High-reliability switchgear with adaptive intelligent insulation and integrated control system

Abstract

The invention discloses high-reliability switch equipment with self-adaptive intelligent insulation and an integrated control system, and relates to the technical field of power system equipment. The high-reliability switch equipment integrated control system with the self-adaptive intelligent insulation function comprises a fluid influence factor analysis module, a fluid flow velocity analysis module and a fluid flow velocity regulation and control module. The fluid flow velocity influence data is obtained through the obtained external influence data of the bidirectional fluid heat dissipation system, whether first fluid flow velocity regulation is carried out is judged, then the flow velocity data and the fluid influence factors of each cooling channel monitoring point are obtained to obtain the fluid flow velocity analysis index, and whether second fluid flow velocity regulation is carried out is judged. And finally, fluid flow velocity analysis in the next preset time period is carried out after fluid flow velocity regulation and control, whether fluid flow velocity regulation and control are carried out or not is judged, the effect of more accurately monitoring the fluid flow velocity uniformity condition in the cooling channel is achieved, and the problem that in the prior art, fluid pipeline heat distribution uniformity control is not accurate is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system equipment, and in particular to high-reliability switchgear with adaptive intelligent insulation and an integrated control system. Background Art

[0002] With the increasing complexity of power systems and rising reliability requirements, the intelligent and adaptive capabilities of switchgear and control systems have become key development directions. High-reliability switchgear with adaptive intelligent insulation can automatically adjust operating parameters in changing electrical environments, ensuring stable system operation and minimizing the risk of failure. Integrated control systems seamlessly connect multiple devices and functional modules to optimize the operational efficiency and safety of power systems. This type of equipment is widely used in critical infrastructure sectors such as electricity, transportation, and communications, and is crucial for improving system stability, reducing maintenance costs, and extending equipment life.

[0003] Existing switchgear and control systems typically rely on traditional mechanical or electrical control methods. While these methods provide basic functionality, they often lack sufficient adaptability and intelligence in complex power environments. The insulation materials used in traditional equipment often have limitations and are easily affected by environmental factors such as temperature and humidity, reducing system reliability and stability. Furthermore, integrated control systems often suffer from information processing lags and untimely responses. With the development of intelligent technology, modern high-reliability switchgear utilizes adaptive intelligent insulation technology, which can automatically adjust its operating state based on changing external conditions, improving system stability and response speed, and ensuring efficient and safe operation in diverse environments.

[0004] For example, the environmentally friendly gas-insulated intelligent sensing enclosed switchgear announced in the invention patent with announcement number CN118281746B includes: a bottom box, a control room, and an enclosed switch structure room. The control room and the enclosed switch structure room are both arranged on the upper side of the bottom box, and the interior of the bottom box is provided with a partition for separating the space; a self-ventilation mechanism is arranged inside the bottom box and on one side of the partition, which includes a platform, and a ventilation unit is provided on the upper side of the platform for realizing ventilation by monitoring the short-circuit status; the ventilation unit includes two air cylinders fixed on the platform, and pistons are slidably provided inside the two air cylinders.

[0005] For example, the intelligent solid-insulated vacuum switchgear disclosed in the invention patent with announcement number CN104124641B includes: a main busbar chamber and a switch chamber arranged above it. The switch chamber includes three independently arranged insulators, wherein each phase insulator includes an isolating switch, a main switch and a grounding switch sealed therein and designed as a vacuum switch. A mechanism transmission box is arranged above the switch chamber and is connected to the switch chamber for transmission.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, traditional natural cooling or simple forced cooling methods are difficult to meet the requirements and are prone to local overheating, affecting equipment performance and life. By introducing a two-way fluid heat dissipation system, precise temperature management is achieved, effectively solving the problem of temperature rise control and extending the service life of the equipment. However, in the two-way fluid heat dissipation system, the control of the fluid flow rate is not precise enough, and there is a problem of inaccurate control of the uniformity of heat distribution in the fluid pipeline. Summary of the Invention

[0008] The embodiments of the present application solve the problem of inaccurate control of heat distribution uniformity in fluid pipelines in the prior art by providing high-reliability switchgear and integrated control systems with adaptive intelligent insulation, and achieve more accurate monitoring of the uniformity of fluid flow rate in cooling channels.

[0009] An embodiment of the present application provides a high-reliability switchgear with adaptive intelligent insulation, including: an adaptive insulation system, a bionic porous structure shell, a bidirectional fluid heat dissipation system, a self-optimizing operating system based on deep reinforcement learning, a fiber optic sensing network, a modular structure and an electromagnetic locking mechanism, and a distributed control system based on blockchain; the adaptive insulation system is used to adjust the insulation performance in real time according to environmental conditions and load changes; the bionic porous structure shell is used to be designed using bionic principles to improve the insulation effect and reduce the weight of the equipment; the bidirectional fluid heat dissipation system includes a microchannel method and an intelligent flow control device to achieve precise temperature management; the fiber optic sensing network is used to achieve all-round, high-precision status monitoring and fault warning.

[0010] An embodiment of the present application provides a high-reliability switchgear integrated control system with adaptive intelligent insulation, including: a fluid influence factor analysis module, a fluid flow rate analysis module and a fluid flow rate control module: wherein the fluid influence factor analysis module is used to obtain fluid flow rate influence data by acquiring external influence data of the bidirectional fluid heat dissipation system within a preset time period, and determine whether to perform a first fluid flow rate control; the fluid flow rate analysis module is used to obtain the flow rate data and fluid influence factor of each cooling channel monitoring point to obtain a fluid flow rate analysis index, and determine whether to perform a second fluid flow rate control; the fluid flow rate control module is used to perform fluid flow rate analysis in the next preset time period after performing fluid flow rate control, and determine whether to perform fluid flow rate control, and the fluid flow rate control includes a first fluid flow rate control and a second fluid flow rate control.

[0011] Furthermore, the fluid flow rate influencing data includes noise data and fluid influencing factors, the noise data includes noise intensity and noise frequency, and the fluid influencing factors include fluid characteristic change influencing factors and external environment influencing factors; the specific process of judging whether to perform the first fluid flow rate regulation is as follows: obtaining reference noise data from a preset database, the reference noise data includes a noise intensity threshold and a reference noise frequency range; comparing the noise intensity with the noise intensity threshold, if the noise intensity is not greater than the noise intensity threshold, directly executing the fluid flow rate analysis module; if the noise intensity is greater than the noise intensity threshold, performing the first fluid flow rate regulation based on the result of comparing the noise frequency with the reference noise frequency range; the first fluid flow rate regulation includes increasing the valve opening and decreasing the valve opening; increasing the valve opening is a regulation method for the noise frequency data within the low-frequency noise range; decreasing the valve opening is a regulation method for the noise frequency data within the high-frequency noise range.

