Motor power distribution system automatic switching control method and system

Abstract

The invention discloses an automatic switching control method and system for a motor power distribution system, and the method comprises the steps: connecting a normally-closed auxiliary contact of a vacuum circuit breaker into a coupler in series, and triggering an insulation monitoring device to switch a working mode when the state of the contact changes; acquiring electric field intensity distribution and a leakage current path of a motor insulation medium by adopting an insulation monitoring device, constructing an electric field-impedance coupling map, and identifying a field intensity concentration area; performing clustering analysis on the minimum field intensity point to form a dominant node, establishing a candidate path, and selecting a path with the minimum impedance gradient as a switching path; monitoring arc characteristic parameters of the vacuum circuit breaker, extracting arc energy density, converting the arc energy density into magnetic field stored energy, releasing reverse electromotive force, and extracting an attenuation coefficient through buffer voltage to correct a switching position; and an impedance matching window is generated, and a comprehensive switching control instruction is generated and executed in combination with the optimized switching time and the energy recovery coefficient, so that intelligent control of the switching position and time is realized, the arc energy is recovered, and the system safety and economical efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor power distribution system control, in particular to a motor power distribution system automatic switching control method and system. BACKGROUND

[0002] As the core power equipment of industrial production, the intelligent level of switching control of motor power distribution system directly affects the safety and economy of system operation. The traditional power distribution switching method mainly relies on manual judgment and preset program control, lacks real-time monitoring mechanism based on the state of auxiliary contact of vacuum circuit breaker, and is difficult to accurately perceive the change of motor working state and respond in time, and lacks deep analysis ability for motor insulation state and electric field distribution.

[0003] The prior art has the problem of low energy utilization efficiency in the switching process, a large amount of energy generated by arc discharge is directly consumed without effective recycling, and the selection of switching time lacks scientific basis, often leading to excessive electrical shock, shortening of equipment life and other problems. In addition, the traditional method cannot realize dynamic optimization adjustment of switching position, and has insufficient adaptability in the face of load fluctuation and environmental change, which is difficult to meet the urgent needs of modern industry for high reliability, high efficiency and intelligence of power distribution system. SUMMARY

[0004] The present application discloses a motor power distribution system automatic switching control method and system, which establishes a monitoring mechanism through auxiliary contact of vacuum circuit breaker, constructs an electric field-impedance coupling map to identify field strength concentration area and plan switching path at the minimum field strength point, monitors arc characteristic parameters to realize conversion of magnetic field energy storage to reverse electromotive force, combines buffer voltage decay coefficient to correct switching position and impedance matching window to determine switching time, generates comprehensive control instructions integrating space, time and energy information, and realizes intelligent automatic switching.

[0005] The present application discloses a motor power distribution system automatic switching control method and system, which establishes a monitoring mechanism through auxiliary contact of vacuum circuit breaker, constructs an electric field-impedance coupling map to identify field strength concentration area and plan switching path at the minimum field strength point, monitors arc characteristic parameters to realize conversion of magnetic field energy storage to reverse electromotive force, combines buffer voltage decay coefficient to correct switching position and impedance matching window to determine switching time, generates comprehensive control instructions integrating space, time and energy information, and realizes intelligent automatic switching. An insulation monitoring device is used to obtain the electric field intensity distribution and leakage current path of motor insulation medium, the field strength gradient is extracted from the electric field intensity distribution, the impedance change trajectory is tracked along the leakage current path, and the field strength gradient and the impedance change trajectory are superimposed to form an electric field-impedance coupling map; The field strength concentration area is identified through the electric field-impedance coupling map, the minimum field strength point is searched in the field strength concentration area, the switching path is planned through the minimum field strength point, and the first switching position is determined according to the impedance distribution on the switching path; The arc characteristic parameters when the normally closed auxiliary contact of the vacuum circuit breaker is broken are monitored, the arc energy density is separated from the arc characteristic parameters, the arc energy density is converted into magnetic field storage energy, and the energy recovery coefficient is generated according to the accumulation rate of the magnetic field storage energy; The real-time load impedance and the power supply line impedance are extracted based on the impedance change trajectory, difference analysis is performed on the real-time load impedance and the power supply line impedance to generate an impedance matching window, the energy recovery coefficient is combined with the impedance matching window to determine an optimal switching time; The reverse electromotive force is released at the optimal switching time by using the magnetic field storage energy, the amplitude modulation is performed on the reverse electromotive force to generate a buffer voltage, the decay coefficient is extracted from the buffer voltage, and the second switching position is formed by correcting the first switching position according to the decay coefficient; The comprehensive switching control instruction is generated based on the second switching position, the optimal switching time and the energy recovery coefficient, and the motor power distribution system automatic switching is completed by executing the comprehensive switching control instruction.

[0006] The second aspect of the present application provides a motor power distribution system automatic switching control system, comprising: The electric field monitoring module is used for acquiring the electric field intensity distribution and the leakage current path of the motor insulation medium by using an insulation monitoring device, extracting the field strength gradient from the electric field intensity distribution, tracking the impedance change trajectory along the leakage current path, and superimposing the field strength gradient and the impedance change trajectory to form an electric field-impedance coupling map; The path planning module is used for identifying a field strength concentration area through the electric field-impedance coupling map, searching for a minimum field strength point in the field strength concentration area, planning a switching path via the minimum field strength point, and determining a first switching position according to the impedance distribution on the switching path; The energy recovery module is used for monitoring the arc characteristic parameters when the normally closed auxiliary contact of the vacuum circuit breaker is broken, separating the arc energy density from the arc characteristic parameters, converting the arc energy density into magnetic field storage energy, and generating an energy recovery coefficient according to the accumulation rate of the magnetic field storage energy; The opportunity determination module is used for extracting the real-time load impedance and the power supply line impedance based on the impedance change trajectory, performing difference analysis on the real-time load impedance and the power supply line impedance to generate an impedance matching window, and determining an optimal switching time in combination with the energy recovery coefficient and the impedance matching window; The position correction module is used for releasing a reverse electromotive force at the optimal switching time by using the magnetic field storage energy, performing amplitude modulation on the reverse electromotive force to generate a buffer voltage, extracting a decay coefficient from the buffer voltage, and forming a second switching position by correcting the first switching position according to the decay coefficient; The switching execution module is configured to generate a comprehensive switching control instruction based on the second switching position, the optimized switching time and the energy recovery coefficient, and execute the comprehensive switching control instruction to complete automatic switching of the motor power distribution system.

[0007] The beneficial effects of the present application are embodied in the following points: 1. An automatic triggering mechanism based on the state of auxiliary contact is established, combining the electric field-impedance coupling map analysis technology, the electric field intensity distribution and the impedance change trajectory are fused, the stereoscopic monitoring of the motor insulation state and the accurate positioning of the field intensity concentration area are realized. The strategy of planning the switching path at the minimum field intensity point avoids the safety risk of the high electric field intensity area, through the hierarchical management of the master node and the slave node and the selection of the impedance gradient minimum path, the electrical safety and the path optimization effect of the switching process are ensured. 2. The conversion mechanism of arc energy density to magnetic field energy storage is established, through real-time monitoring of the arc characteristic parameters of the vacuum circuit breaker and energy separation technology, the recycling of arc discharge energy is realized. Combined with the amplitude modulation of the back electromotive force to generate a buffer voltage, through envelope curve analysis and inflection point sequence identification to extract the decay coefficient, the dynamic correction of the switching position is realized, and the energy utilization efficiency of the system is improved. 3. The comprehensive switching control strategy integrating spatial, temporal and energy three-dimensional information is constructed, through the coding of the second switching position, the time sequence marking of the optimized switching time and the compensation modulation of the energy recovery coefficient, the comprehensive switching control instruction is generated. The control instruction realizes the full-automatic switching of the motor power distribution system, reduces the demand for manual intervention, improves the consistency and reliability of the switching operation, and reduces the system operation cost through energy recovery and optimization control.

[0008] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0009] The drawings herein show specific examples of the technical solutions described in the present application, and constitute part of the specification together with the specific embodiments, for explaining the technical solutions, principles and effects of the present application.

[0010] Unless specifically stated or defined otherwise, the same reference signs in different drawings represent the same or similar technical features, and different reference signs may also be used to represent the same or similar technical features.

[0011] Figure 1 is a flow diagram of a motor power distribution system automatic switching control method of the present application.

[0012] Figure 2 is a structural block diagram of a motor power distribution system automatic switching control system of the present application. DETAILED DESCRIPTION

[0013] In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and

[0014] It is to be understood that the terminology "including", "comprising", "consisting" and "consisting essentially of" used in the specification and the appended claims, are used in the sense of open ended inclusion, that is, to include, but not limited to, the recited features, integers, steps, operations, elements, and / or components.

[0015] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" or "in a various embodiment" or "in other embodiments" in various places throughout this specification are not necessarily referring to the same embodiment, unless otherwise specified. The terms "including", "comprising", "consisting essentially of" and "consisting of" are used interchangeably, unless otherwise specified.

[0016] The technical solutions of the embodiments of the present application are introduced as follows.

[0017] As shown in Figure 1 The motor power distribution system automatic switching control method provided by the embodiments of the present application includes the following steps S110-S160: In step S110, the insulation monitoring device is used to obtain the electric field intensity distribution and the leakage current path of the motor insulation medium, the field strength gradient is extracted from the electric field intensity distribution, the impedance change trajectory is tracked along the leakage current path, and the field strength gradient and the impedance change trajectory are superimposed to form an electric field-impedance coupling map.

