Electrical cabinet condensation risk assessment method based on time-space characteristics and related equipment

Abstract

The invention belongs to the field of condensation prevention, and discloses an electrical cabinet condensation risk assessment method and related equipment based on time-space characteristics, and the method comprises the steps: collecting outdoor meteorological data of a transformer substation, data in a power distribution room, data in an inner wall cabinet of an electrical cabinet and cable trench data in real time, and outputting a condensation occurrence probability through a pre-constructed condensation occurrence probability model; according to the method, the limitation of response lag of a traditional dehumidification method is overcome, the weight can be dynamically adjusted according to the time-space characteristics at the current moment, and therefore the condensation risk index is calculated, and the condensation risk of the electrical cabinet is evaluated in time. According to the method, the condensation risk prevention capability is remarkably improved, electric power safety accidents caused by the condensation problem are effectively avoided, and the economic loss of electric power enterprises is reduced. The method is more flexible and efficient in energy consumption control, meets the requirements of energy conservation and emission reduction, can better adapt to diversified climate conditions of different areas, and is more remarkable in dehumidification effect especially in high-humidity areas or areas with large day and night temperature difference.

Description

Technical Field

[0001] The present invention belongs to the technical field of anti-condensation, and particularly relates to a method for evaluating the condensation risk of electrical cabinets based on spatio-temporal characteristics and related equipment. Background Art

[0002] With the booming development of the power industry, high-voltage switchgear, as an essential key equipment in the power system, has shown a sharp increase in its quantity. In important places such as substations and distribution rooms, high-voltage switchgear undertakes the important responsibilities of power distribution, control, and protection. Its stable and reliable operation plays a crucial role in ensuring the continuity and safety of power supply. However, during the actual operation process, high-voltage switchgear faces a severe problem - condensation. Condensation refers to the phenomenon that when the water vapor content in the air reaches the saturation state, it will condense into liquid water on the surface of an object when it encounters cold. Inside the high-voltage switchgear, the occurrence of condensation will cause serious damage to the equipment. On the one hand, condensation will lead to a decrease in electrical insulation performance, increasing the risk of electric leakage and short circuit, thus triggering equipment failures or even power outages, posing a huge threat to the safe and stable operation of the power system. On the other hand, long-term condensation will also accelerate the corrosion of metal components, shorten the service life of the equipment, increase the maintenance cost and replacement frequency of the equipment.

[0003] According to incomplete statistics, the number of power safety accidents caused by condensation problems has been on the rise in recent years, bringing huge economic losses to power enterprises. Traditional dehumidification methods, such as installing heaters or dehumidifiers inside the switchgear, although can alleviate the condensation problem to a certain extent, have many limitations. These methods are often controlled based on fixed thresholds and lack the ability to dynamically respond to environmental factors, and cannot adjust the dehumidification strategy in a timely manner according to different spatio-temporal conditions. For example, in the case of frequent weather changes and large humidity fluctuations, traditional methods may have a problem of lagging response, resulting in taking measures only after condensation has formed, and cannot effectively prevent the generation of condensation. In addition, traditional methods usually adopt the way of continuous full-power operation, with high energy consumption, which not only increases the operation cost but also does not meet the requirements of energy conservation and emission reduction. Moreover, due to the vast territory, the climate conditions in different regions vary significantly, and traditional methods are difficult to meet the diverse environmental needs. In some special climate conditions, such as high-humidity regions or regions with large day-night temperature differences, their dehumidification effects are often not satisfactory.

[0004] Thus, it can be seen that traditional anti-condensation measures have a phenomenon of lagging response in the case of frequent weather changes and large humidity fluctuations, and cannot timely evaluate the condensation risk, resulting in the inability to effectively prevent the occurrence of condensation in electrical cabinets. Summary of the Invention

[0005] The present invention provides a method and related equipment for evaluating the condensation risk of electrical cabinets based on spatio-temporal characteristics, so as to solve the technical problem that traditional anti-condensation measures have a phenomenon of response lag in the case of frequent weather changes and large humidity fluctuations, and cannot evaluate the condensation risk in a timely manner, resulting in the inability to effectively prevent the occurrence of condensation in electrical cabinets.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A method for evaluating the condensation risk of electrical cabinets based on spatio-temporal characteristics, including: Real-time collection of outdoor meteorological data of the substation, indoor data of the distribution room, data inside the inner wall of the electrical cabinet and cable trench data; Input the collected outdoor meteorological data of the substation, indoor data of the distribution room and data inside the inner wall of the electrical cabinet into a pre-constructed condensation occurrence probability model, and output the condensation occurrence probability; wherein the condensation occurrence probability model is constructed based on the outdoor meteorological data of the substation, indoor data of the distribution room, data inside the inner wall of the electrical cabinet and cable trench data; Based on the condensation occurrence probability and the spatio-temporal characteristic dynamic weight at the current moment, obtain the condensation risk index; the condensation risk index is used to evaluate the condensation risk of the electrical cabinet.

[0007] Further, the real-time collection of outdoor meteorological data of the substation, indoor data of the distribution room, data inside the inner wall of the electrical cabinet and cable trench data includes: Real-time collection of outdoor meteorological data of the substation, and the outdoor meteorological data of the substation includes wind direction, outdoor temperature, outdoor humidity, precipitation and outdoor dew point; Real-time collection of real-time indoor data of the distribution room, and the indoor data of the distribution room includes indoor temperature, indoor humidity, indoor dew point, indoor water content and indoor water content percentage; Real-time collection of data inside the inner wall of the electrical cabinet, and the data inside the inner wall of the electrical cabinet includes cabinet temperature, cabinet humidity and the internal temperature of the anti-condensation device; Real-time collection of cable trench data, and the cable trench data includes cable trench temperature, cable trench humidity, cable trench dew point, cable trench air velocity, cable trench water content and cable trench water content percentage.

[0008] Further, before inputting the collected outdoor meteorological data of the substation, indoor data of the distribution room and data inside the inner wall of the electrical cabinet into a pre-constructed condensation occurrence probability model and outputting the condensation occurrence probability, it further includes: Perform data preprocessing on the collected outdoor meteorological data of the substation, indoor data of the distribution room and data inside the inner wall of the electrical cabinet; the data preprocessing includes outlier filtering operation on the original data and time synchronization operation for the original data; the source data includes outdoor meteorological data of the substation, indoor data of the distribution room and data inside the inner wall of the electrical cabinet.

