An ultrahigh-voltage substation construction environment multi-dimensional influence coordinated regulation method

Abstract

The application discloses a kind of superhigh voltage substation construction environment multidimensional influence coordinated control method, it is related to superhigh voltage substation construction environment control technical field, it aims at solving the technical problem of low control precision and insufficient sealing reliability caused by the dynamic coupling interference of multidimensional environmental parameters when GIS equipment splicing of superhigh voltage substation in high-altitude heavy ice area, including the following steps: S1, construct splicing microenvironment multidimensional parameter perception module, collect the environmental pressure, SF6 gas characteristics, environmental temperature and humidity and splicing surface temperature core parameters of high-altitude heavy ice area superhigh voltage substation GIS equipment field splicing area;S2, based on the collected parameters, excavate the dynamic coupling interference law of each parameter under high-altitude low air pressure, and construct a coupled interference suppression multidimensional coordinated control model;S3, according to the model output, the linkage control instruction containing interference avoidance strategy has the advantages of accurate and stable splicing microenvironment and guaranteeing the sealing performance of equipment splicing.

Description

Technical Field

[0001] This invention relates to the field of construction environment control technology for ultra-high voltage substations, and more specifically, to a method for coordinated control of multi-dimensional influences on the construction environment of ultra-high voltage substations. Background Technology

[0002] In the construction of ultra-high voltage substations in high-altitude heavy icing areas, on-site splicing of GIS equipment is a critical process, and the sealing performance of the splicing surface directly determines the long-term operational safety of the equipment. This region is characterized by high altitude, low air pressure (only 60%-70% of standard atmospheric pressure), extreme low temperatures reaching below -30℃, large diurnal temperature variations, and frequent snow and ice weather. This leads to dynamic fluctuations in four core parameters of the splicing area: ambient air pressure, SF6 (sulfur hexafluoride) gas characteristics, ambient temperature and humidity, and splicing surface temperature. Furthermore, significant coupling interference exists between these parameters. Existing technologies mostly adopt a single-parameter independent control mode, which triggers the action of a single actuator only through a fixed threshold. This neither fully considers the dynamic coupling interference law of various parameters under high altitude and low air pressure environment, nor does it have a mechanism for predicting and avoiding the mutual influence of control actions. This results in insufficient control accuracy and delayed parameter correction, making it difficult to synchronously stabilize the core parameters of the spliced ​​microenvironment within the suitable range, which directly affects the reliability of the splicing and sealing of GIS equipment. In view of this, we propose a collaborative control method for the multi-dimensional influence of the construction environment of ultra-high voltage substations. Summary of the Invention

[0003] The purpose of this invention is to provide a method for coordinated control of multi-dimensional influences on the construction environment of ultra-high voltage substations, in order to solve the technical problems of low control accuracy and insufficient reliability of splicing and sealing caused by dynamic coupling interference of multi-dimensional environmental parameters when splicing GIS equipment in ultra-high voltage substations in high-altitude heavy icing areas.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for coordinated control of multi-dimensional influences on the construction environment of ultra-high voltage substations, comprising the following steps: S1. Construct a multi-dimensional parameter sensing module for splicing microenvironment to collect core parameters such as ambient air pressure, SF6 gas characteristics, ambient temperature and humidity, and splicing surface temperature in the splicing area of ​​the GIS equipment of the ultra-high voltage substation in the high-altitude heavy icing zone. S2. Based on the collected parameters, we can explore the dynamic coupling interference law of each parameter under high altitude and low air pressure, and construct a coupling interference suppression type multi-dimensional collaborative regulation model. S3. Based on the model output, the linkage control command with interference avoidance strategy is used to drive the coordinated action of the air pressure compensation, gas purification, temperature and humidity adjustment and splicing surface temperature control actuators to avoid parameter interference between control actions. S4. Real-time acquisition of parameters after regulation and control is fed back to the regulation model. Combined with the coupling interference suppression strategy, the regulation command is dynamically corrected to form a closed-loop coordinated regulation.

[0005] Preferably, in step S1, the construction of the multi-dimensional parameter sensing module for splicing microenvironments includes: Distributed sensing units are deployed around the splicing area of ​​the GIS equipment. The sensing units include a pressure sensing component for collecting ambient air pressure, a gas sensing component for collecting SF6 gas characteristic parameters, a temperature and humidity sensing component for collecting ambient temperature and humidity, and a contact temperature sensing component for collecting the temperature of the splicing surface. The raw data collected by each sensing component is preprocessed to reduce noise and remove abnormal interference data to form a standardized sensing dataset. Establish a timestamp synchronization mechanism for sensing data to ensure the consistency of the collection time sequence of parameters from different dimensions.

[0006] Preferably, in step S2, the construction of the coupling interference suppression type multidimensional collaborative regulation model includes: Based on the low-pressure environment characteristics of high-altitude heavy icing areas, a parameter dynamic coupling correlation database is established. The database contains historical correlation data and coupling interference case data of air pressure, SF6 gas characteristics, temperature and humidity and splicing surface temperature under different splicing conditions. An adaptive correlation algorithm is used to mine the dynamic coupling interference patterns among the parameters in the database, and to determine the weight allocation relationship between the core control parameters and the auxiliary control parameters, as well as the interference suppression threshold. A multi-objective optimization function with interference suppression constraints is constructed, with the optimization objectives being the stability of the splicing microenvironment, the reliability of the splicing seal, and the minimization of mutual interference of the control actions. The control range of each actuator is constrained, and an initial collaborative control model is generated. The initial model was trained and optimized using historical control data and interference case data; Among them, the dynamic coupling interference pattern mining adopts an adaptive correlation algorithm, the formula of which is as follows: ; in, for Time parameters With parameters The dynamic coupling interference coefficient, for Time parameters With parameters covariance, for Time parameters variance for Time parameters variance This is the time-series correction factor under high-altitude, low-pressure conditions; The formula for determining the parameter weight allocation relationship based on the coupling interference coefficient is as follows: ; in, For the first The adjustment weight of each parameter, This refers to the total number of parameters involved in regulation.