[0018] Specifically, the normally closed auxiliary contact of the vacuum circuit breaker is connected in series with the coupler. When the state of the auxiliary contact changes, the coupler signals trigger the insulation monitoring device to automatically determine the working state of the motor and switch the working mode. After the working mode is switched on, the insulation monitoring device starts to perform deep electrical analysis. By arranging a multi-point electric field probe array between the motor stator winding and the casing, the spatial distribution of the electric field strength inside the insulating medium is monitored in real time. The electric field probe uses a non-contact capacitive sensor, and the sensor head is equipped with a shielding electrode to effectively suppress external electromagnetic interference. The probe array is arranged according to the cylindrical coordinate system, forming a three-dimensional monitoring grid covering the entire insulation area, uniformly distributed in the radial, circumferential and axial directions, ensuring the integrity of the monitoring range. The insulation monitoring device is equipped with a high-impedance preamplifier and a high-precision analog-to-digital converter, with wide measurement range and high measurement accuracy. Synchronous measurement of leakage current path, through the arrangement of micro current sensor on the insulation surface, using Hall effect principle to detect the flow direction and size of leakage current. The current sensor is made of flexible substrate, with ultra-thin design closely attached to the insulation surface, covering a wide measurement range from microampere to milliampere. The data acquisition system uses synchronous triggering mode to ensure the time synchronization and spatial correlation of the electric field strength distribution and leakage current path data.

[0019] In some embodiments, the superimposing the field strength gradient and the impedance change trajectory to form an electric field-impedance coupling map comprises: constructing an electric field distribution vector field according to the field strength gradient; forming a field-path coupling relationship by combining the impedance change trajectory and the electric field distribution vector field; extracting a distortion concentrated area in the field-path coupling relationship; and forming an electric field-impedance coupling map based on the distribution density of the distortion concentrated area.

[0020] An electric field distribution vector field is constructed according to the field strength gradient. The electric field strength vector is calculated by the basic physical relationship wherein E is the electric field strength vector, φ is the electric potential scalar field, is the gradient operator. The electric field strength gradient is calculated by the potential difference of adjacent monitoring points, and the radial, circumferential and axial field strength gradient components are calculated respectively. The complete field strength gradient tensor is constructed by the gradient components in three directions. The discrete field strength gradient data is spatially continuous by high-precision interpolation method, generating a smooth electric field distribution vector field. The electric field distribution vector field is represented in the form of an arrow diagram, with the arrow direction representing the electric field direction, the arrow length representing the field strength, and the color depth representing the strength of the field strength gradient. The vector field is analyzed for divergence to identify charge accumulation areas, and for rotation to detect the non-conservative characteristics of the field distribution. The streamlines of the vector field are established, and the spatial structure and strength variation law of the electric field are intuitively displayed by the distribution pattern of the electric field lines. The visualization of the vector field reveals the complex distribution pattern of the electric field inside the insulating medium, laying a foundation for subsequent field-path coupling analysis.

[0021] The field-path coupling relationship is formed by combining the impedance change trajectory and the electric field distribution vector field. The impedance change trajectory obtained by tracking along the leakage current path is spatially superimposed and analyzed with the constructed electric field distribution vector field. A three-dimensional mathematical description of the leakage current path is established, and a parametric curve equation is used to represent the spatial orientation of the current path. At each position point along the impedance change trajectory, the electric field distribution vector field value at the corresponding position is extracted, and a three-way relationship between the position coordinates, impedance value, and electric field strength is established. The correlation between the electric field strength and the impedance change is calculated, and the statistical analysis method is used to evaluate the degree of association between the two. The coupling strength function of the electric field strength and the impedance value is constructed to quantify the local coupling degree of the electric field and the impedance. The variation law of the coupling strength along the path is analyzed, and the peak position and variation gradient of the coupling strength are identified. The electric field-current density relationship J = σE is established, where J is the current density vector, σ is the conductivity, and E is the electric field strength vector. At the same time, σ = 1 / Z, where Z is the impedance value. Through the field-path coupling relationship analysis, the spatial correspondence between the electric field concentration area and the high impedance area is identified, and the physical mechanism and evolution law of insulation degradation are revealed.

[0022] The distortion concentration area is extracted within the field-path coupling relationship. Based on the established field-path coupling relationship, the spatial area where the electric field distribution and the impedance distribution deviate significantly from the normal mode is identified. The distortion degree index D = |E_measured-E_theoretical| / E_theoretical is defined, where D is the distortion degree index, E_measured is the measured electric field strength, and E_theoretical is the theoretical uniform electric field strength. When the distortion degree index exceeds the preset threshold, the area is determined as a distortion concentration area. The clustering analysis method is used to spatially group the distortion points, and the distortion points are divided into different concentration areas. The geometric characteristics of each distortion concentration area are calculated, including the area, centroid position, principal axis direction, and eccentricity of the spatial distribution characteristics. The electric field strength distribution characteristics of the distortion concentration area are analyzed, and the mean, standard deviation, peak value, and gradient change rate of the electric field strength in the area are calculated. The influence range of the distortion concentration area on the surrounding electric field distribution is evaluated, and the influence boundary and propagation characteristics are determined by the electric field disturbance decay law. A hierarchical system of distortion concentration areas is established, and the areas are divided into three levels of slight, moderate, and severe according to the distortion degree and influence range. The position coordinates, distortion intensity, influence range, and classification results of each distortion concentration area are recorded.

[0023] The electric field-impedance coupling map is formed based on the distribution density of the distortion concentrated region. The spatial density distribution of the distortion concentrated region is calculated using the kernel density estimation method, and the degree of concentration and distribution pattern of the distortion points are determined through statistical analysis. The coordinate system of the map is established, with the horizontal axis representing the radial position and the vertical axis representing the axial position, and the color or contour representing the strong and weak distribution of the distortion density. Different levels of distortion concentrated regions are marked in the electric field-impedance coupling map, and different colors and symbols are used to distinguish between slight, moderate, and severe distortion regions. The isodistortion density lines are drawn to connect the spatial points with the same distortion density, forming the contour map of the distortion distribution. The electric field distribution vector field and impedance change trajectory information are superimposed to simultaneously display the electric field direction, intensity distribution, and impedance change path in the same map. The mathematical expression of the map is established considering the electric field intensity, impedance distribution, and distortion degree, realizing the unified representation of multiple physical quantities. The generated electric field-impedance coupling map directly displays the coupling characteristics of the electric field and impedance inside the insulating medium, clearly identifies the spatial position and severity of the weak insulation region, and provides a visual analysis tool for insulation state evaluation, fault location, and preventive maintenance.

[0024] In step S120, the field strength concentrated region is identified through the electric field-impedance coupling map, the minimum field strength point is searched in the field strength concentrated region, the switching path is planned through the minimum field strength point, and the first switching position is determined according to the impedance distribution on the switching path.

[0025] Specifically, the field strength concentrated region is identified through the electric field-impedance coupling map. In the electric field-impedance coupling map, an adaptive threshold segmentation method is applied, and the field strength concentration determination threshold is set to 1.5 times the average field strength in the electric field-impedance coupling map. Through the color distribution information of the electric field-impedance coupling map, the connected region exceeding the threshold is identified as the field strength concentrated region. Morphological opening and closing operations are used on the identified region in the electric field-impedance coupling map to filter out noise and smooth the boundary, eliminating isolated noise points and internal cavities in the electric field-impedance coupling map. Based on the spatial coordinate information of the electric field-impedance coupling map, the geometric feature parameters of each field strength concentrated region are calculated, including area, perimeter, circularity, and long axis direction. Through the gradient information of the electric field-impedance coupling map, the region growing algorithm is used to expand the boundary of the field strength concentrated region from the seed point until the field strength value in the electric field-impedance coupling map is below the expansion threshold. The field strength distribution characteristics in the field strength concentrated region are statistically analyzed in the electric field-impedance coupling map, including maximum, minimum, mean, and standard deviation. According to the numerical analysis of the electric field-impedance coupling map, a hierarchical classification system of the field strength concentrated region is established, and the regions in the electric field-impedance coupling map are divided into first, second, and third concentrated regions.

[0026] The minimum field strength points in the field strength concentration area are searched. Within the identified field strength concentration area, the local minimum value points of field strength are systematically searched as potential safe switching nodes. It is ensured that the local extreme value search algorithm is used in the field strength concentration area to traverse each grid point in the field strength concentration area to find the local minimum value of field strength. The minimum field strength points searched in the field strength concentration area are spatially verified to check whether their neighborhoods in the field strength concentration area indeed form local depressions. The stability index of each minimum field strength point in the field strength concentration area is calculated to evaluate its stability degree by analyzing the directionality of the neighborhood gradient. A safety evaluation system for the minimum field strength points in the field strength concentration area is established to comprehensively consider the numerical size of the minimum field strength points in the field strength concentration area and the electrical characteristics of the surrounding environment. The importance of multiple minimum field strength points in the field strength concentration area is sorted, and they are arranged in ascending order according to the field strength values in the field strength concentration area. The spatial clustering method is used to group the minimum field strength points in the field strength concentration area, and the points close to each other in the field strength concentration area are merged into the same candidate area. The detailed information of each minimum field strength point in the field strength concentration area is recorded, including the accurate coordinates in the field strength concentration area, the field strength value and the stability index.

[0027] In some embodiments, the switching path is planned via the minimum field strength points, including: clustering analysis of the minimum field strength points to form dominant nodes and subordinate nodes; establishing candidate paths from the dominant nodes to the subordinate nodes; detecting impedance changes along the candidate paths to form a set of mutation points; selecting the path with the smallest impedance gradient from the set of mutation points as the switching path.

