the original A, B and C three-phase current data is collected every 10 seconds within 1 minute, and 6 groups of data are obtained. For each group of three-phase current data, the effective value of each phase current is calculated. Taking the A phase as an example, the 100 current instantaneous values collected within 10 seconds are squared, summed, averaged and then square rooted to obtain the current effective value of the first 10 seconds, which is 150 A. In this way, the A-phase current effective values corresponding to the 6 groups of data are calculated as 150 A, 152 A, 155 A, 160 A, 165 A and 170 A. These current effective values arranged in time sequence and the corresponding collection time points are integrated to form the time sequence of the A-phase current effective value of the CB-05 circuit breaker. Similarly, the B and C phase data are processed to generate circuit breaker current effective value time sequence characteristic data containing information such as the trend of the three-phase current effective value with time and the fluctuation amplitude, which completely records the current effective value time sequence characteristics of the circuit breaker within 1 minute.

[0101] Step S342: Circuit breaker temperature time sequence characteristic analysis is performed on the circuit breaker temperature data corresponding to the updated circuit breaker state attribute matrix to generate circuit breaker temperature time sequence characteristic data.

[0102] In the embodiment of the application, in the 5G base station, a temperature sensor is installed at the key position (such as the contact point and the wiring terminal) of each circuit breaker, and the temperature data is collected at a period of 1 minute. Taking the CB-05 circuit breaker as an example, the 6 groups of data collected by the temperature sensor at the contact point within 1 minute are 40℃, 42℃, 45℃, 48℃, 52℃ and 55℃. These temperature data are arranged in time sequence, and the temperature change difference between adjacent data (such as the difference between the second group and the first group is 2℃) is calculated, and the temperature rising rate (such as from 40℃ to 55℃, the rising rate within 1 minute is 15℃ / minute) is also calculated. The temperature value, collection time, temperature change difference and rising rate are summarized to generate circuit breaker temperature time sequence characteristic data, which clearly presents the characteristics such as the trend of the temperature of the circuit breaker with time and the change rate.

[0103] Step S343: Circuit breaker voltage harmonic time sequence characteristic analysis is performed on the circuit breaker current data corresponding to the updated circuit breaker state attribute matrix to generate circuit breaker voltage harmonic time sequence characteristic data.

[0104] In the embodiment of the present application, the voltage signals at both ends of each circuit breaker are collected by the voltage transformer, and after being converted into digital signals by the signal processing device, the digital signals are transmitted to the system. Taking the circuit breaker as an example, the voltage data is collected every 5 seconds within 1 minute, and the harmonic analysis is performed on the voltage data collected each time. The voltage signal is decomposed into fundamental wave and harmonic components by Fourier transform, and the 2nd harmonic content is calculated to be 3%, the 3rd harmonic content is calculated to be 5%, and the 5th harmonic content is calculated to be 2%. The change of the harmonic content with time is recorded each time, such as at the 10th second, the 2nd harmonic content rises from 3% to 4%. The harmonic content, collection time, harmonic content change trend and other information are integrated to generate the circuit breaker voltage harmonic time sequence characteristic data, which fully shows the dynamic change characteristics of the voltage harmonic of the circuit breaker within 1 minute.

[0105] Step S344: performing circuit breaker current-temperature anomaly analysis based on the circuit breaker current effective value time sequence characteristic data and the circuit breaker temperature time sequence characteristic data, and generating circuit breaker current-temperature anomaly data;

[0106] In the embodiment of the present application, the circuit breaker current effective value time sequence characteristic data and the temperature time sequence characteristic data are associated and analyzed. It is found by observation that when the current effective value rises from 150A to 170A, the temperature rises from 40℃ to 55℃, and the change ratio of current and temperature is (55℃-40℃) / (170A-150A)=0.75℃ / A. The threshold range of the current-temperature change ratio of the circuit breaker under normal circumstances is set to 0.5℃ / A-0.6℃ / A, and since the current ratio exceeds the threshold range, it is determined that the circuit breaker has current-temperature anomaly. The time interval of the anomaly (such as from the 0th second to the 60th second), the current change range (150A-170A), the temperature change range (40℃-55℃) and other information are recorded to generate the circuit breaker current-temperature anomaly data, and the abnormal association of the current and temperature of the circuit breaker within a specific time period is determined.

