A remote operation and maintenance intelligent service system for high-voltage switchgear

Abstract

The present invention relates to the technical field of intelligent operation and maintenance of high-voltage electrical equipment, and in particular to an intelligent service system for remote operation and maintenance of high-voltage switchgear. It comprises the following parts: a data acquisition module: acquiring external gas density data, internal gas density data, ambient temperature data and internal temperature data of the circuit breaker in a high-altitude environment; a monitoring module: using the instantaneous change data of the external gas density data and the ambient temperature data as analysis data of the internal field strength of the circuit breaker; and optimizing the supply of accessories for the circuit breaker based on the analysis data. The present invention suppresses false alarms by decoupling environmental disturbances, optimizes the priority of accessory replacement based on three-dimensional field strength reconstruction, integrates multi-source data to calibrate the supply chain, and matches the supply cycle with the field strength degradation trend; combines the field strength deviation with the component correlation to generate a maintenance path, and constructs a closed-loop system from abnormality perception to precise scheduling, thereby improving the operation and maintenance efficiency and decision reliability of high-voltage equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of high-voltage electrical equipment, and in particular to an intelligent service system for remote operation and maintenance of high-voltage switchgear. Background Art

[0002] As critical high-voltage switchgear in power systems, high-voltage circuit breakers (HVCBs) undertake the core functions of rapidly isolating short-circuit faults and controlling system operating status. Their reliability directly impacts grid security. As ultra-high-voltage and ultra-high-voltage transmission networks extend to complex environments such as high altitudes and extreme cold, equipment is constantly exposed to harsh operating conditions such as low air pressure, strong radiation, and drastic day-night temperature swings. Traditional operation and maintenance technologies have significant limitations: First, conventional gas density monitoring relies on a fixed threshold alarm mechanism, which cannot effectively distinguish between natural fluctuations in SF6 gas density caused by low-pressure environments and actual leakage risks, significantly increasing the false alarm rate. Second, existing status assessments are mostly based on regular maintenance and local sensor data, making it difficult to perceive the three-dimensional electric field intensity distribution within the equipment in real time. This makes it difficult to detect hidden defects such as early partial discharge and floating potential in a timely manner. Third, mainstream prediction models are mostly trained with laboratory steady-state data, ignoring the dynamic coupling effect between the thermodynamic properties of gases and the electromagnetic field distribution in high-altitude environments, resulting in a significant decrease in prediction accuracy during field deployment. Although the Industrial Internet of Things and machine learning technologies are gradually being applied to equipment monitoring, purely data-driven approaches, lacking the integration of multi-physics coupling mechanisms, can easily produce field strength inferences that contradict actual operating conditions. Building a high-precision operation and maintenance system that integrates physical laws with real-time data has become a technical bottleneck in the intelligent upgrade of high-voltage switchgear. Summary of the Invention

[0003] In order to overcome the shortcoming of insufficient operation and maintenance accuracy of high-voltage circuit breakers under multi-physical field coupling, the present invention provides an intelligent service system for remote operation and maintenance of high-voltage switchgear.

[0004] The technical implementation scheme of the present invention is: a high-voltage switchgear remote operation and maintenance intelligent service system, including the following parts:

[0005] Data acquisition module: obtains external gas density data, internal gas density data, ambient temperature data and internal temperature data of the circuit breaker in a high-altitude environment;

[0006] Monitoring module: using the instantaneous change data of the external gas density data and the ambient temperature data as analysis data of the internal field strength of the circuit breaker; optimizing the supply of accessories for the circuit breaker based on the analysis data;

[0007] Analysis and processing module: calculates confidence weight based on the external gas density data, ambient temperature data and internal monitoring data; calculates comprehensive field strength based on the confidence weight;

[0008] Decision optimization module: Based on the calculated value of comprehensive field strength, the updated scheduling scoring formula is used to determine the supply of circuit breaker accessories.

