Switch control method, system and device on magnetic control column based on primary and secondary fusion

Abstract

The present invention provides a switch control method, system and device on a magnetic control column based on primary and secondary fusion, and relates to the technical field of switch control on a magnetic control column. The method comprises: collecting initial switch data, digitally processing the initial data to obtain a digital data set; passing the digital data set through a controller to receive a switch control instruction; converting the control instruction into a magnetic control signal through a magnetic control column; fusing the digital data set and the magnetic control signal to obtain an intelligent control model; performing fault monitoring on the switch control according to the intelligent control model to obtain monitoring data; analyzing the switch control state according to the monitoring data, transmitting the analysis data to the controller, and issuing an alarm in time. The present invention can realize intelligent switch control, enable the intelligent opening control to accurately locate the fault, and greatly improve the efficiency and accuracy of switch control.

Description

Technical Field

[0001] The present invention relates to the technical field of switch control on a magnetic control column, and in particular to a switch control method, system and device on a magnetic control column based on primary and secondary fusion. Background Art

[0002] In the process of switching control on the magnetic control column, intelligent control has a crucial influence on the magnetic field response of the switch control magnetic control column. In order to ensure the accuracy and reliability of the magnetic control column under switch control, intelligent control of the switch control is an indispensable link.

[0003] Traditional switch control methods often have a single on-off state, which is accompanied by the start and close of the magnetic control column. This method is not only simple, but also slow in response and difficult to achieve precise control.

[0004] It is easy to produce overshoot and oscillation, is difficult to adapt to complex control requirements and is easily affected by human factors, resulting in the accuracy and reliability of switch control cannot be fully guaranteed.

[0005] With the rapid development of artificial intelligence technology, switch control technology is increasingly being used in the field of electrical equipment. By collecting, processing and analyzing switch control data, feature information related to switch control can be extracted, thereby enabling rapid and accurate evaluation of switch control.

[0006] However, the existing switch control method still has certain limitations. For example, the method often has a single function and is difficult to accurately locate the switch control fault. Summary of the invention

[0007] In order to make the switch control technology more intelligent and locate the switch control fault more accurately, the present invention provides a switch control method, system and device on a magnetic control column based on primary and secondary fusion, which can realize more intelligent switch control and more accurate fault location, greatly improving the efficiency and accuracy of switch control.

[0008] In order to solve the above technical problems, the technical solutions provided by the present invention are as follows:

[0009] In the first aspect, a switch control method on a magnetic control column based on primary and secondary fusion includes the following steps:

[0010] Collecting initial switch data, and digitizing the initial data to obtain a digital data set;

[0011] passing the digitized data set through a controller to receive switch control instructions;

[0012] The control command is converted into a magnetic control signal through a magnetic control column;

[0013] The digital data set and the magnetic control signal are integrated to obtain an intelligent control model;

[0014] The switch control is monitored for faults according to the intelligent control model to obtain monitoring data, including:

[0015] The switch control is processed in sections through an intelligent control model to obtain the power parameters of three-phase current and voltage of multiple section switch controls;

[0016] Fault identification is performed based on power parameters, and the corresponding parameters are reported to the intelligent control model in the form specified by the protocol through the switch control grid for real-time communication;

[0017] According to fault identification, multi-agent technology is introduced and combined with the matrix algorithm. Each section of switch control is set as an agent, and the types of nodes where the section agents are located are divided into central management layer, regional management layer and local execution layer.

[0018] Each level cooperates with others to exchange data based on the established fault location strategy, and determines the fault direction of each section agent according to the current parameters and phase information sampled by the switch control to obtain the fault section agent location;

[0019] Analyze the switch control status according to the monitoring data, transmit the analysis data to the controller, and issue an alarm in time, including:

[0020] Acquire the section fault location through the fault monitoring data and transmit the fault location information to the intelligent control model;

[0021] The intelligent control model compares the fault location information data with the input switch control status information to obtain the fault difference;

[0022] The fault signal is transmitted to the controller through the intelligent control model according to the fault difference, so as to respond to the alarm system of the controller to issue an alarm.

[0023] Further, the initial switch data is collected and digitized to obtain a digital data set, including:

[0024] Obtain initial current and voltage data of critical points of switch opening and closing states;

[0025] Performing redundant detection on the initial current and voltage data, and performing mean integration on the redundant detection data to obtain mean data;

[0026] The mean data is cleaned to obtain mean enhanced data;

[0027] The mean augmented data are digitally encoded to obtain a digitized dataset in binary form.