[0107] Step S345: performing circuit breaker voltage-temperature rise anomaly data analysis based on the circuit breaker voltage harmonic time sequence characteristic data and the circuit breaker temperature time sequence characteristic data, and generating circuit breaker voltage-temperature rise anomaly data;

[0108] In the embodiment of the present application, the circuit breaker voltage harmonic time sequence characteristic data and the temperature time sequence characteristic data are cross-analyzed. It is found that when the 3rd harmonic content rises from 5% to 7%, the temperature rises from 45℃ to 52℃ in the same time period. The historical operation data of the circuit breaker is consulted to establish a corresponding relationship model of the voltage harmonic content and the temperature rise. The model shows that under normal circumstances, the temperature should rise by 0.8℃ for every 1% rise in the 3rd harmonic content. However, in this case, the 3rd harmonic content rises by 2%, and the temperature rises by 7℃, which is higher than the predicted value (2% x 0.8℃ = 1.6℃). It is determined that the circuit breaker has voltage-temperature rise abnormalities, and information such as the time of the abnormality, the harmonic content change, the actual temperature rise and the difference between the predicted temperature rise is recorded to generate circuit breaker voltage-temperature rise abnormal data and determine the abnormal correlation between the voltage harmonic and the temperature rise of the circuit breaker.

[0109] Step S346: The circuit breaker second abnormal state data is analyzed based on the circuit breaker current-temperature abnormal data and the circuit breaker voltage-temperature rise abnormal data, and the circuit breaker second abnormal state data is generated.

[0110] In the embodiment of the present application, the circuit breaker current-temperature abnormal data and the voltage-temperature rise abnormal data are analyzed in combination with the to-be-detected circuit breaker state data. It is found that the current-temperature abnormality is mainly caused by the abnormal rise in temperature due to the increase in current, and there may be problems such as excessive load or increased contact resistance; the voltage-temperature rise abnormality indicates that the temperature rise caused by the change in voltage harmonic does not conform to the normal law, and there may be problems such as harmonic pollution of the power grid or decreased filtering performance of the circuit breaker. It is comprehensively determined that the circuit breaker has an abnormal state caused by multiple factors, and information such as the abnormal type (current-temperature abnormality, voltage-temperature rise abnormality), the abnormal reason (excessive load, harmonic pollution, etc.), and the change in the involved parameters is summarized to generate the circuit breaker second abnormal state data. The same analysis is performed on all to-be-detected circuit breakers to finally form a complete set of circuit breaker second abnormal state data, which fully reflects the potential abnormal conditions of each circuit breaker.

[0111] Further, step S344 includes the following steps:

[0112] The circuit breaker current-temperature time sequence characteristic data is generated based on the circuit breaker current effective value time sequence characteristic data and the circuit breaker temperature time sequence characteristic data;

[0113] The circuit breaker current-temperature correlation analysis is performed according to the circuit breaker current-temperature time sequence characteristic data to generate the circuit breaker current-temperature correlation data, and the circuit breaker current-temperature abnormality analysis is performed through the circuit breaker current-temperature correlation data to generate the circuit breaker current-temperature abnormal data.