[0009] Preferably, the data acquisition module acquires external gas density data, internal gas density data, ambient temperature data, and internal temperature data of the circuit breaker in a high-altitude environment, including:

[0010] Using external gas density data and ambient temperature data as first monitoring data;

[0011] The internal gas density data and the internal temperature data of the equipment are used as the second monitoring data;

[0012] When the first monitoring data undergoes periodic changes, an optimization period determination of the second monitoring data is triggered;

[0013] The periodic changes are periodic changes in external gas density and ambient temperature in a high-altitude environment;

[0014] The optimization period is defined as a period during which the second monitoring data does not undergo synchronous changes during the periodic changes of the first monitoring data.

[0015] Preferably, the period during which the second monitoring data does not undergo synchronous changes during the periodic changes of the first monitoring data includes:

[0016] The lack of the same change is quantified by the phase difference formula, which is as follows:

[0017]

[0018] in, is the phase difference, is the change of external environmental parameters, is the internal parameter variation, is the environmental conduction delay, is the sampling window length, is the sampling time interval, is the sequence number of the discrete sequence;

[0019] Judgment rules: When > When , it is marked as a cycle that needs to be optimized. is the preset phase difference threshold;

[0020] Extract the first monitoring data that continuously exceeds the preset threshold for a duration of ≥ As the component moments, is the shortest time of continuous exceeding threshold value;

[0021] constructing an optimization period of the second monitoring data based on the composition time;

[0022] Based on the optimization cycle, a preliminary scheduling plan for circuit breaker accessories is generated.

[0023] Preferably, generating a preliminary scheduling plan for circuit breaker accessories based on the optimization period includes:

[0024] The preliminary scheduling score value of the circuit breaker accessories is obtained through the scheduling score formula. The scheduling score formula is as follows:

[0025]

[0026] in, is the scheduling score value, is the phase difference, For components The basic maintenance weight of is the current timestamp, For components Last maintenance timestamp, is the dynamic remaining life ratio, For components The theoretical maintenance cycle, For associated components The state anomaly score, 、 is the weight coefficient, For components A collection of associated components.

[0027] Preferably, the monitoring module uses the instantaneous change data of the external gas density data and the ambient temperature data as analysis data of the internal field strength of the circuit breaker; and optimizes the supply of accessories for the circuit breaker based on the analysis data, including:

[0028] The instantaneous change rate of the external gas density is constructed by taking the moment before the instantaneous change of the external gas density as the starting point and the moment after the instantaneous change as the end point; the instantaneous change rate data of the external gas density and the ambient temperature during the entire change period are synchronously recorded;

[0029] Based on the instantaneous change rate of the external gas density and the instantaneous change rate of the ambient temperature, the internal field strength data of the circuit breaker is obtained.

[0030] Preferably, the acquiring of the internal field strength data of the circuit breaker based on the instantaneous change rate of the external gas density and the instantaneous change rate of the ambient temperature includes:

[0031] Normalize the instantaneous change rate of external gas density, the instantaneous change rate of ambient temperature and the change rate of internal field strength respectively;

[0032] The normalized instantaneous change rate of external gas density, ambient temperature and internal field strength are used as inputs;

[0033] The predicted value of internal field strength is used as output;

[0034] Maxwell's equations and gas state equations are used as physical constraints;

[0035] Finite element simulation is used to generate full-field field strength distribution data under different working conditions. Combined with the collected data before and after abnormal events in actual operation and maintenance, a field strength inference model for circuit breaker blind spots is constructed.

[0036] Preferably, the calculating of the confidence weight based on the external gas density data, the ambient temperature data and the internal monitoring data includes:

[0037]

[0038]

[0039] in, is the instantaneous change in external gas density, is the instantaneous change of ambient temperature, The critical value of environmental mutation set by experience, is the confidence weight of the external environment data, is the confidence weight of the internal monitoring data;

[0040] If the confidence weight of the external environment data exceeds a first preset threshold, increasing the confidence weight of the external environment data;

[0041] If the confidence weight of the external environment data is lower than the second preset threshold, the confidence weight of the internal monitoring data is increased.

