High and low voltage switchgear control method and system

Abstract

The invention discloses a high and low voltage switchgear control method and system, and relates to the technical field of electrical equipment state control, and the method comprises the steps: collecting the state data of a high and low voltage switchgear, generating a state variable matrix, calculating a normalized time change rate, and calculating the relative entropy variation between state variables by using KL divergence. Correcting the normalized time change rate, constructing a normalized state change rate matrix, calculating geodesic distances among state variables by using Fisher information measurement, and constructing embedded coordinates; the generalized Laplace operator of the embedded coordinates is calculated, the change rate of the embedded coordinates is calculated in combination with the normative field theory, and the state of the switch equipment is monitored and adjusted; embedded coordinates are constructed through Fisher-Rao measurement and multi-dimensional scaling analysis, the state analysis depth and the comprehensiveness of state monitoring are improved, the change rate of the embedded coordinates is calculated in combination with the standard field theory, and the sensitivity and the anti-noise capacity of anomaly detection are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment state control, and in particular to a high and low voltage switch equipment control method and system. Background Art

[0002] As an important part of the power system, the main function of high and low voltage switchgear is to control the switching of current to ensure the safe and stable operation of the power system. With the development of power industry technology, especially the emergence of smart grids, the requirements for the intelligence, precision and automation of switchgear are gradually increasing. Modern high and low voltage switchgear not only needs to have basic switching functions, but also needs to monitor the equipment status in real time and predict the health status and operating performance of the equipment through data analysis to ensure the efficient operation of the power system and reduce the probability of failure.

[0003] Existing technologies still have some key deficiencies. Traditional equipment monitoring systems mostly rely on regular manual inspections and empirical judgments, making it difficult to achieve real-time dynamic monitoring and fault prediction, and have poor adaptability to equipment status changes and external environments. Existing equipment status monitoring methods mostly use simple threshold judgments or basic statistical analysis, lack in-depth dynamic modeling and efficient intelligent decision-making capabilities, and often cannot accurately reflect the actual status and potential faults of the equipment. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a high and low voltage switch equipment control method and system to solve the problem that existing equipment status monitoring methods mostly use simple threshold judgment or basic statistical analysis, lack in-depth dynamic modeling and efficient intelligent decision-making capabilities, and often cannot accurately reflect the actual status and potential fault problems of the equipment.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a high and low voltage switchgear control method, which comprises:

[0008] Collect the state data of high and low voltage switchgear, generate the state variable matrix, calculate the normalized time change rate, use KL divergence to calculate the relative entropy variation between state variables, correct the normalized time change rate, construct the normalized state change rate matrix, use Fisher information metric to calculate the geodesic distance between state variables, and construct the embedded coordinates;

[0009] Calculate the generalized Laplace operator of the embedded coordinates, combine it with gauge field theory, calculate the rate of change of the embedded coordinates, monitor the state of the switchgear and make adjustments;

[0010] Build a visual interface to display monitoring results and store the status data generated by collection and analysis.

[0011] As a preferred solution of the high and low voltage switchgear control method of the present invention, the collecting of the status data of the high and low voltage switchgear and constructing the embedded coordinates includes:

[0012] Collecting status data of high and low voltage switchgear through intelligent sensors, including current, voltage, power, temperature, electromagnetic field strength, laser displacement and contact resistance sensors;

[0013] The state data includes current, voltage, load power, temperature, electromagnetic interference intensity, switch contact gap, contact resistance and corresponding time interval data;

[0014] The time window is set using the maximum information entropy criterion, and the time window is used as the number of rows of the state variable matrix. The preprocessed state data is sorted in chronological order to generate the state variable matrix.

[0015] Use the steady-state probability distribution method to set the reference state value and calculate the normalized time rate of change;

[0016] The relative entropy variation between state variables is calculated using KL divergence, and the normalized time rate of change is corrected using an exponential decay function;

[0017] For the corrected normalized time rate of change, a normalized state rate of change matrix is ​​constructed using a matrix scaling method;

[0018] Extract the elements in the normalized state change rate matrix, calculate the Shannon entropy using the Shannon entropy formula, and calculate the equivalent temperature using the law of conservation of energy;

[0019] The reciprocal temperature parameter is calculated using the inverse of the Boltzmann constant;

[0020] Computing the generalized partition function using the Boltzmann factor ;

[0021] The normalized probability is calculated using the maximum entropy probability distribution, the diagonal elements of the Fisher information metric matrix are calculated using the Fisher-Rao metric, and the Fisher information metric matrix is ​​constructed;

[0022] The geodesic distances between state variables are calculated using Fisher information metric, a geodesic distance matrix is ​​constructed, and the embedded coordinates are constructed using multidimensional scaling analysis methods.