[0009] Further, inputting the collected outdoor meteorological data of the substation, the data in the distribution room, and the data in the inner cabinet of the electrical cabinet into the pre-constructed condensation occurrence probability model to output the condensation occurrence probability includes: Inputting the collected outdoor meteorological data of the substation, the data in the distribution room, and the data in the inner cabinet of the electrical cabinet into the pre-constructed condensation occurrence probability model; Calculating the dew point temperature based on the relative humidity inside the cabinet. The specific formula is as follows:

[0010] In the formula, represents the dew point temperature; , respectively represent preset parameters; represents the humidity inside the cabinet; The condensation occurrence probability is calculated using the following formula:

[0011] In the formula, represents the condensation occurrence probability; represents the temperature inside the cabinet; represents the temperature inside the cabinet; represents the precipitation; represents the air velocity in the cable trench; α, β, and γ respectively represent dynamic weight coefficients.

[0012] Further, obtaining the condensation risk index based on the condensation occurrence probability and the dynamic weights of the spatio-temporal characteristics at the current moment includes: Inputting the condensation occurrence probability and the dynamic weights of the spatio-temporal characteristics at the current moment into the condensation risk index calculation formula to obtain the condensation risk index; the dynamic weights of the spatio-temporal characteristics at the current moment include the weight of the equipment importance level and the weight of the environmental complexity; the specific calculation formula is as follows:

[0013] In the formula, R represents the condensation risk index; I device represents the equipment importance level; W1 represents the weight of the equipment importance level; W2 represents the weight of the environmental complexity; represents the environmental complexity.

[0014] Further, after obtaining the condensation risk index based on the condensation occurrence probability and the dynamic weights of the spatio-temporal characteristics at the current moment, it further includes: Evaluating the condensation risk of the electrical cabinet using the condensation risk index to obtain the evaluation result; the specific evaluation result is as follows: If the condensation risk index < the first risk threshold, it is determined that the electrical cabinet is in a low-risk state; If the first risk threshold ≤ condensation risk index < the second risk threshold, it is determined that the electrical cabinet is in a medium-risk state; If the condensation risk index ≥ the second risk threshold, it is determined that the electrical cabinet is in a high-risk state.

[0015] Further, after using the condensation risk index to evaluate the condensation risk of the electrical cabinet and obtaining the evaluation result, it further includes: Implementing a hierarchical control strategy according to the evaluation result; the hierarchical control strategy includes: If the electrical cabinet is in a low-risk state, ventilation measures are adopted; If the electrical cabinet is in a medium-risk state, ventilation and intermittent dehumidification measures are adopted; If the electrical cabinet is in a high-risk state, full-power dehumidification and full-power heating measures are adopted.

[0016] An electrical cabinet condensation risk assessment system based on spatio-temporal characteristics, including: A data acquisition module for real-time acquisition of outdoor substation meteorological data, indoor substation data, data inside the inner wall of the electrical cabinet, and cable trench data; A probability prediction module for inputting the acquired outdoor substation meteorological data, indoor substation data, and data inside the inner wall of the electrical cabinet into a pre-constructed condensation occurrence probability model and outputting the condensation occurrence probability; wherein the condensation occurrence probability model is constructed based on outdoor substation meteorological data, indoor substation data, data inside the inner wall of the electrical cabinet, and cable trench data; A condensation risk calculation module for obtaining a condensation risk index based on the condensation occurrence probability and the spatio-temporal characteristic dynamic weight at the current moment; the condensation risk index is used to evaluate the condensation risk of the electrical cabinet.

[0017] A device, including: A memory for storing a computer program; A processor for implementing the steps of the above-mentioned electrical cabinet condensation risk assessment method based on spatio-temporal characteristics when executing the computer program.

[0018] A computer-readable storage medium storing a computer program, and the computer program is used to implement the steps of the above-mentioned electrical cabinet condensation risk assessment method based on spatio-temporal characteristics when executed by a processor.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for evaluating the condensation risk of electrical cabinets based on spatio-temporal characteristics. This method collects real-time outdoor meteorological data of substations, indoor data of distribution rooms, data inside the inner wall of electrical cabinets, and cable trench data, and uses a pre-constructed condensation occurrence probability model to output the condensation occurrence probability. This not only overcomes the limitation of the lagging response of traditional dehumidification methods but also can dynamically adjust the weights according to the spatio-temporal characteristics at the current moment, thereby calculating the condensation risk index and realizing the timely evaluation of the condensation risk of electrical cabinets. This method significantly improves the prevention ability of condensation risk, effectively avoids power safety accidents caused by condensation problems, and reduces the economic losses of power enterprises. At the same time, compared with the traditional dehumidification method of continuous full-power operation, this method is more flexible and efficient in energy consumption control, meets the requirements of energy conservation and emission reduction, and can better adapt to the diverse climate conditions in different regions. Especially in high-humidity regions or regions with large temperature differences between day and night, its dehumidification effect is more significant.

[0020] Preferably, in the present invention, the outdoor meteorological data of the substation includes wind direction, outdoor temperature, outdoor humidity, precipitation, and outdoor dew point; the indoor data of the distribution room includes indoor temperature, indoor humidity, indoor dew point, indoor water content, and indoor water content percentage; the data inside the inner wall of the electrical cabinet includes the temperature inside the cabinet, the humidity inside the cabinet, and the temperature inside the dew condensation removal device; the cable trench data includes cable trench temperature, cable trench humidity, cable trench dew point, cable trench air velocity, cable trench water content, and cable trench water content percentage. The above data provides a rich information basis for the condensation risk assessment. By accurately collecting these data, the actual conditions of the electrical cabinet and its surrounding environment can be more accurately reflected, thereby improving the accuracy and reliability of the condensation risk assessment.

[0021] Preferably, in the present invention, data preprocessing, including outlier filtering and time synchronization operations, is performed before the data is input into the model, which can effectively avoid the influence of incorrect data on the model prediction results and improve the prediction accuracy. At the same time, the time synchronization operation also ensures the real-time and consistency of the data, providing reliable data support for the dynamic assessment of the condensation risk.

[0022] Preferably, in the present invention, by introducing the dew point temperature calculation and the condensation occurrence probability formula, this method can quantitatively evaluate the possibility of condensation occurrence based on the real-time collected data. This quantitative evaluation method is more accurate and scientific than the traditional qualitative evaluation method and can provide more powerful support for formulating effective preventive measures.

[0023] Preferably, in the present invention, based on the condensation occurrence probability, combined with the spatio-temporal characteristic dynamic weights at the current moment (such as the equipment importance level and the environmental complexity), this method can more comprehensively evaluate the condensation risk of the electrical cabinet. This way of comprehensively considering various factors makes the evaluation results more accurate and comprehensive, which helps to formulate more targeted preventive measures.