[0028] The minimum field strength points are clustered to form dominant nodes and subordinate nodes. All the minimum field strength points searched are taken as input data, and a hierarchical clustering method is used for node grouping and hierarchical analysis. The Euclidean distance matrix d = √[(x1-x2)²+(y1-y2)²+(z1-z2)²] between the minimum field strength points is calculated, where d is the distance between the points, (x1, y1, z1) and (x2, y2, z2) are the spatial coordinates of the two points, and a complete mapping relationship of the distance between the points is constructed. Ward linkage criteria are used for clustering merging, which determines the optimal clustering result by minimizing the within-class variance. The clustering distance threshold is set to 0.8 times the average distance between the minimum field strength points, and the points with a distance less than the threshold are merged into the same node. In each cluster, the point with the smallest field strength value is selected as the dominant node, and the remaining points are subordinate nodes. The dominant node represents the most stable position of the electric field in the region, and has the advantage condition of being the starting point or the end point of switching. The influence radius of the dominant node is calculated, and the subordinate nodes within the radius are dominated by the dominant node. A hierarchical relationship diagram of the dominant nodes and the subordinate nodes is established to clearly show the dominant and subordinate relationship between the nodes. The dominant nodes are sorted according to their importance, considering the field strength value, influence radius and number of subordinate nodes.

[0029] The autonomous leading node establishes candidate paths to the subordinate node. The A* path search algorithm is used to calculate the optimal candidate path from the leading node to the subordinate node, and the heuristic function combines the Euclidean distance and the electric field strength to optimize the search efficiency of the candidate path. Strict constraints are set during the candidate path planning process to ensure that the candidate path does not pass through high-risk areas in the field strength concentration area and must detour or choose a safe channel. The geometric parameters of each candidate path from the leading node to the subordinate node are calculated, including the total length of the candidate path, the number of turns, the maximum curvature, and the average curvature. The electric field environment characteristics of the candidate path are evaluated, and the field strength distribution, gradient change, and number of abnormal points on the candidate path are counted. A candidate path cost function is established, which considers multiple factors such as candidate path length, field strength risk, and geometric complexity. All candidate paths from the leading node to the subordinate node are prioritized, and the candidate path with a smaller cost function value is selected as the preferred path set. Detailed information of each candidate path is recorded, including the candidate path coordinate sequence, intermediate node position, and key feature parameters.

[0030] The impedance change along the candidate path is detected to form a set of mutation points. On the established candidate path, the change of impedance value is detected point by point to identify the position of the impedance sudden change on the candidate path. The sliding window technique is used to move along the candidate path, and the window length is set to 5% of the total length of the candidate path. The statistical characteristics of the impedance value within the window on the candidate path are calculated. The impedance mutation intensity I = |Z(i+1)-Z(i)| / σ is defined, where I is the mutation intensity, Z(i) is the impedance value of the ith sampling point, and σ is the impedance standard deviation. When the mutation intensity I > 2, it is determined as the impedance mutation point on the candidate path. The first-order difference and second-order difference methods are used to detect the gradient and acceleration of the impedance change on the candidate path to identify the inflection point position of the impedance change on the candidate path. Cluster analysis is performed on the detected mutation points on the candidate path to merge the mutation points close in distance on the candidate path into a mutation region, avoiding redundant mutation point detection. The mutation intensity index of each mutation point on the candidate path is calculated to reflect the severity and influence range of the impedance change on the candidate path. A classification system of mutation points on the candidate path is established, and the mutation points on the candidate path are divided into three categories: slow-changing type, sudden-changing type, and jumping type. All the detected mutation points on the candidate path are summarized to form a complete set of mutation points, and the position coordinates, mutation intensity, and classification type of each mutation point on the candidate path are recorded.

[0031] For example, the path with the smallest impedance gradient is selected from the set of mutation points as the switching path, including: identifying the impedance change characteristics of each path according to the set of mutation points; constructing a path smoothness matrix based on the impedance change characteristics; extracting a curvature sequence from the path smoothness matrix; determining the path corresponding to the minimum value in the curvature sequence as the switching path.

[0032] The impedance variation characteristics of each path are identified based on the set of mutation points. Based on the statistical data of the mutation point set, the distribution pattern of the number of mutation points on each candidate path is calculated, and the proportion and density of sudden, gradual, and jump mutation points in the mutation point set are calculated. The spatial information of the mutation point set is used to analyze the distribution pattern of the mutation points on the candidate paths, identifying the locations of dense and sparse mutation areas within the mutation point set. Using the clustered data of the mutation point set, the clustering index and dispersion of the mutation points are calculated, quantifying the spatial clustering characteristics of the mutation point set. The intensity information of the mutation point set is used to calculate the frequency domain characteristics of the impedance variation on the candidate paths, and the main frequency components of the mutation point set are analyzed using a fast Fourier transform. Time domain statistical features are extracted from the mutation point set, including the peak intensity, valley depth, rising slope, and falling slope of the impedance variation. An impedance variation feature description vector based on the mutation point set is established. For each candidate path, a comprehensive feature vector containing the mutation density, frequency characteristics, and statistical parameters of the mutation point set is generated. Through similarity analysis of the mutation point set, candidate paths with similar impedance variation patterns are grouped, identifying the variation patterns and common characteristics within the mutation point set.

[0033] A path smoothness matrix is ​​constructed based on the impedance variation characteristics. The impedance variation characteristic parameters of each candidate path are systematically organized, and the structured path smoothness matrix is ​​constructed using the statistical data of the impedance variation characteristics. A comprehensive evaluation function S = Σwᵢ·fᵢ is established, where S is the comprehensive smoothness score, wᵢ is the weight coefficient of the i-th indicator, and fᵢ is the normalized value of the i-th smoothness indicator. Based on the gradient information of the impedance variation characteristics, a multi-level evaluation index system for the path smoothness matrix is ​​established, including dimensions such as gradient uniformity, mutation point density, and frequency component concentration. Each evaluation index of the path smoothness matrix is ​​normalized using the frequency component of the impedance variation characteristics. The Z-score normalization method is used to map the different dimensional indicators of the impedance variation characteristics to a unified interval. The analytic hierarchy process is used to determine the weight coefficients of each evaluation index of the path smoothness matrix based on the continuity parameter of the impedance variation characteristics. The main mode of the impedance variation characteristics is used to reduce the dimensionality of the path smoothness matrix and extract the principal component factors that reflect the essential characteristics of the impedance variation characteristics. Through a comprehensive analysis of impedance variation characteristics, the comprehensive smoothness score of each candidate path in the path smoothness matrix is ​​calculated, combining multiple impedance variation characteristics into a single value. A mathematical representation of the path smoothness matrix based on impedance variation characteristics is established, with each element of the path smoothness matrix representing the smoothness evaluation result of a specific path under a specific impedance variation characteristic indicator.

[0034] Extract curvature sequence from path smoothness matrix. Based on the constructed path smoothness matrix, the integrated smoothness of each candidate path is extracted from the score data of the path smoothness matrix, forming a one-dimensional numerical sequence reflecting the overall smoothness characteristics of the path. Identify the top several rows of data in the path smoothness matrix with the best smoothness, corresponding to the candidate paths with better curvature performance. Use the gradient uniformity index in the path smoothness matrix to calculate the curvature distribution of the candidate path in combination with the geometric curvature. Through the frequency component data of the path smoothness matrix, the local curvature value κ = |dθ / ds| of each sampling point on the path is calculated using the three-point arc method, where κ is the curvature, θ is the tangent angle, and s is the arc length parameter. Combine the change continuity parameter in the path smoothness matrix to calculate the curvature distribution statistical characteristics of each candidate path, including the average curvature, maximum curvature, and curvature coefficient of variation. Based on the comprehensive score of the path smoothness matrix, establish the correlation analysis of geometric curvature and impedance change smoothness, and study the mutual relationship between path geometry and electrical property change. The multi-dimensional indicators in the path smoothness matrix and the geometric curvature are comprehensively evaluated by weighted fusion, and a comprehensive evaluation function of curvature and smoothness is designed. From the evaluation results of the path smoothness matrix, a standardized curvature sequence is generated, and each sequence element integrates the evaluation data of the path smoothness matrix and the geometric curvature characteristics. Sort and filter the curvature sequence to identify the top several candidate paths with the best curvature performance.

[0035] Determine the path corresponding to the minimum value in the curvature sequence as the switching path. Multiple verifications are performed on the minimum value of the curvature sequence to confirm the superior performance of the path through independent geometric analysis and electrical analysis. Test the engineering feasibility of the path corresponding to the minimum value of the curvature sequence, and evaluate the safety, accessibility and economy of the switching path in actual operation. Perform sensitivity analysis of the switching path to study the influence degree of small changes in the parameters in the curvature sequence on the switching performance. Establish a performance evaluation report for the switching path, recording the geometric parameters, electrical characteristics, safety indicators and operation requirements of the optimal path in the curvature sequence. The candidate path with the smallest value in the curvature sequence is formally determined as the final switching path, completing the decision-making process of the switching path selection. Perform fine optimization on the determined switching path to further improve the smoothness and safety of the optimal path in the curvature sequence through local path adjustment. Establish a standardized description document for the switching path, including the coordinates, technical parameters, operation procedures and safety precautions of the optimal path in the curvature sequence.

[0036] The first switching position is determined according to the impedance distribution on the switching path. The impedance change rate dZ / ds on the switching path is calculated, where dZ is the impedance change amount and ds is the path microelement, and a stable region with the minimum change rate on the switching path is identified. Equidistant sampling is performed on the switching path, and the sampling point interval is set to 2% of the total length of the switching path to ensure sufficient resolution of the impedance distribution on the switching path. The impedance value and impedance gradient of each sampling point on the switching path are calculated, and a complete impedance distribution function Z(s) along the switching path is established. A smoothing filtering method is used to reduce the noise of the impedance distribution curve on the switching path and eliminate the influence of local disturbances. The morphological characteristics of the impedance distribution curve on the switching path are analyzed, and the relatively stable platform region and the sharply changing transition region on the switching path are identified. Selection criteria for the switching position on the switching path are established, and positions with moderate impedance values, small gradients, and good local stability on the switching path are preferentially selected. The impedance characteristics, geometric position, and operational convenience on the switching path are considered comprehensively to determine a candidate point set for the first switching position on the switching path. The safety of the candidate points on the switching path is evaluated, and the electrical safety margin and operation risk level of each position on the switching path are analyzed. The candidate points on the switching path are comprehensively sorted by a multi-criteria decision-making method, and the position with the optimal comprehensive evaluation on the switching path is selected as the first switching position.