[0114] In the embodiment of the present application, the current effective value time sequence characteristic data of the circuit breaker is obtained, and the current effective values within the past 10 minutes are 120A, 130A, 140A, 150A, 160A, 170A, 180A, 190A, 200A, and 210A at intervals of every minute. The temperature time sequence characteristic data is obtained from step S342, and the temperature values corresponding to every minute are 30℃, 32℃, 35℃, 38℃, 42℃, 46℃, 50℃, 55℃, 60℃, and 65℃. The current effective value and the temperature value at the same time point are one-to-one corresponding combination to form a new time sequence data set. For example, the first minute corresponds to (120A, 30℃), the second minute corresponds to (130A, 32℃), and so on. The circuit breaker current-temperature time sequence characteristic data containing 10 groups of data is generated, which completely presents the synchronous change of the current and the temperature of the circuit breaker within 10 minutes. The circuit breaker current-temperature time sequence characteristic data is analyzed. The change amount of the current and the temperature at adjacent time points is calculated, such as from the first minute to the second minute, the current change amount is 130A-120A=10A, and the temperature change amount is 32℃-30℃=2℃. The current and temperature change amount at each adjacent time point within 10 minutes is calculated in turn, and the number of times that the temperature rises when the current increases, the number of times that the temperature decreases when the current decreases, and other information are counted, and the correlation analysis is performed by using the convolutional neural network algorithm. If 8 groups of data present the trend that the temperature rises when the current increases, and the ratio of the current change amount to the temperature change amount fluctuates within a certain range, it can be determined that the current and the temperature have strong correlation. The change amount data, the trend statistical results, and the ratio range of the two are arranged to generate the circuit breaker current-temperature correlation data, and the correlation between the current and the temperature of the circuit breaker is determined. The abnormality is judged according to the circuit breaker current-temperature correlation data. It is known that under the normal operating state of the circuit breaker, the temperature should rise by 1.5℃-2℃ when the current increases by 10A. Through the automatic analysis by the convolutional neural network algorithm, it is obtained that at the seventh minute to the eighth minute, the current increases from 180A to 190A by 10A, but the temperature rises from 50℃ to 55℃ by 5℃, which exceeds the normal rising range. At the same time, such abnormal change occurs 3 times within the past 10 minutes. It is determined that the circuit breaker has current-temperature abnormality, and the specific time interval of the abnormality occurrence (such as 3 intervals of 7-8 minutes), the specific values of the current and the temperature at the abnormality, the degree of deviation from the normal range, and other information are recorded to generate the circuit breaker current-temperature abnormality data, and the abnormality of the current and the temperature relationship of the circuit breaker within a specific time period is accurately identified.

[0115] Further, step S345 includes the following steps:

[0116] According to the circuit breaker voltage harmonic distortion time sequence characteristic data and the circuit breaker temperature time sequence characteristic data, circuit breaker voltage harmonic-temperature rise mapping relationship analysis is performed, circuit breaker voltage harmonic-temperature rise mapping relationship data is generated, and circuit breaker voltage-temperature rise abnormal data analysis is performed through the circuit breaker voltage harmonic-temperature rise mapping relationship data, to generate circuit breaker voltage-temperature rise abnormal data.

[0117] In the embodiment of the application, the voltage harmonic distortion time sequence characteristic data of the circuit breaker is obtained, and data is collected every 5 minutes within the past 30 minutes, wherein the 3rd harmonic distortion rates are 4%, 5%, 6%, 7%, 8%, and 9% in turn; the temperature time sequence characteristic data thereof is obtained, and the corresponding temperature values every 5 minutes are 35℃, 37℃, 40℃, 43℃, 47℃, and 52℃ in turn. The voltage 3rd harmonic distortion rate and the temperature value at the same time point are correspondingly arranged to construct an initial data group. For example, the first 5 minutes correspond to (4%, 35℃), the second 5 minutes correspond to (5%, 37℃), and so on, to form a corresponding sequence containing 6 groups of data. Then, the 6 groups of data are analyzed, and the change amounts of the voltage harmonic distortion rate and the temperature at adjacent time points are calculated. From the first 5 minutes to the second 5 minutes, the change amount of the 3rd harmonic distortion rate is 5%-4%=1%, and the change amount of the temperature is 37℃-35℃=2℃. The change amounts of each adjacent time point in the 6 groups of data are calculated in turn, and the temperature rise when the harmonic distortion rate increases is counted. It is found that the temperature rises by about 2℃ on average with each 1% increase of the 3rd harmonic distortion rate. The change amount data, average change relationship, and other information are summarized to generate circuit breaker voltage harmonic-temperature rise mapping relationship data, and the corresponding relationship between the voltage harmonic and the temperature rise of the circuit breaker is determined. Abnormal analysis is performed according to the generated voltage harmonic-temperature rise mapping relationship data. It is known that, under the normal operating state of the circuit breaker, the temperature rise should be between 1.5℃ and 2.5℃ with each 1% increase of the 3rd harmonic distortion rate. It is found from the mapping relationship data that, from the fourth 5 minutes to the fifth 5 minutes, the 3rd harmonic distortion rate increases from 7% to 8%, which increases by 1%, but the temperature rises from 43℃ to 47℃, which rises by 4℃, exceeding the normal rise range. At the same time, such abnormal change occurs twice in the 6 groups of data. It is determined that the circuit breaker has voltage-temperature rise abnormality, and information such as the specific time interval of the abnormality occurrence (such as 2 intervals of the fourth 5 minutes to the fifth 5 minutes), the specific values of the voltage harmonic distortion rate and the temperature at the time of the abnormality, and the degree of deviation from the normal range of the abnormality is recorded to generate circuit breaker voltage-temperature rise abnormal data, so as to accurately identify the abnormal situation of the voltage harmonic and the temperature rise relationship of the circuit breaker within a specific time period.