[0007] Preferably, in step S3, the actions of driving the SF6 gas purification actuator based on the model output including interference avoidance strategies include: When the collaborative control model determines that the SF6 gas characteristic parameters deviate from the adaptation range, it outputs an SF6 gas purification control sub-command containing a gas pressure interference avoidance strategy. Based on this sub-command, the SF6 gas purification device is started, and the gas circulation pump is simultaneously linked to adjust the circulation rate, and the high-frequency acquisition mode of the pressure sensing component is triggered synchronously. Real-time collection of SF6 gas characteristic parameters and ambient air pressure data after purification is fed back to the collaborative control model. The model dynamically adjusts the operating power of the purification unit and the circulation rate of the circulating pump based on the feedback data to avoid coupling interference between gas purification actions and air pressure stability, until the SF6 gas characteristic parameters return to the suitable range.

[0008] Preferably, in step S3, the action of driving the pressure compensation actuator based on the model output including the interference avoidance strategy linkage control command includes: The collaborative control model calculates the pressure compensation amount, including gas disturbance avoidance, based on the sensed environmental pressure data and the spliced ​​microenvironment target pressure threshold, combined with the current state of SF6 gas characteristic parameters. According to the output of the air pressure compensation control sub-command, the air pressure compensation pump is started, and the appropriate gas is injected into the spliced ​​sealed chamber through the adjustable air intake valve. During the pressure compensation process, a strategy combining segmented compensation and interference prediction is adopted. In the initial stage, gas is injected at a relatively high rate. When the gas pressure approaches the target threshold, the injection rate is reduced and the potential interference to the SF6 gas characteristics is predicted in real time. Simultaneously collect data on air pressure changes and SF6 gas characteristics within the sealed chamber, and feed them back to the model in real time. The model then dynamically corrects the compensation rate and valve opening.

[0009] Preferably, in step S3, the actions of driving the temperature and humidity adjustment and splicing surface temperature control actuators in coordination, based on the model output including interference avoidance strategies, include: The collaborative control model is based on the sensed temperature and humidity data and the splicing surface temperature data. It explores the coupling and interference relationship between the two and outputs temperature and humidity control sub-instructions and splicing surface temperature control sub-instructions with mutual interference avoidance strategies. The temperature and humidity control actuator activates the heating or dehumidification components according to the temperature and humidity control sub-instruction to adjust the temperature and humidity of the spliced ​​microenvironment; The splicing surface temperature control actuator performs precise temperature control on the splicing surface through a flexible heating film according to the splicing surface temperature control sub-instruction; During the control process, the model links the temperature and humidity regulation with the splicing surface temperature control in real time, and dynamically adjusts the operating status of the actuator according to the changes in the parameters of the two, so as to avoid the interference of temperature regulation on humidity and the coupling effect of splicing surface temperature control on ambient temperature and humidity.

[0010] Preferably, in step S4, the real-time acquisition and feedback of parameters after regulation to the regulation model, combined with the dynamic correction of the regulation command in conjunction with the coupling interference suppression strategy, includes: A real-time feedback data transmission channel is constructed, and wired and wireless redundant transmission methods are adopted to transmit the parameters of each dimension and the operating status data of the actuators after regulation to the collaborative regulation model in real time. The model compares and analyzes the feedback data with the target parameter range, calculates the parameter deviation value, and identifies the type and intensity of coupling interference in the current control process. When the deviation exceeds the allowable range, the model adjusts the control parameters of each actuator based on the preset deviation correction algorithm and coupling interference suppression strategy. If the deviation value still fails to return to the allowable range after multiple consecutive corrections, the model will activate the control strategy optimization mechanism, re-match the control parameter weights and interference suppression scheme, and generate a new linkage control instruction. The formulas for calculating parameter deviation and correcting control parameters based on coupling interference are as follows: ; in, for Time of the first The deviation value of each parameter, for Time of the first The target value of each parameter for Time of the first The actual feedback value of each parameter; First, the parameter deviation is normalized, and then a formula for the correction of the control parameter combined with the coupling interference is constructed, as follows: ; in, for Time of the first Dimensionless deviation rate of each parameter; The formula for adjusting the control parameter is: ; in, for Time of the first The adjustment amount of the control parameters of each implementing agency This is the proportionality coefficient. The integral coefficient is... The differential coefficients are... For coupling interference adaptation coefficients, For the first The adjustment weight of each parameter, for Time parameters With parameters The dynamic coupling interference coefficient, for Time of the first The dimensionless deviation rate of each parameter It is the integral variable.

[0011] Preferably, it also includes a step for early warning of abnormal microenvironment regulation: During the closed-loop coordinated control process, the coordinated control model monitors the changing trends of each sensing parameter, the operating status of the actuator, and the intensity of coupling interference between parameters in real time. When the sensing parameters change abruptly, the actuator operates abnormally, the control command fails to execute, or the coupling interference intensity exceeds the suppression threshold, the model determines that the control is in an abnormal state and activates the abnormal warning mechanism. The system issues warning signals through audible and visual warning components, and simultaneously records the time of the anomaly, abnormal parameter data, actuator status information, and interference type, generating an anomaly report.