[0028] Furthermore, the control command is converted into a magnetic control signal through the magnetic control column, including:

[0029] The controller receives the control instruction and transmits the control instruction through the network to obtain the instruction signal;

[0030] Decoding the command signal to obtain a signal recognizable by the controller;

[0031] Filter the identifiable signal to obtain stable signal data;

[0032] The stable signal data is passed through the current coil inside the magnetic control column to generate a magnetic field, so that the positive voltage output establishment time of the switch controller is 3.795μs, and the recovery time during stable signal conversion is 1.396μs; the negative voltage output establishment time is 5.293μs, and the recovery time during stable signal conversion is 2.257μs. When the switch is working in steady state, the DC power consumption is 16.81μA, which is converted into a magnetic control signal.

[0033] Furthermore, the digital data set and the magnetic control signal are fused to obtain an intelligent control model, including:

[0034] Acquire the state, current and voltage values ​​in the digital data set and the magnetic control signal to obtain a characteristic data set;

[0035] Synchronize the timestamps of different data sources in the feature data set to obtain the corresponding feature data set;

[0036] Normalize the corresponding feature data set and remove redundant or irrelevant features to obtain a secondary feature data set;

[0037] Interactively combine the secondary feature data sets, and filter and predict different data sources of the secondary feature data sets to obtain the optimal estimate;

[0038] Perform weighted averaging on the weights in the optimal estimate to obtain the fusion result;

[0039] The fusion results are mapped to the control model for debugging to obtain an intelligent control model.

[0040] Furthermore, the fusion results are mapped to the control model for debugging to obtain an intelligent control model, including:

[0041] Predict the feature data set obtained in real time according to the fusion result to obtain a real-time feature data set;

[0042] Based on the real-time feature data set, the control model is debugged according to the power supply voltage of 2.8V, the negative voltage establishment time of 6.12μs, the conversion rate of 4μs, the power consumption of 44.4μA during the steady-state operation of the switch, and the negative voltage establishment time of 6.5μs, the static power consumption of 55.9μA during the steady-state operation of the switch to obtain an intelligent control model;

[0043] Adopt controller hardware-in-the-loop technology according to various parameters of the intelligent control model to obtain diversified verification functions;

[0044] Based on the diversified verification function, a delayed response is generated according to the interface between the switch and the magnetic control column. The simulation results of different interface delays are compared through the CHIL test system to obtain the delay that should be controlled within 50μs.

[0045] Secondly, the switch control system on the magnetic control column based on primary and secondary fusion includes:

[0046] The acquisition module is used to acquire the initial switch data, digitize the initial data to obtain a digital data set; receive the switch control instruction through the controller according to the digital data set; and convert the control instruction into a magnetic control signal through the magnetic control column;

[0047] A fusion module is used to fuse the digital data set and the magnetic control signal to obtain an intelligent control model;

[0048] The monitoring module is used to monitor the switch control for faults according to the intelligent control model to obtain monitoring data, including:

[0049] The switch control is processed in sections through an intelligent control model to obtain the power parameters of three-phase current and voltage of multiple section switch controls;

[0050] Fault identification is performed based on power parameters, and the corresponding parameters are reported to the intelligent control model in the form specified by the protocol through the switch control grid for real-time communication;

[0051] According to fault identification, multi-agent technology is introduced and combined with the matrix algorithm. Each section of switch control is set as an agent, and the types of nodes where the section agents are located are divided into central management layer, regional management layer and local execution layer.

[0052] Each level cooperates with others to exchange data based on the established fault location strategy, and determines the fault direction of each section agent according to the current parameters and phase information sampled by the switch control to obtain the fault section agent location;

[0053] Analyze the switch control status according to the monitoring data, transmit the analysis data to the controller, and issue an alarm in time, including:

[0054] Acquire the section fault location through the fault monitoring data and transmit the fault location information to the intelligent control model;

[0055] The intelligent control model compares the fault location information data with the input switch control status information to obtain the fault difference;

[0056] The fault signal is transmitted to the controller through the intelligent control model according to the fault difference, so as to respond to the alarm system of the controller to issue an alarm.

[0057] In the third aspect, a switch control device on a magnetic control column based on primary and secondary fusion is characterized by comprising:

[0058] one or more processors;

[0059] A storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the switch control method on the magnetic control column based on primary and secondary fusion.

[0060] In a fourth aspect, a computer-readable storage medium stores a program, which, when executed by a processor, implements a switch control method on a magnetic control column based on primary and secondary fusion.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The above scheme of the present invention realizes intelligent control technology for switches and accurately locates switch control faults, greatly improving switch control efficiency and accuracy. At the same time, the method can also avoid problems that may occur in traditional methods, such as slow response speed, difficulty in achieving precise control, and inability to adapt to complex control requirements. By intelligently controlling the magnetic control column and monitoring control faults in real time, fault problems can be discovered in time and alarms can be issued, thereby avoiding inconvenience and losses caused by faults. This can not only improve work efficiency, but also reduce safety risks caused by faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings are used for further understanding of the present invention and are used to explain the present invention together with the embodiments of the present invention, but do not constitute a limitation of the present invention.