[0024] Preferably, in the present invention, by setting different risk thresholds, the method can divide the condensation risk of the electrical cabinet into three levels: low risk, medium risk, and high risk. This hierarchical evaluation method helps to take different preventive measures for electrical cabinets with different risk levels, thereby more effectively reducing the incidence of condensation accidents.

[0025] Preferably, in the present invention, by implementing a hierarchical control strategy according to the condensation risk assessment results, precise control of the condensation risk of the electrical cabinet can be achieved. This hierarchical control strategy can not only effectively prevent the occurrence of condensation accidents, but also reduce energy consumption and operating costs on the premise of ensuring safety, meeting the requirements of energy conservation and emission reduction. At the same time, this strategy also provides clear operation guidance for the operation and maintenance personnel of the power system, improving work efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flowchart of a method for evaluating the condensation risk of an electrical cabinet based on spatio-temporal characteristics provided by the present invention; Figure 2 It is a schematic structural diagram of a system for evaluating the condensation risk of an electrical cabinet based on spatio-temporal characteristics provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] To further understand the content of the present invention, the following will describe the present invention in detail with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and not for limiting it.

[0028] As mentioned in the background art: For the anti-condensation problem of switch cabinets (electrical cabinets), the existing dehumidification methods are easily affected by seasonal changes, day-night temperature differences, and the variability of the external environment, and it is difficult to meet the requirements of the current complex and changing power operation environment; it is impossible to accurately predict the time of condensation occurrence, and it often starts after the condensation occurs. Since it takes some time to remove the condensation, there are still significant safety hazards before it is completely removed; in addition, because the end time cannot be predicted after starting, it leads to long-term full-power operation, increasing energy consumption and a series of other problems; moreover, in places with a vast territory, the environmental climate differences are significant, and the time of the high-incidence period of condensation is also different accordingly. In some places, it is in a high-incidence state of condensation all year round, while in some areas, condensation only occurs under specific circumstances. In addition, even in the same area, at different spatial positions, such as the high-incidence area and the low-incidence area of condensation, there are obvious differences in the time, frequency, and degree of condensation occurrence; due to the complexity of this spatio-temporal characteristic, it is difficult for traditional dehumidification methods to comprehensively and effectively solve the problem.

[0029] Embodiment 1 To solve the above problems, this embodiment provides a method for evaluating the condensation risk of electrical cabinets based on spatio-temporal characteristics. This method is applicable to the condensation monitoring and prevention of high-voltage switch cabinets in places such as substations and distribution rooms. It realizes dynamic anti-condensation strategies through multi-dimensional environmental parameter fusion and intelligent algorithms. This method realizes real-time monitoring and intelligent analysis of the environmental parameters inside the switch cabinet through digital technology, automatically adjusts the dehumidification strategy according to spatio-temporal changes, and ensures that condensation can be effectively prevented at any time and any place. This not only improves the dehumidification efficiency and reliability of the switch cabinet, but also greatly reduces the risk of power safety accidents caused by condensation, which is of great significance and necessity for ensuring the safe and stable operation of the power system.

[0030] Collect real-time outdoor meteorological data of the substation, indoor data of the distribution room, data inside the inner wall of the electrical cabinet, and data of the cable trench. Input the collected outdoor meteorological data of the substation, indoor data of the distribution room, and data inside the inner wall of the electrical cabinet into a pre-constructed condensation occurrence probability model to output the condensation occurrence probability; wherein the condensation occurrence probability model is constructed based on the outdoor meteorological data of the substation, indoor data of the distribution room, data inside the inner wall of the electrical cabinet, and data of the cable trench. Obtain the condensation risk index based on the condensation occurrence probability and the dynamic weight of the spatio-temporal characteristics at the current moment; the condensation risk index is used to evaluate the condensation risk of the electrical cabinet.

[0031] In this embodiment, the more specific steps are as follows: The first step is multi-source data collection.

[0032] In order to comprehensively and accurately grasp the environmental information of the electrical cabinet, this embodiment adopts the method of multi-source data collection, deploys various types of sensors at different positions, and obtains relevant environmental parameters in real time. The specific layout is as follows: 1.1. Outdoor meteorological six-element sensors of the substation.

[0033] Deploy meteorological six-element sensors outdoors in the substation, including wind speed, wind direction, outdoor temperature, outdoor humidity, outdoor precipitation, and outdoor dew point. These sensors are connected to the central server through RS485 interfaces, and the data update frequency is 1 time / minute.

[0034] In this embodiment, wind speed is one of the important factors affecting condensation in electrical cabinets. When the wind speed is relatively high, under weather conditions with high humidity, it will accelerate the air flow speed, making it easier for the humid outdoor air to enter the interior of the electrical cabinet, thus increasing the risk of condensation. The wind direction determines the direction of moisture intrusion into the electrical cabinet. If the wind direction points to the ventilation openings or gaps of the electrical cabinet, then moisture is more likely to enter the cabinet, increasing the possibility of condensation. The ambient temperature has a direct impact on the formation of condensation. When the outdoor temperature is relatively low, a large temperature difference is formed with the temperature inside the electrical cabinet, and this temperature difference will cause water vapor to condense into dew on low-temperature parts such as the inner wall of the electrical cabinet. Humidity refers to the content of water vapor in the air. In a high-humidity environment, the water vapor in the air is more likely to reach the saturation state, and once it encounters the surface of a low-temperature object, it will quickly condense into liquid water. The increase in precipitation will significantly increase the humidity of the outdoor air, thereby affecting the humidity environment inside the electrical cabinet. The outdoor dew point temperature reflects the temperature at which the air reaches the saturation state under the current temperature and humidity conditions. When the outdoor dew point temperature is lower than the temperature inside the electrical cabinet, if outdoor air enters the electrical cabinet, as the temperature decreases, the water vapor in the air may reach the saturation state and form condensation.

[0035] 1.2. Five-element sensors inside the substation in the distribution room.

[0036] Install five-element sensors inside the substation on the walls or ceilings of the distribution room to measure the indoor temperature, indoor humidity, indoor dew point, indoor water content, and indoor water content percentage. These sensors are connected to the anti-condensation device with an edge computing terminal using the Lora communication method, and the data update frequency is 1 time per 30 seconds.