[0012] Furthermore, the specific acquisition process of the fluid influencing factor is as follows: obtaining external influence data, the external influence data including noise data, fluid characteristic data and external environment data, the fluid characteristic data including fluid density and fluid viscosity, and the external environment data including ambient wind speed and ambient temperature; obtaining reference flow rate influence data from a preset database, the reference flow rate influence data including a reference fluid characteristic range and a wind speed limit; obtaining a fluid influence weight factor from a preset database, the fluid influence weight factor including a fluid characteristic weight factor and an external environment weight factor, the fluid characteristic weight factor including a fluid density weight factor and a fluid viscosity weight factor, and the external environment weight factor including an ambient wind speed weight factor and an ambient temperature weight factor; performing fluid characteristic anomaly analysis on the fluid characteristic data and the corresponding reference fluid characteristic range to obtain a fluid characteristic anomaly evaluation value, and the fluid characteristic anomaly analysis is used to quantify the fluid characteristic variation. the degree of abnormality of the fluid characteristic; combining the fluid characteristic abnormality evaluation value with the corresponding fluid characteristic weight factor to perform comprehensive fluid characteristic impact processing to obtain the fluid characteristic impact factor, the fluid characteristic comprehensive impact processing is used to comprehensively quantify the degree of influence of fluid density and fluid viscosity on fluid flow rate according to the fluid characteristic weight factor, and the fluid characteristic impact factor is used to quantify the degree of influence of fluid characteristic changes on fluid flow rate; performing wind speed deviation processing on the ambient wind speed and the corresponding wind speed limit to obtain a wind speed deviation value, and the wind speed deviation processing is used to quantify the gap between the ambient wind speed and the wind speed limit; combining the wind speed deviation value and the ambient temperature with the corresponding external environment weight factor to perform comprehensive external environment impact processing to obtain the external environment impact factor, the external environment comprehensive impact processing is used to comprehensively quantify the degree of influence of ambient temperature and ambient wind speed on heat dissipation performance according to the external environment weight factor, and the external environment impact factor is used to quantify the degree of influence of external environment changes on heat dissipation performance.

[0013] Furthermore, the flow velocity data includes the flow rate at the monitoring point, the flow velocity at the monitoring point, the symmetry of the flow profile at the monitoring point, the energy loss at the monitoring point and the turbulence intensity at the monitoring point; the symmetry of the flow profile at the monitoring point indicates that a flow velocity distribution diagram in the cooling channel is obtained by a multi-point flow velocity sensor to determine whether there is uneven flow velocity; the flow velocity data is obtained by obtaining data collected at all preset moments in a preset time period from all monitoring points in each cooling channel and performing data processing, and the data processing indicates that data reflecting the data situation in the preset time period is obtained by processing each instantaneous data.

[0014] Furthermore, the flow velocity data and fluid influencing factors of each cooling channel monitoring point are obtained to obtain the fluid flow velocity analysis index, and the specific steps are as follows: obtaining reference weights from a preset database, the reference weights including influencing factor weights and flow velocity data analysis weights, the influencing factor weights including fluid weights and external weights, the flow velocity data analysis weights including flow weights, flow velocity weights, flow profile symmetry weights, energy loss weights and turbulence intensity weights; performing influencing factor correction difference operations on the fluid characteristic influencing factors and the external environment influencing factors to obtain corresponding fluid influence correction values, the influencing factor correction difference operations are used to quantify the correction degree of the fluid influence factors; the fluid influence correction values are combined with the corresponding influencing factor weights to perform influencing The comprehensive quantitative operation of the impact factors is used to obtain a comprehensive impact factor, and the comprehensive quantitative operation of the impact factors is used to comprehensively quantify the degree of influence of the fluid characteristic impact factors and the external environment impact factors on the fluid flow rate; based on the flow rate data and the corresponding flow rate data analysis weight, the flow rate comprehensive processing is performed to obtain a flow rate analysis value, and the flow rate comprehensive processing is used to comprehensively quantify the fluid flow rate situation reflected by the flow rate data; the comprehensive impact factors and the flow rate analysis value are subjected to fluid flow rate comprehensive processing to obtain a fluid flow rate analysis index, and the fluid flow rate comprehensive processing is used to comprehensively quantify the specific situation of the fluid flow rate in the corresponding cooling channel under the condition of considering the correction of the fluid impact factors; the fluid flow rate analysis index represents data for comprehensively quantifying the specific situation of the fluid flow rate in the cooling channel.

[0015] Furthermore, the specific process of determining whether to perform a second fluid flow rate regulation is as follows: obtaining the fluid flow rate analysis index of all cooling channels to obtain a fluid flow rate analysis set, performing set data processing on the fluid flow rate analysis set to obtain a set variance, and the set data processing means performing a variance operation on the fluid flow rate analysis set; if the set variance is not greater than the reference set variance, obtaining the external influence data for the next preset time period; if the set variance is greater than the reference set variance, performing a second fluid flow rate regulation, and the second fluid flow rate regulation means taking different segmented control measures for different cooling channels according to the fluid flow rate analysis data of each cooling channel and the reference set variance.

[0016] Furthermore, different segmented control measures are taken for different cooling channels, and the specific process is as follows: a reference set value is obtained based on the reference set variance, and a set mean of the fluid flow rate analysis set is obtained; deviation processing is performed on each cooling channel and the set mean to obtain the flow rate deviation value of the corresponding cooling channel; if the flow rate deviation value is not greater than the reference set value, the corresponding cooling channel is recorded as a uniform channel, and no segmented control measures are taken; if the flow rate deviation value is greater than the reference set value, the corresponding cooling channel is recorded as a deviated channel, and control measures are taken; the specific steps of taking control measures are: all deviated channels are distinguished by pipe shape, if there is no elbow in the cooling channel and the cooling channel exceeds the preset length, the fluid inlet of the cooling channel is segmented in sequence according to the preset length and the segmented positions are marked as valve position deployment points; if there is an elbow in the cooling channel, the valve position deployment points are marked at both ends of the elbow.

[0017] Furthermore, after the fluid flow rate regulation is performed, the fluid flow rate analysis of the next preset time period is performed to determine whether the fluid flow rate regulation is performed. The specific steps are as follows: Step 1, obtain external influence data to obtain the fluid influence factor and obtain the fluid influence factor of the previous preset time period; Step 2, perform fluid influence factor proportional control on the fluid influence factor of the previous preset time period to obtain the fluid influence weight factor of the current preset time period, and the fluid influence factor proportional control is used to adjust the degree of influence of the fluid influence weight factor on the fluid influence factor; Step 3, perform fluid flow rate analysis based on the obtained fluid influence weight factor to determine whether to perform fluid flow rate regulation; Step 4, if fluid flow rate regulation is performed, perform fluid influence factor proportional control until the number of fluid influence factor proportional controls reaches the preset number of regulation times. If the preset number of regulation times is reached and fluid flow rate regulation is performed, a pipeline operation and maintenance prompt is issued. If the preset number of regulation times is not reached and fluid flow rate regulation is performed, fluid influence factor proportional control is performed. If fluid flow rate regulation is not performed without exceeding the preset number of regulation times, the external influence data of the next preset time period is obtained.

[0018] Furthermore, the proportional control of the fluid influence factor is performed, and the specific process is as follows: the fluid influence factor and the fluid influence factor of the previous preset time period are proportionally controlled to obtain the weight change ratio, and the proportional control processing is used to quantify the degree of change of the fluid influence factor as the preset time period changes; the weight change ratio and the fluid influence weight factor are dynamically controlled to obtain the fluid influence weight factor of the current preset time period, and the dynamic control processing is used to adjust the degree of influence of the fluid influence weight factor on the fluid influence factor.

[0019] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0020] 1. By obtaining the external influence data of the two-way fluid heat dissipation system within a preset time period, the fluid flow rate influence data is obtained, and it is determined whether to perform the first fluid flow rate regulation. Then, the flow rate data and fluid influence factor of each cooling channel monitoring point are obtained to obtain the fluid flow rate analysis index, and it is determined whether to perform the second fluid flow rate regulation. Finally, after the fluid flow rate regulation is performed, the fluid flow rate analysis of the next preset time period is performed to determine whether to perform the fluid flow rate regulation, so as to more timely evaluate the fluid flow rate condition of the cooling channel, thereby achieving more accurate monitoring of the uniformity of the fluid flow rate in the cooling channel, and effectively solving the problem of inaccurate control of the uniformity of heat distribution in the fluid pipeline in the existing technology.