[0118] Further, step S4 comprises the following steps:

[0119] Step S41: performing circuit breaker digital intelligent control analysis based on the circuit breaker global abnormal state causal characteristic data to generate circuit breaker digital intelligent control data;

[0120] Step S42: transmitting the breaker global abnormal state causal feature data and the breaker digital intelligent control data to the matrix node corresponding to the breaker interconnection state data in the breaker state attribute matrix to perform breaker state abnormality detection information mapping and breaker interconnection state feedback control processing, and generating a breaker intelligent control attribute matrix.

[0121] In the embodiment of the application, it is assumed that the breaker numbered CB-03 in the base station has a problem, and its global abnormal state causal feature data shows that the long-term current fluctuation leads to contact wear, contact pressure drop, and then causes the contact resistance to increase, and finally leads to the abnormal increase of the current effective value and the temperature exceeding the standard. Based on this causal feature data, the system starts digital intelligent control analysis. For the problem of contact pressure drop, the system determines to adjust the pressure regulating device built-in the breaker to increase the contact pressure from the current 800 Pa to the normal range of 1000-1200 Pa; for the problem of contact resistance increase, the system sets to continuously monitor the current and temperature changes after the pressure adjustment, and if the abnormality is not eliminated, the system starts load reduction control to gradually reduce the breaker load current from the current 200 A to 160 A. The information such as the control strategies formulated for the abnormal reasons, the specific adjustment parameters (pressure target value, current load reduction amplitude), and the execution sequence (first adjust the pressure and then reduce the load) is integrated to generate the breaker digital intelligent control data. The implementation logic is to formulate control measures to eliminate the root cause of the abnormality according to the abnormal causal chain, and to convert the measures into executable parameter and instruction sequence. Still taking the CB-03 breaker as an example, the digital intelligent control data (pressure adjustment to 1000 Pa, load reduction to 160 A) and the global abnormal state causal feature data (contact wear leading to pressure drop, etc.) are transmitted to the matrix node corresponding to the CB-03 breaker interconnection state data in the breaker state attribute matrix. In the matrix, the abnormal causal information and the control strategy are associated and labeled, for example, in the matrix CB-03 corresponding row, the abnormal reason of “contact pressure drop” is mapped to the control instruction of “adjusting the pressure to 1000 Pa”. At the same time, the feedback control processing is started: after the pressure regulating device completes the pressure adjustment operation, the sensor collects the contact pressure data in real time and feeds back to the matrix, and if the pressure does not reach 1000 Pa, the adjustment parameter is adjusted again; after the load reduction operation, the current and temperature data are continuously monitored, and if the abnormality still exists, the load reduction amplitude is further adjusted. After multiple feedback adjustments, the final abnormality detection information, control execution state, real-time monitoring data, and other information are integrated to generate a breaker intelligent control attribute matrix containing the control information of the CB-03 breaker and other breakers.

[0122] Further, step S5 comprises the following steps:

[0123] Step S51: Obtain the communication device space parameter, the base station belonging association data of the communication device, and the region belonging association data of the communication device;

[0124] Step S52: Establish a communication device topology node according to the communication device space parameter, establish a base station sub-topology structure according to the communication device topology node of the communication device and the base station belonging association data of the communication device, and establish a communication device multi-level space topology structure according to the region belonging association data of the communication device and the base station sub-topology structure;

[0125] Step S53: Transmit the intelligent control attribute matrix of the circuit breaker to the communication device topology node corresponding to the communication device multi-level space topology structure for circuit breaker multi-level monitoring mapping, generate circuit breaker multi-level monitoring topology data, and transmit the circuit breaker multi-level monitoring topology data to the terminal for circuit breaker multi-level monitoring feedback operation.