[0023] As a preferred solution of the high and low voltage switchgear control method of the present invention, wherein: the generalized Laplace operator of the embedded coordinates is calculated, combined with the gauge field theory, to calculate the rate of change of the embedded coordinates, including:

[0024] The time derivatives of the embedded coordinates are calculated using the central difference method;

[0025] The gauge field connection of the state variables is constructed based on the diagonal elements of the Fisher information metric matrix and normalized;

[0026] The curvature tensor of the gauge field is calculated using covariant derivatives and normalized;

[0027] The determinant of the Fisher information metric matrix is ​​calculated using LU decomposition, the inverse matrix of the Fisher information metric matrix is ​​calculated using Cholesky decomposition, and the partial derivatives of the embedded coordinates are calculated using the finite difference method;

[0028] Compute the generalized Laplace operator of embedded coordinates using the Laplace-Beltrami operator;

[0029] Combined with gauge field theory, the rate of change of the embedded coordinates is calculated.

[0030] As a preferred solution of the high and low voltage switchgear control method of the present invention, the monitoring of the switchgear state and adjusting the state include:

[0031] Use statistical analysis to set the state threshold, compare the change rate with the state threshold, and when the change rate is less than the state threshold, it is judged as a normal state and monitoring continues;

[0032] When the rate of change is greater than or equal to the state threshold, it is judged as an abnormal state;

[0033] The gradient of the normalized gauge field connection is calculated using the variational method and adjusted in combination with the diagonal elements of the Fisher information metric matrix;

[0034] Calculate the difference between the rate of change and the state threshold, which is defined as the control error term;

[0035] Use empirical rules to set adjustment factors and calculate control offsets;

[0036] Calculate the optimal control input using the forward update method and execute it;

[0037] The change rate of the embedded coordinates of the optimized control input is calculated and monitored, and the adjustment is stopped when the change rate is less than the state threshold.

[0038] As a preferred solution of the high and low voltage switchgear control method of the present invention, after collecting the state data, a pre-processing operation is first performed.

[0039] As a preferred solution of the high and low voltage switchgear control method of the present invention, wherein: the construction of a visual interface to display the monitoring results includes:

[0040] Use the front-end framework React.js to build a visualization interface, including the main chart area and the top information bar;

[0041] Display monitoring results in the main chart area and the rate of change of embedded coordinates in the top information bar;

[0042] Users who have passed real-name verification are allowed to view the information.

[0043] As a preferred solution of the high and low voltage switchgear control method of the present invention, the state data generated by storing, collecting and analyzing includes:

[0044] The collected status data and monitoring results generated by analysis are stored in the central database, and security access measures are set. The central database will back up the stored data to the cloud and regularly perform integrity checks on the stored data and backup data. After the test is completed, the integrity test record is generated and stored synchronously in the central database.

[0045] In a second aspect, the present invention provides a high and low voltage switchgear control method, comprising:

[0046] The collection and embedding module is used to collect the state data of high and low voltage switchgear, generate the state variable matrix, calculate the normalized time change rate, use KL divergence to calculate the relative entropy variation between state variables, correct the normalized time change rate, construct the normalized state change rate matrix, use Fisher information metric to calculate the geodesic distance between state variables, and construct the embedding coordinates;

[0047] The calculation monitoring module is used to calculate the generalized Laplace operator of the embedded coordinates, and calculate the rate of change of the embedded coordinates in combination with the gauge field theory, so as to monitor the state of the switchgear and make adjustments;

[0048] The visualization storage module is used to build a visualization interface to display monitoring results and store the status data generated by collection and analysis.

[0049] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the high and low voltage switch device control method as described in the first aspect of the present invention is implemented.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the high and low voltage switchgear control method as described in the first aspect of the present invention is implemented.