[0021] 2. By obtaining the reference weight from the preset database, and performing the influence factor correction difference operation on the fluid characteristic influence factor and the external environment influence factor respectively, the corresponding fluid influence correction value is obtained, and then the fluid influence correction value is combined with the corresponding influence factor weight to perform the influence factor comprehensive quantification operation to obtain the comprehensive influence factor, and then the symmetry of the flow profile of the monitoring point is correlated to obtain the symmetry correlation value, and then the flow velocity is comprehensively processed based on the flow velocity data and the corresponding flow velocity data analysis weight to obtain the flow velocity analysis value, and finally the comprehensive influence factor and the flow velocity analysis value are comprehensively processed to obtain the fluid flow velocity analysis index, so as to timely understand the situation of the fluid flow velocity in the cooling channel, and thus realize the accurate analysis of the uniformity of the fluid flow velocity in the cooling channel.

[0022] 3. By obtaining the fluid flow rate analysis index of all cooling channels, a fluid flow rate analysis set is obtained, and the fluid flow rate analysis set is processed to obtain the set variance. Then, the reference set variance is obtained from the preset database and compared with the set variance: if the set variance is not greater than the reference set variance, the external influence data of the next preset time period is obtained. If the set variance is greater than the reference set variance, a second fluid flow rate control is performed, so that timely measures can be taken for cooling channels with uneven fluid flow rates, thereby achieving accurate control of the uniformity of fluid flow rates in cooling channels.

[0023] 4. By obtaining external influence data and obtaining reference flow rate influence data and fluid influence weight factors from a preset database, the fluid characteristic abnormality evaluation value is obtained according to the fluid characteristic data and the corresponding reference fluid characteristic range, and then the fluid characteristic influence factor is obtained by combining the fluid characteristic abnormality evaluation value with the corresponding fluid characteristic weight factor. Then, the wind speed deviation value is obtained based on the ambient wind speed and the corresponding wind speed limit value, and finally, the external environment influence factor is obtained by combining the wind speed deviation value and the ambient temperature with the corresponding external environment weight factor, thereby more fully considering the influence of other objective influencing factors on the fluid flow rate, and thus obtaining a more accurate fluid flow rate analysis index.

[0024] 5. Obtain the reference set value through the reference set variance, and obtain the set mean of the fluid flow rate analysis set. Then, obtain the flow rate deviation value of the cooling channel based on each cooling channel and the set mean. If the flow rate deviation value is not greater than the reference set value, the corresponding cooling channel is recorded as a uniform channel and no segmented control measures are taken. Otherwise, the corresponding cooling channel is recorded as a deviated channel and control measures are taken. In this way, corresponding control measures are taken according to the actual situation of the cooling channel, thereby ensuring the uniformity of heat dissipation of the bidirectional fluid heat dissipation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic structural diagram of a high-reliability switchgear integrated control system with adaptive intelligent insulation provided by an embodiment of the present application;

[0026] Figure 2 A schematic structural diagram of a high-reliability switchgear with adaptive intelligent insulation provided by an embodiment of the present application;

[0027] Figure 3 A schematic structural diagram of a bidirectional fluid heat dissipation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiments of the present application solve the problem of inaccurate control of heat distribution uniformity in fluid pipelines in the prior art by providing a high-reliability switchgear and integrated control system with adaptive intelligent insulation. The system obtains external influence data of a bidirectional fluid heat dissipation system within a preset time period to obtain fluid flow velocity influence data, and determines whether to perform a first fluid flow velocity regulation. Reference weights are then obtained from a preset database, and influence factor correction difference operations are performed on the fluid characteristic influence factors and the external environment influence factors to obtain corresponding fluid influence correction values. The fluid influence correction values are then combined with the corresponding influence factor weights to perform an influence factor comprehensive quantification operation to obtain a comprehensive influence factor. A correlation operation is then performed on the symmetry of the flow profile at the monitoring point to obtain a symmetry correlation value. Comprehensive flow velocity processing is then performed based on the flow velocity data and the corresponding flow velocity data analysis weights to obtain a flow velocity analysis value. The comprehensive influence factor and the flow velocity analysis value are then subjected to fluid flow velocity comprehensive processing to obtain a fluid flow velocity analysis index, and a determination is made whether to perform a second fluid flow velocity regulation. Finally, after the fluid flow velocity regulation is performed, a fluid flow velocity analysis is performed for the next preset time period to determine whether to perform fluid flow velocity regulation, thereby achieving more accurate monitoring of the uniformity of the fluid flow velocity in the cooling channel.

[0029] The technical solution in the embodiment of the present application is to solve the problem of inaccurate control of heat distribution uniformity in the fluid pipeline. The overall idea is as follows:

[0030] By obtaining the external influence data of the two-way fluid heat dissipation system, the fluid flow rate influence data is obtained, and it is judged whether to perform the first fluid flow rate regulation. Then, the flow rate data and fluid influence factor of each cooling channel monitoring point are obtained to obtain the fluid flow rate analysis index, and it is judged whether to perform the second fluid flow rate regulation. Finally, after the fluid flow rate regulation is performed, the fluid flow rate analysis of the next preset time period is performed to determine whether to perform the fluid flow rate regulation, thereby achieving the effect of more accurately monitoring the uniformity of the fluid flow rate in the cooling channel.

[0031] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] like Figure 1 As shown, it is a structural schematic diagram of a high-reliability switchgear integrated control system with adaptive intelligent insulation provided in an embodiment of the present application. The high-reliability switchgear integrated control system with adaptive intelligent insulation provided in an embodiment of the present application includes: a fluid influence factor analysis module, a fluid flow rate analysis module and a fluid flow rate control module: wherein the fluid influence factor analysis module is used to obtain fluid flow rate influence data by obtaining external influence data of the bidirectional fluid heat dissipation system within a preset time period, and judge whether to perform the first fluid flow rate control; the fluid flow rate analysis module is used to obtain the flow rate data and fluid influence factor of each cooling channel monitoring point to obtain a fluid flow rate analysis index, and judge whether to perform the second fluid flow rate control; the fluid flow rate control module is used to perform fluid flow rate analysis in the next preset time period after performing fluid flow rate control, and judge whether to perform fluid flow rate control, and the fluid flow rate control includes the first fluid flow rate control and the second fluid flow rate control.

[0033] In this embodiment, a preset number of preset moments are set within each preset time period, and a preset number of monitoring points are also preset on the cooling channel. By collecting data from the monitoring points on the cooling channel at the preset moments within the preset time period, subsequent fluid flow rate related analysis is performed based on the collected data. These preset numbers are set by preset staff; and through the synergistic effect of each module in this integrated system, more accurate monitoring of the uniformity of the fluid flow rate in the cooling channel is achieved, and corresponding measures can be taken in a timely manner.

[0034] like Figure 2As shown, it is a structural schematic diagram of a high-reliability switchgear with adaptive intelligent insulation provided by an embodiment of the present application. The high-reliability switchgear with adaptive intelligent insulation provided by an embodiment of the present application includes: an adaptive insulation system, a bionic porous structure shell, a bidirectional fluid heat dissipation system, a self-optimizing operating system based on deep reinforcement learning, a fiber optic sensing network, a modular structure and an electromagnetic locking mechanism, and a distributed control system based on blockchain; the adaptive insulation system includes intelligent materials and nanosensors; the adaptive insulation system is used to adjust the insulation performance in real time according to environmental conditions and load changes; the bionic porous structure shell is used to adopt bionic principles to improve the insulation effect and reduce the weight of the equipment; the bidirectional fluid heat dissipation system includes a microchannel method and an intelligent flow control device to achieve precise temperature management; the self-optimizing operating system based on deep reinforcement learning is used to autonomously adjust the equipment operating parameters according to historical data and real-time status; the fiber optic sensing network is used to combine with artificial intelligence algorithms to achieve all-round, high-precision status monitoring and fault warning; the modular structure simplifies the installation and maintenance process by adopting an electromagnetic locking method, improves system reliability and flexibility; the distributed control system based on blockchain realizes safe collaboration and intelligent scheduling between devices.