[0126] In the embodiment of the present application, the spatial parameters of all communication devices in a certain area of 5G base stations are obtained through the Internet of Things positioning system, including latitude and longitude coordinates, altitude, physical distance between devices, etc. At the same time, the base station association data of the communication devices is extracted from the base station management database, and the specific base station to which each device belongs is determined, such as device X belongs to base station A. In addition, according to the administrative division code table, the region association data of the communication device is obtained to determine the area where the device is located. These three types of data containing accurate spatial position, base station attribution relationship and regional administrative attribution are integrated to form a complete communication device association data set. The implementation logic is to integrate physical space position, management attribution relationship, administrative region division and other information through multi-source data collection to provide basic data for subsequent topology construction. Based on the spatial parameters of the communication devices, a topology node is created for each communication device. For example, the topology node of device X contains its spatial parameters such as latitude and longitude coordinates, altitude, as well as attribute information such as device type and function. Then, according to the base station association data, the device topology nodes in the same base station are associated to establish a base station sub-topology structure. For example, the topology nodes of devices X, Y and Z in base station A are associated through a wired connection relationship to form a local network structure centered on base station A. According to the region association data, the base station sub-topology structures in the same region are aggregated to establish a regional-level topology layer, and finally a multi-level spatial topology structure of communication devices including device level, base station level and regional level is formed. The data in the intelligent control attribute matrix of the circuit breaker is mapped to the constructed multi-level spatial topology structure of the communication devices. For example, the control attribute data (pressure regulation to 1000 Pa, load reduction to 160 A, etc.) of a certain circuit breaker CB-03 is mapped to the circuit breaker node connected by device X of base station A in the topology structure. In the topology structure, not only the real-time state data of the circuit breaker is displayed, but also the multi-level information such as the base station and region where it is located is associated and displayed. When the state of the circuit breaker changes, the system automatically marks the abnormality in the topology structure and generates multi-level monitoring topology data containing device level, base station level and regional level abnormal information. For example, when the temperature of CB-03 exceeds the standard, the topology data not only shows the abnormal details of the circuit breaker, but also shows the influence of the circuit breaker on the power supply system of base station A and the potential risk of the 5G network in the region where it is located. Finally, the monitoring topology data containing spatial position information and multi-level associated influence is transmitted to the operation and maintenance terminal through a special communication channel.

[0127] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the appended claims and not by the above description, therefore all variations falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the present application.

[0128] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. A method for digital monitoring of a circuit breaker based on Internet of Things, characterized in that, The method comprises the following steps: Step S1: using the Internet of Things cluster monitoring equipment to monitor the circuit breaker connected to the communication equipment, to obtain circuit breaker monitoring data; based on the circuit breaker monitoring data, extracting circuit breaker state attribute to construct a circuit breaker state attribute matrix; Step S2: performing matrix node dynamic update processing on the circuit breaker state attribute matrix to generate an updated circuit breaker state attribute matrix; Step S3: based on the updated circuit breaker state attribute matrix, performing circuit breaker global abnormal state analysis to generate circuit breaker global abnormal state data; according to the circuit breaker global abnormal state data, performing circuit breaker global abnormal state causal feature analysis to generate circuit breaker global abnormal state causal feature data; Step S4: based on the circuit breaker global abnormal state causal feature data, performing circuit breaker state abnormality detection information mapping and circuit breaker connectivity state feedback control processing on the updated circuit breaker state attribute matrix to generate a circuit breaker intelligent control attribute matrix; Step S5: establishing a multi-level spatial topology structure of the communication equipment; transmitting the circuit breaker intelligent control attribute matrix to the multi-level spatial topology structure of the communication equipment for circuit breaker multi-level monitoring mapping to generate circuit breaker multi-level monitoring topology data, and transmitting the circuit breaker multi-level monitoring topology data to the terminal for circuit breaker multi-level monitoring feedback operation.