[0042] Preferably, the calculating of the comprehensive field strength based on the confidence weight includes: the comprehensive field strength calculation formula is as follows,

[0043]

[0044] in, is the weighted comprehensive field strength value, is the field strength value directly monitored by the internal sensor, is the predicted value of the circuit breaker blind area field strength inference model.

[0045] Preferably, the decision optimization module: based on the comprehensive field strength calculation value, uses the updated scheduling scoring formula to determine the supply of circuit breaker accessories, including: updating the scheduling scoring formula as follows,

[0046]

[0047] in, Score the updated dynamic scheduling, is the scheduling score value, is the field strength safety threshold, is the field intensity deviation sensitivity coefficient;

[0048] Based on the updated scheduling score value, relevant information is sent to the management staff.

[0049] Preferably, the sending of relevant information to the management personnel based on the updated scheduling score value includes:

[0050] The updated scheduling score value is pushed to the management terminal in a visual form and associated with the work order processing process.

[0051] Beneficial effects: The present invention separates high-altitude gas fluctuations from the intrinsic state of the equipment through the environmental disturbance decoupling mechanism, suppressing redundant work orders caused by false alarms; accurately quantifies defect levels based on three-dimensional field strength dynamic reconstruction technology, drives differentiated parts replacement priority division, and optimizes dynamic inventory resource allocation; further realizes two-way calibration of simulation data and real-time monitoring of the supply chain through a confidence fusion model, ensuring adaptive matching of the parts supply cycle and the field strength degradation trend; finally, relies on a dynamic scoring closed-loop mechanism to integrate field strength deviation and component correlation, generates multi-objective collaborative maintenance path planning, and simultaneously improves operation and maintenance response efficiency and decision reliability, forming a full-link closed-loop optimization system from electromagnetic field anomaly perception to supply chain precise scheduling, and provides intelligent decision-making support for preventive maintenance of high-voltage equipment under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a structural diagram of the intelligent service system for remote operation and maintenance of high-voltage switchgear according to the present invention;

[0053] Figure 2 This is a flow chart of optimizing cycle determination of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] A high-voltage switchgear remote operation and maintenance intelligent service system, such as Figure 1 As shown, it includes the following parts:

[0056] Data acquisition module: obtains external gas density data, internal gas density data, ambient temperature data and internal temperature data of the circuit breaker in a high-altitude environment;

[0057] Monitoring module: using the instantaneous change data of the external gas density data and the ambient temperature data as analysis data of the internal field strength of the circuit breaker; optimizing the supply of accessories for the circuit breaker based on the analysis data;

[0058] Analysis and processing module: calculates confidence weights based on the external gas density data, ambient temperature data and internal monitoring data; calculates comprehensive field strength based on the confidence weights; decision optimization module: determines the supply of circuit breaker accessories based on the calculated comprehensive field strength value using an updated scheduling scoring formula.

[0059] Data acquisition module: obtains the external gas density data, internal gas density data, ambient temperature data and internal temperature data of the circuit breaker in a high-altitude environment, including:

[0060] Using external gas density data and ambient temperature data as first monitoring data;

[0061] The internal gas density data and the internal temperature data of the equipment are used as the second monitoring data;

[0062] When the first monitoring data undergoes periodic changes, an optimization period determination of the second monitoring data is triggered;

[0063] The periodic changes are periodic changes in external gas density and ambient temperature in a high-altitude environment;

[0064] The optimization period is defined as a period during which the second monitoring data does not undergo synchronous changes during the periodic changes of the first monitoring data.

[0065] A further explanation is that Figure 2As shown, by separating high-altitude environmental data (primary monitoring data) and circuit breaker internal data (secondary monitoring data), a coupled environmental-equipment monitoring system is constructed. Its core principle is to dynamically calibrate the frequency of internal equipment status monitoring by leveraging cyclical environmental changes. At high altitudes, external air density and temperature fluctuate regularly (cyclically) due to factors such as diurnal and seasonal variations. Ideally, the circuit breaker's internal data should remain stable under a well-sealed condition. If internal data does not fluctuate synchronously with cyclical external environmental changes (for example, external temperature rises while internal air density remains constant or decreases abnormally), this indicates a potential leak or component degradation, triggering targeted testing. In this case, the system uses the external variation cycle as a benchmark to shorten the internal data monitoring interval (optimize the cycle), achieving an upgrade from "fixed inspection" to "environmentally responsive monitoring." This not only avoids misjudgments caused by high-altitude environmental interference, but also enables rapid response to actual anomalies, achieving efficient allocation of operation and maintenance resources.