[0012] Preferably, the output of the linkage control command containing the interference avoidance strategy adopts a priority allocation mechanism: The collaborative control model prioritizes control based on the degree of influence of each parameter on the splicing and sealing performance of GIS equipment and the intensity of coupling interference transmission. Among them, the control of SF6 gas characteristic parameters is the highest priority, the control of splicing surface temperature and ambient air pressure is the second highest priority, and the control of ambient temperature and humidity is the normal priority. When multiple parameters deviate from the fit range at the same time, the model outputs adjustment instructions in order of priority, prioritizing the return to stability of high-priority parameters, while embedding interference protection strategies for low-priority parameters during the adjustment of high-priority parameters. After the high-priority parameters have been adjusted to meet the target, the regular priority parameters will then be adjusted.

[0013] Preferably, it also includes a self-calibration step for the collaborative regulation model: Periodically collect sensing data, control commands, control effect data, and coupling interference suppression effect data during the closed-loop control process to form a model self-calibration dataset; Based on the analysis of model regulation error and interference suppression deviation using self-calibration dataset, the adaptation deviation between model parameters and actual construction conditions in high-altitude heavy icing areas is identified. An adaptive calibration algorithm is used to correct the parameter weights, coupling interference pattern identification logic, and interference suppression strategy of the collaborative control model, and to update the model database. The core algorithm formula for model self-calibration is as follows, based on adjusting the weights of the error correction parameters: ; in, For the first calibration The adjustment weight of each parameter, For weight calibration coefficients, for The overall control error of the time-matter model The preset allowable control error threshold, for Time of the first Deviation values ​​of each parameter; At the same time, the time-series correction factor for high-altitude, low-pressure environments is adjusted: ; in, After calibration Timing correction factor at time step, These are the timing correction factors before calibration. To correct the coefficients and calibrate the coefficients, for The average value of the coupling interference coefficients among all parameters at time 1. This is the preset ideal average value of the coupling interference coefficient.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a multi-dimensional collaborative control model with coupling interference suppression. Based on an adaptive correlation algorithm, it accurately quantifies the dynamic coupling interference intensity between various parameters. Combined with a parameter weight allocation formula, it scientifically allocates control resources and outputs a linkage control command containing interference avoidance strategies. This drives the coordinated action of the air pressure compensation, gas purification, temperature and humidity regulation, and splicing surface temperature control actuators. This effect fundamentally avoids secondary interference of single parameter control on other parameters, ensuring that the core parameters of the splicing microenvironment are synchronously and stably within the suitable range, significantly improving the reliability of GIS equipment splicing sealing.

[0015] 2. This invention also achieves real-time acquisition and high-speed feedback of parameters after regulation through a closed-loop collaborative control mechanism and wired / wireless redundant transmission channels. Combined with parameter deviation calculation, normalization processing, and control parameter correction formula, the control command is dynamically corrected. This effectively addresses the rapid fluctuations and control lag issues of parameters in high-altitude environments, not only improving the control response speed and accuracy, but also specifically suppressing coupling interference between different parameters, ensuring the coordination and effectiveness of control actions, and avoiding overshoot or steady-state deviation during parameter correction.

[0016] 3. This invention also employs a priority control mechanism, prioritizing control parameters based on their impact on the splicing sealing performance. When multiple parameters simultaneously deviate from their suitable range, priority is given to ensuring the stability of key parameters related to SF6 gas characteristics, thus optimizing the allocation of control resources. Combined with a splicing microenvironment control anomaly early warning mechanism, it can monitor abnormal situations such as parameter mutations and actuator malfunctions in real time. Through audible and visual warnings, anomaly report generation, and emergency shutdown protection, it prevents the escalation of faults. Simultaneously, by incorporating a self-calibration step of the collaborative control model, parameter weights and time-series correction coefficients are periodically adjusted, ensuring the model continuously adapts to the complex and variable construction environment of high-altitude heavy icing areas. This effect not only improves the safety and efficiency of the construction process but also ensures the long-term stability, anti-interference ability, and environmental adaptability of the control model. Attached Figure Description

[0017] Figure 1 This is the overall flowchart of the collaborative control of multi-dimensional influences on the construction environment of ultra-high voltage substations according to the present invention; Figure 2 This is a flowchart illustrating the construction process of the multi-dimensional parameter sensing module for splicing microenvironments in this invention. Detailed Implementation

[0018] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.

[0019] Example 1, such as Figure 1 and Figure 2 As shown, this invention provides a method for coordinated control of multi-dimensional influences on the construction environment of ultra-high voltage substations, comprising the following steps: S1. Construct a multi-dimensional parameter sensing module for splicing microenvironment to collect core parameters such as ambient air pressure, SF6 (sulfur hexafluoride) gas characteristics, ambient temperature and humidity, and splicing surface temperature in the splicing area of ​​the GIS equipment of the ultra-high voltage substation in the high-altitude heavy icing area. S2. Based on the collected parameters, we can explore the dynamic coupling interference law of each parameter under high altitude and low air pressure, and construct a coupling interference suppression type multi-dimensional collaborative regulation model. S3. Based on the model output, the linkage control command with interference avoidance strategy is used to drive the coordinated action of the air pressure compensation, gas purification, temperature and humidity adjustment and splicing surface temperature control actuators to avoid parameter interference between control actions. S4. Real-time acquisition and control parameters are fed back to the control model. Combined with the coupling interference suppression strategy, the control commands are dynamically corrected to form a closed-loop collaborative control, ensuring the reliability of the splicing and sealing of GIS equipment.