2. The Internet of Things based digital monitoring method of circuit breaker as claimed in claim 1 wherein, Step S1 comprises the following steps: Step S11: using the Internet of Things cluster monitoring equipment to monitor the circuit breaker connected to the communication equipment, to obtain circuit breaker monitoring data, wherein the circuit breaker monitoring data comprises circuit breaker current data, circuit breaker voltage data, circuit breaker temperature distribution data, circuit breaker wafer pressure distribution data, and circuit breaker connectivity state data; Step S12: performing time sequence synchronization processing on the circuit breaker monitoring data to generate synchronized circuit breaker monitoring data; performing spatial node identification on the synchronized circuit breaker monitoring data to generate circuit breaker spatial node data; Step S13: establishing a circuit breaker state attribute matrix architecture through the circuit breaker connectivity state data in the synchronized circuit breaker monitoring data and the circuit breaker spatial node data, and performing circuit breaker attribute state mapping on the circuit breaker current data, circuit breaker voltage data, and circuit breaker temperature distribution data in the synchronized circuit breaker monitoring data to obtain a circuit breaker state attribute matrix.

3. The IoT based digital monitoring method of circuit breaker as claimed in claim 2 wherein, Step S2 comprises the following steps: Step S21: obtaining circuit breaker current soft regulation parameters; Step S22: setting circuit breaker current short-time limiting threshold and circuit breaker current long-time limiting threshold according to the circuit breaker current soft regulation parameters, wherein the circuit breaker current short-time limiting threshold is greater than the circuit breaker current long-time limiting threshold; Step S23: When the circuit breaker current data in the circuit breaker state attribute matrix is not less than the circuit breaker current short-time limiting threshold and the duration is not less than the preset short-time load timing parameter, or when the circuit breaker current data in the circuit breaker state attribute matrix is not less than the circuit breaker current short-time limiting threshold and the duration is not less than the preset short-time load timing parameter, the circuit breaker soft regulation expansion flow updating processing is performed on the circuit breaker current soft regulation parameter, the updated circuit breaker current soft regulation parameter is generated, and the circuit breaker current upper limit updating operation is performed through the updated circuit breaker current soft regulation parameter; Step S24: The circuit breaker state attribute matrix is dynamically updated based on the circuit breaker current upper limit updating operation to generate an updated circuit breaker state attribute matrix.

4. The Internet of Things based digital monitoring method of circuit breaker as claimed in claim 3 wherein, Step S3 includes the following steps: Step S31: Circuit breaker pressure-uniform flow abnormal node analysis is performed on the circuit breaker current data corresponding to the updated circuit breaker state attribute matrix and the circuit breaker wafer pressure distribution data to generate circuit breaker pressure-uniform flow abnormal distribution data; Step S32: Circuit breaker first abnormal state recognition is performed based on the circuit breaker pressure-uniform flow abnormal distribution data to generate circuit breaker first abnormal state data; Step S33: Circuit breaker first abnormal state data that is not related to the circuit breaker first abnormal state data in the updated circuit breaker state attribute matrix is screened out as to-be-detected circuit breaker state data; Step S34: Circuit breaker second abnormal state data analysis is performed on the to-be-detected circuit breaker state data based on the updated circuit breaker state attribute matrix to generate circuit breaker second abnormal state data; Step S35: Circuit breaker global abnormal state analysis is performed according to the circuit breaker first abnormal state data and the circuit breaker second abnormal state data to generate circuit breaker global abnormal state data; Step S36: Circuit breaker global abnormal state causal feature analysis is performed according to the circuit breaker global abnormal state data to generate circuit breaker global abnormal state causal feature data.

5. The Internet of Things based digital monitoring method of circuit breaker as claimed in claim 4 wherein, Step S31 includes the following steps: Branch current waveform analysis is performed on the circuit breaker current data corresponding to the updated circuit breaker state attribute matrix to generate circuit breaker branch current waveform data; Circuit breaker branch current analysis is performed through the circuit breaker branch current waveform data to generate circuit breaker branch current data; Circuit breaker pressure-uniform flow abnormal node analysis is performed according to the circuit breaker wafer pressure distribution data and the circuit breaker branch current data to generate circuit breaker pressure-uniform flow abnormal distribution data.