[0035] In this embodiment, through the combination of smart materials and nanosensors, real-time dynamic adjustment of insulation performance is achieved, effectively responding to different environmental conditions and load changes. The unique bionic porous structure design significantly improves the insulation effect while greatly reducing the weight of the equipment. The innovative two-way fluid heat dissipation system integrates microchannel technology and intelligent flow control to achieve precise temperature management and solve the problem of temperature rise control. The equipment also integrates a self-optimizing operating system based on deep reinforcement learning, which can autonomously adjust operating parameters according to historical data and real-time status, greatly improving the adaptability and efficiency of the equipment. The combination of advanced fiber optic sensor networks and artificial intelligence algorithms realizes all-round, high-precision status monitoring and fault warning. The modular design uses innovative electromagnetic locking technology, which not only simplifies the installation and maintenance process, but also improves the reliability and flexibility of the system.

[0036] Specifically, the adaptive insulation system consists of smart materials and nanosensors. The smart materials are electric field-responsive and can adjust their insulation properties in real time based on the strength of the electric field. Specifically, a new type of electroactive polymer composite material is used, which can change its molecular structure under the influence of an electric field, thereby adjusting the insulation strength. The nanosensors, which use carbon nanotube-based composite sensors, are distributed throughout the insulation material and monitor parameters such as temperature, humidity, and electric field strength in real time. The system analyzes sensor data using intelligent algorithms and dynamically adjusts the performance of the smart materials to achieve adaptive regulation of insulation performance. For example, in high humidity environments, the system enhances the hydrophobicity of the material, improving insulation strength.

[0037] It should be added that the high-reliability switchgear with adaptive intelligent insulation of the present invention mainly consists of the following parts: an adaptive insulation system (1), a bionic porous structure shell (2), a bidirectional fluid heat dissipation system (3), a self-optimizing operating system based on deep reinforcement learning (4), a fiber optic sensing network (5), a modular structure and electromagnetic locking mechanism (6), a distributed control system based on blockchain (7), an edge computing unit (8) and an acoustic imaging detection system (9).

[0038] Specifically, such as Figure 3 Figure 2 shows the schematic structure of a bidirectional fluid cooling system that integrates microchannel technology and an intelligent flow control device. Microchannel technology uses an etching process to create micron-scale cooling channels on a metal substrate, significantly increasing the heat dissipation area and improving heat dissipation efficiency. The intelligent flow control device, consisting of a micropump, solenoid valve, and flow sensor, adjusts the coolant flow rate based on real-time temperature data for precise temperature management. The system also utilizes environmentally friendly phase change materials, such as paraffin-based phase change materials, as a heat dissipation medium, further enhancing heat dissipation. During normal operation, the phase change material absorbs heat and undergoes a phase change, releasing heat when the load decreases to balance temperature fluctuations.

[0039] It should be noted that the bidirectional fluid cooling system (3) includes a microchannel heat exchanger (31), an intelligent flow control valve (32), a thermal management controller (33), and an environmentally friendly phase change material storage tank (34). The microchannel heat exchanger uses an aluminum alloy substrate, and a cooling channel network with a width of 100 microns and a depth of 200 microns is produced by a chemical etching process. The intelligent flow control valve is driven by piezoelectric ceramics, and the response time is less than 10 milliseconds. The thermal management controller is based on the PID algorithm (Proportional-Integral-Derivative Control) to achieve precise temperature control. The environmentally friendly phase change material is octadecyl octadecanoate, with a phase change temperature of 28-30°C and a latent heat of 200 J / g. The bidirectional fluid cooling system (3) adjusts the coolant flow according to real-time temperature data to achieve precise temperature management and control the temperature rise within 80% of the rated value.

[0040] Furthermore, the fluid flow rate influencing data includes noise data and fluid influencing factors, the noise data includes noise intensity and noise frequency, and the fluid influencing factors include fluid characteristic change influencing factors and external environment influencing factors; judging whether to perform the first fluid flow rate regulation, the specific process is as follows: obtaining reference noise data from a preset database, the reference noise data includes a noise intensity threshold and a reference noise frequency range, the reference noise frequency range includes a low-frequency noise range and a high-frequency noise range; comparing the noise intensity with the noise intensity threshold, if the noise intensity is not greater than the noise intensity threshold, directly executing the fluid flow rate analysis module; if the noise intensity is greater than the noise intensity threshold, performing the first fluid flow rate regulation based on the result of comparing the noise frequency with the reference noise frequency range; the first fluid flow rate regulation includes increasing the valve opening and decreasing the valve opening; increasing the valve opening is a control method for the noise frequency data to be within the low-frequency noise range; decreasing the valve opening is a control method for the noise frequency data to be within the high-frequency noise range.

[0041] In this embodiment, the noise intensity is measured by using a noise meter (volume meter) to measure the intensity of the sound, and the noise frequency is measured by using a spectrum analyzer; increasing the valve opening and decreasing the valve opening are specifically obtained by constructing a mapping set of noise frequency and valve opening adjustment multiples, and the real-time noise frequency is input into the mapping set to obtain the corresponding valve opening adjustment multiple, and the valve opening is adjusted according to the valve opening adjustment multiple; through the analysis of the embodiments of the present application, the cooling channels that need to be fluid flow rate controlled are preliminarily screened, and the corresponding first fluid flow rate control is taken in time, which improves the efficiency of monitoring the fluid flow rate condition and achieves the effect of more accurate monitoring of the uniformity of the fluid flow rate in the cooling channel.

[0042] Specifically, reference noise data is obtained from a preset database. In one specific embodiment, the noise intensity threshold is set by a preset staff member based on the operating conditions of the bidirectional fluid cooling system. The reference noise frequency range is obtained by consulting a reference database. The reference database indicates that the low-frequency noise range is 20 Hz to 500 Hz, and the high-frequency noise range is 500 Hz to 10 kHz.

[0043] Specifically, the specific acquisition process of the fluid influencing factor is as follows: obtain external influencing data, which includes noise data, fluid characteristic data and external environment data, the fluid characteristic data includes fluid density and fluid viscosity, and the external environment data includes ambient wind speed and ambient temperature; obtain reference flow rate influence data from a preset database, the reference flow rate influence data includes a reference fluid characteristic range and a wind speed limit, the reference fluid characteristic range includes a reference fluid density range and a reference fluid viscosity range, the reference fluid density range represents the range corresponding to the maximum fluid density and the minimum fluid density, and the reference fluid viscosity range represents the range corresponding to the maximum fluid viscosity and the minimum fluid viscosity; obtain fluid influence weight factors from a preset database, the fluid influence weight factors include fluid characteristic weight factors and external environment weight factors, the fluid characteristic weight factors include fluid density weight factors and fluid viscosity weight factors, and the external environment weight factors include ambient wind speed weight factors and ambient temperature weight factors; perform fluid characteristic anomaly analysis on the fluid characteristic data and the corresponding reference fluid characteristic range to obtain a fluid characteristic anomaly evaluation value (i.e. and ), fluid property anomaly analysis and processing is used to quantify the degree of abnormality in fluid property changes; combining the fluid property anomaly assessment value with the corresponding fluid property weight factor to perform comprehensive fluid property impact processing to obtain the fluid property impact factor. The fluid property comprehensive impact processing is used to comprehensively quantify the degree of influence of fluid density and fluid viscosity on fluid flow rate based on the fluid property weight factor. The fluid property impact factor is used to quantify the degree of influence of fluid property changes on fluid flow rate. The specific restricted expression of the fluid property impact factor is as follows:

[0044] ;

[0045] Where, Indicates the number of the preset time period, , Indicates the total number of preset time periods, represents the fluid density at the t-th preset time period, represents the fluid viscosity at the t-th preset time period, represents the maximum density of the fluid, represents the maximum viscosity of the fluid, represents the minimum density of the fluid, represents the minimum viscosity of the fluid, represents the fluid density weighting factor, represents the fluid viscosity weight factor, Indicates the fluid property influencing factor in the t-th preset time period.

