A terminal box mechanism box internal environment monitoring and fault display device

Abstract

The present invention discloses a terminal box mechanism box internal environment monitoring and fault display device, and relates to the terminal box monitoring technology field. The device includes a temperature sensor, a humidity sensor and a temperature and humidity display device. The sensor is installed at a key position inside the terminal box, and the temperature and humidity data are collected in real time through a microprocessor and a data transmission module. The display device can be detachably installed on the outside, integrated with a data receiving module and a display screen, supports communication with a video inspection system, and can be connected to a heating and dehumidification device to maintain the stability of the internal environment. The device collects data, pre-processes abnormal data, uses a GRU model to predict temperature and humidity trends, extracts features and performs K-means clustering analysis, and finally formulates inspection strategies for different environmental conditions. The device can not only display temperature, humidity and equipment status in real time, but also transmit data to a central database through the Internet of Things, which improves the intelligence level of equipment management and provides strong support for ensuring the normal operation of facilities.

Description

Technical Field

[0001] The invention relates to the technical field of terminal box monitoring, and in particular to a terminal box mechanism box internal environment monitoring and fault display device. Background Art

[0002] In the power system, the stable operation of substation equipment is very important. As important components of substation equipment, the stability of the internal environment and normal working state of the terminal box and mechanism box directly affect the reliability of the entire substation system.

[0003] Traditional inspections of substation equipment mainly rely on regular manual on-site inspections. This method has problems such as low efficiency, lack of timeliness, and difficulty in covering all details. For example, in some remote substations, manual inspections take a long time and may not be able to detect environmental changes or potential faults inside the equipment in a timely manner.

[0004] The internal environment of the terminal box and the mechanism box is easily affected by factors such as temperature, humidity, and dust. Harsh environmental conditions may cause electrical component aging, poor contact, short circuits, and other faults. For example, in a humid environment, the terminals may rust, affecting the conductivity; high temperatures may cause the insulation material to age. Once a terminal box or mechanism box fails, if it is not discovered and handled in time, it may cause a wider range of equipment failures and even lead to power grid accidents.

[0005] To sum up, the emergence of an internal environment monitoring and fault display device for terminal box mechanism box suitable for remote inspection of substation is to cope with the importance of substation equipment inspection, the shortcomings of traditional inspection methods, the growth of remote inspection demand, the complexity of the internal environment and the requirement for timely handling of faults, so as to improve the reliability and stability of the power system. Summary of the invention

[0006] The present invention provides a terminal box mechanism box internal environment monitoring and fault display device, which aims to solve the problem of delayed detection of heating and dehumidification device failure in the terminal box, greatly reduce the manual inspection and maintenance cycle, and effectively improve the equipment health and the reliability of safe operation of the power grid. The device is of great significance in improving inspection efficiency, ensuring the reliable operation of the power system, reducing costs, realizing intelligent management, ensuring personnel safety, and enhancing emergency response capabilities.

[0007] The technical solution adopted by the present invention is:

[0008] A terminal box mechanism internal environment monitoring and fault display device, comprising a temperature sensor, a humidity sensor, and a terminal box temperature and humidity display device; the temperature sensor and humidity sensor are installed at key positions inside the terminal box, and the temperature sensor and humidity sensor have built-in microprocessors and data transmission modules; the terminal box temperature and humidity display device is detachably installed on the outside of the terminal box through a wall-mounted bracket, and is internally integrated with a data receiving module, a display screen, and a communication interface that matches an existing video remote patrol system. It is connected to the temperature sensor and humidity sensor for data transmission through the data receiving module, and the terminal box temperature and humidity display device is also connected to a heating and dehumidification device installed inside the terminal box.

[0009] Furthermore, the temperature sensor and humidity sensor collect environmental data inside the terminal box in real time; the terminal box temperature and humidity display device collects and displays the temperature and humidity values ​​inside the terminal box and the working status of the heating and dehumidification device, and uses the Internet of Things to transmit the collected temperature and humidity data and heating and dehumidification device status data to the central database.

