Intelligent electric control cabinet remote operation and maintenance system based on Internet of Things

Abstract

The invention relates to the technical field of intelligent operation and maintenance of electric control cabinets, and discloses an intelligent electric control cabinet remote operation and maintenance system based on the Internet of Things, which comprises a central control board, data acquisition equipment, a data transmission module, a data cleaning module, a fault detection module, a wireless communication module and a mobile terminal, the central control board is respectively connected with the data transmission module and the data cleaning module, the data transmission module is also connected with the data acquisition equipment, and the data cleaning module is also connected with the fault detection module. According to the intelligent electric control cabinet remote operation and maintenance system based on the Internet of Things, the operation state, fault information, early warning signals and the like of the intelligent electric control cabinet can be sensed and monitored by using the Internet of Things technology, and data analysis is automatically performed on the information through the fault detection technology; therefore, real-time monitoring, data statistics and maintenance management of the intelligent electric control cabinet are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of electric control cabinets, and particularly to an intelligent remote operation and maintenance system for electric control cabinets based on the Internet of Things. Background Art

[0002] As an important part of the power system, intelligent electric control cabinets play a crucial role and provide an indispensable guarantee for the reliability and stability of the power system. However, there are some problems in the operation and management of intelligent electric control cabinets. Therefore, it is very necessary to strengthen the refined management of intelligent electric control cabinets. In the traditional management mode and operation and maintenance process of intelligent electric control cabinets, manual operation and management have always been the main operation and management methods. Manual inspection is achieved by checking and recording the operating conditions of equipment, which is inefficient and prone to errors. This management method requires a considerable amount of human resources. Personnel must conduct comprehensive inspections, maintenance, and repairs on the equipment to ensure that problems can be handled in a timely manner when they occur during the operation of the equipment. Therefore, the management level and efficiency of intelligent electric control cabinets have been greatly affected. However, in today's era characterized by high efficiency, speed, and informatization, this management method appears too cumbersome and inefficient, and more efficient solutions need to be sought.

[0003] Traditional operation and maintenance processes usually adopt two methods: planned maintenance and emergency repair to ensure the stability and reliability of the system. In order to ensure the regular maintenance of equipment, fault prevention, and extension of service life, planned maintenance is required; when equipment fails, emergency repair is needed to be able to handle it in a timely manner. This requires operation and maintenance management personnel to always pay attention to the safe and stable state of the electric control cabinet and formulate emergency plans according to the actual situation. However, due to excessive reliance on manual operation, there are problems such as untimely information transmission and non-standard equipment management in this operation and maintenance process, which may lead to potential safety hazards and fault risks of the equipment, and also affect the normal operation of the power system. Summary of the Invention

[0004] The present invention provides an intelligent remote operation and maintenance system for electric control cabinets based on the Internet of Things. By using Internet of Things technology, it can sense and monitor the operating status, fault information, warning signals, etc. of intelligent electric control cabinets, and automatically analyze these information through fault detection technology, so as to realize the real-time monitoring, data statistics, and maintenance management of intelligent electric control cabinets.

[0005] The present invention provides an intelligent remote operation and maintenance system for electric control cabinets based on the Internet of Things, including a central console, data acquisition equipment, a data transmission module, a data cleaning module, a fault detection module, a wireless communication module, and a mobile terminal. The central console is respectively connected to the data transmission module and the data cleaning module. The data transmission module is also connected to the data acquisition equipment, and the data cleaning module is also connected to the fault detection module;

[0006] The data acquisition device is used to collect the operation data of the intelligent electric control cabinet and transmit it to the central console through the data transmission module;

[0007] The data cleaning module is used to filter and denoise the collected operation data through a filter and a noise remover to eliminate interference information in the data, and then normalize the operation data to form standardized data;

[0008] The fault detection module is used to perform intelligent fault detection on the operation data of the intelligent electric control cabinet by using the preset principal component analysis method and output the detection result of the intelligent electric control cabinet;

[0009] The wireless communication module is used to transmit the detection result of the intelligent electric control cabinet to the mobile terminal of the operation and maintenance personnel, so that the operation and maintenance personnel can receive the fault information in a timely and accurate manner and make corresponding handling.