[0037] In step S130, the arc characteristic parameters of the vacuum circuit breaker normally closed auxiliary contact during breaking are monitored, the arc energy density is separated from the arc characteristic parameters, the arc energy density is converted into magnetic field energy storage, and the energy recovery coefficient is generated according to the accumulation rate of the magnetic field energy storage.

[0038] Specifically, the arc characteristic parameters during the breaking of the normally closed auxiliary contact of the vacuum circuit breaker are monitored. During the breaking process of the normally closed auxiliary contact of the vacuum circuit breaker, multi-dimensional characteristic data of arc discharge are synchronously collected by high-frequency current sensors and high-voltage probes. In view of the special structure of the normally closed auxiliary contact of the vacuum circuit breaker, a fiber-optic spectrometer is installed at the contact gap to monitor the spectral changes and intensity distribution of the arc characteristic parameters during the breaking of the normally closed auxiliary contact of the vacuum circuit breaker in real time. A Hall effect magnetic field sensor array is arranged around the normally closed auxiliary contact of the vacuum circuit breaker to capture the magnetic field component in the arc characteristic parameters during the breaking of the normally closed auxiliary contact of the vacuum circuit breaker. The variation of the arc characteristic parameters during the breaking process of the normally closed auxiliary contact of the vacuum circuit breaker is recorded by a high-speed camera system, and the morphological evolution of the arc column is analyzed. A wide-band voltage transformer is configured to monitor the voltage waveform in the arc characteristic parameters between the normally closed auxiliary contacts of the vacuum circuit breaker, and the sampling frequency is set to the order of megahertz. A multi-dimensional data model of the arc characteristic parameters during the breaking of the normally closed auxiliary contact of the vacuum circuit breaker is established, including the current peak value, duration, voltage gradient, magnetic field strength, spectral power density, and geometric size. The arc characteristic parameters collected during the breaking of the normally closed auxiliary contact of the vacuum circuit breaker are synchronously processed to ensure the time reference consistency of various types of data of the arc characteristic parameters.

[0039] The arc energy density is separated from the arc characteristic parameters. Based on the monitored arc characteristic parameters, the energy analysis method is used to extract the distribution characteristics of the arc energy density from the arc characteristic parameters. Using the current and voltage data in the arc characteristic parameters, the arc instantaneous power P(t)=U(t)·I(t) is calculated, which provides the power basis for the calculation of the arc energy density. Through the spectral data analysis of the arc characteristic parameters, the contribution of different wavelengths to the arc energy density is identified, and the spectral distribution function of the arc energy density is established. Combined with the geometric size information in the arc characteristic parameters, the arc volume variation is calculated, and the total energy is allocated to obtain the arc energy density ρ=E / V, where ρ is the arc energy density, E is the total energy, and V is the arc volume. Using the magnetic field strength data of the arc characteristic parameters, the influence of the magnetic field on the distribution of the arc energy density is analyzed, and the concentrated and diffused regions of the arc energy density are identified. The arc characteristic parameters are decomposed in the frequency domain, the contribution of different frequency components to the arc energy density is extracted, and the frequency domain spectrum of the arc energy density is established. Through the time sequence analysis of the arc characteristic parameters, the time evolution law of the arc energy density is tracked, and the key moments of the accumulation and release of the arc energy density are identified. The correlation model between the arc energy density and the arc characteristic parameters is established to realize the accurate separation and quantitative calculation of the arc energy density.

[0040] The arc energy density is converted into magnetic field energy storage. Based on the spatial distribution data of the arc energy density, the current density distribution is calculated according to the current intensity and the cross-sectional area in the arc region, providing the current basis for the calculation of the magnetic field energy storage. Through the gradient information of the arc energy density, the spatial mapping relationship of the magnetic field strength is constructed, and the Ampere loop law is applied to establish the correlation between the magnetic induction intensity and the magnetic field energy storage. The heat energy loss in the conversion process of the arc energy density is considered, and the conversion efficiency model is established to calculate the effective part of the arc energy density that can be actually converted into the magnetic field energy storage. Based on the time-varying characteristics of the arc energy density, the dynamic accumulation process of the magnetic field energy storage is analyzed, and the magnetic field energy storage density u = B² / (2μ0) is established, where u is the magnetic field energy storage density, B is the magnetic induction intensity, and μ0 is the vacuum permeability. Through the spatial gradient analysis of the arc energy density, the distribution mode of the magnetic field energy storage is identified, and the concentration area and diffusion boundary of the magnetic field energy storage are determined. The converted magnetic field energy storage is spatially integrated to calculate the total magnetic field energy storage W = ∫udV, where W is the total magnetic field energy storage, u is the magnetic field energy storage density, and V is the integration volume, to obtain the complete magnetic field energy storage distribution. A conversion mapping table of the arc energy density to the magnetic field energy storage is established to record the energy flow and loss distribution in the magnetic field energy storage conversion process.

[0041] In some embodiments, the energy recovery coefficient is generated according to the accumulation rate of the magnetic field energy storage, including: performing time domain analysis on the accumulation rate of the magnetic field energy storage to obtain instantaneous power variation; evaluating the energy conversion efficiency according to the instantaneous power variation to form an efficiency curve; extracting an energy aggregation point from the peak interval of the efficiency curve; and generating an energy recovery coefficient according to the time distribution of the energy aggregation point.

[0042] The accumulation rate of the magnetic field energy storage is analyzed in time domain to obtain the instantaneous power variation. The accumulation rate of the magnetic field energy storage is sampled in high-precision time domain with a sampling interval of microseconds to ensure capturing the transient change details of the accumulation rate of the magnetic field energy storage. The accumulation rate of the magnetic field energy storage is subjected to sliding average processing to eliminate the interference of high-frequency noise on the calculation of the instantaneous power variation. The accumulation rate of the magnetic field energy storage is correlated and analyzed with the arc power at the corresponding time to calculate the instantaneous power variation ΔP = P(t + Δt) - P(t), where ΔP is the instantaneous power variation and Δt is the time interval. A mathematical relationship model of the accumulation rate of the magnetic field energy storage and the instantaneous power variation is established to identify the influence law of the accumulation rate of the magnetic field energy storage on the instantaneous power variation. The accumulation rate of the magnetic field energy storage is subjected to frequency spectrum analysis to identify the contribution degree of the dominant frequency component to the instantaneous power variation. The statistical characteristics of the instantaneous power variation are extracted through the fluctuation characteristic analysis of the accumulation rate of the magnetic field energy storage, including the mean, variance, peak value and change rate of the instantaneous power variation. The complete time sequence data of the instantaneous power variation in the accumulation rate analysis process of the magnetic field energy storage are recorded to establish a dynamic file of the instantaneous power variation.

[0043] The efficiency curve is formed according to the evaluation of energy conversion efficiency based on the instantaneous power change. The instantaneous energy conversion efficiency η(t) = P_out(t) / P_in(t) is calculated, where η(t) is the instantaneous efficiency, and the real-time calculation is performed using the instantaneous power change data. Using the gradient information of the instantaneous power change, the rate and trend of the efficiency curve change are analyzed, and the efficiency improvement and decline periods caused by the instantaneous power change are identified. Through statistical analysis of the instantaneous power change, the probability density function of the efficiency curve distribution is established, and the random influence of the instantaneous power change on the efficiency curve is revealed. The instantaneous power change is arranged in chronological order, and the continuous efficiency curve is drawn, with the horizontal axis being time and the vertical axis being conversion efficiency. The efficiency curve is smoothed, and the local fluctuations caused by the instantaneous power change are eliminated by using the cubic spline interpolation method, highlighting the main trend of the efficiency curve. The morphological characteristics of the efficiency curve are analyzed, and the distribution position and duration of the efficiency curve peak, valley, rising section and falling section are identified. A mathematical description model of the efficiency curve is established, and a polynomial fitting is used to represent the relationship between the efficiency curve and time.

[0044] The energy aggregation point is generated by extracting the peak interval of the efficiency curve. The efficiency curve peak detection threshold is set, and the points exceeding 1.2 times the average value of the efficiency curve are selected as candidate peak points to ensure that the extracted peak has significance. Cluster analysis is performed on the peak points detected on the efficiency curve, and adjacent peaks with short time intervals are merged into the peak interval of the efficiency curve. The average efficiency and duration of each peak interval of the efficiency curve are calculated to evaluate the overall performance of energy conversion in the interval. In each peak interval of the efficiency curve, the time point with the most concentrated energy conversion is identified as the energy aggregation point, which corresponds to the maximum instantaneous energy storage increment. The spatial distribution characteristics of the efficiency curve peak interval are analyzed, and the spatial coordinates and influence range of the energy aggregation point in the arc region are calculated. The strength evaluation index of the energy aggregation point is established, considering factors such as the peak height of the efficiency curve, the interval width and the cumulative amount of the energy aggregation point. The importance of multiple energy aggregation points extracted in the efficiency curve is sorted, and the key energy aggregation point with the largest contribution to the overall energy recovery is identified. The detailed parameters of the energy aggregation point are recorded, including the position in the efficiency curve, the duration, the peak efficiency and the cumulative energy of the energy aggregation point.