[0010] Furthermore, the terminal box temperature and humidity display device is also provided with a preprocessing module, which performs abnormal data cleaning, data alignment, and data standardization on the temperature and humidity numerical data inside the terminal box through the preprocessing module.

[0011] A terminal box mechanism box internal environment monitoring method, the terminal box mechanism box internal environment monitoring method is based on the terminal box mechanism box internal environment monitoring and fault display device, comprising the following steps:

[0012] Step 1, data collection and preprocessing: the background management system collects and standardizes the temperature and humidity data of the terminal box;

[0013] Step 2, GRU time series prediction: The background management system trains the GRU model and predicts the future temperature and humidity trends based on the real-time collected environmental data inside the terminal box;

[0014] Step 3, feature extraction: The background management system extracts the temperature and humidity fluctuation range, trend, and heating device status characteristics based on the real-time collected terminal box environment data;

[0015] Step 4, K-means clustering: The backend management system clusters the terminal boxes based on the real-time collected environmental data inside the terminal boxes, and generates patrol strategies based on the family classification;

[0016] Step 5, strategy formulation: The background management system formulates high-frequency patrols for high-volatility clusters and low-frequency patrols for stable clusters.

[0017] Furthermore, during the data collection and preprocessing process, the terminal box temperature and humidity display device collects the sensor data using the following formula:

[0018] Temperature and humidity sampling data:

[0019] x t ={T t ,H t}

[0020] In the formula, x t represents the state vector at time t; T t is the temperature value at time t; H t is the humidity value at time t;

[0021] Status S of the heating and dehumidifying device t , recorded as a binary signal:

[0022]

[0023] The preprocessing module of the terminal box temperature and humidity display device performs data standardization on the temperature and humidity data of each time step:

[0024]

[0025] In the formula, represents the standardized temperature and humidity data at time t; x t represents the state vector at time t; μ x is the mean of temperature and humidity data; σ x is the standard deviation of temperature and humidity data;

[0026] The pre-processing module of the terminal box temperature and humidity display device performs preliminary abnormal value filtering by setting the upper and lower limits of temperature and humidity:

[0027]

[0028] In the formula, represents the temperature and humidity data after preliminary outlier filtering at time t; T min , T max is the effective range of temperature; T t Represents the original temperature and humidity data vector at time t; H min , H max is the effective range of humidity; H t represents the humidity value at time t;

[0029] The preprocessing module of the terminal box temperature and humidity display device synchronizes the temperature and humidity data with the timestamp of the equipment working status to perform data alignment.

[0030] Furthermore, the process of training the GRU model in the background management system is as follows:

[0031] Step 2.1, data preparation: Assume that the historical time series of temperature and humidity is:

[0032] X={x 1 ,x 2 ,...,x t}

[0033] Where X represents the historical time series of temperature and humidity; t is the temperature and humidity feature vector of the tth time step, including the temperature T t and humidity H t ;

[0034] Predict the temperature and humidity values ​​for the next k time steps using label data:

[0035] Y={y t+1 ,y t+2 ,...,y t+k ,}

[0036] Where Y represents the predicted temperature and humidity value sequence; t+1 Represents the predicted temperature and humidity values ​​at the t+1th time step; y t+2 Represents the predicted temperature and humidity values ​​at the t+2th time step; y t+k Represents the predicted temperature and humidity values ​​at the t+kth time step;

[0037] Step 2.2, GRU unit structure: The update formula of GRU is as follows:

[0038] z t =σ(W z ·[h t-1 ,x t ]+b z )

[0039] In the formula, z t is the update gate, which controls the degree of retention of historical information in the current state; W z is the weight matrix; h t-1 is the hidden state of the previous moment; x t Input data of the current time step; b z is the bias; σ is the Sigmoid activation function, ranging from 0 to 1;

[0040] Reset Gate t :

[0041] r t =σ(W r ·[h t-1 ,x t ]+b r )