[0010] Further, the fault detection module uses principal component analysis to extract and reduce the dimensions of the operation data to remove redundant information in the operation data;

[0011] The operation data after feature extraction is divided into normal data and fault data to train and test the model, and the model control limit is obtained by using the normal operation data after feature extraction and SEP α , judge hotellingT 2 and whether the SPE statistic exceeds the control limit at the same time to identify the fault data, including training the fault detection model and testing the fault detection model;

[0012] Training the fault detection model: Use the sample data mean calculation formula to calculate the eigenvalues and eigenvectors, obtain all the principal components by projecting on the eigenvectors of the eigenvalues, set the objective function of principal component analysis to obtain the number of principal components, and while retaining the effective information, reduce the dimension of the original experimental data; Use the normal data after feature extraction and calculate the control limits of the two statistics and SEP α ;

[0013] Testing the fault detection model: Calculate the statistic information in the operation sample data, and calculate the two statistic information hotellingT 2 and SPE respectively. The fault detection process is to judge by calculating whether the two statistic information exceeds the control limit at the same time. Once the statistic information exceeds the set two control limits, it proves that the fault data is detected.

[0014] Further, the sample data mean calculation formula and the objective function of principal component analysis are:

[0015] Suppose there are N original operating data samples \(x_1, x_2, \ldots, x_N\) N \(\in R^M\) M , each sample is M - dimensional. First, centralize the original operating data and calculate the sample data mean:

[0016]

[0017] According to the above two - dimensional data, to ensure the maximum variance of the principal components of the random variable, the objective function of principal component analysis is set as:

[0018]

[0019] where \(Y = [y_1, y_2, \ldots, y_N]\in R^M\) d represents the required linear matrix after projection, and C represents the covariance matrix after mean - value processing, and the expression is: M

[0020]

[0021] Solve the objective function of principal component analysis to obtain the generalized eigenvalue equation, and the specific expression is:

[0022] Cy_i i =\(\lambda_i\) i y_i i (\(i = 1, 2, \ldots, N\))

[0023] where \(\lambda_i\) i is the eigenvalue, and \(y_i\) i represents the corresponding eigenvector. Arrange the eigenvalues in descending order. The principal component component corresponding to the largest eigenvalue is projected in the feature space, and the obtained is the first principal component, and all principal component information is obtained in turn.

[0024] Furthermore, it also includes a condensation detection and alarm subsystem. The condensation detection and alarm subsystem is connected to the central console. The condensation detection and alarm subsystem includes a condensation detection and alarm module, a wireless signal transmission module, a background monitoring module, and a condensation elimination module;

[0025] The condensation detection and alarm module uses a condensation induction sensor, adopts active contact condensation, and forms multiple combined condensation induction circuits at positions in the electric control cabinet where condensation is likely to occur. The induction circuit monitors and alarms when the condensation diameter \(\leq1\) mm, and automatically conducts when the condensation just gathers and forms and has not caused harm to the equipment, and transmits the signal;

[0026] ​The wireless signal transmission module includes a wireless alarm communication unit and a transmitting antenna, and adopts a two-stage communication method to ensure the reliability of the wireless communication link. The wireless alarm module installed on the device to be detected is the first stage, and the wireless communication and relay module and the second-stage wireless relay module installed at the center of the detection area are the second stage;

[0027] The background monitoring module communicates with the site wirelessly, automatically completes patrol inspection, automatic alarm, and automatic recording. It consists of a computer, a wireless communication unit, and configuration software, and records the detection and alarm history curves of the entire condensation detection and alarm subsystem;

[0028] When the condensation sensor detects the generation of condensation in the electric control cabinet, the condensation elimination module starts the treatment equipment in the electric control cabinet to eliminate the condensation.