[0064] Figure 1 It is a flow chart of a method for controlling a switch on a magnetic control column based on primary and secondary fusion provided by an embodiment of the present invention.

[0065] Figure 2 It is a schematic diagram of a switch control system on a magnetic control column based on primary and secondary fusion provided by an embodiment of the present invention; DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0067] In order to better understand the above technical solution, the technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0068] It should be pointed out that the above steps are only a preferred implementation order. In the specific implementation process, some steps can be replaced without affecting the overall implementation effect. In order to more clearly explain the technical solution of this application, the following content explains this solution in a preferred manner.

[0069] like Figure 1 As shown, the present invention provides a switch control method on a magnetic control column based on primary and secondary fusion, and the method comprises the following steps:

[0070] Step 11, collecting the initial switch data, and digitizing the initial data to obtain a digital data set; specifically, the initial switch control data can be obtained by collecting the switch control opening and closing state, voltage and current power parameter data, and then, by digitizing the switch control opening and closing state, voltage and current power parameter data, the operation of digitizing the initial switch control data is completed, thereby obtaining the switch control digital data. The contents described in the following embodiments are all described in detail with the switch control opening and closing state, voltage and current power parameter data as the switch control initial data;

[0071] Step 12, passing the digital data set through the controller to receive the switch control instruction; specifically, a PLC controller can be used as a control center, and the Modbus protocol can be used to communicate with the PLC controller. The PLC controller can receive the digital data set from the data acquisition module, and generate a control instruction based on a preset control strategy to determine whether the switch needs to be controlled. If the switch needs to be controlled, the PLC controller communicates with the magnetic control column through the Ethernet network. The magnetic control column receives the instruction and converts it into a corresponding magnetic control signal to control the on and off state of the switch, and vice versa;

[0072] The system collects the operating status data of the switch through sensors, processes it digitally, and transmits it to the controller through the communication network. The controller determines whether the switch needs to be controlled based on the preset control strategy and the received data, and generates corresponding control instructions to send to the magnetic control column, ultimately realizing remote control of the switch. The system uses digital technology to realize intelligent control of the switch, improve control efficiency and reliability, and can achieve more flexible management and maintenance through the cloud platform.

[0073] Step 13, converting the control instruction into a magnetic control signal through the magnetic control column;

[0074] Step 14, fusing the digital data set and the magnetic control signal to obtain an intelligent control model;

[0075] Step 15, performing fault monitoring on the switch control according to the intelligent control model to obtain monitoring data;

[0076] Step 16, analyze the switch control status according to the monitoring data, transmit the analysis data to the controller, and issue an alarm in time.

[0077] In the embodiment of the present invention, in the intelligent switch control, the whole process starts with collecting the initial state data of the switch, involving the collection of parameters such as current, voltage, and temperature. These analog signals are converted into digital signals by an analog-to-digital converter (ADC) to form a digital data set, which is convenient for storage, transmission and analysis, and provides accurate basic data for subsequent processing. Then, the controller receives control instructions from the host computer or other intelligent devices, and combines these instructions with the digital data set, so that the control of the switch is more flexible and intelligent, and can respond quickly according to real-time data and external instructions; in addition, through the magnetic control column, the electrical control instructions are converted into magnetic control signals, which can accurately control the physical state of the switch and improve the accuracy and reliability of the operation. The digital data set and the magnetic control signal are further integrated, and an intelligent control model is generated using algorithms such as machine learning. The model can automatically optimize the control strategy and improve the efficiency and stability of the system; the intelligent control model also has a real-time monitoring function, which can identify abnormalities or faults in the operation of the switch and generate monitoring data. These data are used to analyze the control state of the switch, such as detecting whether there are problems such as overload and short circuit. The analysis results are then transmitted to the controller, and an alarm is issued in time. This series of intelligent monitoring and analysis enables operators or automatic systems to understand the switch status in a timely manner and take appropriate measures, such as adjusting control strategies or issuing alarms, thereby enhancing the safety and reliability of the system. The entire process forms a closed-loop control system to ensure efficient and safe switch operation.