[0037] The indoor temperature has an important impact on the saturated water vapor content of the air inside the electrical cabinet. At a relatively low temperature, the saturated water vapor content of the air is relatively low and it is easier to reach the saturation state, thus increasing the possibility of condensation. Humidity directly reflects the humidity of the air in the distribution room. In a high-humidity environment, the water vapor inside the electrical cabinet is more likely to reach saturation and condense into dew. The dew point is one of the key indicators for judging whether condensation will occur. When the indoor dew point temperature is higher than the temperature of some parts inside the electrical cabinet, water vapor will condense into liquid water on these low-temperature parts. The water content represents the mass of water vapor contained in a unit volume of air, and the water content percentage refers to the percentage of the water vapor mass in the total air mass. High water content and high water content percentage both indicate that the air humidity is relatively high, increasing the risk of condensation.

[0038] 1.3. Three-element sensors inside the electrical cabinet on the inner wall of the cabinet.

[0039] Install three-element sensors inside the electrical cabinet on the inner wall of the cabinet to measure the ambient temperature, ambient humidity, and the temperature inside the anti-condensation device. These sensors are connected to the anti-condensation device with an edge computing terminal through the RS485 interface, and the data update frequency is 1 time per 10 seconds.

[0040] The ambient temperature inside the cabinet is one of the important factors determining whether condensation will occur. The low-temperature parts inside the cabinet, such as the surfaces of metal components and insulating materials, are often the main areas where condensation occurs. The ambient humidity reflects the humidity of the air inside the electrical cabinet. In a high-humidity environment, water vapor is more likely to become saturated and condense. The internal temperature of the anti-condensation device can reflect the working state of the device. If the internal temperature of the device is too high or too low, it may affect its dehumidification effect and then affect the humidity environment inside the cabinet.

[0041] 1.4 Cable trench six-parameter sensor Deploy six-parameter sensors for the cable trench at the entrance and middle of the cable trench to measure the cable trench temperature, cable trench humidity, cable trench dew point, air velocity, water content in the cable trench, and percentage of water content in the cable trench. These sensors are connected to the anti-condensation device with an edge computing terminal through the RS485 interface, and the data update frequency is 1 time per 15 seconds.

[0042] The cable trench temperature affects the air temperature entering the electrical cabinet. If the cable trench temperature is low, when the air entering the electrical cabinet mixes with the high-temperature air inside the cabinet, it may cause a local temperature drop and increase the risk of condensation. Humidity reflects the humidity of the air in the cable trench. After the high-humidity cable trench air enters the electrical cabinet, it will increase the humidity inside the cabinet and easily cause condensation. The cable trench dew point temperature is also an important indicator for judging whether condensation will occur. When the cable trench dew point temperature is lower than the temperature inside the electrical cabinet, condensation may form after the cable trench air enters the electrical cabinet. The air velocity affects the transfer speed of moisture in the cable trench. If the air velocity is large, the moisture will enter the electrical cabinet faster and accelerate the formation of condensation. The water content and percentage of water content further reflect the humidity condition of the air in the cable trench. High water content and high percentage of water content indicate that the cable trench air has high humidity and is more likely to enter the electrical cabinet and cause condensation.

[0043] The specific sensor deployment method is as follows: Further, the meteorological six-parameter sensor is installed outdoors in the substation and is connected to the central server through RS485.

[0044] Further, the five-parameter sensor inside the station is connected to the anti-condensation device (with an edge computing terminal) through the Lora communication method.

[0045] Further, the three-parameter sensor inside the cabinet is connected to the anti-condensation device (with an edge computing terminal) through RS485.

[0046] Further, the six-parameter sensor for the cable trench is connected to the anti-condensation device (with an edge computing terminal) through RS485.

[0047] Furthermore, the central server includes a set of monitoring applications running in a virtual machine image, which remotely operate and control the remote devices through VNC technology.

[0048] Furthermore, data is transmitted between the condensation device (with an edge computing terminal) and the central server through the APOLLOMQTT protocol.

[0049] Furthermore, the central server continuously improves and optimizes the condensation generation mechanism algorithm through self-learning, and obtains accurate results for all electrical cabinets through big data analysis.

[0050] Furthermore, all data is viewed and analyzed in real time through the management interface.

[0051] After the above sensor deployment is completed, the outdoor meteorological data of the substation, the data in the distribution room, the data inside the inner wall of the electrical cabinet, and the data in the cable trench are collected in real time. Among them, the outdoor meteorological data of the substation includes wind direction, outdoor temperature, outdoor humidity, precipitation, and outdoor dew point; the data in the distribution room includes indoor temperature, indoor humidity, indoor dew point, indoor water content, and indoor water content percentage; the data inside the inner wall of the electrical cabinet includes cabinet temperature, cabinet humidity, and the temperature inside the condensation removal device; the data in the cable trench includes cable trench temperature, cable trench humidity, cable trench dew point, cable trench air velocity, cable trench water content, and cable trench water content percentage; the above data provides a rich information basis for the condensation risk assessment. By accurately collecting these data, the actual conditions of the electrical cabinet and its surrounding environment can be more accurately reflected, thereby improving the accuracy and reliability of the condensation risk assessment.

[0052] Step 2: Data preprocessing.

[0053] The collected raw data may have problems such as noise and outliers. To ensure the accuracy of subsequent analysis, the data needs to be preprocessed.

[0054] 2.1. Outlier data removal.

[0055] The sliding window method is used to process the collected data, and the window size is set to 10. The sliding window method is a commonly used method for processing time series data. It slides a fixed-size window over the data sequence and performs statistical analysis on the data within the window to identify outlier data. Specifically, for the data within each window, its mean and standard deviation are calculated. If the deviation of a data point from the mean of the data within the window exceeds a certain threshold (usually 3 times the standard deviation), then this data point is considered outlier data and is removed. In this way, abnormal data caused by sensor failures, interference, etc. can be effectively filtered out, improving the quality of the data.

[0056] 2.2. Timestamp alignment.

[0057] Since the data acquisition frequencies and times of different sensors may vary, in order to ensure the temporal consistency of multi-source data, it is necessary to align the timestamps of the data through NTP (Network Time Protocol). NTP is a protocol used to synchronize clocks in a computer network, which can keep the clocks of different devices in precise synchronization. Through the NTP protocol, the timestamps of the data collected by all sensors are uniformly adjusted to the same time standard, ensuring that the data from different sensors can be compared and processed in the same time dimension during subsequent analysis and calculations.