[0042] In the formula, r t To reset the gate, control how to combine the historical state and the current input; W ris the weight matrix; h t-1 is the hidden state of the previous moment; x t Input data of the current time step; b r is the bias; σ is the Sigmoid activation function, ranging from 0 to 1;

[0043] Candidate hidden state h t :

[0044] h t =tanh(W h ·[r t ⊙h t-1 ,x t ]+b h )

[0045] In the formula, h t is the candidate hidden state of the current time step; r t is the reset gate; W h is the weight matrix; b h is bias;

[0046] It is element-by-element multiplication; tanh is the hyperbolic tangent function, with a range of -1, 1;

[0047] Hide status updateh t :

[0048] h t =z t ⊙h t-1 +(1-z t )h t

[0049] In the formula, h t is the hidden state of the current time step, which is a combination of historical information and current input; t is the update gate. Step 2.3, output layer: The hidden state h is transformed into t Converted into predicted value y t :

[0050] y t =W o ·h t +b o

[0051] In the formula, y t is the predicted value of the current time step; h t The hidden state at the current time step; W o is the output layer weight; b o is the bias for the output layer.

[0052] Furthermore, during the K-means clustering process, the data is prepared as follows:

[0053] Extract features from the GRU model or directly use historical data to construct the feature vector of each terminal box:

[0054] V={v 1 ,v 2 ,...,v n}

[0055] Where V is the set of feature vectors; v i is the characteristic vector of the i-th terminal box, which includes the temperature fluctuation range, the number of times the heating device starts and stops, and the mean value of temperature and humidity; n is the number of terminal boxes;

[0056] Temperature and humidity fluctuation range:

[0057] In the formula, is the fluctuation range of temperature and humidity; max(T) is the highest temperature measured in time period t; min(T) is the lowest temperature measured in time period t; is the humidity fluctuation range; max(H) is the highest humidity measured in the time period t; min(H) is the lowest humidity measured in the time period t;

[0058] The average temperature and humidity is and That is, the mean temperature and humidity of the i-th terminal box in a given time period;

[0059] The number of starts and stops of the heating and dehumidification device is S count .

[0060] Furthermore, the K-means clustering process includes the following steps:

[0061] Step 4.1, Initialize cluster centers: Randomly select K points as the initial cluster centers {μ 1 ,μ 2 ,...,μ k};

[0062] In the formula, μ 1 is the center of the first cluster; μ k is the center of the kth cluster;

[0063] Step 4.2, calculate cluster assignment: assign each sample to the nearest cluster based on the Euclidean distance:

[0064]

[0065] In the formula, C i is the sample set belonging to the i-th cluster; v j is the jth sample; μ i is the cluster center of the i-th cluster; ||vj -μ i || 2 For sample v j To cluster center μ i The square of the Euclidean distance; Represents the index of other clusters in all clusters;

[0066] Step 4.3, calculate the new center of each cluster as the mean of the samples in the cluster:

[0067]

[0068] In the formula, μ i is the new center of the i-th cluster; |C i | is the number of samples in the i-th cluster; Represents all samples v belonging to the i-th cluster j Perform summation;

[0069] Step 4.4, iterative optimization: repeat steps 4.2 and 4.3 until the cluster centers converge or the maximum number of iterations is reached.

[0070] Furthermore, the optimization goal of the K-means clustering process is to minimize the intra-cluster sum of squares WCSS;

[0071]

[0072] Where WCSS is the minimum intra-cluster sum of squares; K represents the total number of clusters in the cluster; i is the index of the cluster; v j is the jth sample; C i is the sample set belonging to the i-th cluster; μ i is the cluster center of the ith cluster.

[0073] Furthermore, the patrol strategy is based on a strategy classification condition, and the strategy classification condition includes:

[0074] High-frequency inspection: temperature and humidity fluctuation range R T , R H High, heating and dehumidification device operating frequency S count high;

[0075] Regular inspections: Temperature and humidity trends show moderate fluctuations and normal operating frequency;

[0076] Low-frequency inspection: low temperature and humidity fluctuation range, low operating frequency, and no obvious abnormalities;

[0077] Generate strategies through logical conditions:

[0078]

[0079] The beneficial effects of the present invention are:

[0080] 1. Real-time monitoring of environmental data: Through real-time monitoring of temperature sensors and humidity sensors, the environmental conditions inside the terminal box can be timely understood. This helps to avoid equipment failures caused by extreme environmental conditions and ensure the normal operation of the equipment.