[0078] In a preferred embodiment of the present invention, the above step 11 may include:

[0079] Step 111, obtaining initial current and voltage data of critical points of switch opening and closing states;

[0080] Step 112, performing redundancy detection on the initial current and voltage data, and performing mean integration on the redundant detection data to obtain mean data;

[0081] Step 113, cleaning the mean data to obtain mean enhanced data;

[0082] Step 114 , digitally encode the mean enhanced data to obtain a binary digital data set.

[0083] In an embodiment of the present invention, at the critical moment of the switch operation (such as the switching point of the opening and closing state) through step 111, the system will measure and record the initial data of the current and voltage. These data reflect the electrical performance of the switch under a specific operating state. By obtaining the current and voltage data at the critical point, it can help analyze the performance and state of the switch, and provide an important basis for subsequent data processing and fault diagnosis. Step 112 performs redundancy detection on the collected current and voltage data, identifies and excludes repeated or invalid data points. Then, the valid data is averaged and integrated into a representative data value. This step can reduce the complexity of data processing and improve the reliability of the data. The mean data simplifies the data structure and facilitates subsequent analysis and processing. Step 113 data cleaning includes steps such as removing noise and correcting deviations. Through these processes, the quality and accuracy of the data are improved. The cleaned mean enhanced data is more accurate and reliable, providing high-quality input for digital processing and subsequent analysis. Step 114 converts the cleaned mean data into binary form, that is, digital coding. This step involves converting the data format to make it suitable for electronic processing systems. The digitally encoded data is easy to store, transmit and process in digital systems, improving the efficiency of data processing and the overall performance of the system.

[0084] In another preferred embodiment of the present invention, first, step 111 obtains the initial current and voltage data of the critical point of the switch opening and closing state. At the critical moment of the switch operation (such as the switching point of the opening and closing state), the system will measure and record the initial data of the current and voltage. These data reflect the electrical performance of the switch in a specific operating state. By obtaining the current and voltage data of the critical point, it can help analyze the performance and state of the switch, and provide an important basis for subsequent data processing and fault diagnosis. Then step 112 performs redundancy detection on the initial current and voltage data, identifies and excludes repeated or invalid data points, and then calculates the mean of the valid data to integrate it into a representative data value. This step can reduce the complexity of data processing and improve the reliability of data. The mean data simplifies the data structure and facilitates subsequent analysis and processing. Then step 113 cleans the mean data, including steps such as removing noise and correcting deviations, and improves the quality and accuracy of the data through these processes. The cleaned mean enhanced data is more accurate and reliable, providing high-quality input for digital processing and subsequent analysis. Finally, step 114 converts the cleaned mean data into binary form, that is, digital encoding, so that it is suitable for electronic processing systems. Digitally encoded data is easy to store, transmit and process in digital systems, which improves the efficiency of data processing and the overall performance of the system. Through the above steps, from obtaining initial data to converting it into a digital data set, each step is aimed at improving the quality and processing efficiency of the data, ensuring that the system can accurately analyze and control the state of the switch, thereby improving the reliability and performance of the system.

[0085] In a preferred embodiment of the present invention, the above step 13 may include:

[0086] Step 131, the controller receives the control instruction and transmits the control instruction via the network to obtain an instruction signal;

[0087] Step 132, decoding the command signal to obtain a signal recognizable by the controller;

[0088] Step 133, filtering the identifiable signal to obtain stable signal data;

[0089] Step 134, the stable signal data is passed through the current coil inside the magnetic control column to generate a magnetic field, so that the positive voltage output establishment time of the switch controller is 3.795μs, and the recovery time during the stable signal conversion is 1.396μs; the negative voltage output establishment time is 5.293μs, and the recovery time during the stable signal conversion is 2.257μs. When the switch is working in a steady state, the DC power consumption is 16.81μA, which is converted into a magnetic control signal.

[0090] In an embodiment of the present invention, in step 131, the controller first receives control instructions from the user or the upper system, which are generated by sensor data or other input sources, and transmitted to a specific device or node through the network to form a command signal. Then, in step 132, the controller decodes the received command signal into a recognizable format through a decoder, and then filters these signals through step 133 to remove noise and interference to obtain a stable control signal. These stable signals are transmitted through the current coil inside the magnetic control column to generate a magnetic field to control the output state of the switch. Finally, in step 134, after receiving the control signal, the switch controller can establish and restore positive and negative voltage outputs in a very short time, while maintaining low DC power consumption in steady-state operation. This whole process ensures the rapid response, low power consumption and precise control of the system, so that the control system operates efficiently, accurately and reliably in a complex environment.