[0058] Step 3: Dew point temperature calculation: The dew point temperature inside the electrical cabinet is one of the important parameters for judging whether condensation will occur. Based on the collected relative humidity inside the cabinet The dew point temperature is calculated through the following formula :

[0059] In the formula, represents the dew point temperature; a and b respectively represent preset parameters; represents the humidity inside the cabinet; In this embodiment, a = 17.27; b = 237.7; These two parameters are determined based on empirical formulas and are applicable to the temperature range of 0~60°C; This formula is based on physical principles and takes into account the influence of the relative humidity inside the cabinet on the dew point temperature. The higher the relative humidity, the closer the dew point temperature is to the current temperature, indicating that the air is closer to the saturated state and the possibility of condensation is greater. By calculating the dew point temperature, key parameters can be provided for subsequent calculation of the condensation probability.

[0060] Step 4: Calculate the condensation occurrence probability.

[0061] The condensation occurrence probability is calculated using the condensation mechanism mathematical model, and the specific formula is as follows:

[0062] In the formula, represents the condensation occurrence probability; represents the temperature inside the cabinet; represents the temperature inside the cabinet; represents the precipitation; represents the air velocity in the cable trench; α, β, and γ respectively represent dynamic weight coefficients; The above dynamic weight coefficients are determined by fitting historical data, and the default values are α = 0.6, β = 0.3, and γ = 0.1 respectively.

[0063] In this embodiment, the pre - constructed condensation probability model comprehensively considers the influence of environmental factors in multiple dimensions on the condensation phenomenon. The difference between the cabinet temperature and the dew point temperature reflects the degree of air unsaturation. The smaller the difference, the closer the air is to the saturated state, and the greater the possibility of condensation. The air flow velocity in the cable trench affects the transfer speed of moisture. The greater the flow velocity, the easier it is for moisture to enter the electrical cabinet, increasing the risk of condensation. The increase in precipitation will cause the outdoor humidity to rise, thereby affecting the humidity environment inside the electrical cabinet. The influence of precipitation on the condensation probability is described by a logarithmic function to describe the influence of precipitation on the condensation probability. The outdoor temperature is used as the denominator to adjust the entire probability value, considering the comprehensive influence of the outdoor environmental temperature on the condensation phenomenon. By inputting relevant parameters, the possibility of condensation occurrence can be predicted, providing a basis for subsequent risk assessment and preventive measures.

[0064] Step 5: Calculate the condensation risk index.

[0065] In this embodiment, in order to more comprehensively evaluate the harm degree of condensation to the electrical cabinet, it is necessary to calculate the condensation risk index. The calculation formula is as follows:

[0066] In the formula, R represents the condensation risk index; I device represents the equipment importance level; W1 represents the weight of the equipment importance level; W2 represents the weight of the environmental complexity; represents the environmental complexity, which is calculated based on factors such as temperature and humidity fluctuations, wind speed variance, etc.; by default, W1 = 0.7 and W2 = 0.3.

[0067] Among them, the equipment importance level is divided into levels 1 - 5, which reflects the value and criticality of this electrical cabinet in the power system. For some important electrical cabinets, such as the main transformer switch cabinet, bus switch cabinet, etc., their equipment importance levels are relatively high. Once a condensation fault occurs, it may have a serious impact on the safe and stable operation of the entire power system. The environmental complexity reflects the degree of influence of environmental factors on the condensation risk. Under environmental conditions with large temperature and humidity fluctuations and large wind speed variance, the occurrence of condensation is more difficult to predict and control, so the environmental complexity is higher. By combining the condensation occurrence probability with the equipment importance and environmental complexity, the condensation risk can be comprehensively evaluated, providing a scientific basis for formulating reasonable preventive measures.

[0068] In this embodiment, it also includes setting different warning thresholds according to the obtained condensation risk index and taking corresponding warning and preventive measures. The specific hierarchical control strategy is as follows: 6.1 Low - risk situation When the condensation risk index R < 0.3, it indicates that the electrical cabinet is in a low-risk state. At this time, only the ventilation measure is started, and the ventilation wind speed is set to ≤ 2 m / s. Ventilation can promote the air exchange inside and outside the electrical cabinet, reduce the humidity inside the cabinet, and reduce the formation of condensation. In the low-risk situation, adopting a lower ventilation wind speed can reduce energy consumption while ensuring the dehumidification effect.

[0069] 6.2 Medium-risk situation When 0.3 ≤ condensation risk index R < 0.7, the electrical cabinet is in a medium-risk state. At this time, the measures of ventilation + intermittent dehumidification are taken, and the duty cycle of intermittent dehumidification is set to 50%. Ventilation can continue to promote air exchange, and intermittent dehumidification can further reduce the humidity inside the cabinet on the basis of ventilation. A duty cycle of 50% means that the dehumidification equipment works for 50% of the time and stops working for 50% of the time, which can reduce the energy consumption of the equipment while ensuring the dehumidification effect.

[0070] 6.3 High-risk situation When the condensation risk index R ≥ 0.7, the electrical cabinet is in a high-risk state. At this time, the measures of full-power dehumidification + heating are executed, and the temperature inside the electrical cabinet is raised to the dew point + 2°C. Full-power dehumidification can quickly reduce the humidity inside the cabinet, and heating can increase the temperature inside the cabinet, making the air far from the saturated state, thus effectively preventing the formation of condensation. Raising the temperature to the dew point + 2°C can ensure that the air inside the cabinet has sufficient unsaturation and reduce the possibility of condensation.

[0071] 6.4 Energy-saving optimization In order to achieve the energy-saving goal, during the low-load period at night (such as 22:00 - 6:00), the power of the dehumidification equipment is automatically reduced to 70% of the normal power. At night, the load of the power system is low, and the heat generation inside the electrical cabinet is relatively small. At this time, the risk of condensation is relatively low. By reducing the power of the dehumidification equipment, the energy consumption can be reduced while ensuring the basic dehumidification effect, and the energy utilization efficiency can be improved.

[0072] The specific control strategies implemented above are shown in Table 1: Table 1 is the anti-condensation control strategy table

[0073] The above condensation risk index thresholds can also be dynamically adjusted according to environmental patterns divided by seasons and day and night.

[0074] It can be seen that this embodiment provides a method for evaluating the condensation risk of electrical cabinets based on spatio-temporal characteristics. By fusing spatio-temporal characteristics, the spatio-temporal dynamic weights are dynamically adjusted, that is, the time dimension and the space dimension; that is, the environmental mode is divided according to seasons and day and night, and the anti-condensation threshold is dynamically adjusted; and a spatial coupling model is established by combining multi-layer parameters of meteorology, within the station, inside the cabinet and cable trenches; then, by deploying sensors for six meteorological elements (wind speed, wind direction, temperature, humidity, precipitation, dew point), five elements within the station (temperature, humidity, etc.), three elements inside the cabinet and six elements of the cable trench, full-scenario coverage is achieved; finally, by constructing a mathematical model of the condensation mechanism, risk prediction is realized based on the collaboration of edge computing and cloud computing; at the same time, the anti-condensation measures such as dehumidification, heating, and ventilation are dynamically adjusted to balance safety and energy consumption.