[0051] The beneficial effects of the present invention are as follows: the present invention collects state data of high and low voltage switchgear, generates a state variable matrix, calculates a normalized time change rate, uses KL divergence to calculate the relative entropy variation between state variables, corrects the normalized time change rate, constructs a normalized state change rate matrix, uses Fisher information metric to calculate the geodesic distance between state variables, and constructs embedded coordinates; calculates the generalized Laplace operator of the embedded coordinates, combines the gauge field theory, calculates the change rate of the embedded coordinates, monitors the state of the switchgear and makes adjustments; solves the problems of low sensitivity in abnormality detection, weak ability to capture nonlinear features, and insufficient characterization of state evolution mechanisms in traditional technologies, improves the depth of state analysis and the comprehensiveness of state monitoring, and enhances the sensitivity and noise resistance of abnormality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0053] Figure 1 This is a flow chart of the high and low voltage switchgear control method in Example 1.

[0054] Figure 2 This is a structural diagram of the high and low voltage switchgear control system in Example 1.

[0055] Figure 3 This is a flowchart for constructing embedded coordinates in Example 1.

[0056] Figure 4 This is a flow chart for monitoring and adjusting the status of a switch device in Example 1. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0060] Example 1, reference Figures 1 to 4 , which is the first embodiment of the present invention, and provides a high and low voltage switch device control method, comprising the following steps:

[0061] S1. Collect the state data of high and low voltage switchgear, generate the state variable matrix, calculate the normalized time change rate, use KL divergence to calculate the relative entropy variation between state variables, correct the normalized time change rate, construct the normalized state change rate matrix, use Fisher information metric to calculate the geodesic distance between state variables, and construct the embedded coordinates;

[0062] Specifically, the status data of high and low voltage switchgear is collected and embedded coordinates are constructed, including:

[0063] Collecting status data of high and low voltage switchgear through intelligent sensors, including current, voltage, power, temperature, electromagnetic field strength, laser displacement and contact resistance sensors;

[0064] The state data includes current, voltage, load power, temperature, electromagnetic interference intensity, switch contact gap, contact resistance and corresponding time interval data;

[0065] Preprocess the state data, including time synchronization, smoothing the state data using the Kalman filter algorithm, removing noise introduced by sensor errors or external factors, identifying and deleting abnormal data using the Z-Score method, supplementing missing data using the spline interpolation algorithm, and normalizing the state data;

[0066] The time window is set using the maximum information entropy criterion, and the time window is used as the number of rows of the state variable matrix. The preprocessed state data is sorted in chronological order to generate the state variable matrix.

[0067] Use the steady-state probability distribution method to set the reference state value and calculate the normalized time rate of change. The formula is:

[0068] ,

[0069] in is the normalized time rate of change of the jth state variable at the i-th time point, is the jth state variable value at the i-th time point, indicating the state data at the preprocessed time point, is the baseline value, For the time interval between adjacent state variables, calculate the average rate of change of all normalized time rates of change;

[0070] The relative entropy variation between state variables is calculated using KL divergence, and the normalized time rate of change is corrected using an exponential decay function. The formula is:

[0071] ,

[0072] in is the normalized time rate of change of the jth state variable at the ith time point after correction, is the relative entropy variation between the jth state variable and the average rate of change, is the normalized time rate of change of the jth state variable, is the average rate of change of the jth state variable;

[0073] For the corrected normalized time rate of change, a normalized state rate of change matrix is ​​constructed using a matrix scaling method;

[0074] Extract the elements in the normalized state change rate matrix, use the Shannon entropy formula to calculate the Shannon entropy, and use the law of conservation of energy to calculate the equivalent temperature. The formula is:

[0075] ,

[0076] in is the equivalent temperature, T is the temperature, P is the load power, and C is the specific heat capacity of the equipment;

[0077] The reciprocal temperature parameter is calculated using the reciprocal of the Boltzmann constant, as follows:

[0078] ,

[0079] in is the reciprocal temperature parameter, is the Boltzmann constant;

[0080] Computing the generalized partition function using the Boltzmann factor , the formula is:

[0081] ,

[0082] Where N is the number of state variables, is the reciprocal temperature parameter of the ith device, is the Shannon entropy of the i-th state variable;

[0083] The normalized probability is calculated using the maximum entropy probability distribution, the diagonal elements of the Fisher information metric matrix are calculated using the Fisher-Rao metric, and the Fisher information metric matrix is ​​constructed. The formula is:

[0084] ,

[0085] in is the diagonal element of the Fisher information metric matrix, corresponding to the local information metric of the kth state variable, is the normalized probability of the kth state variable, is the change increment of the kth state variable in the normalized state change rate matrix, is the logarithm of the generalized partition function, which is used to calculate the second-order rate of change of the state variable, is the increment of the logarithm of the generalized partition function with respect to the kth state variable The second-order partial derivative of ;

[0086] The geodesic distances between state variables are calculated using Fisher information metric, a geodesic distance matrix is ​​constructed, and the embedded coordinates are constructed using multidimensional scaling analysis methods.