[0091] In a preferred embodiment of the present invention, the above step 134 may include:

[0092] Step 1341, positive pressure output establishment time 3.795μs: ensure that the switch controller establishes positive pressure output within 3.795 microseconds after receiving the control signal; positive pressure recovery time 1.396μs: when the signal is converted, the controller can recover to a stable state within 1.396 microseconds; negative pressure output establishment time 5.293μs: after receiving the corresponding signal, the controller can establish negative pressure output within 5.293 microseconds; negative pressure recovery time 2.257μs: when the signal is converted, the controller can recover to a stable state within 2.257 microseconds; DC power consumption when the switch is working in steady state is 16.81μA: when working in steady state, the DC power consumption of the controller is 16.81 microamperes;

[0093] The above time parameters ensure that the controller can respond to the control signal in a very short time, improving the response speed of the system; in steady-state operation, the low-power design of the controller reduces energy consumption and improves the energy efficiency of the system; fast setup and recovery time ensures accurate conversion of control signals and enhances the accuracy and stability of control;

[0094] Step 1342, constructing a DPDT RF control framework according to the parameters in step 1341, wherein RF1-RF3 and RF2-RF3 share the same parallel branch, VG_p_3 is the gate bias of the power tube of the branch, and VB_p_3 is the body end bias; RF1-RF4 and RF2-RF4 share the same parallel branch, VG_p_4 is the gate bias of the power tube of the branch, and VB_p_4 is the body end bias;

[0095] By sharing the same parallel branch between RF1-RF3 and RF2-RF3, as well as between RF1-RF4 and RF2-RF4, the required hardware resources, costs and space requirements can be effectively reduced. This design allows the RF path to share the same power tube and bias settings, thereby optimizing the efficiency and compactness of the overall design; by setting appropriate gate bias (VG_p_3, VG_p_4) and body bias (VB_p_3, VB_p_4) for the parallel branch, stable transmission of the RF signal and efficient power management can be ensured. This helps to improve the overall quality and reliability of the signal and reduce signal distortion and interference; and the parallel branches with independent bias settings allow for more flexible circuit adjustment and optimization, allowing the system to be quickly adjusted for different operating conditions and application requirements. This flexibility is necessary for high-performance RF applications; Improve system scalability and compatibility: Using standardized parallel branches and bias configurations, it can be more easily integrated with other RF components and systems, improving the compatibility and scalability of the overall system; Reasonable bias settings can help reduce overall power consumption, especially in high-frequency operations. Effective management of power consumption is the key to improving energy efficiency and extending equipment life.

[0096] In a preferred embodiment of the present invention, the above step 14 may include:

[0097] Step 141, acquiring the state, current and voltage values ​​in the digitized data set and the magnetic control signal to obtain a characteristic data set;

[0098] Step 142, synchronizing the timestamps of different data sources in the feature data set to obtain the corresponding feature data set;

[0099] Step 143, normalizing the corresponding feature data set and removing redundant or irrelevant features to obtain a secondary feature data set;

[0100] Step 144, interactively combining the secondary feature data sets, and filtering and predicting different data sources of the secondary feature data sets to obtain an optimal estimate;

[0101] Step 145, performing weighted averaging on the weights in the optimal estimation to obtain a fusion result;

[0102] Step 146, mapping the fusion result to the control model for debugging to obtain an intelligent control model.

[0103] In an embodiment of the present invention, the process starts with obtaining the state parameters and magnetic control signals of the equipment in operation in step 141, and collecting and digitizing these data into feature data sets through sensors and data acquisition systems to ensure the accuracy and real-time nature of the data. Then, step 142 synchronously processes different data sources through timestamps to ensure the consistency of data time and improve the accuracy and reliability of the data set. Then, step 143 normalizes the data and removes redundant or irrelevant features to obtain a secondary feature data set, making the data more suitable for the training of the machine learning model, improving the processing speed and the generalization ability of the model. Step 144 uses the interactive algorithm model to interactively combine and filter the secondary feature data set to obtain the optimal valuation, so that the future state or trend can be predicted more accurately. Then step 145 performs weighted averaging on the weights in the optimal valuation to obtain a fusion result, balance the influence of each feature, and improve the accuracy and robustness of the valuation. Finally, step 146 maps the fusion result to the control model for debugging to obtain an intelligent control model, realize data-driven intelligent control, improve the automation and intelligence level of the control system, and enhance the adaptability and efficiency of the system. The entire process realizes a closed loop from data collection to intelligent control by efficiently processing and analyzing data, significantly improving the performance and intelligence level of the system.