[0046] The wind speed deviation processing is performed on the ambient wind speed and the corresponding wind speed limit to obtain the wind speed deviation value. The wind speed deviation processing is used to quantify the difference between the ambient wind speed and the wind speed limit. The wind speed deviation value and the ambient temperature are combined with the corresponding external environment weight factor to perform the external environment comprehensive impact processing to obtain the external environment impact factor. The external environment comprehensive impact processing is used to comprehensively quantify the degree of influence of the ambient temperature and ambient wind speed on the heat dissipation performance according to the external environment weight factor. The external environment impact factor is used to quantify the degree of influence of external environment changes on the heat dissipation performance. The specific limiting expression of the external environment impact factor is as follows:

[0047] ;

[0048] Where, Indicates the number of the preset time period, , Indicates the total number of preset time periods, Indicates the ambient wind speed in the t-th preset time period, Indicates the ambient temperature in the t-th preset time period, Indicates the wind speed limit, represents the ambient wind speed weight factor, represents the ambient temperature weight factor, Represents the external environment influencing factor in the t-th preset time period.

[0049] It should be understood that the fluid property influence factor algorithm combines the fluid property data, the reference fluid property range and the corresponding fluid property weight factor for comprehensive analysis to obtain the fluid property influence factor. In the formula, as the degree of deviation of the fluid density from the corresponding reference fluid density range increases, the corresponding fluid property influence factor becomes larger, indicating that the change in fluid density may have a greater impact on the fluid flow rate; similarly, as the degree of deviation of the fluid viscosity from the corresponding reference fluid viscosity range increases, the corresponding fluid property influence factor becomes larger, indicating that the change in fluid viscosity has a greater impact on the heat dissipation of the cooling channel; the comprehensive impact processing of fluid properties means adding the results of multiplying the fluid property abnormality assessment value with the corresponding fluid property weight factor; the comprehensive impact processing of the external environment means adding the results of multiplying the wind speed deviation value with the corresponding external environment weight factor; by analyzing the fluid property influence factor, the impact of the change in fluid properties on the fluid flow rate is taken into account, making the analysis of the fluid flow rate more accurate, so that the corresponding fluid flow rate control measures can be taken in time to ensure the uniformity of the fluid flow rate.

[0050] In this embodiment, the external environment impact factor algorithm combines the external environment data, the wind speed limit and the corresponding external environment weight factor for comprehensive analysis to obtain the external environment impact factor. In the formula, when the ambient wind speed is closer to the wind speed limit and the ambient wind speed is less than the wind speed limit, the corresponding external environment impact factor is smaller, indicating that the ambient wind speed has a smaller impact on the heat dissipation of the cooling channel. Conversely, it means that the impact of the ambient wind speed on the cooling channel has reached the maximum, and the corresponding external environment impact factor is also larger; when the ambient temperature is higher, the greater the impact on the heat dissipation performance of the cooling channel, and the corresponding external environment impact factor is also larger; through the analysis of the external environment factor algorithm, the influence of the two major factors of wind speed and temperature in the external environment on the cooling channel is taken into account, so that the flow rate analysis of the fluid in the cooling channel is more accurate, so that corresponding measures can be taken in time.

[0051] Specifically, the reference flow rate impact data is obtained from a preset database. In a specific embodiment, the reference fluid property range can be obtained by consulting the data. Generally, in a liquid cooling system, the reference fluid density range should generally be between 800-1200 kg / m³, and the reference fluid viscosity range should generally be between 1.0×10 -6 to 1.0×10 -3 Pa·s (for water or similar liquids); the wind speed limit is set by the preset staff according to the working conditions of the two-way fluid cooling system.

[0052] It should be added that the fluid density is obtained by using a densitometer (such as a buoyancy method) to measure the density of the fluid, and the fluid viscosity is obtained by using a viscometer (such as a rotational viscometer or a capillary viscometer) to measure the viscosity of the fluid; the ambient temperature is directly measured by using a thermometer (such as a digital thermometer, an infrared thermometer, a glass thermometer, etc.), and the ambient wind speed is obtained by using an anemometer (such as a handheld anemometer, a hot film anemometer, etc.) to measure the wind speed in the environment; and the fluid characteristic data and external environment data used in this embodiment are the average values of the data collected at all preset times within a preset time period at the monitoring points on the cooling channel.

[0053] Specifically, the fluid property weighting factor is obtained from a preset database. The fluid property weighting factor represents the degree of influence of the fluid property data on the fluid property influencing factor. Each fluid property data and the fluid property weighting factor have a unique mapping relationship, and the value range is between 0 and 1. For example, a mapping set of fluid property data and preset fluid property weighting factors is constructed, and the real-time fluid density and fluid viscosity are input into the mapping set to obtain the fluid density weighting factor and fluid viscosity weighting factor, respectively, which represent the degree of influence of the fluid density and fluid viscosity on the fluid property influencing factor, and the sum of the two is 1.

[0054] Specifically, the external environment weighting factor is obtained from a preset database. The external environment weighting factor represents the degree of influence of the external environment data on the external environment influencing factor. Each external environment data and the external environment influencing factor have a unique mapping relationship, and the value range is between 0 and 1. For example, a mapping set of external environment data and preset external environment weighting factors is constructed, and the real-time ambient wind speed and ambient temperature are input into the mapping set to obtain the ambient wind speed weighting factor and ambient temperature weighting factor, respectively, which represent the degree of influence of the ambient wind speed and ambient temperature on the external environment influencing factor, and the sum of the two is 1.

[0055] Furthermore, the flow rate data includes the flow rate at the monitoring point, the flow rate at the monitoring point, the symmetry of the flow profile at the monitoring point, the energy loss at the monitoring point and the turbulence intensity at the monitoring point; the symmetry of the flow profile at the monitoring point indicates that a flow velocity distribution diagram in the cooling channel is obtained by using a multi-point flow velocity sensor to determine whether there is uneven flow velocity; the flow rate data is obtained by obtaining data collected at all preset moments in a preset time period from all monitoring points in each cooling channel and performing data processing, and data processing indicates that data reflecting the data situation in the preset time period is obtained by processing each instantaneous data.

[0056] In this embodiment, the flow rate at the monitoring point is obtained by a flow meter deployed at the monitoring point; the flow velocity at the monitoring point is measured by an ultrasonic flow meter deployed at the monitoring point; the symmetry of the flow profile at the monitoring point is determined by obtaining a flow velocity distribution diagram in the heat dissipation channel using a multi-point flow velocity sensor, and dividing the flow velocity distribution diagram into left and right symmetrical parts to obtain flow velocity data on the left and right sides, respectively. The symmetry index representing the symmetry of the flow profile at the monitoring point is obtained based on the following formula:

[0057] ;

[0058] What needs to be explained is that Indicates the flow rate data on the left. Indicates the flow rate data on the right. Represents the symmetry index.

[0059] The energy loss at the monitoring point is calculated by the pressure loss at the monitoring point. The pressure loss is calculated using the following formula:

[0060] ;

[0061] What needs to be explained is that Indicates the pressure loss, is the friction factor, is the heat dissipation channel length, is the diameter of the heat dissipation channel, is the fluid density, is the real-time flow rate, where the friction factor is selected according to the surface roughness of the heat dissipation channel.

[0062] The turbulence intensity at the monitoring point is obtained by measuring the flow velocity fluctuation in real time with a hot wire anemometer and calculating the corresponding standard deviation.

[0063] The acquisition of the above data provides a data basis for subsequent fluid flow rate analysis and also helps in the accurate implementation of fluid flow rate.