[0045] The energy recovery coefficient is generated according to the time distribution of the energy aggregation point. The distribution density of the energy aggregation point on the time axis is analyzed, the average interval time of the energy aggregation point and the distribution uniformity index are calculated. The calculation formula of the energy recovery coefficient K = Σ (E_i * w_i) / E_total is established, wherein K is the energy recovery coefficient, E_i is the energy value of the i-th energy aggregation point, and w_i is the corresponding weight factor. According to the intensity distribution of the energy aggregation point, a weight distribution strategy is designed, and the energy aggregation point with high intensity obtains a larger weight coefficient. Considering the time distribution characteristics of the energy aggregation point, a time decay factor is introduced, and the weight of the early appearing energy aggregation point is appropriately improved. Through the spatial correlation analysis of the energy aggregation point, a multi-point cooperative energy recovery coefficient correction mechanism is established. The generated energy recovery coefficient is normalized to limit the energy recovery coefficient value range to 0 to 1, which is convenient for engineering application and performance comparison. A hierarchical evaluation standard of the energy recovery coefficient is established, and the recovery performance is divided into excellent, good, general and poor four levels according to the energy recovery coefficient value. The generation process and calculation parameters of the energy recovery coefficient are recorded to form a complete energy aggregation point distribution file and an energy recovery coefficient evaluation report.

[0046] In step S140, the real-time load impedance and the power supply line impedance are extracted based on the impedance change trajectory, the real-time load impedance is subtracted from the power supply line impedance, the impedance matching window is generated by difference analysis, and the optimal switching time is determined by combining the energy recovery coefficient and the impedance matching window.

[0047] Specifically, the real-time load impedance and the power supply line impedance are extracted based on the impedance change trajectory. By performing time-frequency analysis on the impedance change trajectory, the low-frequency component caused by load change and the high-frequency component caused by line characteristics in the impedance change trajectory are identified. An impedance separation model Z_total(t)=Z_load(t)+Z_line(t) is established, wherein Z_total is the total impedance of the impedance change trajectory, Z_load is the real-time load impedance, and Z_line is the power supply line impedance. The dynamic change of the real-time load impedance is extracted from the impedance change trajectory by using the time domain characteristics of the impedance change trajectory and adopting Kalman filtering method. The basic characteristic value of the power supply line impedance is identified by steady-state analysis of the impedance change trajectory, and a time-invariant model of the power supply line impedance is established. The impedance change trajectory is decomposed by wavelet, the high-frequency details are taken as the power supply line impedance component, and the low-frequency approximation is taken as the real-time load impedance component. A parameter identification model of the impedance change trajectory is established, and the parameters of the real-time load impedance and the power supply line impedance are identified in real time by using recursive least squares method. The time sequence data of the real-time load impedance and the power supply line impedance extracted from the impedance change trajectory are recorded, and a complete file of impedance separation results is established.

[0048] The real-time load impedance is subtracted from the power supply line impedance to generate an impedance matching window. Based on the extracted real-time load impedance and power supply line impedance data, the impedance difference ΔZ(t) = |Z_load(t)-Z_line(t)| between the real-time load impedance and the power supply line impedance is calculated, where ΔZ is the impedance difference, reflecting the matching degree of the real-time load impedance and the power supply line impedance. An impedance matching degree evaluation index M(t) = 1 / (1+ΔZ(t) / Z_ref) is established, where M is the matching degree, Z_ref is the reference impedance value, and the closer the matching degree is to 1, the more matched the real-time load impedance and the power supply line impedance are. The difference between the real-time load impedance and the power supply line impedance is analyzed in a sliding window, with a window length of 10 ms, and the statistical characteristics of the difference between the real-time load impedance and the power supply line impedance in the window are calculated. The impedance matching threshold is set to 20% of the reference impedance, and when the difference between the real-time load impedance and the power supply line impedance is less than the threshold, it is marked as a matching state. By aggregating the time of consecutive matching state points, an impedance matching window is formed, each impedance matching window recording the start time, end time and matching degree of the real-time load impedance and the power supply line impedance. The distribution of the impedance matching window is analyzed, and the average duration, occurrence frequency and stability index of the impedance matching window are counted. A mathematical description model of the impedance matching window is established, recording the time boundary, real-time load impedance value, power supply line impedance value and matching quality score of each impedance matching window.

[0049] In some embodiments, the combination of the energy recovery coefficient and the impedance matching window determines the optimal switching time, including: constructing an energy release time sequence according to the energy recovery coefficient; performing stability evaluation on the impedance matching window to form a matching reliability; modulating the energy release time sequence with the matching reliability as a weight to form a weighted time sequence; determining the optimal switching time from the intersection of the weighted time sequence and the impedance matching window.

[0050] The energy release time sequence is constructed according to the energy recovery coefficient. The energy recovery coefficient is arranged in time sequence to form a continuous time function K(t) of the energy recovery coefficient, which describes the evolution of the energy release capability over time. The energy recovery coefficient is subjected to first-order difference operation to calculate the energy release rate and identify the time period with the most drastic change in the energy recovery coefficient. A mathematical model E_release(t)=K(t)·f(t) of the energy release time sequence is established, where E_release is the energy release time sequence and f(t) is a time modulation function. The key moments of energy release are identified through peak detection of the energy recovery coefficient, and these moments are taken as feature points of the energy release time sequence. The energy recovery coefficient is subjected to spectral analysis to extract the dominant frequency component and construct a periodic model of the energy release time sequence. A cumulative distribution function of the energy release time sequence is established to describe the probability distribution characteristics of the energy release time sequence. The statistical characteristics of the energy recovery coefficient are used to generate a random model of the energy release time sequence to simulate the uncertainty of the energy release time sequence. The complete data of the energy release time sequence are recorded, including time stamp, energy recovery coefficient value, release rate and cumulative release amount.

[0051] The stability of the impedance matching window is evaluated to form a matching reliability. The standard deviation σ_ΔZ of the impedance difference in the impedance matching window is calculated, and the smaller the standard deviation is, the more stable the impedance matching window is. A matching reliability calculation formula R(t)=exp(-σ_ΔZ / σ_ref) is established, where R is the matching reliability, σ_ref is the reference standard deviation, and the matching reliability value is between 0 and 1. The duration distribution of the impedance matching window is analyzed, and the impedance matching window with longer duration has higher matching reliability. The clarity of the impedance matching window boundary is evaluated, and the matching reliability of the impedance matching window with a fuzzy boundary is correspondingly reduced. The frequency analysis of the repeated appearance of the impedance matching window is performed, and the matching mode with frequent appearance has higher matching reliability. A multi-dimensional evaluation model of the stability of the impedance matching window is established, which comprehensively considers the duration, matching degree, boundary clarity and repetition frequency. The matching reliability is normalized to map the matching reliability of different impedance matching windows to a unified evaluation interval. The matching reliability value of each impedance matching window is recorded to establish an association database of the matching reliability and the characteristics of the impedance matching window.

[0052] The energy release time series is weighted and modulated by the matching reliability to form a weighted time series. The calculation formula of the weighted time series is W(t)=E_release(t)·R(t), where W is the weighted time series, E_release is the energy release time series, and R is the matching reliability. Through the weight modulation of the matching reliability, the importance of the time period with high matching reliability in the weighted time series is highlighted, and the influence of the unreliable period is suppressed. The difference between the weighted time series and the original energy release time series is analyzed to evaluate the influence of the matching reliability modulation on the characteristics of the weighted time series. The weighted time series is smoothed to eliminate the mutation caused by the change of the matching reliability and maintain the continuity of the weighted time series. The statistical model of the weighted time series is established to calculate the mean, variance and probability distribution characteristics of the weighted time series. Through the peak value analysis of the weighted time series, the preferred time period with high energy release and high matching reliability is identified. The time-frequency analysis of the weighted time series is performed to study the influence of the weight modulation on the frequency domain characteristics of the weighted time series. The generation process and modulation parameters of the weighted time series are recorded to establish a complete weight modulation archive.

[0053] The determination of the optimal switching time from the intersection of the weighted time series and the impedance matching window includes: constructing a time probability field based on the weighted time series; making the impedance matching window pass through the time probability field to generate an intersection trajectory; searching for a probability peak point along the intersection trajectory to form a candidate time set; and generating an optimal switching time according to the probability weight ordering of the candidate time set.

[0054] A time probability field is constructed based on the weighted time series. The weighted time series is normalized to map the numerical value to the probability interval of 0 to 1, P(t)=W(t) / max(W(t)), where P is the time probability and W is the weighted time series value. A continuous distribution model of the time probability field is established, and a Gaussian kernel function is used for smooth interpolation of the discrete weighted time series points. A two-dimensional time probability field is constructed, with the horizontal axis representing time and the vertical axis representing probability density, and the color depth representing the intensity of the time probability field distribution. Through statistical analysis of the weighted time series, the mean, variance and skewness of the time probability field are calculated. The mathematical expression of the time probability field is P(t)=Σw_i·G(t-t_i,σ), where w_i is the weight, G is the Gaussian function, t_i is the sampling point of the weighted time series, and σ is the smoothing parameter. Contour analysis is performed on the time probability field to identify the distribution pattern of the high probability area and the low probability area of the time probability field. The construction parameters and distribution characteristics of the time probability field are recorded to establish a digital representation of the time probability field.

[0055] The impedance matching window crosses the time probability field to generate a cross trajectory. The time boundary information of the impedance matching window is projected onto the time probability field to analyze the interaction between the impedance matching window and the time probability field. A crossing algorithm is established to simulate the moving trajectory of the impedance matching window in the time probability field and record the probability changes of the impedance matching window during the crossing process. The crossing intensity I(t)=P(t)·M(t) is calculated during the crossing process, where I is the crossing intensity, P is the time probability field value, and M is the impedance matching window indicator function. A geometric description of the cross trajectory is established to record the path coordinates and direction changes of the impedance matching window crossing the time probability field. The morphological characteristics of the cross trajectory are analyzed to identify the straight line segment, curve segment and turning point distribution of the cross trajectory. Through the dynamic analysis of the impedance matching window crossing the time probability field, a differential equation model of the cross trajectory evolution is established. The cross trajectory is smoothed to eliminate the discontinuity of the impedance matching window boundary causing the cross trajectory mutation. The complete cross trajectory data generated by the impedance matching window crossing the time probability field is recorded, including the cross trajectory coordinates, crossing intensity and geometric characteristics.