[0087] Traditional rate of change analysis directly uses the original value or simple filtering, without combining dynamic correction of distribution differences. The KL divergence is used to quantify the degree of deviation of the state variable from the average rate of change, and then adjusted in the form of exponential decay so that the rate of change reflects the balance between local anomalies and global trends. The corrected rate of change not only retains the time dynamics, but also eliminates the influence of noise through entropy variation, which improves the sensitivity of anomaly detection in complex multivariate scenarios (such as a surge in electromagnetic interference). The combination of KL divergence and exponential decay integrates information theory and signal processing across fields, and is not widely used in rate of change correction for equipment status monitoring. Traditional status monitoring only uses physical temperature, and does not combine load power with entropy as an equivalent indicator. Load power and Shannon entropy are introduced into equivalent temperature through energy conservation, and a thermodynamic analogy is constructed to reflect the "information thermal state" of equipment operation. Equivalent temperature combines physical parameters (temperature, power) and information parameters (entropy), providing a multi-dimensional representation of equipment status. Compared with single temperature monitoring, it can warn of anomalies earlier (such as contact resistance). Increased potential failure), traditional equipment monitoring does not use generalized partition function for state distribution modeling, Fisher information is mostly used for parameter optimization rather than state space analysis, Shannon entropy is converted into probability weights through Boltzmann factors, and the local information sensitivity of state variables is calculated by combining Fisher information metric to construct a geometric representation of multidimensional state space. The Fisher information metric matrix reveals the nonlinear dependence between state variables (such as the coupling effect of current and contact resistance). Compared with traditional correlation analysis, it can more accurately capture complex fault modes. Traditional state analysis mostly uses Euclidean distance or correlation coefficient, and does not introduce geodesic distance of information geometry. The geodesic distance between state variables is calculated by Fisher information metric matrix, combined with MDS embedded coordinates, to reveal the nonlinear geometric relationship between variables. The geodesic distance reflects the intrinsic distribution curvature of state variables (such as the nonlinear effect of voltage mutation on contact resistance), which can more accurately capture the dynamic correlation between multidimensional states than Euclidean distance;

[0088] In addition to the traditional parameters such as current, voltage, load power, temperature, etc., the newly added "electromagnetic interference intensity" and "switch contact gap" have significantly improved the comprehensiveness of state monitoring. Compared with the fixed time window, the adaptability of the maximum information entropy criterion can dynamically capture the key change points in the data to avoid information loss or redundancy. The KL divergence quantifies the relative entropy variation between state variables, and the exponential decay function is combined to correct the change rate, effectively filter out noise interference, and highlight the significance of abnormal states. The Fisher-Rao metric is combined with the geodesic distance to capture the nonlinear geometric characteristics between state variables. The embedded coordinates are generated through MDS mapping, which significantly improves the accuracy and visualization of state representation.

[0089] S2, calculate the generalized Laplace operator of the embedded coordinates, combine the gauge field theory, calculate the rate of change of the embedded coordinates, monitor the state of the switch device and make adjustments;

[0090] Specifically, the generalized Laplace operator of the embedded coordinates is calculated, and the rate of change of the embedded coordinates is calculated by combining the gauge field theory, including:

[0091] The time derivatives of the embedded coordinates are calculated using the central difference method;

[0092] The canonical field connection of the state variables is constructed based on the diagonal elements of the Fisher information metric matrix and normalized. The formula is:

[0093] ,

[0094] in is the gauge field connection of the kth embedding coordinate, is the time derivative of the kth embedding coordinate at time t, where is the diagonal element of the Fisher information metric matrix, corresponding to the local information metric of the kth state variable;

[0095] The curvature tensor of the gauge field is calculated using covariant derivatives and normalized;

[0096] The determinant of the Fisher information metric matrix is ​​calculated using LU decomposition, the inverse matrix of the Fisher information metric matrix is ​​calculated using Cholesky decomposition, and the partial derivatives of the embedded coordinates are calculated using the finite difference method;