[0104] In a preferred embodiment of the present invention, the above step 146 may include:

[0105] Step 1461, predicting the feature data set obtained in real time according to the fusion result to obtain a real-time feature data set;

[0106] Step 1462, based on the real-time characteristic data set, the control model is debugged according to the power supply voltage of 2.8V, the negative pressure establishment time of 6.12μs, the conversion rate of 4μs, the power consumption of 44.4μA during the steady-state operation of the switch, and the negative pressure establishment time of 6.5μs, and the static power consumption of 55.9μA during the steady-state operation of the switch, so as to obtain an intelligent control model;

[0107] Step 1463, using controller hardware-in-the-loop technology according to various parameters of the intelligent control model to obtain diversified verification functions;

[0108] Step 1464, based on the diversified verification function, a delayed response is generated according to the link of the switch and the magnetic control column interface, and the simulation results of different interface delays are compared through the CHIL test system to obtain that the delay should be controlled within 50 μs.

[0109] In the embodiment of the present invention, step 1461 acquires feature data sets in real time by fusing data from different sources, and these data sets contain the operating status and environmental information of the current system. The real-time feature data sets are analyzed using a prediction model to predict the future behavior or state of the system. Step 1462 debugs the control model according to parameters such as power supply voltage, negative pressure establishment time, conversion rate, etc. to ensure the stability and accuracy of the model in actual operation. Step 1463 uses controller hardware-in-the-loop technology (HIL) to verify the model. This technology can test the control model in a real hardware environment to improve the authenticity and reliability of the test. Step 1464 uses HIL technology to verify the performance of the control model under different conditions to ensure the versatility and adaptability of the model. The delay of the switch and the magnetic control column interface is simulated by the CHIL test system, and the impact of different interface delays is compared to ensure that the delay is controlled within 50μs, thereby improving the response speed and stability of the system. Through these methods, the future behavior of the system can be predicted more accurately, adjustments or countermeasures can be made in advance, the stability of the system can be enhanced, and the probability of failure can be reduced. The use of HIL technology improves the efficiency and reliability of testing, optimizes the system response time by precisely controlling interface delays, enhances the versatility and adaptability of the control model, and ensures that it can work stably in different environments and conditions.

[0110] In a preferred embodiment of the present invention, the above step 15 may include:

[0111] Step 151, performing segmented processing on the switch control through the intelligent control model to obtain power parameters of three-phase current and voltage of multiple segment switch controls;

[0112] Step 152, fault identification is performed according to power parameters, and the corresponding parameters are reported to the intelligent control model in a form specified by the protocol through the switch control grid for real-time communication;

[0113] Step 153, according to the fault identification, the multi-agent technology is introduced, combined with the matrix algorithm, each section of the switch control is set as an agent, and the type of the node where the section agent is located is divided into the central management layer, the regional management layer and the local execution layer;

[0114] Step 154 ​​, each level cooperates with each other to exchange data according to the established fault location strategy, and determines the fault direction at each section agent based on the current parameters and phase information sampled by the switch control to obtain the fault section agent location.

[0115] In the embodiment of the present invention, step 151 divides the switch control operation into multiple sections through the intelligent control model, and monitors the power parameters of the three-phase current and voltage of each section one by one, so as to more accurately grasp the operating status of each section and timely discover and handle abnormalities. Step 152 The model performs fault identification based on the collected power parameters, and reports the relevant parameters to the intelligent control model in the form specified by the regulations through the switch control grid to achieve real-time communication and rapid response. Step 153 introduces multi-agent technology and matrix algorithm, and the switch control of each section is set as an independent agent. The agent nodes are classified into the central management layer, the regional management layer and the local execution layer to ensure the coordination and efficient operation of the system. In step 154, each level cooperates according to the formulated fault location strategy to ensure effective data interaction, determine the fault direction according to the sampled current parameters and phase information, and achieve accurate fault section location. Through this segmented processing, accurate monitoring and control are achieved to improve the reliability and stability of the power grid; real-time fault identification and response are achieved to reduce the impact of faults; efficient management and coordination are achieved to optimize resource allocation and fault handling processes; the direction and location of faults are accurately determined to improve the efficiency and accuracy of fault handling, thereby improving the overall safety and reliability of the power grid and ensuring the stability of power supply;

[0116] In another preferred embodiment of the present invention,

[0117] (1) The network description matrix D numbers the sectionalizers and the distribution areas corresponding to the sectionalizers in the distribution network topology to be sought. The row elements of the matrix D represent the divided area i, and the column elements represent the sectionalizer j corresponding to the area. An n×n matrix can be obtained. The elements of D are defined as ; The tidal current direction is obtained through tidal current calculation.

[0118] (2) The fault information matrix G is an n×n diagonal matrix. If the direction of the section switch current is the same as the specified positive direction of the current during the fault, then 1 is uploaded; if the direction is the opposite, then -1 is uploaded; if no fault current is experienced, then 0 is uploaded.