[0075] In addition, a method for evaluating the condensation risk of electrical cabinets based on spatio-temporal characteristics provided by this embodiment further includes: Step 7, Feedback and optimization.

[0076] According to the actual prevention effect, such as whether condensation is effectively controlled, the energy consumption of equipment operation, etc., the model parameters and equipment parameters are adjusted by feedback.

[0077] 7.1, Adjustment of model parameters.

[0078] Adjust the dynamic weight coefficients α, β, and γ in the condensation occurrence probability model. If it is found during actual operation that the influence degree of certain factors on condensation is inconsistent with the model setting, these coefficients can be re-fitted by analyzing historical data to make the model more accurately reflect the actual situation. For example, if it is found that the air flow velocity in the cable trench has a greater influence on condensation than the model setting, the value of β can be appropriately increased.

[0079] 7.2, Adjustment of equipment parameters Adjust the parameters such as the dehumidification capacity, heating power, and ventilation volume of the dehumidification equipment. If it is found that the effect of a certain prevention measure is not ideal, the corresponding equipment parameters can be adjusted to improve the dehumidification effect. For example, if in a high-risk situation, the measure of full-power dehumidification + heating still cannot effectively control condensation, the dehumidification capacity can be appropriately increased or the heating power can be increased.

[0080] Through continuous feedback and optimization, the accuracy of prediction and prevention can be improved, ensuring the safe and stable operation of the electrical cabinet.

[0081] This embodiment provides a method for evaluating the condensation risk of electrical cabinets based on spatio-temporal characteristics, which has the following advantages: Adopting this method significantly improves the prediction accuracy, with the model accuracy rate > 95% (the traditional method < 85%); the energy consumption is optimized, and the energy consumption of equipment operation under the dynamic strategy is reduced by 18% - 30%; the adaptability is significantly enhanced, supporting automatic calibration of parameters across regions, with the false alarm rate < 5%, effectively solving the problems existing in the traditional anti-condensation method and ensuring the safe and reliable operation of the power system.

[0082] Apply the evaluation method provided in this embodiment to a specific scenario for implementation. The implementation process is as follows: First, application in the high-voltage switchgear of a certain substation.

[0083] Application background: This substation is located in a coastal area, with high air humidity throughout the year and a large temperature difference between day and night. Such climatic conditions make the high-voltage switchgear extremely prone to condensation, seriously affecting the safe and stable operation of the equipment. The previous traditional anti-condensation measures have poor effects.

[0084] Implementation method: Install a digital anti-condensation device for electrical cabinets considering spatio-temporal characteristics. Deploy meteorological six-element sensors outdoors in the substation to monitor parameters such as wind speed, wind direction, temperature, humidity, precipitation, and dew point in real time; install five-element sensors in the substation indoors to monitor key data such as temperature and humidity in the substation; set corresponding sensors in the high-voltage switchgear and cable trench respectively to comprehensively collect environmental data. The data collected by the sensors is transmitted to the edge computing terminal and central server of the anti-condensation device through a specific communication protocol.

[0085] Application effect: After the device operates, the condensation prediction accuracy rate is as high as 96%, which is significantly improved compared with the traditional method. By dynamically adjusting the anti-condensation strategy, such as automatically strengthening the dehumidification and heating functions at night when the humidity is high and the temperature is low, the energy consumption of the equipment is reduced by 26%. At the same time, due to accurate prediction and timely preventive measures, the number of failures of the high-voltage switchgear in this substation caused by condensation has been significantly reduced, ensuring the stability of power supply.

[0086] Second, multi-regional distributed substations Application background: Multiple substations managed by a certain power company are distributed in different regions, covering the southern region with high humidity, the northern region with dryness and large temperature difference between day and night, and some mountainous areas. The environmental differences in each region are significant, and the traditional unified anti-condensation scheme is difficult to meet the needs of different substations.

[0087] Implementation method: Digital anti-condensation devices considering spatio-temporal characteristics are deployed in each substation. According to the characteristics of different regions, the device automatically calibrates parameters. For example, in the high-humidity areas in the south, the warning threshold of the condensation risk index (R) is reduced, and the dehumidification function is preferentially activated; in the dry areas in the north, the R threshold is appropriately increased to avoid false triggering. Sensors in each substation continuously collect data, and through edge computing and cloud computing technologies, remote centralized monitoring and management of electrical cabinets in all substations are realized.

[0088] Application effect: After a period of operation, this set of devices has shown good adaptability in substations in different regions. The overall false alarm rate is controlled within 4%, effectively avoiding unnecessary equipment operations. While ensuring the safe operation of electrical cabinets, cross-regional efficient management is achieved, greatly improving the reliability and operation and maintenance efficiency of the power system.

[0089] Third, the power distribution room of urban rail transit.

[0090] Application background: The power distribution rooms of urban rail transit are distributed at different stations. Some stations are located underground, with a relatively enclosed environment and high humidity. Coupled with the heat and vibration generated by train operation, the electrical cabinets face a greater risk of condensation, posing a potential threat to the safe operation of rail transit.

[0091] Implementation method: Install digital anti-condensation devices on the electrical cabinets in the power distribution rooms of rail transit. By arranging sensors at different positions in the station, information such as meteorological data, in-station environmental parameters, temperature and humidity inside the electrical cabinet and in the cable trench is collected. The device intelligently adjusts the anti-condensation measures according to the collected data, combined with the train operation schedule (considering time characteristics). For example, during the peak period of train operation, ventilation is strengthened to discharge heat and moisture; during the period when trains are out of service at night, the dehumidification power is appropriately reduced to achieve energy-saving operation.

[0092] Application effect: After the device is put into use, the condensation risk of the electrical cabinet is effectively reduced, ensuring the stable operation of the power supply system of rail transit. According to statistics, the number of equipment failures caused by condensation problems has been reduced by more than 80%, improving the reliability of urban rail transit, reducing train delays caused by power supply failures, and enhancing the travel experience of passengers.