[0097] The generalized Laplace operator of the embedded coordinates is calculated using the Laplace-Beltrami operator as follows:

[0098] ,

[0099] in is the generalized Laplace operator of the kth embedding coordinate, is the determinant of the Fisher information measure matrix, is the inverse matrix of the Fisher information metric matrix, which represents the information metric relationship between different state variables. is the partial derivative of the kth embedded coordinate and the jth embedded coordinate;

[0100] Combined with the gauge field theory, the rate of change of the embedded coordinates is calculated as follows:

[0101] ,

[0102] in is the rate of change of the kth embedding coordinate, is the normalized gauge field connection of the kth embedding coordinate, is the normalized curvature tensor of the kth and jth gauge fields.

[0103] Traditional information geometry uses Fisher metrics to calculate geodesic distances or parameter sensitivity, but does not directly construct gauge field connections. Gauge field theory is mostly used in physical fields (such as electromagnetic fields) and is not widely used in embedded coordinate analysis. The diagonal elements of the information metric are used as normalization factors and combined with time derivatives to construct gauge field connections to achieve geometric representation of the dynamic evolution of state variables. Traditional embedded coordinate analysis (such as MDS) only generates static coordinates and does not calculate dynamic rates. Gauge field theory is not widely used in rate prediction of information geometry. The normalized gauge field connection and the curvature tensor are added to construct the change rate, which integrates local dynamics (connection) and global geometry (curvature). The change rate reflects both time evolution and coupling between variables. Compared with simple derivatives, it can more accurately predict the dynamic evolution of the state space (such as the mutation trend of the device state).

[0104] Compared with the one-sided difference method, the central difference method has higher accuracy, can effectively reduce truncation errors, and provide accurate estimates of the changes in embedded coordinates over time. LU decomposition efficiently calculates the determinant, and Cholesky decomposition ensures the numerical stability of the inverse matrix. The combination of the two significantly reduces the computational complexity of complex matrix operations.

[0105] Further, monitoring of switchgear status and making adjustments include:

[0106] Use statistical analysis to set the state threshold, compare the change rate with the state threshold, and when the change rate is less than the state threshold, it is judged as a normal state and monitoring continues;

[0107] When the rate of change is greater than or equal to the state threshold, it is judged as an abnormal state;

[0108] The gradient of the normalized gauge field connection is calculated using the variational method and adjusted by combining the diagonal elements of the Fisher information metric matrix. The formula is:

[0109] ,

[0110] in is the gradient of the kth gauge field connection after adjustment, is the normalized gradient of the kth gauge field connection;

[0111] Calculate the difference between the rate of change and the state threshold, which is defined as the control error term;

[0112] Use the rule of thumb to set the adjustment factor and calculate the control offset. The formula is:

[0113] ,

[0114] in is the kth control offset, is the adjustment factor, is the kth control error term;

[0115] Calculate the optimal control input using the forward update method and execute it;

[0116] The change rate of the embedded coordinates of the optimized control input is calculated and monitored, and the adjustment is stopped when the change rate is less than the state threshold.

[0117] The statistical analysis method sets the state threshold in a data-driven manner, which is more adaptable than the fixed threshold and can dynamically reflect the changes in the equipment operating conditions. The abnormal judgment method based on the change rate makes full use of the dynamic trend information and transcends the limitations of traditional static parameter monitoring. The variational method combines the diagonal elements of the Fisher information metric matrix to accurately calculate the gradient of the gauge field connection, providing a dual basis of geometry and information theory for state adjustment. The control error term quantifies the degree of abnormality and provides a specific goal for adjustment. The empirical rule introduces domain knowledge and balances computational complexity and practicality. The forward update method ensures that the control input is dynamically adjusted with the state change through iterative optimization. Compared with the traditional feedback control, it is more forward-looking. By recalculating the change rate of the embedded coordinates, the adjustment effect is verified and the stop condition is set to form a closed-loop control mechanism.

[0118] S3, build a visual interface to display monitoring results, store, collect and analyze the status data generated;

[0119] Specifically, a visual interface is built to display the monitoring results, including:

[0120] Use the front-end framework React.js to build a visualization interface, including the main chart area and the top information bar;

[0121] Display monitoring results in the main chart area and the rate of change of embedded coordinates in the top information bar;

[0122] Users who have passed real-name verification are allowed to view the information.