[0020] In an embodiment of the present invention, S1, the construction of the multi-dimensional parameter sensing module for splicing microenvironments includes: Distributed sensing units are deployed around the splicing area of ​​the GIS equipment. The sensing units include a pressure sensing component for collecting ambient air pressure, a gas sensing component for collecting SF6 gas characteristic parameters, a temperature and humidity sensing component for collecting ambient temperature and humidity, and a contact temperature sensing component for collecting the temperature of the splicing surface. The raw data collected by each sensing component is preprocessed to reduce noise and remove abnormal interference data to form a standardized sensing dataset. Establish a timestamp synchronization mechanism for sensing data to ensure the consistency of the collection time sequence of parameters in different dimensions, and provide accurate data support for mining the dynamic coupling interference patterns of parameters under high altitude and low air pressure.

[0021] In an embodiment of the present invention, S2, the construction of the coupling interference suppression type multidimensional collaborative regulation model includes: Based on the low-pressure environment characteristics of high-altitude heavy icing areas, a parameter dynamic coupling correlation database is established. The database contains historical correlation data and coupling interference case data of air pressure, SF6 gas characteristics, temperature and humidity and splicing surface temperature under different splicing conditions. An adaptive correlation algorithm is used to mine the dynamic coupling interference patterns among the parameters in the database, and to determine the weight allocation relationship between the core control parameters and the auxiliary control parameters, as well as the interference suppression threshold. A multi-objective optimization function with interference suppression constraints is constructed, with the optimization objectives being the stability of the splicing microenvironment, the reliability of the splicing seal, and the minimization of mutual interference of the control actions. The control range of each actuator is constrained, and an initial collaborative control model is generated. The initial model was trained and optimized using historical control data and interference case data to improve the model's adaptability to complex coupled imbalance scenarios at high altitudes and the accuracy of interference suppression. Among them, the dynamic coupling interference pattern mining adopts an adaptive correlation algorithm, the formula of which is as follows: ; in, for Time parameters With parameters The dynamic coupling interference coefficient is mainly used to quantify the mutual interference intensity between two parameters under dynamic conditions of high altitude and low air pressure, providing a basis for interference quantification for subsequent control strategies. for Time parameters With parameters The covariance is used to characterize the degree of linear correlation between the changing trends of two parameters and is a basic indicator for judging whether there is a coupling relationship between the parameters. for Time parameters The variance is used to characterize the parameter. The degree of dispersion of its own values ​​provides data support for the normalization of covariance; for Time parameters The variance, the effect of Consistency is used to eliminate the influence of the parameter's own numerical scale on the determination of the degree of correlation. It is a time-series correction coefficient for high-altitude, low-pressure environments, used to adapt to the impact of dynamic changes in the high-altitude environment over time (such as time-series fluctuations in air pressure and environmental changes caused by diurnal temperature differences) on parameter coupling relationships, and to improve the environmental adaptability of the coupling interference coefficient. Formula for dynamic coupling interference coefficient: First, calculate using covariance. The linear correlation between two parameters (such as ambient air pressure and SF6 gas characteristics) at different times is determined. Then, the covariance is normalized by the square root of the product of the variances of the two parameters to eliminate the interference of the difference in the numerical scale of different parameters on the determination of the correlation degree. Finally, a time series correction coefficient under high altitude and low air pressure environment is introduced to perform environmental adaptation correction on the normalized correlation results, and finally a coefficient that can accurately reflect the mutual interference intensity of the two parameters in the dynamic environment at high altitude is obtained. The formula for determining the parameter weight allocation relationship based on the coupling interference coefficient is as follows: ; in, For the first The control weight of each parameter is used to quantify the allocation of control resources. The larger the weight value, the higher the priority of the parameter in coordinated control and the more abundant the control resources obtained. The total number of parameters involved in regulation, specifically the total number of the four core parameters: ambient air pressure, SF6 gas characteristics, ambient temperature and humidity, and splicing surface temperature. Parameter weighting formula: First, calculate the difference between 1 and the absolute value of the maximum coupling interference coefficient corresponding to a single parameter. This difference directly represents the degree to which a parameter is affected by other parameters (the larger the difference, the less affected it is). Then, sum the above differences for all parameters. Finally, divide the difference of a single parameter by the sum to achieve the normalization of the control weight allocation, ensuring that the sum of the weights of all parameters is 1, thus providing a quantitative basis for the allocation of control resources. The two sets of formulas mentioned above, through the logical connection of "coupling interference quantification - scientific weight allocation," effectively address the technical pain points of traditional control schemes, such as the difficulty in quantifying parameter coupling interference in high-altitude environments and the subjective and arbitrary allocation of control weights. The dynamic coupling interference coefficient formula, combined with the temporal dynamic characteristics of high-altitude environments, achieves precise quantification of the degree of coupling interference between different parameters, clearly characterizing the correlation interference patterns of core parameters over time under high-altitude, low-pressure environments. This provides an objective and accurate basis for the formulation of subsequent control strategies. The parameter weight formula, based on the quantification results of the coupling interference coefficient, achieves the scientific allocation of control weights through difference calculation and normalized summation. This ensures that control resources are tilted towards core parameters less affected by interference, guaranteeing the targetedness and rationality of coordinated control from the basic decision-making level. This lays the core algorithmic foundation for constructing a coupling interference suppression-type multi-dimensional coordinated control model.

[0022] In an embodiment of the present invention, S3, the action of driving the SF6 gas purification actuator based on the model output including the interference avoidance strategy is as follows: When the collaborative control model determines that the SF6 gas characteristic parameters deviate from the adaptation range, it outputs an SF6 gas purification control sub-command containing a gas pressure interference avoidance strategy. Based on this sub-command, the SF6 gas purification device is started, and the gas circulation pump is simultaneously linked to adjust the circulation rate, and the high-frequency acquisition mode of the pressure sensing component is triggered synchronously. Real-time collection of SF6 gas characteristic parameters and ambient air pressure data after purification is fed back to the collaborative control model. The model dynamically adjusts the operating power of the purification unit and the circulation rate of the circulating pump based on the feedback data to avoid coupling interference between gas purification actions and air pressure stability, until the SF6 gas characteristic parameters return to the suitable range.