[0064] Furthermore, the flow velocity data and fluid influencing factors of each cooling channel monitoring point are obtained to obtain the fluid flow velocity analysis index. The specific steps are as follows: obtain reference weights from the preset database, the reference weights include influencing factor weights and flow velocity data analysis weights, the influencing factor weights include fluid weights and external weights, and the flow velocity data analysis weights include flow weights, flow velocity weights, flow profile symmetry weights, energy loss weights and turbulence intensity weights; perform influencing factor correction difference operations on the fluid characteristic influencing factors and the external environment influencing factors to obtain corresponding fluid influence correction values, and the influencing factor correction difference operation is used to quantify the correction degree of the fluid influence factors; combine the fluid influence correction values with the corresponding influencing factor weights to perform influencing factor comprehensive quantification operations to obtain the comprehensive influence factor (i.e. ), the comprehensive quantification operation of influencing factors is used to comprehensively quantify the degree of influence of fluid characteristic influencing factors and external environment influencing factors on fluid flow rate; based on the flow rate data and the corresponding flow rate data analysis weight, the flow rate comprehensive processing is performed to obtain the flow rate analysis value, and the flow rate comprehensive processing is used to comprehensively quantify the fluid flow rate situation reflected by the flow rate data; the comprehensive influencing factors and the flow rate analysis value are subjected to fluid flow rate comprehensive processing to obtain the fluid flow rate analysis index, and the fluid flow rate comprehensive processing is used to comprehensively quantify the specific conditions of the fluid flow rate in the corresponding cooling channel under the consideration of the correction of the fluid influencing factors; the fluid flow rate analysis index represents the data for comprehensively quantifying the specific conditions of the fluid flow rate in the cooling channel.

[0065] The specific limiting expression of the fluid velocity analysis index is as follows:

[0066] ;

[0067] ;

[0068] Where, Indicates the number of the preset time period, , Indicates the total number of preset time periods, represents the flow rate of the monitoring point in the t-th preset time period, represents the flow rate at the monitoring point in the t-th preset time period, Indicates the symmetry of the flow profile at the monitoring point in the t-th preset time period, Indicates the energy loss of the monitoring point in the t-th preset time period, represents the turbulence intensity of the monitoring point in the t-th preset time period, represents the traffic weight, represents the flow velocity weight, represents the flow section symmetry weight, represents the energy loss weight, represents the turbulence intensity weight, represents the fluid weight, represents the external weight, represents the comprehensive impact factor of the t-th preset time period, represents the fluid characteristic influencing factor of the t-th preset time period, represents the external environment influencing factor of the t-th preset time period, Represents the fluid flow rate analysis index of the t-th preset time period.

[0069] In this embodiment, the algorithm combines the flow rate data, the fluid influencing factor and the reference weight for comprehensive analysis to obtain the fluid flow rate analysis index, wherein, as the flow rate data and the comprehensive influencing factor change, the corresponding fluid flow rate analysis index also changes accordingly, wherein, as the flow rate data and the comprehensive influencing factor increase, the corresponding fluid flow rate analysis index also increases, indicating that the specific condition of the fluid in the cooling channel may fluctuate more; and the comprehensive influencing factor is affected by the change of the fluid influencing factor. When the fluid characteristic influencing factor and the external environment influencing factor increase, the corresponding comprehensive influencing factor is smaller, indicating that the fluid influencing factor is larger, and the degree of correction of the fluid flow rate analysis index should be The higher the value, the higher the impact factor correction difference operation means performing a difference operation on the fluid impact factor with the value 1, the comprehensive quantification operation of the impact factor means performing an average operation on the results of multiplying the fluid impact correction value with the corresponding impact factor weight, the comprehensive flow rate processing means summing the results of multiplying the flow rate data with the corresponding flow rate data analysis weight, and the comprehensive fluid flow rate processing means multiplying the comprehensive impact factor and the flow rate analysis value; through the analysis of the fluid flow rate analysis index, it is more conducive to monitoring the uneven heat distribution of the cooling system caused by the uneven fluid flow rate in the cooling channel, and taking corresponding control measures in time to make the change of the fluid flow rate tend to be uniform.

[0070] Specifically, the influence factor weight is obtained from a preset database, and the influence factor weight represents the degree of influence of the fluid influence factor on the fluid flow rate analysis index. Each fluid influence factor and the influence factor weight have a unique mapping relationship, and the value range is between 0 and 1. For example, a mapping set of fluid influence factors and preset influence factor weights is constructed, and the real-time fluid characteristic influence factors and external environment influence factors are input into the mapping set to obtain fluid weights and external weights, respectively, representing the degree of influence of the fluid characteristic influence factors and external environment influence factors on the fluid flow rate analysis index, and the sum of the two is 1.

[0071] Specifically, the flow rate data analysis weight is obtained from a preset database, and the flow rate data analysis weight represents the degree of influence of the flow rate data on the fluid flow rate analysis index. Each flow rate data has a unique mapping relationship with the flow rate data analysis weight, and the value range is between 0 and 1; for example, a mapping set of flow rate data and preset flow rate data analysis weights is constructed, and the real-time monitoring point flow rate, monitoring point flow rate, monitoring point flow profile symmetry, monitoring point energy loss and monitoring point turbulence intensity are input into the mapping set to obtain flow weight, flow rate weight, flow profile symmetry weight, energy loss weight and turbulence intensity weight, which respectively represent the degree of influence of the monitoring point flow rate, monitoring point flow rate, monitoring point flow profile symmetry, monitoring point energy loss and monitoring point turbulence intensity on the fluid flow rate analysis index, and the sum of the five is 1.

[0072] Furthermore, the specific process of determining whether to perform a second fluid flow rate regulation is as follows: obtaining the fluid flow rate analysis index of all cooling channels to obtain a fluid flow rate analysis set, performing set data processing on the fluid flow rate analysis set to obtain a set variance, where the set data processing means performing a variance operation on the fluid flow rate analysis set; obtaining a reference set variance from a preset database, and comparing it with the set variance: if the set variance is not greater than the reference set variance, obtaining external influence data for the next preset time period; if the set variance is greater than the reference set variance, performing a second fluid flow rate regulation, where the second fluid flow rate regulation means performing analysis based on the fluid flow rate analysis data of each cooling channel and the reference set variance, and taking different segmented control measures for different cooling channels based on different analysis results.

[0073] In this embodiment, the reference set variance is specifically set by a preset staff member based on the working conditions of the bidirectional fluid cooling system; by uniformly processing and comparing the fluid flow rate analysis data of all cooling channels, a more objective analysis is performed on whether the fluid flow rate in the cooling channel is uniform, thereby reducing the singleness of the fluid flow rate analysis conditions of the cooling channel and improving the reliability of the analysis results corresponding to the fluid flow rate analysis.

[0074] Furthermore, different segmented control measures are taken for different cooling channels, and the specific process is as follows: a reference set value is obtained based on the reference set variance, and the set mean of the fluid flow rate analysis set is obtained; the fluid flow rate analysis index of each cooling channel and the set mean are processed with deviation values to obtain the flow rate deviation value of the corresponding cooling channel; the flow rate deviation value is compared with the reference set value: if the flow rate deviation value is not greater than the reference set value, the corresponding cooling channel is recorded as a uniform channel, and no segmented control measures are taken; if the flow rate deviation value is greater than the reference set value, the corresponding cooling channel is recorded as a deviated channel, and control measures are taken; the specific steps for taking control measures are: all deviated channels are distinguished by pipe shape, if there is no elbow in the cooling channel and the cooling channel exceeds the preset length, the fluid inlet of the cooling channel is segmented in sequence according to the preset length and the segmented positions are marked as valve position deployment points; if there is an elbow in the cooling channel, the valve position deployment points are marked at both ends of the elbow.