[0123] React.js' componentized design and virtual DOM technology significantly improve the rendering efficiency and maintainability of the interface. Compared with traditional static HTML or basic JavaScript development, it can update complex monitoring data in real time without reloading the page. The main chart area uses graphical display (such as line charts showing current changes, scatter plots reflecting temperature distribution) to convert complex multi-dimensional state data into intuitive visual information, and places the rate of change of embedded coordinates in the top information bar, providing real-time dynamic key indicators to facilitate users to quickly grasp the evolution speed of device status. The real-name verification mechanism ensures that only authorized users can access monitoring data, significantly improving the system's security and data privacy protection capabilities.

[0124] Furthermore, the status data generated by the storage, collection and analysis includes:

[0125] The collected status data and monitoring results generated by analysis are stored in the central database, and security access measures are set. The central database will back up the stored data to the cloud and regularly perform integrity checks on the stored data and backup data. After the test is completed, the integrity test record is generated and stored synchronously in the central database.

[0126] Centralized storage of status data and monitoring results facilitates efficient data management and unified analysis. Security access measures effectively prevent unauthorized access or data leakage, and ensure the confidentiality and integrity of switchgear status data. Cloud backup significantly improves data disaster resistance through off-site redundant storage. Regular integrity testing ensures that data has not been tampered with or damaged, detects potential problems in a timely manner, generates and stores integrity test records, forms a traceable audit chain, and enhances the transparency and verifiability of data management.

[0127] This embodiment also provides a high and low voltage switchgear control system, including:

[0128] The collection and embedding module is used to collect the state data of high and low voltage switchgear, generate the state variable matrix, calculate the normalized time change rate, use KL divergence to calculate the relative entropy variation between state variables, correct the normalized time change rate, construct the normalized state change rate matrix, use Fisher information metric to calculate the geodesic distance between state variables, and construct the embedding coordinates;

[0129] The calculation monitoring module is used to calculate the generalized Laplace operator of the embedded coordinates, and calculate the rate of change of the embedded coordinates in combination with the gauge field theory, so as to monitor the state of the switchgear and make adjustments;

[0130] The visualization storage module is used to build a visualization interface to display monitoring results and store the status data generated by collection and analysis.

[0131] This embodiment also provides a computer device, which is suitable for the case of a high and low voltage switch device control method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the high and low voltage switch device control method proposed in the above embodiment.

[0132] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0133] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the high and low voltage switch device control method proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0134] In summary, the present invention collects state data of high and low voltage switchgear, generates a state variable matrix, calculates the normalized time rate of change, uses KL divergence to calculate the relative entropy variation between state variables, corrects the normalized time rate of change, constructs a normalized state change rate matrix, uses Fisher information metric to calculate the geodesic distance between state variables, and constructs embedded coordinates; calculates the generalized Laplace operator of the embedded coordinates, combines the gauge field theory, calculates the rate of change of the embedded coordinates, monitors the state of the switchgear and makes adjustments; solves the problems of low sensitivity in anomaly detection, weak ability to capture nonlinear features, and insufficient characterization of state evolution mechanisms in traditional technologies, improves the depth of state analysis and the comprehensiveness of state monitoring, and enhances the sensitivity and noise resistance of anomaly detection.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A high and low voltage switchgear control method, characterized in that: include, Collect the state data of high and low voltage switchgear, generate the state variable matrix, calculate the normalized time change rate, use KL divergence to calculate the relative entropy variation between state variables, correct the normalized time change rate, construct the normalized state change rate matrix, use Fisher information metric to calculate the geodesic distance between state variables, and construct the embedded coordinates; Calculate the generalized Laplace operator of the embedded coordinates, combine it with gauge field theory, calculate the rate of change of the embedded coordinates, monitor the state of the switchgear and make adjustments; Build a visual interface to display monitoring results and store the status data generated by collection and analysis.