[0023] In an embodiment of the present invention, S3, the action of driving the air pressure compensation actuator based on the linkage control command containing the interference avoidance strategy output by the model includes: The collaborative control model calculates the pressure compensation amount, including gas disturbance avoidance, based on the sensed environmental pressure data and the spliced ​​microenvironment target pressure threshold, combined with the current state of SF6 gas characteristic parameters. According to the output of the air pressure compensation control sub-command, the air pressure compensation pump is started, and the appropriate gas is injected into the spliced ​​sealed chamber through the adjustable air intake valve. During the pressure compensation process, a strategy combining segmented compensation and interference prediction is adopted. In the initial stage, gas is injected at a relatively high rate. When the gas pressure approaches the target threshold, the injection rate is reduced and the potential interference to the SF6 gas characteristics is predicted in real time. Simultaneously collect data on air pressure changes and SF6 gas characteristics within the sealed chamber, and feed them back to the model in real time. The model dynamically corrects the compensation rate and valve opening to achieve a coordinated guarantee of precise air pressure balance and stable gas characteristics.

[0024] In an embodiment of the present invention, S3, the coordinated action of driving the temperature and humidity adjustment and splicing surface temperature control actuators based on the model output including interference avoidance strategy includes: The collaborative control model is based on the sensed temperature and humidity data and the splicing surface temperature data. It explores the coupling and interference relationship between the two and outputs temperature and humidity control sub-instructions and splicing surface temperature control sub-instructions with mutual interference avoidance strategies. The temperature and humidity control actuator activates the heating or dehumidification components according to the temperature and humidity control sub-instruction to adjust the temperature and humidity of the spliced ​​microenvironment; The splicing surface temperature control actuator performs precise temperature control on the splicing surface through a flexible heating film according to the splicing surface temperature control sub-instruction; During the control process, the model links the temperature and humidity adjustment with the splicing surface temperature control in real time. It dynamically adjusts the operating status of the actuators according to the changes in the parameters of both, avoiding the interference of temperature control on humidity and the coupling effect of splicing surface temperature control on ambient temperature and humidity, and ensuring that the temperature and humidity and splicing surface temperature are synchronously and stably within the appropriate range.

[0025] In an embodiment of the present invention, S4 involves real-time acquisition of the parameters after regulation and feedback to the regulation model, and dynamic correction of the regulation command in conjunction with a coupling interference suppression strategy, including: A real-time feedback data transmission channel is constructed, and wired and wireless redundant transmission methods are adopted to transmit the parameters of each dimension and the operating status data of the actuators after regulation to the collaborative regulation model in real time. The model compares and analyzes the feedback data with the target parameter range, calculates the parameter deviation value, and identifies the type and intensity of coupling interference in the current control process. When the deviation exceeds the allowable range, the model adjusts the control parameters of each actuator based on the preset deviation correction algorithm and coupling interference suppression strategy. If the deviation value still fails to return to the allowable range after multiple consecutive corrections, the model activates the control strategy optimization mechanism, re-matches the control parameter weights and interference suppression scheme, and generates new linkage control instructions to ensure the effectiveness and anti-interference capability of closed-loop control. The formulas for calculating parameter deviation and correcting control parameters based on coupling interference are as follows: ; in, for Time of the first The deviation values ​​of each parameter are used to directly characterize the degree of deviation between the actual state and the target state of the parameter, and are the core basic data for adjusting the parameter. for Time of the first The target value of each parameter, that is, the ideal value that the parameter needs to be maintained in the micro-environment of high-altitude GIS equipment splicing, provides a benchmark for deviation calculation; for Time of the first The actual feedback value of each parameter, that is, the real-time actual data of the parameters collected by the sensing module, is used to compare with the target value to calculate the deviation; First, the parameter deviation is normalized, and then a formula for the correction of the control parameter combined with the coupling interference is constructed, as follows: ; in, for Time of the first Dimensionless deviation rate of each parameter; The formula for adjusting the control parameter is: ; in, for Time of the first The adjustment amount of the control parameters of each actuator is used to drive the actuator (such as a pressure compensation pump, SF6 purification unit) to adjust its operating status, so as to realize parameter deviation correction and coupling interference suppression. It is a proportional coefficient used to quickly respond to parameter deviations and improve the immediacy of control. It is the integral coefficient, used to eliminate static deviations of parameters and improve the steady-state accuracy of regulation; The differential coefficient is used to predict the trend of parameter deviation changes, suppress overshoot, and improve control stability. The coupling interference adaptation coefficient has the same dimensions as the control parameter. Its core function is to transform the dimensionless coupling interference term into a dimension that matches the control parameter, thus ensuring the uniformity of the formula dimensions. For the first The adjustment weights of each parameter are used to quantify and allocate coupling interference suppression resources, ensuring the priority of interference suppression for core parameters; for Time parameters With parameters The dynamic coupling interference coefficient is used to quantify the coupling interference strength between two parameters, providing a quantitative basis for interference suppression. for Time of the first The dimensionless deviation rate of each parameter is used to characterize the degree of deviation of the coupling parameter, providing data support for quantifying the impact of coupling interference on the current parameter; As an integral variable, it represents the integration time interval and is used to calculate the integral cumulative amount of parameter deviation and eliminate static deviation; This set of formulas constructs a complete control logic of "deviation calculation - dimensional unification - collaborative correction," effectively solving the technical pain points of inconsistent dimensions of multiple parameters and difficulty in accurately suppressing coupled interference in traditional control schemes. Through dimensionless normalization, the dimensional differences between different types of parameters are eliminated, providing a feasible path for the fusion analysis of multi-parameter coupled interference. The formula for correcting the control parameters combines the steady-state accuracy and dynamic response advantages of classic PID control, while accurately quantifying and suppressing cross-parameter interference through coupled interference terms, avoiding secondary interference of other parameters by single-parameter control. Ultimately, precise collaborative control of the GIS equipment splicing microenvironment under high-altitude, low-pressure conditions is achieved, ensuring the coordination and effectiveness of control actions, and further improving the stability of the splicing microenvironment and the reliability of splicing sealing.