[0066] During the periodic change of the first monitoring data, the period during which the second monitoring data does not undergo synchronous change includes:

[0067] The lack of the same change is quantified by the phase difference formula, which is as follows:

[0068]

[0069] in, is the phase difference, is the change of external environmental parameters, is the internal parameter variation, is the environmental conduction delay, is the sampling window length, is the sampling time interval, is the sequence number of the discrete sequence;

[0070] Judgment rules: When > When , it is marked as a cycle that needs to be optimized. is the preset phase difference threshold;

[0071] Extract the first monitoring data that continuously exceeds the preset threshold for a duration of ≥ As the component moments, is the shortest time of continuous exceeding threshold value;

[0072] constructing an optimization period of the second monitoring data based on the composition time;

[0073] Based on the optimization cycle, a preliminary scheduling plan for circuit breaker accessories is generated.

[0074] Further explanation is that the change in the external environmental parameter is the comprehensive change of the first monitoring data (external gas density and ambient temperature), and is calculated as follows: ,in, and are the weight coefficients of external gas density and ambient temperature respectively; and are the changes in external gas density and ambient temperature, , ;

[0075] The internal parameter variation is the comprehensive change rate of the second monitoring data (internal gas density and equipment ambient temperature), calculated in the same way;

[0076] Quantify the mismatch between internal and external parameters by phase difference: the external environment ( ) and the circuit breaker interior ( ) is delayed by the time difference After correction, calculate the window period The mean absolute deviation within ).when Exceeding the threshold , indicating that the internal response is out of sync with the external stimulus (for example, the internal gas density does not shrink as expected when the external temperature rises), triggering the optimization of the monitoring period. The formula essentially builds a dynamic differential tracker - external periodic disturbances are regarded as "driving signals", and internal response delays or anomalies are regarded as "system transfer function distortion". The differential difference is used to capture fault characteristics such as insulation degradation and seal leakage ( The larger it is, the lower the system health).

[0077] For example, the temperature difference between day and night in the plateau is 40°C (external temperature +30°C during the day → -10°C at night). A normal circuit breaker is well sealed, and the daily fluctuation of the internal gas density should be less than 5%. One day, the following was monitored:

[0078] External temperature change rate =1.67℃ / h (daytime temperature rise);

[0079] Internal density change rate =-0.8kg / m³ / h (abnormal leakage, =2h lag), calculated =1.25> (set to 0.5), it is judged as failure. The optimization period starts high-frequency monitoring from 12:00 to 16:00 when the external temperature changes drastically, accurately capturing the leakage point.

[0080] Based on the optimization cycle, a preliminary scheduling plan for circuit breaker accessories is generated, including:

[0081] The preliminary scheduling score value of the circuit breaker accessories is obtained through the scheduling score formula. The scheduling score formula is as follows:

[0082]

[0083] in, is the scheduling score value, is the phase difference, For components The basic maintenance weight of is the current timestamp, For components Last maintenance timestamp, is the dynamic remaining life ratio, For components The theoretical maintenance cycle, For associated components The state anomaly score, 、 is the weight coefficient, For components A collection of associated components.

[0084] Further explanation is that through the environment-equipment dynamic decoupling analysis ( ) and lifespan-correlation score ( ) to achieve accurate operation and maintenance decisions.