[0093] Example 2 As Figure 1 shown, this embodiment provides a method for evaluating the condensation risk of electrical cabinets based on spatio-temporal characteristics, including the following steps: Collect real-time outdoor meteorological data of the substation, in-station data, data on the inner wall and inside the electrical cabinet, and cable trench data; Input the collected outdoor meteorological data of the substation, the data in the distribution room, and the data inside the inner cabinet of the electrical cabinet into the pre-constructed condensation occurrence probability model to output the condensation occurrence probability; wherein the condensation occurrence probability model is constructed based on the outdoor meteorological data of the substation, the data in the distribution room, the data inside the inner cabinet of the electrical cabinet, and the data in the cable trench. Obtain the condensation risk index based on the condensation occurrence probability and the dynamic weight of the spatio-temporal characteristics at the current moment; the condensation risk index is used to evaluate the condensation risk of the electrical cabinet.

[0094] As Figure 2 As shown, this embodiment also provides an electrical cabinet condensation risk assessment system based on spatio-temporal characteristics, including: a data acquisition module for real-time collecting the outdoor meteorological data of the substation, the data in the distribution room, the data inside the inner cabinet of the electrical cabinet, and the data in the cable trench; a probability prediction module for inputting the collected outdoor meteorological data of the substation, the data in the distribution room, and the data inside the inner cabinet of the electrical cabinet into the pre-constructed condensation occurrence probability model to output the condensation occurrence probability; wherein the condensation occurrence probability model is constructed based on the outdoor meteorological data of the substation, the data in the distribution room, the data inside the inner cabinet of the electrical cabinet, and the data in the cable trench; a condensation risk calculation module for obtaining the condensation risk index based on the condensation occurrence probability and the dynamic weight of the spatio-temporal characteristics at the current moment; the condensation risk index is used to evaluate the condensation risk of the electrical cabinet.

[0095] The present invention also provides a device, including: a memory for storing a computer program; a processor for implementing the steps of the electrical cabinet condensation risk assessment method based on spatio-temporal characteristics when executing the computer program.

[0096] When the processor executes the computer program, it implements the above steps of the electrical cabinet condensation risk assessment based on spatio-temporal characteristics, for example: real-time collecting the outdoor meteorological data of the substation, the data in the distribution room, the data inside the inner cabinet of the electrical cabinet, and the data in the cable trench; inputting the collected outdoor meteorological data of the substation, the data in the distribution room, and the data inside the inner cabinet of the electrical cabinet into the pre-constructed condensation occurrence probability model to output the condensation occurrence probability; wherein the condensation occurrence probability model is constructed based on the outdoor meteorological data of the substation, the data in the distribution room, the data inside the inner cabinet of the electrical cabinet, and the data in the cable trench; obtaining the condensation risk index based on the condensation occurrence probability and the dynamic weight of the spatio-temporal characteristics at the current moment; the condensation risk index is used to evaluate the condensation risk of the electrical cabinet.

[0097] Alternatively, when the processor executes the computer program, it implements the functions of the various modules in the above system. For example, a data acquisition module for real-time acquisition of outdoor meteorological data of a substation, indoor distribution room data, data inside the inner cabinet of an electrical cabinet, and cable trench data; a probability prediction module for inputting the acquired outdoor meteorological data of the substation, indoor distribution room data, and data inside the inner cabinet of the electrical cabinet into a pre-constructed condensation occurrence probability model and outputting the condensation occurrence probability; wherein the condensation occurrence probability model is constructed based on the outdoor meteorological data of the substation, indoor distribution room data, data inside the inner cabinet of the electrical cabinet, and cable trench data; a condensation risk calculation module for obtaining a condensation risk index based on the condensation occurrence probability and the dynamic weight of the spatio-temporal characteristics at the current moment; and the condensation risk index is used to evaluate the condensation risk of the electrical cabinet.

[0098] Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used to describe the execution process of the computer program in the electrical cabinet condensation risk assessment device based on spatio-temporal characteristics. For example, the computer program can be divided into a data acquisition module, a probability prediction module, and a condensation risk calculation module; the specific functions of each module are as follows: a data acquisition module for real-time acquisition of outdoor meteorological data of a substation, indoor distribution room data, data inside the inner cabinet of an electrical cabinet, and cable trench data; a probability prediction module for inputting the acquired outdoor meteorological data of the substation, indoor distribution room data, and data inside the inner cabinet of the electrical cabinet into a pre-constructed condensation occurrence probability model and outputting the condensation occurrence probability; wherein the condensation occurrence probability model is constructed based on the outdoor meteorological data of the substation, indoor distribution room data, data inside the inner cabinet of the electrical cabinet, and cable trench data; a condensation risk calculation module for obtaining a condensation risk index based on the condensation occurrence probability and the dynamic weight of the spatio-temporal characteristics at the current moment; and the condensation risk index is used to evaluate the condensation risk of the electrical cabinet.

[0099] The electrical cabinet condensation risk assessment device based on spatio-temporal characteristics can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The electrical cabinet condensation risk assessment device based on spatio-temporal characteristics may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above is an example of the electrical cabinet condensation risk assessment device based on spatio-temporal characteristics, and does not constitute a limitation on the electrical cabinet condensation risk assessment device based on spatio-temporal characteristics. It may include more components than the above, or combine some components, or different components. For example, the electrical cabinet condensation risk assessment device based on spatio-temporal characteristics may further include input / output devices, network access devices, a bus, etc.

[0100] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc. The processor is the control center of the electrical cabinet condensation risk assessment based on spatio-temporal characteristics, and connects all parts of the electrical cabinet condensation risk assessment device based on spatio-temporal characteristics through various interfaces and lines.

[0101] The memory can be used to store the computer program and / or modules. The processor realizes various functions of the electrical cabinet condensation risk assessment device based on spatio-temporal characteristics by running or executing the computer program and / or modules stored in the memory, and by calling the data stored in the memory.

[0102] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0103] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the electrical cabinet condensation risk assessment method based on spatio-temporal characteristics are implemented.

[0104] If the modules / units integrated in the electrical cabinet condensation risk assessment system based on spatio-temporal characteristics are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0105] Based on such understanding, all or part of the processes in the above-mentioned electrical cabinet condensation risk assessment method based on spatio-temporal characteristics of the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned electrical cabinet condensation risk assessment method based on spatio-temporal characteristics can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or preset intermediate form, etc.