[0029] Furthermore, the condensation detection and alarm module includes a condensation detection control unit, a condensation induction sensor, a power supply unit, and a wireless transmission unit;

[0030] The condensation detection control unit: after receiving the alarm signal, transmits the condensation signal through the built-in wireless transmission unit to the wireless signal transmission module via the external transmitting antenna; the power supply unit is used for independent power supply of the device; the wireless communication unit can communicate with all communication modules, wireless relays, and central nodes on the site.

[0031] Furthermore, the condensation treatment equipment includes an intelligent semiconductor dehumidifier device and an electric heating fan. The electric heating fan raises the temperature inside the cabinet to destroy the conditions for the formation of condensation, and the semiconductor dehumidifier simultaneously removes the moisture in the air to reduce the humidity inside the cabinet, so as to ensure that no condensation appears in the electric control cabinet.

[0032] Furthermore, it also includes a display module, which is connected to the central console, and is used to display the detection results of the intelligent electric control cabinet and the condensation detection and elimination results.

[0033] Furthermore, the mobile terminal includes a mobile phone, a smart watch, or a tablet computer.

[0034] The beneficial effects of the present invention are:

[0035] The data acquisition device of the present invention collects the operation data of the intelligent electric control cabinet and transmits it to the central control console. The fault detection module uses the preset principal component analysis method to perform intelligent fault detection on the operation data of the intelligent electric control cabinet and outputs the detection result of the intelligent electric control cabinet, and finally transmits it to the mobile terminal of the operation and maintenance personnel, so that the operation and maintenance personnel can receive the fault information in a timely and accurate manner and make corresponding handling. The present invention utilizes the Internet of Things technology to be able to timely sense and monitor the operation status, fault information, warning signals, etc. of the intelligent electric control cabinet, and automatically analyzes these information through fault detection technology, thereby realizing the real-time monitoring, data statistics and maintenance management of the intelligent electric control cabinet. It can not only effectively reduce the demand for human resources, but also improve the operation efficiency of the equipment and the efficiency of maintenance management, thus providing a more efficient, rapid and safe solution for the management and operation and maintenance of the intelligent electric control cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 FIG. is a schematic structural diagram of the remote operation and maintenance system of the intelligent electric control cabinet based on the Internet of Things of the present invention.

[0037] Figure 2 FIG. is a schematic structural diagram of the condensation detection and alarm module in the present invention.

[0038] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] As Figure 1 shown, the present invention provides a remote operation and maintenance system for an intelligent electric control cabinet based on the Internet of Things, including a central control console, a data acquisition device, a data transmission module, a data cleaning module, a fault detection module, a wireless communication module and a mobile terminal. The mobile terminal includes a mobile phone, a smart watch or a tablet computer. The central control console is respectively connected to the data transmission module and the data cleaning module. The data transmission module is also connected to the data acquisition device, and the data cleaning module is also connected to the fault detection module.

[0041] The data acquisition device is used to collect the operation data of the intelligent electric control cabinet and transmit it to the central console through the data transmission module. Sensors and data acquisition devices such as temperature sensors and power meters are deployed to collect the real-time operation data inside the electric control cabinet. These sensors and data acquisition devices are connected to a network to obtain and transmit data. A central console is established to connect the data transmission networks of each intelligent electric control cabinet through Internet of Things technology, centrally manage and monitor the data. Finally, cloud computing and big data technologies are used to store and process the transmitted data, providing decision-making support for operation and maintenance personnel, real-time monitoring and analyzing the data of intelligent electric control cabinets, and greatly improving the operation and maintenance efficiency.

[0042] The data cleaning module is used to filter and denoise the collected operation data through a filter and a noise remover to eliminate the interference information in the data, and then normalize the operation data to form standardized data.

[0043] The fault detection module is used to perform intelligent fault detection on the operation data of the intelligent electric control cabinet by using the preset principal component analysis method and output the detection result of the intelligent electric control cabinet.