[0075] In this embodiment, the preset length is specifically set by the preset staff according to the working conditions of the bidirectional fluid cooling system; the corresponding reference set value is obtained by performing square root processing on the reference set variance, the deviation value processing represents the difference operation between the fluid flow rate analysis index of each cooling channel and the set mean, and the valve position deployment point represents a prompt for the preset staff to deploy the throttle valve and control valve at the position corresponding to the point; the method of this embodiment screens out cooling channels with uneven fluid flow rate, and realizes precise control of the cooling channels, thereby improving the accuracy of the fluid flow rate analysis status of the cooling channels.

[0076] Furthermore, after the fluid flow rate is regulated, the fluid flow rate analysis of the next preset time period is performed to determine whether the fluid flow rate regulation is performed. The specific steps are as follows: Step 1, obtain external influence data to obtain the fluid influence factor and obtain the fluid influence factor of the previous preset time period; Step 2, perform fluid influence factor proportional control on the fluid influence factor of the previous preset time period to obtain the fluid influence weight factor of the current preset time period. The fluid influence factor proportional control is used to adjust the degree of influence of the fluid influence weight factor on the fluid influence factor; Step 3, perform fluid flow rate analysis based on the obtained fluid influence weight factor to determine whether the fluid flow rate regulation is performed; Step 4, if the fluid flow rate regulation is performed, the fluid influence factor is performed. Proportional control is performed until the number of proportional controls of the fluid influence factor reaches the preset number of controls. If the preset number of controls is reached and the fluid flow rate is controlled, a pipeline operation and maintenance prompt is performed. If the preset number of controls is not reached and the fluid flow rate is not controlled, the external influence data of the next preset time period is obtained. The pipeline operation and maintenance prompt indicates that the cooling channel operation and maintenance personnel are prompted to operate and maintain the cooling channel of the two-way fluid cooling system. If the preset number of controls is not reached and the fluid flow rate is controlled, the fluid influence factor proportional control is performed. If the fluid flow rate is not controlled without exceeding the preset number of controls, the external influence data of the next preset time period is obtained. Step five: if the fluid flow rate is not controlled, the external influence data of the next preset time period is obtained.

[0077] In this embodiment, the preset number of adjustments is specifically set by the preset staff according to the working conditions of the two-way fluid cooling system; and by limiting the preset number of adjustments, it is ensured that the system will not have an infinite loop phenomenon; at the same time, by introducing the fluid influencing factor, the influence of objective factors such as the fluid's own characteristics and the external environment on the uneven fluid flow rate is more fully considered, so as to timely analyze the impact on the two-way fluid cooling system and make corresponding corrections, thereby enabling the cooling system to adapt to changes in the external environment and fluid characteristics, and perform corresponding fluid flow rate regulation, thereby ensuring the cooling effect of the two-way fluid cooling system.

[0078] Furthermore, proportional control of the fluid influence factor is performed, and the specific process is as follows: proportional control processing is performed on the fluid influence factor and the fluid influence factor of the previous preset time period to obtain the weight change ratio, and the proportional control processing is used to quantify the degree of change of the fluid influence factor as the preset time period changes; the weight change ratio and the fluid influence weight factor are dynamically controlled to obtain the fluid influence weight factor of the current preset time period, and the dynamic control processing is used to adjust the degree of influence of the fluid influence weight factor on the fluid influence factor.

[0079] In this embodiment, the proportional control processing specifically represents a deviation operation between the fluid influence factor and the fluid influence factor of the previous preset time period, specifically using the fluid influence factor of the previous preset time period as the denominator, and the difference between the fluid influence factor and the fluid influence factor of the previous preset time period as the numerator; the dynamic control processing specifically represents a multiplication operation of the weight change ratio and the fluid influence weight factor. Through the embodiment of the present application, the fluid influence weight factor that changes with the preset time period is obtained, thereby using the change of the fluid over time to obtain a more accurate fluid flow rate analysis index.

[0080] To summarize, the embodiment of the present application obtains the fluid flow rate influence data by acquiring the external influence data of the bidirectional fluid heat dissipation system within a preset time period, and determines whether to perform the first fluid flow rate regulation, and then obtains the flow rate data and fluid influence factor of each cooling channel monitoring point to obtain the fluid flow rate analysis index, and determines whether to perform the second fluid flow rate regulation. Finally, after the fluid flow rate regulation is performed, the fluid flow rate analysis of the next preset time period is performed to determine whether to perform fluid flow rate regulation, thereby more timely evaluating the fluid flow rate condition of the cooling channel, and further achieving more accurate monitoring of the uniformity of the fluid flow rate in the cooling channel, effectively solving the problem of inaccurate control of the uniformity of heat distribution in the fluid pipeline in the prior art.

[0081] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0085] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0086] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. High reliability switchgear with adaptive intelligent insulation, characterized in that, include: Adaptive insulation systems, biomimetic porous structure shells, bidirectional fluid heat dissipation systems, self-optimizing operating systems based on deep reinforcement learning, fiber optic sensor networks, modular structures, electromagnetic locking mechanisms, and blockchain-based distributed control systems; The adaptive insulation system is used to adjust insulation performance in real time according to environmental conditions and load changes; The bionic porous structure shell is designed using bionic principles to improve insulation effect and reduce equipment weight; The bidirectional fluid cooling system includes a microchannel approach and intelligent flow control devices to achieve precise temperature management; The optical fiber sensing network is used to achieve all-round, high-precision status monitoring and fault early warning.

2. High reliability switchgear integrated control system with adaptive intelligent insulation, characterized by: include: Fluid influencing factor analysis module, fluid flow rate analysis module and fluid flow rate control module: The fluid impact factor analysis module is used to obtain the fluid flow rate impact data by acquiring the external impact data of the bidirectional fluid heat dissipation system within a preset time period, and determine whether to perform the first fluid flow rate control; The fluid flow rate analysis module is used to obtain the flow rate data and fluid influencing factors of each cooling channel monitoring point to obtain a fluid flow rate analysis index and determine whether to perform the second fluid flow rate control; The fluid flow rate control module is used to perform fluid flow rate analysis in the next preset time period after performing fluid flow rate control to determine whether to perform fluid flow rate control. The fluid flow rate control includes first fluid flow rate control and second fluid flow rate control.

3. The high-reliability switchgear integrated control system with adaptive intelligent insulation according to claim 2, characterized in that: The fluid flow rate influencing data includes noise data and fluid influencing factors, the noise data includes noise intensity and noise frequency, and the fluid influencing factors include fluid characteristic change influencing factors and external environment influencing factors; The specific process of determining whether to perform flow rate control of the first fluid is as follows: Acquire reference noise data from a preset database, wherein the reference noise data includes a noise intensity threshold and a reference noise frequency range; Compare the noise intensity with the noise intensity threshold, and if the noise intensity is not greater than the noise intensity threshold, directly execute the fluid flow rate analysis module; If the noise intensity is greater than the noise intensity threshold, regulating the flow rate of the first fluid based on a result of comparing the noise frequency with a reference noise frequency range; The first fluid flow rate control includes increasing the valve opening and decreasing the valve opening; The method of increasing the valve opening is to control the noise frequency data within the low-frequency noise range; The reduction of the valve opening is a control method for keeping the noise frequency data within the high-frequency noise range.