[0119] (3) Fault judgment matrix P

[0120] P is calculated from the network description matrix D and the fault information matrix G.

[0121]

[0122] In the fault area, the current direction is inflow, and there is no outflow, so the row where all non-zero elements in P are 1 is set to be the row where the fault area is located.

[0123] In another preferred embodiment of the present invention, the above step 16 may include:

[0124] Step 161, obtaining the section fault location through the fault monitoring data, and transmitting the fault location information to the intelligent control model;

[0125] Step 162, the intelligent control model compares the fault location information data with the input switch control state information to obtain a fault difference;

[0126] Step 163, transmit the fault signal to the controller through the intelligent control model according to the fault difference, so as to respond to the controller alarm system to issue an alarm

[0127] In an embodiment of the present invention, based on the fault monitoring data obtained in step 15, sensors are installed in key sections of the power grid through step 161 to monitor parameters such as current and voltage in real time. The system can automatically analyze the data and accurately identify the fault section when the parameters are abnormal, using complex data analysis techniques such as Fourier transform or waveform analysis. Then, through step 162, real-time monitoring and positioning technology can quickly identify the fault location, significantly shorten the fault response and recovery time, improve the reliability of the power grid, and reduce economic losses and safety risks. The fault location information is then transmitted to the intelligent control model, which has preset switch control state information. Finally, step 163 compares the actual fault location with the expected control state, and the model calculates the fault difference, helping the operator to quickly understand the nature and severity of the fault, formulate effective response measures, and provide data support for future prevention. Once the fault difference is determined, the intelligent control model sends a signal to the controller, starts the alarm system, and warns the relevant personnel in the form of sound, light signals, etc., to ensure the fault alarm in the first time and take emergency measures to prevent the fault from expanding. This series of steps constitutes an efficient, real-time grid fault monitoring and response system, which not only improves the efficiency and safety of grid operation, but also reduces potential economic losses and ensures the stability and reliability of power supply.

[0128] like Figure 2 As shown, the present invention also provides a switch control system on a magnetic control column based on primary and secondary fusion, including:

[0129] The acquisition module 10 is used to acquire the initial switch data, digitize the initial data to obtain a digital data set; receive the switch control instruction through the controller according to the digital data set; and convert the control instruction into a magnetic control signal through the magnetic control column;

[0130] A fusion module 20 is used to fuse the digital data set and the magnetic control signal to obtain an intelligent control model;

[0131] The monitoring module 30 is used to perform fault monitoring on the switch control according to the intelligent control model to obtain monitoring data; analyze the switch control state according to the monitoring data, transmit the analysis data to the controller, and issue an alarm in time.

[0132] The embodiment of the present invention also provides a switch control device on a magnetic control column based on primary and secondary fusion, comprising: a processor, a memory storing a computer program, and when the computer program is run by the processor, the method described above is executed. All implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0133] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0134] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0136] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0137] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0138] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0139] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0140] In addition, it should be pointed out that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it can be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in hardware, firmware, software or a combination thereof in any computing device (including processors, storage media, etc.) or a network of computing devices, which can be achieved by those of ordinary skill in the art using basic programming skills after reading the description of the present invention.

[0141] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0142] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A switch control method on a magnetic control column based on primary and secondary fusion, characterized in that: The following steps are involved: Collecting initial switch data, and digitizing the initial data to obtain a digital data set; passing the digitized data set through a controller to receive switch control instructions; The control command is converted into a magnetic control signal through a magnetic control column; The digital data set and the magnetic control signal are integrated to obtain an intelligent control model; The switch control is monitored for faults according to the intelligent control model to obtain monitoring data, including: The switch control is processed in sections through an intelligent control model to obtain the power parameters of three-phase current and voltage of multiple section switch controls; Fault identification is performed based on power parameters, and the corresponding parameters are reported to the intelligent control model in the form specified by 104 protocol through the switch control grid for real-time communication; According to fault identification, multi-agent technology is introduced and combined with the matrix algorithm. Each section of switch control is set as an agent, and the types of nodes where the section agents are located are divided into central management layer, regional management layer and local execution layer. Each level cooperates with others to exchange data based on the established fault location strategy, and determines the fault direction of each section agent according to the current parameters and phase information sampled by the switch control to obtain the fault section agent location; Analyze the switch control status according to the monitoring data, transmit the analysis data to the controller, and issue an alarm in time, including: Acquire the section fault location through the fault monitoring data and transmit the fault location information to the intelligent control model; The intelligent control model compares the fault location information data with the input switch control status information to obtain the fault difference; The fault signal is transmitted to the controller through the intelligent control model according to the fault difference, so as to respond to the alarm system of the controller to issue an alarm.