[0044] The fault detection module uses principal component analysis to extract features and reduce the dimension of the operation data to remove the redundant information in the operation data. Principal component analysis is an unsupervised multivariate statistical data dimension reduction method that linearly combines the original data variables into new variables through the change of the feature space, maps the data with high dimension to a low-dimensional subspace, extracts the main feature information of the data, reduces the dimension of the original data, and improves the model accuracy. Principal component analysis establishes an optimal feature space. Taking two-dimensional data as an example, the original data is projected onto a new coordinate axis to reduce the dimension of the original data, and to ensure that the most feature information is retained, the projection needs to be distributed with the largest variance in the first principal component.

[0045] The operation data after feature extraction is divided into normal data and fault data to train and test the model. The number of kernel principal components of the sample data is determined according to the cumulative contribution rate, the dimension of the original operation data is reduced, and the model control limit is obtained by using the data of the electrical equipment working normally after feature extraction. And SEP α , judge hotellingT 2 And whether the SPE statistic exceeds the control limit at the same time to identify the fault data, and compare the detection effect of the fault detection model. Due to the situation of different data ranges and data units in the original experimental data, first standardize the original operation data, perform mean standardization calculation on the original operation data, unify the dimension, and eliminate the inconsistency due to data characteristics:

[0046]

[0047] Among them, the data standard deviation is represented by σ; the data mean is represented by α. Fault detection based on principal component analysis includes training a fault detection model and testing a fault detection model:

[0048] Training the fault detection model: Calculate eigenvalues and eigenvectors using the sample data mean calculation formula, obtain all principal components by taking the projections on the eigenvectors of the eigenvalues, set the objective function using principal component analysis to find the number of principal components, and while retaining the effective information, achieve dimensionality reduction of the original experimental data; Use the normal data after feature extraction and calculate the control limits of two statistics And SEP α ;

[0049] Testing the fault detection model: Calculate the statistical information in the running sample data, and calculate the two statistical information hotellingT 2 And SPE respectively. The fault detection process is to judge by calculating whether the two statistical information exceed the control limits simultaneously in real time. Once the statistical information exceeds the set two control limits, it proves that the fault data is detected.

[0050] T 2 The T statistic is a multivariate statistical method used to judge whether a given observation is significantly deviated from the central position of other observations. It measures the distance between a sample point and the center of the data set. T 2 The calculation of is usually based on the principal component scores of the sample. In the context of PCA, its definition is usually:

[0051] T 2 = xTP∧ -1 PTx

[0052] Among them, x is the observation value, P is the principal component loading matrix, and Λ is the diagonal matrix of the variance (eigenvalue) of the principal component. T 2 T is usually compared with a critical value, which is determined based on the F-distribution. If the T 2 value of a sample exceeds this critical value, then the sample is regarded as abnormal or deviated from the center.

[0053] SPE is used to measure the difference between the observed value and the value predicted by the PCA model. It measures the distance between the sample point and the PCA model plane, reflecting the amount of information in the data that is not captured by the PCA model. In the context of PCA, SPE can be defined as:

[0054] SEP = ||x - x reconstructed || 2

[0055] Among them, x is the original observed value, and x reconstructedAre the observed values reconstructed by the PCA model. Compared with T 2 Similar to that, the value of SPE can be compared with a critical value to determine whether the observation significantly differs from the prediction of the model. If the value of SPE exceeds the critical value, the sample may contain important information not captured by the PCA model and may thus be regarded as an anomaly.