[0056] Along the cross trajectory, search for probability peak points to form a candidate time set. On the generated cross trajectory, a peak detection algorithm is used to search for local maximum points of probability distribution. A probability peak point determination criterion is established, when the probability value of a point on the cross trajectory is greater than that of adjacent points and exceeds 1.5 times the average probability, it is marked as a probability peak point. First derivative analysis is performed on the cross trajectory to identify stationary points with zero probability gradient as potential probability peak points. The sliding window technique is used to move along the cross trajectory, and the maximum probability point is searched in each window to form a preliminary candidate time set of probability peak points. Cluster analysis is performed on the detected probability peak points, and the peak points with close spatial distance are merged into the same candidate time set element. The characteristic parameters of each probability peak point are calculated, including peak height, half-peak width, peak sharpness and surrounding probability gradient. A probability peak point quality evaluation model is established, considering factors such as peak intensity, stability and uniqueness. From all the probability peak points on the cross trajectory, the qualified points are selected to form a candidate time set. The detailed information of each time in the candidate time set is recorded, including time position, probability value, characteristic parameter and quality score.

[0057] The optimized switching time is generated according to the probability weight ordering of the candidate time set. Based on the formed candidate time set, a probability weight-based ordering criterion is established to select the optimal switching time. The probability weight w_i of each time in the candidate time set is calculated, where w_i is the weight of the i th candidate time in the candidate time set, and P_i is the corresponding probability value. A multi-criteria decision model is established, which considers the stability, reachability and engineering practicability of the time in addition to the probability weight. The candidate time set is comprehensively scored, and the scoring formula is S_i = a w_i + b s_i + g r_i, where S is the comprehensive score, s is the stability index, r is the practicability index, and a, b and g are weight coefficients. The candidate time set is sorted in descending order according to the comprehensive score, and the time with the highest score is selected as the optimized switching time. Sensitivity analysis is performed on the sorting result of the candidate time set to study the influence of weight coefficient changes on the sorting result, and the robustness of the selection is ensured. A verification mechanism for the optimized switching time is established, and the switching performance of the selected optimized switching time is verified through simulation analysis. The selection process and decision basis of the optimized switching time are recorded, and a complete time selection file is established. The time with the highest score in the candidate time set is determined as the final optimized switching time, and the optimization selection process of the optimized switching time is completed.

[0058] In step S150, the reverse electromotive force is released at the optimized switching time by utilizing the magnetic field energy storage, the buffer voltage is generated by amplitude modulation of the reverse electromotive force, the decay coefficient is extracted from the buffer voltage, and the second switching position is formed by correcting the first switching position according to the decay coefficient.

[0059] Specifically, the magnetic field energy is stored and released at the optimal switching time to generate the counter electromotive force. Through the rapid discharge mechanism of the magnetic field energy, the stored magnetic field energy is released at the optimal switching time to generate the counter electromotive force to suppress the arc reignition. A magnetic field energy release model dW / dt=-P_release is established, where W is the magnetic field energy, t is the time, dt is the time differential representing a very small change in time, dW / dt is the rate of change of the magnetic field energy with respect to time, i.e., the energy release rate, P_release is the release power, and the negative sign indicates that the magnetic field energy decreases. According to Faraday's law of electromagnetic induction, the counter electromotive force is calculated as ε=-dΦ / dt, where ε is the counter electromotive force, Φ is the magnetic flux, dt is the time differential representing a very small change in time, dΦ / dt is the rate of change of the magnetic flux with respect to time, i.e., the magnetic flux change rate, and the negative sign indicates the direction of the induced electromotive force. The magnetic field energy release controller is triggered synchronously at the optimal switching time to ensure that the magnetic field energy begins to discharge at the precise optimal switching time. Through the spatial distribution information of the magnetic field energy, the field strength distribution of the counter electromotive force in the contact gap is calculated to optimize the spatial effect of the counter electromotive force. The counter electromotive force waveform during the magnetic field energy release process is monitored, and the peak value, rise time, pulse width, and decay characteristics of the counter electromotive force are recorded. The complete data of the magnetic field energy releasing the counter electromotive force at the optimal switching time are recorded, including the release timing, counter electromotive force amplitude, and energy conversion parameters.

[0060] The counter electromotive force is amplitude-modulated to generate a buffer voltage. Based on the obtained counter electromotive force signal, the amplitude modulation technique is used to convert the counter electromotive force into a buffer voltage suitable for circuit protection. The amplitude modulation function U_buffer(t)=A(t)·ε(t) is established, where U_buffer is the buffer voltage, A(t) is the modulation coefficient, and ε(t) is the counter electromotive force. Through peak detection of the counter electromotive force, the dynamic range of the modulation coefficient is determined to ensure that the buffer voltage amplitude is within the safe working range. The pulse width modulation technique is used to shape the counter electromotive force in the time domain to generate a buffer voltage with specific waveform characteristics. Using the frequency spectrum characteristics of the counter electromotive force, a band-pass filter is designed to extract the effective frequency components and suppress high-frequency noise and low-frequency drift in the counter electromotive force. The amplitude control loop of the buffer voltage is established to dynamically adjust the modulation depth of the counter electromotive force according to the real-time changes of the load impedance. Through the phase information of the counter electromotive force, the phase relationship between the buffer voltage and the system voltage is optimized to reduce the voltage impact. The generated buffer voltage is waveform-shaped, and digital filtering technology is used to eliminate glitches and oscillations in the counter electromotive force modulation process. The quality evaluation index of the buffer voltage is established, considering the amplitude stability, waveform distortion, and spectral purity. The complete process of generating the buffer voltage from the counter electromotive force is recorded, including the modulation parameters, buffer voltage waveform, and quality indicators.

[0061] In some embodiments, the extracting the attenuation coefficient from the buffer voltage comprises: converting the buffer voltage into an envelope curve; identifying turning points in the envelope curve to form a sequence of inflection points; segmenting the envelope curve into a rising segment and a decaying segment by using the sequence of inflection points as boundaries; and determining the attenuation coefficient by a ratio of slopes of the rising segment and the decaying segment.

[0062] The buffer voltage is converted into an envelope curve. The buffer voltage is analyzed using a Hilbert transform method to calculate the instantaneous amplitude A(t) = √[u²(t) + û²(t)], where A is the instantaneous amplitude, u is the buffer voltage signal, and û is the Hilbert transform result. The peak value detection algorithm is used to identify the peak points in each cycle of the buffer voltage, and the peak points are connected to form the upper envelope curve of the buffer voltage. A low-pass filtering method is used to smooth the buffer voltage, eliminating the high-frequency carrier components in the buffer voltage and retaining the main trend of the envelope curve. A mathematical description model of the envelope curve is established, and a piecewise function or a spline interpolation is used to represent the continuous change of the envelope curve. The envelope curve is sampled and quantized to convert the continuous envelope signal into a discrete digital sequence for subsequent processing. The statistical characteristics of the envelope curve are analyzed, and parameters such as the mean, variance, peak factor, and wave crest factor of the envelope curve are calculated. The envelope curve is analyzed by spectrum analysis to identify the main frequency components and periodic characteristics of the envelope curve. The correspondence between the envelope curve and the original buffer voltage is established to ensure that the envelope curve accurately reflects the amplitude variation law of the buffer voltage. The processing parameters and conversion quality evaluation results of the buffer voltage converted into the envelope curve are recorded.

[0063] In the envelope curve, the turning points are identified to form a sequence of inflection points. Based on the obtained envelope curve data, a curvature analysis method is used to identify the turning point positions on the envelope curve. The first and second derivatives of the envelope curve are calculated, and the turning point positions on the envelope curve are identified by the sign change of the second derivative. A turning point criterion is established, and when the absolute value of the second derivative of the envelope curve exceeds a threshold value and the signs before and after are opposite, it is marked as a turning point. A sliding window technique is used to move along the envelope curve, and local extrema and inflection points are detected in each window of the envelope curve to form a candidate set of turning points. The detected turning points in the envelope curve are screened, and false inflection points caused by noise are removed to retain the true envelope curve change transitions. The time markers of the inflection point sequence are established, and the time position and amplitude coordinates of each turning point on the envelope curve are recorded. The distribution law of the inflection point sequence is analyzed, and the average interval, distribution density, and variation trend of the turning points in the inflection point sequence are counted. The inflection point sequence is classified, and the inflection points in the inflection point sequence are divided into convex points, concave points, and mixed turning points according to the turning direction. A mathematical description of the inflection point sequence is established, and a vector is used to represent the position, type, and characteristic parameters of each inflection point in the inflection point sequence. The complete inflection point sequence identified in the envelope curve is recorded, including the number, distribution position, and geometric characteristics of the inflection point sequence.

[0064] The envelope curve is segmented into ascending and decaying segments by the inflection point sequence. The peak and valley points of the envelope curve are identified in the inflection point sequence, and the envelope curve is divided into segments by these key points in the inflection point sequence. The segment classification criteria are established, and the envelope curve segments are classified into ascending and decaying segments according to the slope sign of the envelope curve and the turning characteristics of the inflection point sequence. The slope of the ascending segment is calculated as k_up = ΔA / Δt (ascending), where ΔA is the amplitude change and Δt is the time interval between the inflection point sequences. The slope of the decaying segment is calculated as k_down = ΔA / Δt (descending), and the slope is negative, indicating amplitude decay. Geometric feature analysis is performed on each ascending and decaying segment to calculate the segment length, average slope, slope change rate, and linearity. The quality evaluation system of the ascending and decaying segments is established to evaluate the data integrity and fitting accuracy of each ascending and decaying segment. The starting time, ending time, and duration of each ascending and decaying segment are recorded through the time information of the inflection point sequence. The envelope curve segment database is established to store the geometric parameters, statistical features, and quality indicators of all ascending and decaying segments. The alternating rules of the ascending and decaying segments are analyzed to identify the periodicity and regularity of the envelope curve changes.