[0081] 2. Automatic fault warning: The data processing and display system integrated in the device can promptly reflect abnormal changes in temperature and humidity. Once a situation beyond the normal range is detected, the system can immediately issue an alarm to prompt maintenance personnel to conduct inspections and repairs, thereby reducing economic losses caused by equipment failure.

[0082] 3. Data-driven decision support: Real-time data collection and analysis can provide a scientific basis for equipment management. Using the GRU time series prediction model and K-means clustering analysis, reasonable inspection strategies and maintenance plans can be formulated to improve management efficiency and targeted maintenance.

[0083] 4. Improve equipment reliability: Combined with heating and dehumidification devices for environmental regulation, the temperature and humidity of the internal environment can be effectively controlled, the impact of environmental changes on equipment performance can be reduced, and the overall reliability and service life of the equipment can be improved.

[0084] 5. Intelligent management: Through the Internet of Things technology, monitoring data can be uploaded to the central database in real time, realizing centralized monitoring of multiple terminal boxes, improving the level of intelligent management, and facilitating centralized processing and decision-making.

[0085] 6. Data analysis and optimization: The pre-processing module is set up to clean and standardize the data before it is transmitted to the background management system, ensuring the accuracy of the analysis results, thereby optimizing the inspection strategy and maintenance measures and achieving dynamic adjustment.

[0086] 7. Save human resources: Through automatic monitoring, early warning and strategy formulation, the frequency of manual inspections can be reduced, human resources can be saved and work efficiency can be improved.

[0087] In summary, the terminal box mechanism box internal environment monitoring and fault display device can not only improve the safety and stability of the terminal box, but also improve the inspection efficiency through intelligent management means, ensure the reliable operation of the power system, reduce costs, realize intelligent management, ensure personnel safety and enhance emergency response capabilities. It is of great significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0089] Figure 1 It is a schematic diagram of the overall structure of the terminal box mechanism box internal environment monitoring and fault display device of the present invention;

[0090] Figure 2 It is a structural schematic diagram of the temperature and humidity display device of the terminal box of the present invention;

[0091] Figure 3 It is a flow chart of the method for monitoring the internal environment of the terminal box mechanism box of the present invention;

[0092] In the figure: 1. Terminal box temperature and humidity display device; 2. Temperature sensor; 3. Humidity sensor; 4. Wall-mounted bracket. DETAILED DESCRIPTION

[0093] To make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0094] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely represents some embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features and technical solutions in the embodiments can be combined with each other without conflict. It should be noted that similar numbers and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0095] Example 1

[0096] At present, remote inspections of substations cannot cover the internal environment of the substation terminal box. There is a lack of effective monitoring means to monitor the internal environment of the terminal box in real time. With the extension of the on-site inspection cycle, there is a problem of untimely discovery of internal environmental defects and hidden dangers of the substation terminal box. The currently used monitoring means only realize the online monitoring of the temperature and humidity data inside the terminal box, lack of real-time perception of internal faults of the terminal box, and no internal environment assessment of the terminal box and inspection strategy model output to guide operation and maintenance personnel to carry out differentiated operation and maintenance of the terminal box.

[0097] In response to the above problems, this embodiment proposes a terminal box mechanism box internal environment monitoring and fault display device, which is used to realize real-time perception of temperature and humidity data in the terminal box, real-time perception of the status of the heating dehumidifier in the terminal box, and build a terminal box data analysis and strategy output model to improve work efficiency and personnel safety.