[0106] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0107] It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0108] The above embodiments are only one of the implementation manners capable of realizing the technical solution of the present invention. The scope of protection required by the present invention is not only limited by this embodiment, but also includes any changes, substitutions and other implementation manners that are easily conceivable by those skilled in the art within the technical scope disclosed by the present invention. Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or make equivalent substitutions, and any modification or equivalent substitution that does not deviate from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for assessing the condensation risk of electrical cabinets based on spatiotemporal characteristics, characterized in that: include: Real-time collection of substation outdoor meteorological data, distribution room data, electrical cabinet wall cabinet data, and cable trench data; Input the collected outdoor meteorological data of the substation, the data in the distribution room, and the data in the electrical cabinet and wall cabinet into a pre-built condensation occurrence probability model, and output the condensation occurrence probability; wherein the condensation occurrence probability model is constructed based on the outdoor meteorological data of the substation, the data in the distribution room, the data in the electrical cabinet and wall cabinet, and the cable trench data; A condensation risk index is obtained based on the probability of condensation occurrence and the dynamic weight of the spatiotemporal characteristics at the current moment; the condensation risk index is used to evaluate the condensation risk of the electrical cabinet.

2. The electrical cabinet condensation risk assessment method based on time-space characteristics according to claim 1 is characterized in that: The real-time collection of outdoor weather data of the substation, indoor data of the distribution room, data inside the electrical cabinet and wall cabinet, and cable trench data includes: Collecting outdoor meteorological data of the substation in real time, wherein the outdoor meteorological data of the substation includes wind direction, outdoor temperature, outdoor humidity, precipitation and outdoor dew point; Collecting real-time data in the power distribution room, including indoor temperature, indoor humidity, indoor dew point, indoor water content and indoor water content percentage; Collect data in the electrical cabinet and wall cabinet in real time, including cabinet temperature, cabinet humidity and internal temperature of the decondensation device; The cable trench data are collected in real time, and the cable trench data include cable trench temperature, cable trench humidity, cable trench dew point, cable trench air velocity, cable trench moisture content and cable trench moisture percentage.

3. The electrical cabinet condensation risk assessment method based on spatiotemporal characteristics according to claim 1 is characterized in that: The method further includes inputting the collected outdoor meteorological data of the substation, the indoor data of the power distribution room, and the data in the wall cabinet of the electrical cabinet into the pre-built condensation occurrence probability model and outputting the condensation occurrence probability: The collected outdoor meteorological data of the substation, the indoor data of the distribution room, and the data inside the wall cabinet of the electrical cabinet are preprocessed; the data preprocessing includes an abnormal value filtering operation on the original data and a time synchronization operation on the original data; the source data includes the outdoor meteorological data of the substation, the indoor data of the distribution room, and the data inside the wall cabinet of the electrical cabinet.

4. The method for assessing condensation risk of electrical cabinets based on spatiotemporal characteristics according to claim 1, characterized in that: The method of inputting the collected outdoor meteorological data of the substation, the data in the distribution room, and the data in the wall cabinet of the electrical cabinet into the pre-built condensation occurrence probability model and outputting the condensation occurrence probability includes: Input the collected outdoor meteorological data of the substation, the data inside the distribution room, and the data inside the wall cabinet of the electrical cabinet into the pre-built condensation occurrence probability model; The dew point temperature is calculated based on the relative humidity in the cabinet. The specific formula is as follows: In the formula, Indicates the dew point temperature; , Respectively represent preset parameters; Indicates the humidity inside the cabinet; The condensation occurrence probability is calculated using the following formula: In the formula, Indicates the probability of condensation occurring; Indicates the temperature inside the cabinet; Indicates the temperature inside the cabinet; Indicates precipitation; represents the air velocity in the cable trench; α, β and γ represent the dynamic weight coefficients respectively.

5. The method for assessing condensation risk of electrical cabinets based on spatiotemporal characteristics according to claim 4, characterized in that: The condensation risk index is obtained based on the condensation occurrence probability and the dynamic weight of the spatiotemporal characteristics at the current moment, including: The condensation occurrence probability and the dynamic weight of the spatiotemporal characteristics at the current moment are input into the condensation risk index calculation formula to obtain the condensation risk index; the dynamic weight of the spatiotemporal characteristics at the current moment includes the weight of the equipment importance level and the weight of the environmental complexity; the specific calculation formula is as follows: Where, R represents the condensation risk index; I device Indicates the importance level of the equipment; W1 indicates the weight of the importance level of the equipment; W2 indicates the weight of the complexity of the environment; Indicates the complexity of the environment.

6. The method for assessing condensation risk of electrical cabinets based on spatiotemporal characteristics according to claim 1, characterized in that: After obtaining the condensation risk index based on the condensation occurrence probability and the dynamic weight of the spatiotemporal characteristics at the current moment, the method further includes: The condensation risk index is used to evaluate the condensation risk of the electrical cabinet and the evaluation results are obtained; the specific evaluation results are as follows: If the condensation risk index is less than the first risk threshold, the electrical cabinet is judged to be in a low risk state; If the first risk threshold ≤ condensation risk index < second risk threshold, the electrical cabinet is judged to be in a medium risk state; If the condensation risk index ≥ the second risk threshold, the electrical cabinet is judged to be in a high-risk state.

7. The electrical cabinet condensation risk assessment method based on time-space characteristics according to claim 6 is characterized in that: The condensation risk index is used to evaluate the condensation risk of the electrical cabinet, and after the evaluation result is obtained, the method further includes: A hierarchical control strategy is implemented according to the evaluation results; the hierarchical control strategy includes: If the electrical cabinet is in a low risk state, ventilation measures are adopted; If the electrical cabinet is in a medium risk state, ventilation and intermittent dehumidification measures should be adopted; If the electrical cabinet is in a high-risk state, full dehumidification and full heating measures are used.

8. An electrical cabinet condensation risk assessment system based on spatiotemporal characteristics, characterized in that: include: Data acquisition module, used to collect real-time outdoor meteorological data of the substation, data in the distribution room, data in the electrical cabinet and wall cabinet, and cable trench data; A probability estimation module is used to input the collected outdoor meteorological data of the substation, the data in the distribution room, and the data in the electrical cabinet and wall cabinet into a pre-built condensation occurrence probability model, and output the condensation occurrence probability; wherein the condensation occurrence probability model is constructed based on the outdoor meteorological data of the substation, the data in the distribution room, the data in the electrical cabinet and wall cabinet, and the cable trench data; A condensation risk calculation module is used to obtain a condensation risk index based on the probability of condensation occurrence and the dynamic weight of the spatiotemporal characteristics at the current moment; The condensation risk index is used to evaluate the condensation risk of an electrical cabinet.

9. A device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the electrical cabinet condensation risk assessment method based on time-space characteristics as described in any one of claims 1-7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: The computer program, when executed by a processor, is used to implement the steps of the electrical cabinet condensation risk assessment method based on spatiotemporal characteristics as described in any one of claims 1 to 7.

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