4. The high-reliability switchgear integrated control system with adaptive intelligent insulation according to claim 3, characterized in that: The specific process of obtaining the fluid impact factor is as follows: Acquiring external influence data, the external influence data including noise data, fluid characteristic data, and external environment data, the fluid characteristic data including fluid density and fluid viscosity, and the external environment data including ambient wind speed and ambient temperature; Acquire reference flow velocity impact data from a preset database, wherein the reference flow velocity impact data includes a reference fluid characteristic range and a wind speed limit; Obtaining a fluid impact weight factor from a preset database, wherein the fluid impact weight factor includes a fluid property weight factor and an external environment weight factor, wherein the fluid property weight factor includes a fluid density weight factor and a fluid viscosity weight factor, and the external environment weight factor includes an ambient wind speed weight factor and an ambient temperature weight factor; Performing a fluid property anomaly analysis on the fluid property data and the corresponding reference fluid property range to obtain a fluid property anomaly assessment value, wherein the fluid property anomaly analysis is used to quantify the degree of anomaly in the fluid property change; Combine the fluid property abnormality assessment value and the corresponding fluid property weight factor to perform comprehensive fluid property impact processing to obtain a fluid property impact factor. The comprehensive fluid property impact processing is used to comprehensively quantify the degree of influence of fluid density and fluid viscosity on fluid flow rate based on the fluid property weight factor. The fluid property impact factor is used to quantify the degree of influence of fluid property changes on fluid flow rate. Performing wind speed deviation processing on the ambient wind speed and the corresponding wind speed limit to obtain a wind speed deviation value, wherein the wind speed deviation processing is used to quantify the difference between the ambient wind speed and the wind speed limit; The wind speed deviation value and ambient temperature are combined with the corresponding external environment weight factor to perform comprehensive external environment impact processing to obtain the external environment impact factor. The external environment comprehensive impact processing is used to comprehensively quantify the degree of influence of ambient temperature and ambient wind speed on heat dissipation performance according to the external environment weight factor. The external environment impact factor is used to quantify the degree of influence of external environment changes on heat dissipation performance.

5. The high-reliability switchgear integrated control system with adaptive intelligent insulation according to claim 2, characterized in that: The flow velocity data includes the flow rate at the monitoring point, the flow velocity at the monitoring point, the symmetry of the flow profile at the monitoring point, the energy loss at the monitoring point, and the turbulence intensity at the monitoring point; The symmetry of the flow profile at the monitoring point indicates that the flow velocity distribution diagram in the cooling channel is obtained by multi-point flow velocity sensors to determine whether there is uneven flow velocity; The flow rate data is obtained by acquiring data collected from all monitoring points in each cooling channel at all preset moments within a preset time period and performing data processing. The data processing means obtaining data reflecting the data situation within the preset time period by processing each instantaneous data.

6. The high-reliability switchgear integrated control system with adaptive intelligent insulation according to claim 5, characterized in that: The flow rate data and fluid influencing factors of each cooling channel monitoring point are obtained to obtain the fluid flow rate analysis index, and the specific steps are as follows: Obtaining reference weights from a preset database, the reference weights including influencing factor weights and flow velocity data analysis weights, the influencing factor weights including fluid weights and external weights, and the flow velocity data analysis weights including flow weights, flow velocity weights, flow profile symmetry weights, energy loss weights, and turbulence intensity weights; Performing influence factor correction difference calculation on the fluid characteristic influence factor and the external environment influence factor respectively to obtain corresponding fluid influence correction value, wherein the influence factor correction difference calculation is used to quantify the correction degree of the fluid influence factor; The fluid impact correction value is combined with the corresponding impact factor weight to perform an impact factor comprehensive quantification operation to obtain a comprehensive impact factor, wherein the impact factor comprehensive quantification operation is used to comprehensively quantify the degree of influence of the fluid characteristic impact factor and the external environment impact factor on the fluid flow rate; Performing comprehensive flow processing based on the flow velocity data and the corresponding flow velocity data analysis weights to obtain a flow velocity analysis value, wherein the comprehensive flow velocity processing is used to comprehensively quantify the fluid flow velocity reflected by the flow velocity data; Performing a comprehensive fluid flow velocity processing on the comprehensive influencing factor and the flow velocity analysis value to obtain a fluid flow velocity analysis index, wherein the comprehensive fluid flow velocity processing is used to comprehensively quantify the specific condition of the fluid flow velocity in the corresponding cooling channel while taking into account the correction of the fluid influencing factor; The fluid flow rate analysis index represents data that comprehensively quantifies the specific conditions of the fluid flow rate in the cooling channel.

7. The high-reliability switchgear integrated control system with adaptive intelligent insulation according to claim 6, characterized in that: The specific process of determining whether to perform second fluid flow rate regulation is as follows: Obtaining fluid flow rate analysis indices of all cooling channels to obtain a fluid flow rate analysis set, and performing set data processing on the fluid flow rate analysis set to obtain a set variance, wherein the set data processing represents performing a variance operation on the fluid flow rate analysis set; If the aggregate variance is not greater than the reference aggregate variance, then obtain the external impact data for the next preset time period; If the set variance is greater than the reference set variance, a second fluid flow rate control is performed, wherein the second fluid flow rate control means taking different segmented control measures for different cooling channels according to the fluid flow rate analysis data of each cooling channel and the reference set variance.

8. The high-reliability switchgear integrated control system with adaptive intelligent insulation according to claim 7, characterized in that: Different segmented control measures are adopted for different cooling channels, and the specific process is as follows: Obtaining a reference set value according to the reference set variance, and obtaining a set mean of the fluid velocity analysis set; Perform deviation processing on each cooling channel and the ensemble mean to obtain the flow velocity deviation value of the corresponding cooling channel; If the flow rate deviation value is not greater than the reference set value, the corresponding cooling channel is recorded as a uniform channel and no segmented control measures are taken; If the flow rate deviation value is greater than the reference set value, the corresponding cooling channel is recorded as a deviated channel, and control measures are taken; The specific steps of taking regulatory measures are: All deviated channels are distinguished by pipe shape. If the cooling channel does not have an elbow and the cooling channel exceeds the preset length, the cooling channel is segmented in sequence according to the preset length from the fluid inlet and the segmented positions are marked as valve position deployment points; If there is an elbow in the cooling channel, mark the valve position deployment points at both ends of the elbow.

9. The high-reliability switchgear integrated control system with adaptive intelligent insulation according to claim 2, characterized in that: After the fluid flow rate is regulated, the fluid flow rate analysis is performed in the next preset time period to determine whether to perform the fluid flow rate regulation. The specific steps are as follows: Step 1: Obtain external impact data to obtain a fluid impact factor and obtain the fluid impact factor of the previous preset time period; Step 2: Performing fluid influence factor proportional control on the fluid influence factor and the fluid influence factor of the previous preset time period to obtain the fluid influence weight factor of the current preset time period, wherein the fluid influence factor proportional control is used to adjust the degree of influence of the fluid influence weight factor on the fluid influence factor; Step 3: Analyze the fluid flow rate based on the obtained fluid impact weight factor to determine whether to perform fluid flow rate control; Step 4: If fluid flow rate regulation is performed, fluid influence factor proportional control is performed until the number of fluid influence factor proportional controls reaches the preset number of regulation times. If the preset number of regulation times is reached and fluid flow rate regulation is performed, pipeline operation and maintenance prompts are given. If the preset number of regulation times is not reached and fluid flow rate regulation is performed, fluid influence factor proportional control is performed. If fluid flow rate regulation is not performed without exceeding the preset number of regulation times, external influence data for the next preset time period is obtained.

10. The high-reliability switchgear integrated control system with adaptive intelligent insulation according to claim 9, characterized in that: The specific process of controlling the ratio of fluid influencing factors is as follows: Performing proportional control processing on the fluid impact factor and the fluid impact factor of the previous preset time period to obtain a weight change ratio, wherein the proportional control processing is used to quantify the degree of change of the fluid impact factor as the preset time period changes; The weight change ratio and the fluid influence weight factor are dynamically controlled to obtain the fluid influence weight factor of the current preset time period. The dynamic control process is used to adjust the influence degree of the fluid influence weight factor on the fluid influence factor.

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