2. The high and low voltage switchgear control method according to claim 1, characterized in that: The collecting of status data of high and low voltage switchgear and constructing embedded coordinates includes: Collecting status data of high and low voltage switchgear through intelligent sensors, including current, voltage, power, temperature, electromagnetic field strength, laser displacement and contact resistance sensors; The state data includes current, voltage, load power, temperature, electromagnetic interference intensity, switch contact gap, contact resistance and corresponding time interval data; The time window is set using the maximum information entropy criterion, and the time window is used as the number of rows of the state variable matrix. The preprocessed state data is sorted in chronological order to generate the state variable matrix. Use the steady-state probability distribution method to set the reference state value and calculate the normalized time rate of change; The relative entropy variation between state variables is calculated using KL divergence, and the normalized time rate of change is corrected using an exponential decay function; For the corrected normalized time rate of change, a normalized state rate of change matrix is ​​constructed using a matrix scaling method; Extract the elements in the normalized state change rate matrix, calculate the Shannon entropy using the Shannon entropy formula, and calculate the equivalent temperature using the law of conservation of energy; The reciprocal temperature parameter is calculated using the inverse of the Boltzmann constant; Computing the generalized partition function using the Boltzmann factor ; The normalized probability is calculated using the maximum entropy probability distribution, the diagonal elements of the Fisher information metric matrix are calculated using the Fisher-Rao metric, and the Fisher information metric matrix is ​​constructed; The geodesic distances between state variables are calculated using Fisher information metric, a geodesic distance matrix is ​​constructed, and the embedded coordinates are constructed using multidimensional scaling analysis methods.

3. The high and low voltage switchgear control method according to claim 2, characterized in that: The generalized Laplace operator of the embedded coordinates is calculated, combined with the gauge field theory, to calculate the rate of change of the embedded coordinates, including: The time derivatives of the embedded coordinates are calculated using the central difference method; The gauge field connection of the state variables is constructed based on the diagonal elements of the Fisher information metric matrix and normalized; The curvature tensor of the gauge field is calculated using covariant derivatives and normalized; The determinant of the Fisher information metric matrix is ​​calculated using LU decomposition, the inverse matrix of the Fisher information metric matrix is ​​calculated using Cholesky decomposition, and the partial derivatives of the embedded coordinates are calculated using the finite difference method; Compute the generalized Laplace operator of embedded coordinates using the Laplace-Beltrami operator; Combined with gauge field theory, the rate of change of the embedded coordinates is calculated.

4. The high and low voltage switchgear control method according to claim 3, characterized in that: The monitoring of the switchgear status and making adjustments include: Use statistical analysis to set the state threshold, compare the change rate with the state threshold, and when the change rate is less than the state threshold, it is judged as a normal state and monitoring continues; When the rate of change is greater than or equal to the state threshold, it is judged as an abnormal state; The gradient of the normalized gauge field connection is calculated using the variational method and adjusted in combination with the diagonal elements of the Fisher information metric matrix; Calculate the difference between the rate of change and the state threshold, which is defined as the control error term; Use empirical rules to set adjustment factors and calculate control offsets; Calculate the optimal control input using the forward update method and execute it; The change rate of the embedded coordinates of the optimized control input is calculated and monitored, and the adjustment is stopped when the change rate is less than the state threshold.

5. The high and low voltage switchgear control method according to claim 1, characterized in that: After the state data is collected, a pre-processing operation is first performed.

6. The high and low voltage switchgear control method according to claim 4, characterized in that: The construction of a visual interface to display monitoring results includes: Use the front-end framework React.js to build a visualization interface, including the main chart area and the top information bar; Display monitoring results in the main chart area and the rate of change of embedded coordinates in the top information bar; Users who have passed real-name verification are allowed to view the information.

7. The high and low voltage switchgear control method according to claim 4, characterized in that: The state data generated by the storage, collection and analysis includes: The collected status data and monitoring results generated by analysis are stored in the central database, and security access measures are set. The central database will back up the stored data to the cloud and regularly perform integrity checks on the stored data and backup data. After the test is completed, the integrity test record is generated and stored synchronously in the central database.

8. A high and low voltage switchgear control system, based on the high and low voltage switchgear control method according to claim 1, characterized in that: include, The collection and embedding module is used to collect the state data of high and low voltage switchgear, generate the state variable matrix, calculate the normalized time change rate, use KL divergence to calculate the relative entropy variation between state variables, correct the normalized time change rate, construct the normalized state change rate matrix, use Fisher information metric to calculate the geodesic distance between state variables, and construct the embedding coordinates; The calculation monitoring module is used to calculate the generalized Laplace operator of the embedded coordinates, and calculate the rate of change of the embedded coordinates in combination with the gauge field theory, so as to monitor the state of the switchgear and make adjustments; The visualization storage module is used to build a visualization interface to display monitoring results and store the status data generated by collection and analysis.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the high and low voltage switchgear control method according to claim 1 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the high and low voltage switchgear control method according to claim 1 are implemented.

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