[0026] In embodiments of the present invention, a step of splicing microenvironment regulation anomaly early warning is also included: During the closed-loop coordinated control process, the coordinated control model monitors the changing trends of each sensing parameter, the operating status of the actuator, and the intensity of coupling interference between parameters in real time. When the sensing parameters change abruptly, the actuator operates abnormally, the control command fails to execute, or the coupling interference intensity exceeds the suppression threshold, the model determines that the control is in an abnormal state and activates the abnormal warning mechanism. The system issues warning signals through audible and visual warning components, while simultaneously recording the time of anomaly occurrence, abnormal parameter data, actuator status information, and interference type, and generating an anomaly report. The anomaly report is transmitted to the remote monitoring platform to provide maintenance personnel with a basis for handling the anomaly. If necessary, it can trigger the emergency shutdown protection action of the execution mechanism to prevent the anomaly from escalating and affecting the splicing quality.

[0027] In embodiments of the present invention, the output of the linkage control command containing the interference avoidance strategy adopts a priority allocation mechanism: The collaborative control model prioritizes control based on the degree of influence of each parameter on the splicing and sealing performance of GIS equipment and the intensity of coupling interference transmission. Among them, the control of SF6 gas characteristic parameters is the highest priority, the control of splicing surface temperature and ambient air pressure is the second highest priority, and the control of ambient temperature and humidity is the normal priority. When multiple parameters deviate from the fit range at the same time, the model outputs adjustment instructions in order of priority, prioritizing the return to stability of high-priority parameters, while embedding interference protection strategies for low-priority parameters during the adjustment of high-priority parameters. After the high-priority parameters have been controlled to meet the target, the regular priority parameters will be controlled to ensure that the control resources are allocated reasonably and interference between parameters is minimized.

[0028] In embodiments of the present invention, a self-calibration step of the collaborative regulation model is also included: Periodically collect sensing data, control commands, control effect data, and coupling interference suppression effect data during the closed-loop control process to form a model self-calibration dataset; Based on the analysis of model regulation error and interference suppression deviation using self-calibration dataset, the adaptation deviation between model parameters and actual construction conditions in high-altitude heavy icing areas is identified. An adaptive calibration algorithm is used to correct the parameter weights, coupling interference pattern identification logic, and interference suppression strategy of the collaborative control model, and to update the model database. By self-calibrating the model, we ensure that the control model is always adapted to the complex and ever-changing construction environment in high-altitude heavy icing areas, and continuously improve the long-term control accuracy, stability and anti-interference ability. The core algorithm formula for model self-calibration is as follows, based on adjusting the weights of the error correction parameters: ; in, For the first calibration The adjustment weights of each parameter serve as the basic benchmark values ​​for weight calibration. This is the weight calibration coefficient, used to adjust the magnitude of weight correction, avoid sudden weight changes that could lead to instability in the control, and ensure the smoothness and rationality of weight correction. for The comprehensive control error of the time-based model is used to characterize the overall control effect of the current control model and to provide feedback on the effect of weight correction. The preset allowable control error threshold is the benchmark for judging whether the control effect is qualified, and is used to quantify the degree of deviation of the current control error; for Time of the first The deviation value of each parameter is used to characterize the degree of deviation of a single parameter, providing parameter-level feedback for weight adjustment; At the same time, the time-series correction factor for high-altitude, low-pressure environments is adjusted: ; in, After calibration The time series correction factor is the final result after dynamic correction of the time series correction factor, which is used to improve the adaptation accuracy of dynamic changes in high-altitude environment. The timing correction factor before calibration serves as the baseline value for timing correction factor calibration. The calibration coefficient is used to adjust the correction magnitude of the timing correction coefficient, ensuring that the corrected coefficient can accurately adapt to dynamic changes in the environment and avoid over-correction. for The average value of the coupling interference coefficients among all parameters at any given time is used to characterize the overall coupling interference intensity under the current environment, providing environmental interference feedback for timing correction coefficient calibration. The preset ideal average value of the coupling interference coefficient is the benchmark for judging whether the current overall coupling interference intensity is reasonable, and is used to quantify the degree of deviation of the overall interference intensity. This set of formulas constructs a dynamic self-calibration mechanism for the control model, effectively solving the technical pain point that traditional fixed-parameter models are difficult to adapt to the complex and variable construction environment in high-altitude heavy icing areas. Through the weight calibration formula, the control weights can be dynamically adjusted according to the actual control effect and parameter deviation, ensuring that control resources are always tilted towards the core parameters with urgent control needs. Through the time-series correction coefficient calibration formula, the model can accurately adapt to the time-series dynamic changes in the high-altitude environment (such as air pressure fluctuations and time-series changes in ambient temperature and humidity), improving the accuracy of coupling interference coefficient calculation. Ultimately, this achieves continuous adaptation and optimization of the control model to the complex construction environment at high altitudes, ensuring the control accuracy, stability, and anti-interference capability of the model in long-term operation, further strengthening the environmental adaptability and engineering practicality of the coupling interference suppression-type collaborative control model.