[0085] Fault prediction ( Formula): Quantify the phase difference between external environmental fluctuations and internal responses, When the threshold is exceeded, it is marked as a potential failure window;

[0086] Life decay model ( Formula): Dynamic remaining life ratio , , is the cumulative stress, For components Dynamically correct the maintenance cycle and change the theoretical maintenance cycle Compression by stress accumulation index (such as plateau temperature difference cycle accelerates metal fatigue Rising), real-time calculation of component health;

[0087] Intelligent Scheduling (Scheduling Scoring Formula): Scheduling Scoring , For maintenance urgency, fusion phase difference severity ( ), basic weight ( ), remaining life ( Denominator drives urgency) and associated component anomalies ( Multiplying and amplifying risks), output priority scores, and drive optimal configuration of accessory resources.

[0088] Operation and maintenance actions:

[0089] Dynamic monitoring: Focus optimization periodic intensive sampling when exceeding threshold;

[0090] Lifespan warning: <0.3 triggers preventive replacement;

[0091] Collaborative Scheduling: Sort and generate spare parts work orders, giving priority to high-scoring items (such as >8 requires response within 48 hours).

[0092] Example: A circuit breaker in a plateau substation =1.8( = 1.0), the cumulative stress makes From 5 years to 3.2 years ( =0.1), =0.25, associated disconnector =0.5, calculated =ln(2.8)·[2.0+0.5·(24 months / 0.25)]·1.25=9.7, triggering emergency replacement scheduling and simultaneous inspection of related components.

[0093] The monitoring module uses the instantaneous change data of the external gas density data and the ambient temperature data as analysis data of the internal field strength of the circuit breaker; based on the analysis data, optimizes the supply of accessories for the circuit breaker, including:

[0094] The instantaneous change rate of the external gas density is constructed by taking the moment before the instantaneous change of the external gas density as the starting point and the moment after the instantaneous change as the end point; the instantaneous change rate data of the external gas density and the ambient temperature during the entire change period are synchronously recorded;

[0095] Based on the instantaneous change rate of the external gas density and the instantaneous change rate of the ambient temperature, the internal field strength data of the circuit breaker is obtained.

[0096] A further explanation is that, under the constraint of scarcity of internal sensors, by capturing the instantaneous rate of change of gas density and temperature (rather than the steady-state value), the trend of field strength change inside the circuit breaker can be dynamically inverted. The transient difference between the external environment sudden change (such as temperature rise) and the internal gas density response implicitly indicates insulation degradation. After normalization to eliminate dimensional interference, the multi-dimensional time series rate of change is input into the scoring formula (such as ), using external measurable parameters to drive the internal field strength degradation model, replacing "absolute monitoring accuracy" with "change sensitivity" to solve the state deduction problem under incomplete data. For example: when the temperature soars instantaneously, the gas density does not change synchronously, the normalized field strength curve suddenly changes, triggering Abnormality scoring and priority scheduling of related parts for maintenance.

[0097] Acquiring internal field strength data of the circuit breaker based on the instantaneous change rate of the external gas density and the instantaneous change rate of the ambient temperature, including:

[0098] Normalize the instantaneous change rate of external gas density, the instantaneous change rate of ambient temperature and the change rate of internal field strength respectively;

[0099] The normalized instantaneous change rate of external gas density, ambient temperature and internal field strength are used as inputs;

[0100] The predicted value of internal field strength is used as output;

[0101] Maxwell's equations and gas state equations are used as physical constraints;

[0102] Finite element simulation is used to generate full-field field strength distribution data under different working conditions. Combined with the collected data before and after abnormal events in actual operation and maintenance, a field strength inference model for circuit breaker blind spots is constructed.

[0103] A further explanation is that the sensor blind spots are compensated by multi-physics field coupling inference:

[0104] Input enhancement: After normalizing the external gas density, ambient temperature, and field strength change rate, additional temporal correlation analysis (such as dynamic causal networks) is required to extract cross-parameter lag effects;

[0105] Mathematicalization of physical constraints: Maxwell's equations (∇×E=0) and the gas state equation (PV=nRT) are converted into field intensity gradient loss functions, forcing the predicted values ​​to satisfy physical conservation.