2. The switch control method on the magnetic control column based on primary and secondary fusion according to claim 1 is characterized in that: Collect the initial switch data and digitize the initial data to obtain a digital data set, including: Obtain initial current and voltage data of critical points of switch opening and closing states; Performing redundant detection on the initial current and voltage data, and performing mean integration on the redundant detection data to obtain mean data; The mean data is cleaned to obtain mean enhanced data; The mean augmented data are digitally encoded to obtain a digitized dataset in binary form.

3. The switch control method on the magnetic control column based on primary and secondary fusion according to claim 2 is characterized in that: The control instructions are converted into magnetic control signals through the magnetic control column, including: The controller receives the control instruction and transmits the control instruction through the network to obtain the instruction signal; Decoding the command signal to obtain a signal recognizable by the controller; Filter the identifiable signal to obtain stable signal data; The stable signal data is passed through the current coil inside the magnetic control column to generate a magnetic field, so that the positive voltage output establishment time of the switch controller is 3.795μs, and the recovery time during stable signal conversion is 1.396μs; the negative voltage output establishment time is 5.293μs, and the recovery time during stable signal conversion is 2.257μs. When the switch is working in steady state, the DC power consumption is 16.81μA, which is converted into a magnetic control signal.

4. The switch control method on the magnetic control column based on primary and secondary fusion according to claim 3 is characterized in that: The digital data set and the magnetic control signal are combined to obtain an intelligent control model, including: Acquire the state, current and voltage values ​​in the digital data set and the magnetic control signal to obtain a characteristic data set; Synchronize the timestamps of different data sources in the feature data set to obtain the corresponding feature data set; Normalize the corresponding feature data set and remove redundant or irrelevant features to obtain a secondary feature data set; Interactively combine the secondary feature data sets, and filter and predict different data sources of the secondary feature data sets to obtain the optimal estimate; Perform weighted averaging on the weights in the optimal estimate to obtain the fusion result; The fusion results are mapped to the control model for debugging to obtain an intelligent control model.

5. The switch control method on the magnetic control column based on primary and secondary fusion according to claim 4 is characterized in that: The fusion results are mapped to the control model for debugging to obtain an intelligent control model, including: Predict the feature data set obtained in real time according to the fusion result to obtain a real-time feature data set; Based on the real-time feature data set, the control model is debugged according to the power supply voltage of 2.8V, the negative voltage establishment time of 6.12μs, the conversion rate of 4μs, the power consumption of 44.4μA during the steady-state operation of the switch, and the negative voltage establishment time of 6.5μs, the static power consumption of 55.9μA during the steady-state operation of the switch to obtain an intelligent control model; Adopt controller hardware-in-the-loop technology according to various parameters of the intelligent control model to obtain diversified verification functions; Based on the diversified verification function, a delayed response is generated according to the interface between the switch and the magnetic control column. The simulation results of different interface delays are compared through the CHIL test system to obtain the delay that should be controlled within 50μs.

6. The switch control system on the magnetic control column based on primary and secondary fusion is characterized by: include: A collection module (10) is used to collect initial switch data and digitize the initial data to obtain a digitized data set; The controller receives the switch control command according to the digital data set; the control command is converted into a magnetic control signal through the magnetic control column; A fusion module (20) is used to fuse the digital data set and the magnetic control signal to obtain an intelligent control model; The monitoring module (30) is used to perform fault monitoring on the switch control according to the intelligent control model to obtain monitoring data, including: The switch control is processed in sections through an intelligent control model to obtain the power parameters of three-phase current and voltage of multiple section switch controls; Fault identification is performed based on power parameters, and the corresponding parameters are reported to the intelligent control model in the form specified by 104 protocol through the switch control grid for real-time communication; According to fault identification, multi-agent technology is introduced and combined with the matrix algorithm. Each section of switch control is set as an agent, and the types of nodes where the section agents are located are divided into central management layer, regional management layer and local execution layer. Each level cooperates with others to exchange data based on the established fault location strategy, and determines the fault direction of each section agent according to the current parameters and phase information sampled by the switch control to obtain the fault section agent location; Analyze the switch control status according to the monitoring data, transmit the analysis data to the controller, and issue an alarm in time, including: Acquire the section fault location through the fault monitoring data and transmit the fault location information to the intelligent control model; The intelligent control model compares the fault location information data with the input switch control status information to obtain the fault difference; The fault signal is transmitted to the controller through the intelligent control model according to the fault difference, so as to respond to the alarm system of the controller to issue an alarm.

7. A switch control device on a magnetic control column based on primary and secondary fusion, characterized in that: include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

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