[0029] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A method for coordinated control of multi-dimensional influences on the construction environment of ultra-high voltage substations, characterized in that, Includes the following steps: S1. Construct a multi-dimensional parameter sensing module for splicing microenvironment to collect core parameters such as ambient air pressure, SF6 gas characteristics, ambient temperature and humidity, and splicing surface temperature in the splicing area of ​​the GIS equipment of the ultra-high voltage substation in the high-altitude heavy icing zone. S2. Based on the collected parameters, we can explore the dynamic coupling interference law of each parameter under high altitude and low air pressure, and construct a coupling interference suppression type multi-dimensional collaborative regulation model. S3. Based on the model output, the linkage control command with interference avoidance strategy is used to drive the coordinated action of the air pressure compensation, gas purification, temperature and humidity adjustment and splicing surface temperature control actuators to avoid parameter interference between control actions. S4. Real-time acquisition of parameters after regulation and control is fed back to the regulation model. Combined with the coupling interference suppression strategy, the regulation command is dynamically corrected to form a closed-loop coordinated regulation.

2. The method for coordinated control of multi-dimensional influences on the construction environment of an ultra-high voltage substation according to claim 1, characterized in that, In step S1, the construction of the multi-dimensional parameter sensing module for splicing microenvironments includes: Distributed sensing units are deployed around the splicing area of ​​the GIS equipment. The sensing units include a pressure sensing component for collecting ambient air pressure, a gas sensing component for collecting SF6 gas characteristic parameters, a temperature and humidity sensing component for collecting ambient temperature and humidity, and a contact temperature sensing component for collecting the temperature of the splicing surface. The raw data collected by each sensing component is preprocessed to reduce noise and remove abnormal interference data to form a standardized sensing dataset. Establish a timestamp synchronization mechanism for sensing data to ensure the consistency of the collection time sequence of parameters from different dimensions.

3. The method for coordinated control of multi-dimensional influences on the construction environment of an ultra-high voltage substation according to claim 1, characterized in that, In S2, the construction of the coupling interference suppression type multidimensional collaborative regulation model includes: Based on the low-pressure environment characteristics of high-altitude heavy icing areas, a parameter dynamic coupling correlation database is established. The database contains historical correlation data and coupling interference case data of air pressure, SF6 gas characteristics, temperature and humidity and splicing surface temperature under different splicing conditions. An adaptive correlation algorithm is used to mine the dynamic coupling interference patterns among the parameters in the database, and to determine the weight allocation relationship between the core control parameters and the auxiliary control parameters, as well as the interference suppression threshold. A multi-objective optimization function with interference suppression constraints is constructed, with the optimization objectives being the stability of the splicing microenvironment, the reliability of the splicing seal, and the minimization of mutual interference of the control actions. The control range of each actuator is constrained, and an initial collaborative control model is generated. The initial model was trained and optimized using historical control data and interference case data; Among them, the dynamic coupling interference pattern mining adopts an adaptive correlation algorithm, the formula of which is as follows: ； in, for Time parameters With parameters The dynamic coupling interference coefficient, for Time parameters With parameters covariance, for Time parameters variance for Time parameters variance This is the time-series correction factor under high-altitude, low-pressure conditions; The formula for determining the parameter weight allocation relationship based on the coupling interference coefficient is as follows: ； in, For the first The adjustment weight of each parameter, This refers to the total number of parameters involved in regulation.

4. The method for coordinated control of multi-dimensional influences on the construction environment of an ultra-high voltage substation according to claim 1, characterized in that, In step S3, the actions of the SF6 gas purification actuator driven by the linkage control command with interference avoidance strategy output by the model include: When the collaborative control model determines that the SF6 gas characteristic parameters deviate from the adaptation range, it outputs an SF6 gas purification control sub-command containing a gas pressure interference avoidance strategy. Based on this sub-command, the SF6 gas purification device is started, and the gas circulation pump is simultaneously linked to adjust the circulation rate, and the high-frequency acquisition mode of the pressure sensing component is triggered synchronously. Real-time collection of SF6 gas characteristic parameters and ambient air pressure data after purification is fed back to the collaborative control model. The model dynamically adjusts the operating power of the purification unit and the circulation rate of the circulating pump based on the feedback data to avoid coupling interference between gas purification actions and air pressure stability, until the SF6 gas characteristic parameters return to the suitable range.

5. The method for coordinated control of multi-dimensional influences on the construction environment of an ultra-high voltage substation according to claim 1, characterized in that, In step S3, the actions of the pressure compensation actuator driven by the linkage control command with interference avoidance strategy output by the model include: The collaborative control model calculates the pressure compensation amount, including gas disturbance avoidance, based on the sensed environmental pressure data and the spliced ​​microenvironment target pressure threshold, combined with the current state of SF6 gas characteristic parameters. According to the output of the air pressure compensation control sub-command, the air pressure compensation pump is started, and the appropriate gas is injected into the spliced ​​sealed chamber through the adjustable air intake valve. During the pressure compensation process, a strategy combining segmented compensation and interference prediction is adopted. In the initial stage, gas is injected at a relatively high rate. When the gas pressure approaches the target threshold, the injection rate is reduced and the potential interference to the SF6 gas characteristics is predicted in real time. Simultaneously collect data on air pressure changes and SF6 gas characteristics within the sealed chamber, and feed them back to the model in real time. The model then dynamically corrects the compensation rate and valve opening.