[0106] Data Fusion: Finite element simulation generates field strength distributions under extreme operating conditions (such as arc reignition at -40°C). This is then overlaid with sparse field strength data from actual events. Transfer learning is used to construct a generalized field strength map, outputting blind-spot field strength predictions. Externally measurable parameters drive internal insulation failure warnings, guiding the setting of component replacement thresholds (e.g., prioritizing arc extinguisher maintenance when field strength exceeds 25 kV / mm).

[0107] Further explanation is that the normalization is a data standardization method (such as Z-score), which is used to eliminate dimensional differences and improve the model convergence speed; the Maxwell equations are a set of partial differential equations describing the relationship between electric fields and magnetic fields, which are used to constrain the model to conform to the physical laws of electromagnetic fields; the gas state equation is the ideal gas equation (such as PV=nRT), which is used to correlate gas density, temperature and pressure changes; the finite element simulation is a numerical calculation method, which solves the field strength distribution in complex geometric bodies through grid division to generate virtual data required for training; the data before and after the abnormal event are monitoring data before and after the occurrence of circuit breaker faults (such as arc reignition and gas leakage), which are used for model generalization ability training.

[0108] Calculating confidence weights based on the external gas density data, ambient temperature data, and internal monitoring data includes:

[0109]

[0110]

[0111] in, is the instantaneous change in external gas density, is the instantaneous change of ambient temperature, The critical value of environmental mutation set by experience, is the confidence weight of the external environment data, is the confidence weight of the internal monitoring data;

[0112] If the confidence weight of the external environment data exceeds a first preset threshold, increasing the confidence weight of the external environment data;

[0113] If the confidence weight of the external environment data is lower than the second preset threshold, the confidence weight of the internal monitoring data is increased.

[0114] Further explanation is that, assuming that the first preset threshold value is greater than the second preset threshold value (such as >0.7 depends on external data, <0.3 depends on internal data);

[0115] Resolving conflicts between internal and external data credibility through dynamic weight allocation:

[0116] Weighting mechanism: external confidence weight Quantify the intensity of environmental abrupt changes (such as gas leaks >0 or temperature rises suddenly >10℃), if >0.8 (the first preset threshold), it is determined that external interference is dominant, and external data is used first to infer the internal field strength (such as arc restrike risk); if <0.3 (the second preset threshold), the environment is considered stable and depends on the field strength value of the internal sensor.

[0117] Dynamic fusion: continuous transition of weights (e.g. =0.6, internal and external data are fused with a weighted ratio of 6:4 to avoid sudden decision-making.

[0118] Example: The circuit breaker of the plateau substation was suddenly exposed to the sun. =15°C (threshold = 20°C), =0 (seal is normal), then =15 / 20=0.75, trigger the external priority mode, combine the temperature rise rate to infer the risk of local overheating of the insulating medium, and schedule a heat dissipation inspection; if =18kPa (threshold = 20kPa), =5℃, =23 / 20=1.15, exceeding the limit forces external data drive to warn of gas leakage.

[0119] Based on the confidence weight, the comprehensive field strength is calculated, including: the comprehensive field strength calculation formula is as follows,

[0120]

[0121] in, is the weighted comprehensive field strength value, is the field strength value directly monitored by the internal sensor, is the predicted value of the circuit breaker blind area field strength inference model.

[0122] A further explanation is that the accuracy of field strength estimation is improved through dynamic credibility fusion:

[0123] When the external environment changes drastically (such as a sudden rise in temperature causing >0.8), (Blind zone model prediction) weight is increased to prioritize capturing local distortion of the insulating medium (such as field intensity spikes caused by arcs); when the environment is stable ( >0.7), (Sensor measured value) dominates to ensure basic reliability.

[0124] Example: When the circuit breaker is opened, the arc extinguishing chamber Surge Trigger =0.9, model prediction blind spot =28kV / mm (measured =15kV / mm), comprehensive field strength ≈25.2kV / mm, identify dielectric breakdown risk; if the environment is stable ( =0.2), then ≈0.8×15+0.2×16=15.2kV / mm, which fits the actual working conditions.