[0056] The calculation formula for the sample data mean and the objective function of principal component analysis are as follows:

[0057] Suppose there are N original operating data samples \(x_1, x_2, \ldots, x\) N \(\in R\) M , each sample is M-dimensional. First, centralize the original operating data and calculate the sample data mean:

[0058]

[0059] Based on the above two-dimensional data, to ensure the maximum variance of the principal component of the random variable, set the objective function of principal component analysis as:

[0060]

[0061] where \(Y = [y_1, y_2, \ldots, y\) d \(\in R\) M represents the required linear matrix after projection, and C represents the covariance matrix after mean value, and the expression is:

[0062]

[0063] Solve the objective function of principal component analysis and obtain the generalized eigenvalue equation. The specific expression is:

[0064] \(Cy\) i \(=\lambda\) i \(y\) i \((i = 1, 2, \ldots, N)\)

[0065] where \(\lambda\) i is the eigenvalue, and \(y\) i represents the corresponding eigenvector. Arrange the eigenvalues in descending order. Among them, project the principal component component of the largest eigenvalue in the feature space, and the obtained is the first principal component, and all principal component information is obtained in turn.

[0066] The wireless communication module is used to transmit the detection results of the intelligent electric control cabinet to the mobile terminal of the operation and maintenance personnel, so that the operation and maintenance personnel can receive the fault information in a timely and accurate manner and make corresponding handling.

[0067] Fault detection process: Standardize the sample data, divide the data into normal data and fault data, use the normal data as training data, calculate the eigenvectors of the covariance rectangle, obtain the principal components, set the cumulative contribution rate threshold to determine the number of principal components and calculate the control limits; use the fault data as test data, obtain the principal components, calculate the test data statistic, and determine whether SPE > SEP α and If so, it indicates a fault; if not, it indicates normal.

[0068] In one embodiment, the present invention further includes a condensation detection and alarm subsystem, the condensation detection and alarm subsystem is connected to the central console, and the condensation detection and alarm subsystem includes a condensation detection and alarm module, a wireless signal transmission module, a background monitoring module, and a condensation elimination module.

[0069] Condensation detection and alarm module: Use a specially designed large-area maze-type anti-corrosion condensation induction sensor, adopt active contact condensation, and form multiple combined condensation induction circuits at positions inside the box where condensation is likely to occur. The induction circuit has high sensitivity (the monitored and alarmed condensation diameter ≦ 1mm, while the condensation diameter that can cause short-circuit hazards to equipment such as terminal blocks is generally ≦ 4mm), automatically conducts when the condensation just starts to gather and form and has not yet caused harm to the equipment, and transmits the signal to the integrally installed condensation detection and control unit for processing, solving the defect of large measurement errors of the humidity controller. As Figure 2 shown, the condensation detection and alarm module includes a condensation detection and control unit, a condensation induction sensor, a power supply unit, and a wireless transmission unit. After receiving the alarm signal, the condensation detection and control unit transmits the condensation signal through the built-in wireless transmission unit and sends the condensation alarm signal through the external transmitting antenna to the wireless signal transmission module. The power module is used for independent power supply of the equipment, and the wireless transmission unit can communicate with all communication modules, wireless relays, and central nodes on site.

[0070] Wireless signal transmission module: Includes a wireless alarm communication unit and a transmitting antenna, and adopts a two-stage or three-stage (currently configured) communication method to ensure the reliability of the wireless communication link. The wireless transmission module installed in the condensation detection and alarm module is the first stage, and the communication distance can reach 3km without obstruction. The wireless communication and relay module installed at the center of the detection area and the second-stage wireless relay module are the second stage, and are installed at a higher position on site.

[0071] Background monitoring module: The background monitoring module communicates wirelessly with the site, automatically completes patrol inspection, automatic alarm, and automatic recording, records the detection and alarm historical curves of the entire condensation detection and alarm subsystem, and is convenient for query. The working conditions and alarm conditions are clearly expressed on the interface at a glance.

[0072] Dew elimination module: When the dew sensor detects the generation of dew inside the electric control cabinet, in addition to sending out an alarm signal in a timely manner, it will also start the treatment equipment inside the electric control cabinet to eliminate the dew. The dew treatment equipment includes an intelligent semiconductor dehumidifier device and an electric heating fan. When the dew is detected, while alarming, a control instruction is sent to drive the dew elimination device: the electric heating fan raises the temperature inside the cabinet to destroy the conditions for dew formation, and the semiconductor dehumidifier simultaneously removes the moisture in the air to reduce the humidity inside the cabinet, ensuring that no more dew appears inside the electric control cabinet. When the dew sensor detects that the dew inside the electric control cabinet has been eliminated, it automatically or sends a delayed instruction to turn off the electric heating fan and the intelligent semiconductor dehumidifier.