[0065] The attenuation coefficient is determined by the slope ratio of the ascending and decaying segments. The attenuation coefficient calculation formula is established as β = |k_down| / |k_up|, where β is the attenuation coefficient, k_down is the average slope of the decaying segment, and k_up is the average slope of the ascending segment. Through statistical analysis of the slopes of the ascending and decaying segments, the mean, standard deviation, and confidence interval of the slope ratio of the ascending and decaying segments are calculated. The slope ratios of multiple ascending and decaying segments are analyzed comprehensively, and the overall attenuation coefficient is calculated using the weighted average method. The time-varying model of the attenuation coefficient is established to analyze the evolution law of the slope ratio of the ascending and decaying segments over time. The physical reasonableness of the attenuation coefficient calculated by the slope ratio of the ascending and decaying segments is verified through energy analysis of the ascending and decaying segments. The uncertainty evaluation model of the attenuation coefficient is established to quantify the influence of measurement errors of the ascending and decaying segments on the calculation of the attenuation coefficient. The calculated attenuation coefficient is checked to ensure that the value is within the physically achievable range. The complete process of the slope ratio analysis of the ascending and decaying segments is recorded, and the detailed file of the attenuation coefficient calculation is established to complete the conversion calculation from the slope ratio of the ascending and decaying segments to the attenuation coefficient.

[0066] The second switching position is formed according to the attenuation coefficient correction of the first switching position. A position correction algorithm is established to calculate the influence of the electrical environment change of the first switching position by using the voltage attenuation characteristics reflected by the attenuation coefficient. A position offset calculation formula Δx=f(β,Z1,E_local) is designed, where Δx is the position correction amount, β is the attenuation coefficient, Z1 is the impedance of the first switching position, and E_local is the local electric field strength. The stability degree of the electrical environment of the first switching position is determined by the numerical value of the attenuation coefficient. A large attenuation coefficient indicates that the environment changes dramatically and requires a larger correction. A search algorithm for the second switching position is established to find the optimal second switching position in the local area near the first switching position according to the attenuation coefficient constraint. The attenuation characteristics of the electric field around the first switching position are analyzed by using the correlation between the attenuation coefficient and the electric field distribution to identify the second switching position with a more stable electric field. The spatial offset direction and distance of the second switching position relative to the first switching position are determined by the gradient information of the attenuation coefficient. A safety verification mechanism for the second switching position is established to ensure that the corrected second switching position meets the safety requirements under the attenuation coefficient constraint. The complete information of the second switching position, including the coordinates, attenuation coefficient value, correction parameters and performance evaluation, is recorded. The second switching position corrected by the attenuation coefficient is taken as the final switching position.

[0067] In step S160, a comprehensive switching control instruction is generated based on the second switching position, the optimized switching time and the energy recovery coefficient, and executed to complete the automatic switching of the motor power distribution system.

[0068] In some embodiments, the generation of the comprehensive switching control instruction based on the second switching position, the optimized switching time and the energy recovery coefficient includes: encoding the second switching position to generate a position instruction code; adding a time sequence mark to the position instruction code using the optimized switching time to form a space-time instruction; modulating the control strength to generate an energy compensation term by using the energy recovery coefficient; and fusing the space-time instruction and the energy compensation term to generate the comprehensive switching control instruction.

[0069] The second switching position is encoded to generate a position instruction code. A binary encoding method is used to quantize the three-dimensional coordinates of the second switching position, and an encoding rule PC=encode(x,y,z) of the position instruction code is established, where PC is the position instruction code, and x, y, and z are the spatial coordinates of the second switching position. The bit width and resolution of the position instruction code are determined according to the accuracy requirement of the second switching position, so that the encoded position instruction code can accurately represent the second switching position. A mapping table of the second switching position to the position instruction code is established to record the unique position instruction code identifier corresponding to each second switching position. The boundary conditions of the second switching position are checked to ensure that the generated position instruction code is within the effective execution range. Direction information and execution path identifiers are added to the position instruction code using the geometric characteristics of the second switching position. A verification mechanism of the position instruction code is established to detect errors in the encoding process of the second switching position. The generated position instruction code is compressed and optimized to reduce the overhead of data transmission and storage of the second switching position. A decoding algorithm of the position instruction code is established to ensure that the second switching position information can be accurately restored at the receiving end. The complete process of generating the position instruction code from the second switching position encoding is recorded, including the encoding parameters, mapping relationship, and verification results.

[0070] A timing marker is added to the position instruction code using the optimized switching time to form a space-time instruction. An encoding format TS=encode(T_opt,Δt,priority) of the timing marker is established, where TS is the timing marker, T_opt is the optimized switching time, Δt is the time accuracy, and priority is the execution priority. The absolute time and relative time information of the optimized switching time are embedded into the position instruction code to form a space-time instruction containing the time dimension. The execution window and time limit requirements of the position instruction code are set through the timing constraints of the optimized switching time. A synchronization mechanism of the space-time instruction is established to ensure that multiple position instruction codes can be executed in coordination according to the requirements of the optimized switching time. The periodic characteristics of the optimized switching time are used to add repeated execution and loop control identifiers to the space-time instruction. A time calibration method of the space-time instruction is established to synchronize the position instruction code through the reference clock of the optimized switching time. The timing conflict of the space-time instruction is detected to avoid the execution conflict of different position instruction codes at the optimized switching time. A buffering mechanism of the space-time instruction is established to provide time fault tolerance for the fluctuation of the optimized switching time. The detailed process of adding the timing marker to the position instruction code using the optimized switching time to form the space-time instruction is recorded, including the timing encoding rule, synchronization parameters, and conflict handling strategies.

[0071] An energy compensation term is generated by modulating the control intensity with the energy recovery coefficient. A control intensity modulation function G(t) = G0·(1+K·α) is established, where G is the modulated control intensity, G0 is the baseline control intensity, K is the energy recovery coefficient, and a is the modulation coefficient. The power output and energy distribution strategy of the control system are dynamically adjusted by the size of the energy recovery coefficient. The real-time changing energy compensation term is generated by the time-varying characteristics of the energy recovery coefficient, which adapts to the energy recovery state of the system. A calculation model EC = K·P_base·β is established for the energy compensation term, where EC is the energy compensation term, P_base is the baseline power, and β is the compensation coefficient. The amplitude and phase of the energy compensation term are optimized through the correlation analysis of the energy recovery coefficient and the system load. An amplitude limiting mechanism is established for the energy compensation term to prevent excessive compensation caused by fluctuations in the energy recovery coefficient. The trend and adjustment direction of the energy compensation term are predicted using the statistical characteristics of the energy recovery coefficient. A feedback control loop is established for the energy compensation term to adjust the compensation parameters according to the actual recovery effect of the energy recovery coefficient. The complete algorithm for generating the energy compensation term by modulating the control intensity with the energy recovery coefficient is recorded, including the modulation function, the compensation model, and the limiting conditions.

[0072] The spatiotemporal instruction and the energy compensation term are fused to generate a comprehensive switching control instruction. A fusion algorithm CMD = merge(TSI, EC, W) is established, where CMD is the comprehensive switching control instruction, TSI is the spatiotemporal instruction, EC is the energy compensation term, and W is the weight matrix. The spatiotemporal instruction provides accurate execution coordinates and time references for the comprehensive switching control instruction through its position and timing information. The energy modulation information of the energy compensation term is embedded in the comprehensive switching control instruction to optimize and recover energy. A priority coordination mechanism is established for the spatiotemporal instruction and the energy compensation term to handle conflicts and competition during the fusion process to generate the comprehensive switching control instruction. A weighted fusion method is used to linearly combine the spatiotemporal instruction and the energy compensation term, and the weight coefficients are dynamically adjusted according to the system state of the comprehensive switching control instruction. A completeness verification mechanism is established for the comprehensive switching control instruction to ensure that the fused comprehensive switching control instruction contains complete spatiotemporal instruction and energy compensation term information. The format of the comprehensive switching control instruction is standardized to unify the structure and parameter representation of the comprehensive switching control instruction. An execution simulation system is established for the comprehensive switching control instruction to verify the effectiveness of the fusion of the spatiotemporal instruction and the energy compensation term. The detailed process of generating the comprehensive switching control instruction by fusing the spatiotemporal instruction and the energy compensation term is recorded, including the fusion algorithm, weight distribution, and verification results, to complete the final generation of the comprehensive switching control instruction.

[0073] Execute the integrated switching control instruction to achieve automatic switching of the motor distribution system. The second switching position information in the integrated switching control instruction drives the switchgear for the automatic switching of the motor distribution system to the specified position. Utilize the optimized switching timing data in the integrated switching control instruction to precisely control the start and completion times of the automatic switching of the motor distribution system. Based on the energy recovery coefficient parameter in the integrated switching control instruction, optimize energy management and power regulation during the automatic switching of the motor distribution system. Establish a real-time monitoring mechanism for the automatic switching of the motor distribution system to track the execution progress of the integrated switching control instruction and the response status of the automatic switching of the motor distribution system. Through a feedback control loop, adjust the execution parameters of the integrated switching control instruction based on the actual status of the automatic switching of the motor distribution system. Establish a fault detection and protection mechanism for the automatic switching of the motor distribution system to ensure the safety and reliability of the automatic switching process. Evaluate the execution results of the automatic switching of the motor distribution system to verify the effectiveness of the integrated switching control instruction and the performance of the automatic switching of the motor distribution system. Establish a historical record system for the automatic switching of the motor distribution system to store the integrated switching control instruction, execution process, and result data for each automatic switching of the motor distribution system. Record the complete process of executing comprehensive switching control instructions to achieve automatic switching of the motor power distribution system, including execution parameters, monitoring data, performance indicators and system response, to complete the closed-loop control process of automatic switching of the motor power distribution system.