6. The method for coordinated control of multi-dimensional influences on the construction environment of an ultra-high voltage substation according to claim 1, characterized in that, In S3, the actions of the linkage control command with interference avoidance strategy output by the model, which drives the temperature and humidity adjustment and splicing surface temperature control actuators to coordinate, include: The collaborative control model is based on the sensed temperature and humidity data and the splicing surface temperature data. It explores the coupling and interference relationship between the two and outputs temperature and humidity control sub-instructions and splicing surface temperature control sub-instructions with mutual interference avoidance strategies. The temperature and humidity control actuator activates the heating or dehumidification components according to the temperature and humidity control sub-instruction to adjust the temperature and humidity of the spliced ​​microenvironment; The splicing surface temperature control actuator performs precise temperature control on the splicing surface through a flexible heating film according to the splicing surface temperature control sub-instruction; During the control process, the model links the temperature and humidity regulation with the splicing surface temperature control in real time, and dynamically adjusts the operating status of the actuator according to the changes in the parameters of the two, so as to avoid the interference of temperature regulation on humidity and the coupling effect of splicing surface temperature control on ambient temperature and humidity.

7. The method for coordinated control of multi-dimensional influences on the construction environment of an ultra-high voltage substation according to claim 3, characterized in that, In step S4, the parameters after real-time acquisition and control are fed back to the control model, and the control commands are dynamically corrected in conjunction with the coupling interference suppression strategy, including: A real-time feedback data transmission channel is constructed, and wired and wireless redundant transmission methods are adopted to transmit the parameters of each dimension and the operating status data of the actuators after regulation to the collaborative regulation model in real time. The model compares and analyzes the feedback data with the target parameter range, calculates the parameter deviation value, and identifies the type and intensity of coupling interference in the current control process. When the deviation exceeds the allowable range, the model adjusts the control parameters of each actuator based on the preset deviation correction algorithm and coupling interference suppression strategy. If the deviation value still fails to return to the allowable range after multiple consecutive corrections, the model will activate the control strategy optimization mechanism, re-match the control parameter weights and interference suppression scheme, and generate a new linkage control instruction. The formulas for calculating parameter deviation and correcting control parameters based on coupling interference are as follows: ； in, for Time of the first The deviation value of each parameter, for Time of the first The target value of each parameter for Time of the first The actual feedback value of each parameter; First, the parameter deviation is normalized, and then a formula for the correction of the control parameter combined with the coupling interference is constructed, as follows: ； in, for Time of the first Dimensionless deviation rate of each parameter; The formula for adjusting the control parameter is: ； in, for Time of the first The adjustment amount of the control parameters of each implementing agency This is the proportionality coefficient. The integral coefficient is... The differential coefficients are... For coupling interference adaptation coefficients, For the first The adjustment weight of each parameter, for Time parameters With parameters The dynamic coupling interference coefficient, for Time of the first The dimensionless deviation rate of each parameter It is the integral variable.

8. The method for coordinated control of multi-dimensional influences on the construction environment of an ultra-high voltage substation according to claim 1, characterized in that, It also includes steps for early warning of abnormal microenvironment regulation: During the closed-loop coordinated control process, the coordinated control model monitors the changing trends of each sensing parameter, the operating status of the actuator, and the intensity of coupling interference between parameters in real time. When the sensing parameters change abruptly, the actuator operates abnormally, the control command fails to execute, or the coupling interference intensity exceeds the suppression threshold, the model determines that the control is in an abnormal state and activates the abnormal warning mechanism. The system issues warning signals through audible and visual warning components, and simultaneously records the time of the anomaly, abnormal parameter data, actuator status information, and interference type, generating an anomaly report.

9. The method for coordinated control of multi-dimensional influences on the construction environment of an ultra-high voltage substation according to claim 1, characterized in that, The output of the linkage control command containing the interference avoidance strategy adopts a priority allocation mechanism: The collaborative control model prioritizes control based on the degree of influence of each parameter on the splicing and sealing performance of GIS equipment and the intensity of coupling interference transmission. Among them, the control of SF6 gas characteristic parameters is the highest priority, the control of splicing surface temperature and ambient air pressure is the second highest priority, and the control of ambient temperature and humidity is the normal priority. When multiple parameters deviate from the fit range simultaneously, the model outputs adjustment instructions in order of priority, prioritizing the return to stability of high-priority parameters, while embedding interference protection strategies for low-priority parameters during the adjustment of high-priority parameters. After the high-priority parameters have been adjusted to meet the target, the regular priority parameters will then be adjusted.

10. The method for coordinated control of multi-dimensional influences on the construction environment of an ultra-high voltage substation according to claim 3, characterized in that, It also includes a self-calibration step for the collaborative regulation model: Periodically collect sensing data, control commands, control effect data, and coupling interference suppression effect data during the closed-loop control process to form a model self-calibration dataset; Based on the analysis of model regulation error and interference suppression deviation using self-calibration dataset, the adaptation deviation between model parameters and actual construction conditions in high-altitude heavy icing areas is identified. An adaptive calibration algorithm is used to correct the parameter weights, coupling interference pattern identification logic, and interference suppression strategy of the collaborative control model, and to update the model database. The core algorithm formula for model self-calibration is as follows, based on adjusting the weights of the error correction parameters: ； in, For the first calibration The adjustment weight of each parameter, For weight calibration coefficients, for The overall control error of the time-matter model The preset allowable control error threshold, for Time of the first Deviation values ​​of each parameter; At the same time, the time-series correction factor for high-altitude, low-pressure environments is adjusted: ； in, After calibration Timing correction factor at time step, These are the timing correction factors before calibration. To correct the coefficients and calibrate the coefficients, for The average value of the coupling interference coefficients among all parameters at time 1. This is the preset ideal average value of the coupling interference coefficient.

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