[0098] like Figure 1 As shown, the terminal box mechanism box internal environment monitoring and fault display device includes a temperature sensor 2, a humidity sensor 3, and a terminal box temperature and humidity display device 1. Among them, the temperature sensor 2 and the humidity sensor 3 are both Sensirion industrial-grade temperature sensors and humidity sensors, and the temperature sensor 2 and the humidity sensor 3 are installed at key positions inside the terminal box; the temperature sensor 2 and the humidity sensor 3 have built-in microprocessors and data transmission modules, and the sensor shell material is selected from high-temperature resistant, corrosion-resistant and good insulating engineering plastics to protect the sensitive components inside the sensor from complex environmental factors in the terminal box, such as humidity, electromagnetic interference, etc., to ensure that the temperature sensor 2 and the humidity sensor 3 work stably for a long time, accurately measure the temperature and humidity data in the terminal box, and realize real-time perception of the internal environment of the terminal box. The temperature sensor 2 and the humidity sensor 3 use a wired transmission method, such as RS485 bus, to stably transmit data to the terminal box temperature and humidity display device 1 to reduce the risk of interference and loss during data transmission.

[0099] like Figure 1 and Figure 2 As shown, the terminal box temperature and humidity display device 1 is equipped with a wall-mounted bracket 4, which is detachably mounted on the outside of the terminal box through the wall-mounted bracket 4. The terminal box temperature and humidity display device 1 is internally integrated with a data receiving module, a display screen, and a communication interface that matches the existing video remote patrol system. The display screen adopts a high-brightness, high-contrast LCD screen, which can clearly and intuitively display the temperature and humidity values ​​inside the terminal box and the working status of the heating and dehumidification device; the display device shell material is installed on the surface of the outdoor terminal box and needs to withstand harsh environmental conditions such as sun, rain, wind and sand erosion, so it adopts hard plastic material, which has the characteristics of light weight, high strength and corrosion resistance, ensuring the long-term reliable operation of the display device outdoors, providing clear and accurate terminal box internal environment information for operation and maintenance personnel, and at the same time coordinating with the overall environment of the substation to meet actual use needs. The internal circuit board of the terminal box temperature and humidity display device 1 uses high-quality epoxy glass fiber board as the substrate material of the internal circuit; the epoxy glass fiber board has good electrical insulation properties, mechanical strength and stability, and can effectively support and fix electronic components to ensure the normal operation of the circuit and the accuracy and stability of data transmission, providing a solid foundation for the reliable operation of the entire monitoring system, realizing real-time perception of temperature and humidity data in the terminal box and the status of the heating dehumidifier, and providing strong support for differentiated operation and maintenance through data analysis.

[0100] The terminal box temperature and humidity display device 1 is connected to the temperature sensor 2 and the humidity sensor 3 for data transmission via a data receiving module, and the terminal box temperature and humidity display device 1 is also connected to a heating and dehumidification device installed inside the terminal box. In addition, as a preferred technical solution, a preprocessing module is also provided in the terminal box temperature and humidity display device 1 in this embodiment, and the terminal box temperature and humidity display device 1 performs abnormal data cleaning, data alignment, and data standardization on the temperature and humidity numerical data inside the terminal box via the preprocessing module.

[0101] The terminal box mechanism box internal environment monitoring and fault display device uses reasonable circuit layout and modular design to facilitate installation, debugging and maintenance, ensuring the stability and reliability of the entire structure, and seamlessly connecting with the existing equipment in the substation, effectively solving the problem that the current remote inspection of the substation cannot cover the internal environment monitoring of the terminal box, and improving the real-time perception of the status inside the substation terminal box.

[0102] Example 2

[0103] Based on the terminal box mechanism box internal environment monitoring and fault display device provided in Example 1, this embodiment also provides a terminal box mechanism box internal environment monitoring method, such as Figure 3 As shown, the terminal box mechanism box internal environment monitoring method comprises the following steps:

[0104] Step 1, data collection and preprocessing: The background management system collects and standardizes the temperature and humidity data of the terminal box.