[0074] In order to implement the above method embodiment corresponding to a motor power distribution system automatic switching control method to achieve the corresponding functions and technical effects. Figure 2 , Figure 2 The following is a block diagram of an automatic switching control system 200 for a motor power distribution system provided in an embodiment of the present application. For ease of explanation, only the parts related to this embodiment are shown. The automatic switching control system 200 for a motor power distribution system provided in an embodiment of the present application includes: An electric field monitoring module 201 is configured to obtain the electric field intensity distribution and leakage current path of the motor insulation medium using an insulation monitoring device, extract a field intensity gradient from the electric field intensity distribution, trace an impedance change trajectory along the leakage current path, and superimpose the field intensity gradient and the impedance change trajectory to form an electric field-impedance coupling map; a path planning module 202 configured to identify a field strength concentration region using the electric field-impedance coupling map, search for a minimum field strength point within the field strength concentration region, plan a switching path via the minimum field strength point, and determine a first switching position based on an impedance distribution along the switching path; An energy recovery module 203 is configured to monitor arc characteristic parameters when the normally closed auxiliary contacts of the vacuum circuit breaker are disconnected, separate arc energy density from the arc characteristic parameters, convert the arc energy density into magnetic field energy storage, and generate an energy recovery coefficient based on the accumulation rate of the magnetic field energy storage; The opportunity determination module 204 is configured to extract real-time load impedance and power supply line impedance based on the impedance change trajectory, perform difference analysis on the real-time load impedance and the power supply line impedance to generate an impedance matching window, and determine an optimal switching time based on the energy recovery coefficient and the impedance matching window; The position correction module 205 is configured to release a reverse electromotive force at the optimal switching time by using the magnetic field energy storage, generate a buffer voltage by amplitude modulation on the reverse electromotive force, extract a decay coefficient from the buffer voltage, and correct the first switching position to form a second switching position according to the decay coefficient. The switching execution module 206 is configured to generate a comprehensive switching control instruction based on the second switching position, the optimal switching time and the energy recovery coefficient, and execute the comprehensive switching control instruction to complete automatic switching of the motor power distribution system.

[0075] The motor power distribution system automatic switching control system 200 described above can implement a motor power distribution system automatic switching control method according to the method embodiments described above. The optional items in the method embodiments are also applicable to this embodiment, and will not be described in detail here. The remaining content of the present embodiment can refer to the content of the method embodiments described above, and will not be described in detail in this embodiment.

[0076] The purpose of the above embodiments is to exemplarily reproduce and deduce the technical solutions of the present application, and to completely describe the technical solutions, purposes and effects of the present application. The purpose is to make the public understand the disclosure of the present application more thoroughly and comprehensively, and does not limit the protection scope of the present application.

[0077] The above embodiments are also not based on an exhaustive enumeration of the present application. In addition, there can be many other unlisted embodiments. Any substitution and improvement made without violating the concept of the present application is within the protection scope of the present application.

Claims

1. A method for automatic switching control of a motor power distribution system, characterized in that: include: An insulation monitoring device is used to obtain the electric field intensity distribution and leakage current path of the motor insulation medium, a field intensity gradient is extracted from the electric field intensity distribution, an impedance change trajectory is tracked along the leakage current path, and the field intensity gradient and the impedance change trajectory are superimposed to form an electric field-impedance coupling map; identifying a field strength concentration area using the electric field-impedance coupling map, searching for a minimum field strength point within the field strength concentration area, planning a switching path via the minimum field strength point, and determining a first switching position based on an impedance distribution along the switching path; monitoring arc characteristic parameters when the normally closed auxiliary contacts of the vacuum circuit breaker are disconnected, separating arc energy density from the arc characteristic parameters, converting the arc energy density into magnetic field energy storage, and generating an energy recovery coefficient based on an accumulation rate of the magnetic field energy storage; extracting the real-time load impedance and the power supply line impedance based on the impedance change trajectory, performing a difference analysis between the real-time load impedance and the power supply line impedance to generate an impedance matching window, and determining an optimized switching time in combination with the energy recovery coefficient and the impedance matching window; utilizing the magnetic field energy storage to release a back electromotive force at the optimized switching moment, performing amplitude modulation on the back electromotive force to generate a buffer voltage, extracting an attenuation coefficient from the buffer voltage, and modifying the first switching position according to the attenuation coefficient to form a second switching position; A comprehensive switching control instruction is generated based on the second switching position, the optimized switching time and the energy recovery coefficient, and the comprehensive switching control instruction is executed to complete the automatic switching of the motor power distribution system.

2. The method according to claim 1, characterized in that The step of superimposing the field intensity gradient and the impedance change trajectory to form an electric field-impedance coupling map includes: constructing an electric field distribution vector field according to the field intensity gradient; Combining the impedance change trajectory and the electric field distribution vector field to form a field-circuit coupling relationship; extracting a distortion concentration region within the field-circuit coupling relationship; An electric field-impedance coupling map is formed based on the distribution density of the distortion concentrated area.

3. The method according to claim 1, characterized in that Planning a switching path via the minimum field strength point includes: Performing cluster analysis on the minimum field strength points to form dominant nodes and subordinate nodes; establishing a candidate path from the master node to the slave node; Detecting impedance changes along the candidate path to form a set of mutation points; A path with the smallest impedance gradient is selected from the set of mutation points as the switching path.

4. The method according to claim 1, wherein Generating an energy recovery coefficient according to the accumulation rate of the magnetic field energy storage includes: Performing time domain analysis on the accumulation rate of the magnetic field energy storage to obtain instantaneous power changes; Evaluating energy conversion efficiency according to the instantaneous power change to form an efficiency curve; Extracting a peak interval from the efficiency curve to generate an energy concentration point; An energy recovery coefficient is generated according to the time distribution of the energy accumulation points.

5. The method according to claim 1, wherein The determining of the optimized switching time by combining the energy recovery coefficient and the impedance matching window includes: constructing an energy release time series according to the energy recovery coefficient; Performing stability evaluation on the impedance matching window to form matching reliability; Modulating the energy release time series using the matching reliability as a weight to form a weighted time series; An optimized switching time is determined from the intersection of the weighted time series and the impedance matching window.

6. The method according to claim 1, wherein The extracting the attenuation coefficient from the buffer voltage includes: converting the buffer voltage into an envelope curve; identifying turning points in the envelope curve to form an inflection point sequence; dividing the envelope curve into a rising segment and a decaying segment based on the inflection point sequence; The attenuation coefficient is determined by the slope ratio of the rising section and the attenuation section.

7. The method according to claim 1, characterized in that The generating of a comprehensive switching control instruction based on the second switching position, the optimized switching time, and the energy recovery coefficient includes: encoding the second switching position to generate a position instruction code; Adding a timing mark to the position instruction code using the optimized switching moment to form a time-space instruction; The energy recovery coefficient is modulated to control the intensity to generate an energy compensation term; The spatiotemporal instruction and the energy compensation term are combined to generate a comprehensive switching control instruction.

8. The method according to claim 3, characterized in that The selecting a path with the smallest impedance gradient from the set of mutation points as the switching path includes: Identifying impedance change characteristics of each path according to the set of mutation points; Constructing a path smoothness matrix based on the impedance variation characteristics; extracting a curvature sequence from the path smoothness matrix; The path corresponding to the minimum value in the curvature sequence is determined as the switching path.

9. The method according to claim 5, characterized in that The determining of the optimized switching time from the intersection of the weighted time series and the impedance matching window comprises: constructing a temporal probability field based on the weighted time series; Allowing the impedance matching window to cross the time probability field to generate a cross trajectory; Searching for probability peak points along the cross trajectory to form a candidate time set; The optimized switching time is generated according to the probability weight sorting of the candidate time set.

10. An automatic switching control system for a motor power distribution system, characterized in that: include: An electric field monitoring module is configured to obtain the electric field intensity distribution and leakage current path of the motor insulation medium using an insulation monitoring device, extract a field intensity gradient from the electric field intensity distribution, trace an impedance change trajectory along the leakage current path, and superimpose the field intensity gradient and the impedance change trajectory to form an electric field-impedance coupling map; a path planning module, configured to identify a field strength concentration area using the electric field-impedance coupling map, search for a minimum field strength point within the field strength concentration area, plan a switching path via the minimum field strength point, and determine a first switching position based on an impedance distribution along the switching path; an energy recovery module, configured to monitor arc characteristic parameters when the normally closed auxiliary contacts of the vacuum circuit breaker are disconnected, separate arc energy density from the arc characteristic parameters, convert the arc energy density into magnetic field energy storage, and generate an energy recovery coefficient based on the accumulation rate of the magnetic field energy storage; a timing determination module, configured to extract the real-time load impedance and the power supply line impedance based on the impedance change trajectory, perform a difference analysis between the real-time load impedance and the power supply line impedance to generate an impedance matching window, and determine an optimized switching time in combination with the energy recovery coefficient and the impedance matching window; a position correction module, configured to utilize the magnetic field energy storage to release a back electromotive force at the optimized switching moment, perform amplitude modulation on the back electromotive force to generate a buffer voltage, extract an attenuation coefficient from the buffer voltage, and correct the first switching position according to the attenuation coefficient to form a second switching position; A switching execution module is used to generate a comprehensive switching control instruction based on the second switching position, the optimized switching time and the energy recovery coefficient, and execute the comprehensive switching control instruction to complete the automatic switching of the motor power distribution system.

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