[0125] Decision optimization module: Based on the comprehensive field strength calculation value, the updated scheduling scoring formula is used to determine the supply of circuit breaker accessories, including: the updated scheduling scoring formula is as follows,

[0126]

[0127] in, Score the updated dynamic scheduling, is the scheduling score value, is the field strength safety threshold, is the field intensity deviation sensitivity coefficient;

[0128] Based on the updated scheduling score value, relevant information is sent to the management staff.

[0129] A further explanation is that when the comprehensive field strength Deviation from safety threshold When scheduling score Scale up by deviation ( control sensitivity). For example, setting =25kV / mm, if a circuit breaker has a blind spot =30kV / mm, take =0.5, then the score increase is 0.5×(5 / 25)=10%, the original score =80 is updated to 88, surpassing the normal task priority.

[0130] When the field strength exceeds the limit (e.g. >1.2 ), the score rises exponentially, triggering an emergency work order.

[0131] Updated scheduling scoring formula achieves the following goals, grading responses: Define Piecewise functions, such as >0.3, =1.2, accelerating the dispatch of high-risk equipment;

[0132] Data linkage: After the score is updated, the inventory system (such as spare parts inventory) and personnel location data are automatically linked to push the optimal work order based on "priority-accessibility-resource matching" (for example, tasks with a score > 90 and sufficient spare parts are assigned to the nearest operation and maintenance team).

[0133] Example: Multiple circuit breakers in a substation give simultaneous alarms. Device A =95, B equipment ( =85), the system prioritizes device A and automatically assigns vehicle-mounted arc extinguishing chamber accessories to the work order, while also pushing the insulation tester operation guide to the on-site personnel terminal.

[0134] Based on the updated scheduling score, relevant information is sent to managers, including:

[0135] The updated scheduling score value is pushed to the management terminal in a visual form and associated with the work order processing process.

[0136] The above is a detailed introduction to the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, based on the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A high-voltage switchgear remote operation and maintenance intelligent service system, characterized by: Includes the following sections: Data acquisition module: acquires external gas density data, internal gas density data, ambient temperature data, and internal temperature data of the circuit breaker in a high-altitude environment; uses the external gas density data and ambient temperature data as the first monitoring data; and uses the internal gas density data and internal temperature data of the device as the second monitoring data; Monitoring module: using the instantaneous change data of the external gas density data and the ambient temperature data as analysis data of the internal field strength of the circuit breaker; Optimizing the supply of accessories for circuit breakers based on the analysis data; Analysis and processing module: calculating the confidence weight based on the first monitoring data and the second monitoring data; Calculating a comprehensive field strength based on the confidence weight; The calculating the confidence weight based on the first monitoring data and the second monitoring data includes: , in, is the instantaneous change in external gas density, is the instantaneous change of ambient temperature, The critical value of environmental mutation set by experience, is the confidence weight of the first monitoring data, is the confidence weight of the second monitoring data; If the confidence weight of the first monitoring data exceeds a first preset threshold, increasing the confidence weight of the first monitoring data; If the confidence weight of the first monitoring data is lower than the second preset threshold, increasing the confidence weight of the second monitoring data; Decision optimization module: Based on the calculated value of comprehensive field strength, the updated scheduling scoring formula is used to determine the supply of circuit breaker accessories.

2. A high-voltage switchgear remote operation and maintenance intelligent service system according to claim 1, characterized in that: The data acquisition module acquires external gas density data, internal gas density data, ambient temperature data, and internal temperature data of the circuit breaker in a high-altitude environment, including: When the first monitoring data undergoes periodic changes, an optimization period determination of the second monitoring data is triggered; The periodic changes are periodic changes in external gas density and ambient temperature in a high-altitude environment; The optimization period is defined as a period during which the second monitoring data does not undergo synchronous changes during the periodic changes of the first monitoring data.