6. The Internet of Things based digital monitoring method of circuit breaker as claimed in claim 4 wherein, Step S34 includes the following steps: Step S341: Circuit breaker current effective value timing feature analysis is performed on the circuit breaker current data corresponding to the updated circuit breaker state attribute matrix to generate circuit breaker current effective value timing feature data; Step S342: Circuit breaker temperature timing feature analysis is performed on the circuit breaker temperature data corresponding to the updated circuit breaker state attribute matrix to generate circuit breaker temperature timing feature data; Step S343: Circuit breaker voltage harmonic timing feature analysis is performed on the circuit breaker current data corresponding to the updated circuit breaker state attribute matrix to generate circuit breaker voltage harmonic timing feature data; Step S344: based on the circuit breaker current effective value time sequence feature data and the circuit breaker temperature time sequence feature data, the circuit breaker current-temperature abnormality analysis is performed to generate circuit breaker current-temperature abnormality data; Step S345: based on the circuit breaker voltage harmonic time sequence feature data and the circuit breaker temperature time sequence feature data, the circuit breaker voltage-temperature rise abnormality data analysis is performed to generate circuit breaker voltage-temperature rise abnormality data; Step S346: the circuit breaker second abnormal state data analysis is performed on the circuit breaker state data to be detected based on the circuit breaker current-temperature abnormality data and the circuit breaker voltage-temperature rise abnormality data, and the circuit breaker second abnormal state data is generated.

7. The Internet of Things based digital monitoring method of circuit breaker as claimed in claim 6 wherein, Step S344 includes the following steps: Based on the circuit breaker current effective value time sequence feature data and the circuit breaker temperature time sequence feature data, the circuit breaker current-temperature time sequence feature data is analyzed; According to the circuit breaker current-temperature time sequence feature data, the circuit breaker current-temperature correlation analysis is performed to generate circuit breaker current-temperature correlation data; and through the circuit breaker current-temperature correlation data, the circuit breaker current-temperature abnormality analysis is performed to generate circuit breaker current-temperature abnormality data.

8. The Internet of Things based digital monitoring method of circuit breaker as claimed in claim 6 wherein, Step S345 includes the following steps: According to the circuit breaker voltage harmonic distortion time sequence feature data and the circuit breaker temperature time sequence feature data, the circuit breaker voltage harmonic-temperature rise mapping relationship analysis is performed to generate circuit breaker voltage harmonic-temperature rise mapping relationship data, and through the circuit breaker voltage harmonic-temperature rise mapping relationship data, the circuit breaker voltage-temperature rise abnormality data analysis is performed to generate circuit breaker voltage-temperature rise abnormality data.

9. The IoT-based digital monitoring method of circuit breakers according to claim 3, characterized in that, Step S4 includes the following steps: Step S41: based on the circuit breaker global abnormal state causal feature data, the circuit breaker digital intelligent control analysis is performed to generate circuit breaker digital intelligent control data; Step S42: the circuit breaker global abnormal state causal feature data and the circuit breaker digital intelligent control data are transmitted to the matrix node corresponding to the circuit breaker connected state data in the updated circuit breaker state attribute matrix to perform circuit breaker state abnormality detection information mapping and circuit breaker connected state feedback control processing, and the circuit breaker intelligent control attribute matrix is generated.

10. The Internet of Things based digital monitoring method of circuit breakers as claimed in claim 1 wherein, Step S5 includes the following steps: Step S51: obtain the communication equipment space parameters, the base station association data of the communication equipment, and the region association data of the communication equipment; Step S52: establish a communication equipment topology node according to the communication equipment space parameters, establish a base station sub-topological structure according to the communication equipment topology node and the base station association data of the communication equipment, and establish a communication equipment multi-level space topological structure according to the region association data of the communication equipment and the base station sub-topological structure; Step S53: transmit the circuit breaker intelligent control attribute matrix to the communication equipment topology node corresponding to the communication equipment multi-level space topological structure to perform circuit breaker multi-level monitoring mapping, generate circuit breaker multi-level monitoring topological data, and transmit the circuit breaker multi-level monitoring topological data to the terminal to perform circuit breaker multi-level monitoring feedback operation.

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