[0073] In one embodiment, the present invention further includes a display module, the display module is connected to the central console, and the display module is used to display the detection results of the intelligent electric control cabinet and the dew detection and elimination results.

[0074] The data acquisition device of the present invention acquires the operation data of the intelligent electric control cabinet and transmits it to the central console. The fault detection module uses the preset principal component analysis method to perform intelligent fault detection on the operation data of the intelligent electric control cabinet, and outputs the detection results of the intelligent electric control cabinet, and finally transmits them to the mobile terminal of the operation and maintenance personnel, so that the operation and maintenance personnel can receive the fault information in a timely and accurate manner and make corresponding processing. The present invention utilizes the Internet of Things technology to be able to timely sense and monitor the operation status, fault information, warning signals, etc. of the intelligent electric control cabinet, and automatically analyzes these information through fault detection technology, so as to realize the real-time monitoring, data statistics and maintenance management of the intelligent electric control cabinet. It can not only effectively reduce the demand for human resources, but also improve the operation efficiency of the equipment and the efficiency of maintenance management, thus providing a more efficient, rapid and safe solution for the management and operation and maintenance of the intelligent electric control cabinet.

[0075] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, device, article or method. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, device, article or method including that element.

[0076] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. An intelligent electric control cabinet remote operation and maintenance system based on the Internet of Things, characterized in that, It includes a central console, a data acquisition device, a data transmission module, a data cleaning module, a fault detection module, a wireless communication module and a mobile terminal. The central console is respectively connected to the data transmission module and the data cleaning module. The data transmission module is also connected to the data acquisition device, and the data cleaning module is also connected to the fault detection module; The data acquisition device is used to collect the operation data of the intelligent electric control cabinet and transmit it to the central console through the data transmission module; The data cleaning module is used to filter and denoise the collected operation data through a filter and a noise remover to eliminate interference information in the data, and then normalize the operation data to form standardized data; The fault detection module is used to perform intelligent fault detection on the operation data of the intelligent electric control cabinet by using the preset principal component analysis method and output the detection result of the intelligent electric control cabinet; The wireless communication module is used to transmit the detection result of the intelligent electric control cabinet to the mobile terminal of the operation and maintenance personnel, so that the operation and maintenance personnel can receive the fault information in a timely and accurate manner and make corresponding processing.

2. The intelligent electric control cabinet remote operation and maintenance system based on the Internet of Things according to claim 1, characterized in that, The fault detection module uses principal component analysis to extract and reduce the dimension of the operation data to remove redundant information in the operation data; The operation data after feature extraction is divided into normal data and fault data to train and test the model, and the model control limits are obtained using the normal operation data after feature extraction. and SEP α , judge hotellingT 2 and whether the SPE statistic exceeds the control limit at the same time to identify the fault data, including training the fault detection model and testing the fault detection model; Training a fault detection model: Calculate eigenvalues and eigenvectors using the sample data mean calculation formula, obtain all principal components by getting the projections of the eigenvalues on the eigenvectors, set the objective function for principal component analysis to find the number of principal components, and while retaining the effective information, achieve dimensionality reduction of the original experimental data; Use the normal data after feature extraction and calculate the control limits of two statistics and SEP α ; Testing the fault detection model: Calculate the statistical information in the running sample data, and calculate two statistical information, hotellingT 2 and SPE respectively. The fault detection process is judged by calculating whether the two statistical information exceed the control limits in real time. Once the statistical information exceeds the two set control limits, it proves that the fault data is detected.