3. A high-voltage switchgear remote operation and maintenance intelligent service system according to claim 2, characterized in that: The period during which the second monitoring data does not undergo synchronous changes during the periodic changes of the first monitoring data includes: The period during which the second monitoring data does not undergo synchronous changes is quantified using a phase difference formula, which is as follows: in, is the phase difference, is the change of external environmental parameters, is the internal parameter variation, is the environmental conduction delay, is the sampling window length, is the sampling time interval, is the serial number of the discrete sequence; external environmental parameters refer to external gas density data and ambient temperature data, and internal parameters refer to internal gas density data and equipment ambient temperature data; Judgment rules: When > When , it is marked as a cycle that needs to be optimized. is the preset phase difference threshold; Extract the first monitoring data that continuously exceeds the preset threshold for a duration of ≥ As the component moments, is the shortest time of continuous exceeding threshold value; constructing an optimization period of the second monitoring data based on the composition time; Based on the optimization cycle, a preliminary scheduling plan for circuit breaker accessories is generated.

4. A high-voltage switchgear remote operation and maintenance intelligent service system according to claim 3, characterized in that: Generating a preliminary scheduling plan for circuit breaker accessories based on the optimization cycle includes: The preliminary scheduling score value of the circuit breaker accessories is obtained through the scheduling score formula. The scheduling score formula is as follows: in, is the scheduling score value, is the phase difference, For components The basic maintenance weight of is the current timestamp, For components Last maintenance timestamp, is the dynamic remaining life ratio, For components The theoretical maintenance cycle, For associated components The state anomaly score, 、 is the weight coefficient, For components A collection of associated components.

5. The high-voltage switchgear remote operation and maintenance intelligent service system according to claim 1, characterized in that: The monitoring module uses the instantaneous change data of the external gas density data and the ambient temperature data as analysis data of the internal field strength of the circuit breaker; Based on the analysis data, optimize the supply of circuit breaker accessories, including: The instantaneous change rate of the external gas density is constructed by taking the moment before the instantaneous change of the external gas density as the starting point and the moment after the instantaneous change as the end point; the instantaneous change rate data of the external gas density and the ambient temperature during the entire change period are synchronously recorded; Based on the instantaneous change rate of the external gas density and the instantaneous change rate of the ambient temperature, the internal field strength data of the circuit breaker is obtained.

6. A high-voltage switchgear remote operation and maintenance intelligent service system according to claim 5, characterized in that: The acquiring of the internal field strength data of the circuit breaker based on the instantaneous change rate of the external gas density and the instantaneous change rate of the ambient temperature includes: Normalize the instantaneous change rate of external gas density, the instantaneous change rate of ambient temperature and the change rate of internal field strength respectively; The normalized instantaneous change rate of external gas density, ambient temperature and internal field strength are used as inputs; The predicted value of internal field strength is used as output; Maxwell's equations and gas state equations are used as physical constraints; Finite element simulation is used to generate full-field field strength distribution data under different working conditions. Combined with the collected data before and after abnormal events in actual operation and maintenance, a field strength inference model for circuit breaker blind spots is constructed.

7. The high-voltage switchgear remote operation and maintenance intelligent service system according to claim 1, characterized in that: The calculation of the comprehensive field strength based on the confidence weight includes: the comprehensive field strength calculation formula is as follows, in, is the weighted comprehensive field strength value, is the field strength value directly monitored by the internal sensor, is the predicted value of the circuit breaker blind area field strength inference model.

8. The high-voltage switchgear remote operation and maintenance intelligent service system according to claim 7, characterized in that: The decision optimization module: based on the comprehensive field strength calculation value, uses the updated scheduling scoring formula to determine the supply of circuit breaker accessories, including: the updated scheduling scoring formula is as follows, in, Score the updated dynamic scheduling, is the scheduling score value, is the field strength safety threshold, is the field intensity deviation sensitivity coefficient; Based on the updated scheduling score value, relevant information is sent to the management staff.

9. A high-voltage switchgear remote operation and maintenance intelligent service system according to claim 8, characterized in that: The updated scheduling score value is used to send relevant information to the management personnel, including: The updated scheduling score value is pushed to the management terminal in a visual form and associated with the work order processing process.

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