3. The intelligent electric control cabinet remote operation and maintenance system based on the Internet of Things according to claim 2, wherein The sample data mean calculation formula and the objective function of principal component analysis are: Suppose there are N original operation data samples \(x_1, x_2, \ldots, x\) N \(\in R\) M , each sample is M-dimensional. First, centralize the original operation data and calculate the mean of the sample data: According to the above two-dimensional data, to ensure the maximum variance of the principal component of the random variable, the objective function of principal component analysis is set as: where Y = [y1, y2, …, y d ∈ R M represents the required linear matrix after projection, C represents the covariance matrix after mean value, and the expression is: Solve the objective function of principal component analysis and obtain the generalized eigenvalue equation. The specific expression is: Cy i = λ i y i (i = 1, 2, …, N) Among them, λ i is the eigenvalue, and y i represents the corresponding eigenvector. The eigenvalues are arranged in descending order from largest to smallest. Among them, the principal component component of the largest eigenvalue is projected in the feature space, and the obtained result is the first principal component. All the principal component information is obtained in turn.

4. The intelligent electric control cabinet remote operation and maintenance system based on the Internet of Things according to claim 1, characterized in that, It also includes a condensation detection and alarm subsystem. The condensation detection and alarm subsystem is connected to the central console. The condensation detection and alarm subsystem includes a condensation detection and alarm module, a wireless signal transmission module, a background monitoring module and a condensation elimination module; The condensation detection and alarm module uses a condensation induction sensor and adopts active contact condensation to form multiple combined condensation induction circuits at the positions in the electric control cabinet where condensation is likely to occur. The induction circuit monitors and alarms when the condensation diameter ≦ 1mm, and automatically conducts when the condensation just gathers and forms and has not caused harm to the equipment, and transmits the signal; The wireless signal transmission module includes a wireless alarm communication unit and a transmitting antenna, and adopts a two-stage communication method to ensure the reliability of the wireless communication link. The wireless alarm module installed on the detected equipment is the first stage, and the wireless communication and relay module and the second-stage wireless relay module installed at the center of the detection area are the second stage; The background monitoring module communicates with the site wirelessly, automatically completes patrol inspection, automatic alarm and automatic recording. It is composed of a computer, a wireless communication unit and configuration software, and records the detection and alarm history curves of the entire condensation detection and alarm subsystem; When the condensation sensor detects the generation of condensation in the electric control cabinet, the condensation elimination module starts the treatment equipment in the electric control cabinet to eliminate the condensation.

5. The remote operation and maintenance system for intelligent electric control cabinets based on the Internet of Things according to claim 4, characterized in that, The condensation detection and alarm module includes a condensation detection control unit, a condensation induction sensor, a power supply unit and a wireless transmission unit; Dew detection and control unit: After receiving the alarm signal, it sends the dew condensation signal through the built-in wireless transmission unit to the wireless signal transmission module via the external transmitting antenna; the power supply unit is used for the independent power supply of the device; the wireless communication unit can communicate with all communication modules, wireless relays and central nodes on site.

6. The intelligent electric control cabinet remote operation and maintenance system based on the Internet of Things according to claim 4, characterized in that, The dew condensation treatment device includes an intelligent semiconductor dehumidifier device and an electric hot air blower. The electric hot air blower raises the temperature inside the cabinet to destroy the conditions for dew condensation formation, and the semiconductor dehumidifier simultaneously removes moisture in the air to reduce the humidity inside the cabinet, so as to ensure that no dew condensation appears inside the electric control cabinet.

7. The intelligent electric control cabinet remote operation and maintenance system based on the Internet of Things according to claim 5, characterized in that, It also includes a display module. The display module is connected to the central console, and the display module is used to display the detection results of the intelligent electric control cabinet and the results of dew condensation detection and elimination.

8. The intelligent electric control cabinet remote operation and maintenance system based on the Internet of Things according to claim 1, wherein, The mobile terminal includes a mobile phone, a smart watch or a tablet computer.

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