[0105] Specifically, during the data collection and preprocessing process, the terminal box temperature and humidity display device collects the sensor data using the following formula:

[0106] Temperature and humidity sampling data:

[0107] x t ={T t ,H t}

[0108] In the formula, x t represents the state vector at time t; T t is the temperature value at time t; H t is the humidity value at time t;

[0109] Status S of the heating and dehumidifying device t , recorded as a binary signal:

[0110]

[0111] The preprocessing module of the terminal box temperature and humidity display device performs data standardization on the temperature and humidity data of each time step:

[0112]

[0113] In the formula, represents the standardized temperature and humidity data at time t; x t represents the state vector at time t; μ x is the mean of temperature and humidity data; σ x is the standard deviation of temperature and humidity data;

[0114] The data preprocessing process is as follows:

[0115] The pre-processing module of the terminal box temperature and humidity display device performs preliminary abnormal value filtering by setting the upper and lower limits of temperature and humidity:

[0116]

[0117] In the formula, represents the temperature and humidity data after preliminary outlier filtering at time t; T min , T max is the effective range of temperature; T t Represents the original temperature and humidity data vector at time t; H min , H max is the effective range of humidity; H t represents the humidity value at time t;

[0118] The preprocessing module of the terminal box temperature and humidity display device synchronizes the temperature and humidity data with the timestamp of the equipment working status to perform data alignment.

[0119] Step 2, GRU time series prediction: The background management system trains the GRU model and predicts future temperature and humidity trends based on the real-time collected environmental data inside the terminal box.

[0120] Among them, the process of training the GRU model in the background management system is as follows:

[0121] Step 2.1, data preparation: Assume that the historical time series of temperature and humidity is:

[0122] X={x 1 ,x 2 ,...,x t}

[0123] Where X represents the historical time series of temperature and humidity; t is the temperature and humidity feature vector of the tth time step, including the temperature T t and humidity H t ;

[0124] Predict the temperature and humidity values ​​for the next k time steps using label data:

[0125] Y={y t+1 ,y t+2 ,...,yt+k ,}

[0126] Where Y represents the predicted temperature and humidity value sequence; t+1 Represents the predicted temperature and humidity values ​​at the t+1th time step; y t+2 Represents the predicted temperature and humidity values ​​at the t+2th time step; y t+k Represents the predicted temperature and humidity values ​​at the t+kth time step;

[0127] Step 2.2, GRU unit structure: The update formula of GRU is as follows:

[0128] z t =σ(W z ·[h t-1 ,x t ]+b z )

[0129] In the formula, z t is the update gate, which controls the degree of retention of historical information in the current state; W z is the weight matrix; h t-1 is the hidden state of the previous moment; x t Input data of the current time step; b z is the bias; σ is the Sigmoid activation function, ranging from 0 to 1;

[0130] Reset Gate t :

[0131] r t =σ(W r ·[h t-1 ,x t ]+b r )

[0132] In the formula, r t To reset the gate, control how to combine the historical state and the current input; W r is the weight matrix; h t-1 is the hidden state of the previous moment; x t Input data of the current time step; b r is the bias; σ is the Sigmoid activation function, ranging from 0 to 1;

[0133] Candidate hidden state h t :

[0134] h t =tanh(W h ·[r t ⊙h t-1 ,x t ]+b h )

[0135] In the formula, h t is the candidate hidden state of the current time step; r t is the reset gate; W h is the weight matrix; b h is bias;

[0136] It is element-by-element multiplication; tanh is the hyperbolic tangent function, with a range of -1, 1;

[0137] Hide status updateh t :

[0138] h t =z t ⊙h t-1 +(1-z t )⊙h t

[0139] In the formula, h t is the hidden state of the current time step, which is a combination of historical information and current input; t is the update gate. Step 2.3, output layer: The hidden state h is transformed into t Converted into predicted value y t :

[0140] y t =W o ·h t +b o

[0141] In the formula, y t is the predicted value of the current time step; h t The hidden state at the current time step; W o is the output layer weight; b o is the bias for the output layer.

[0142] Step 3, feature extraction: The background management system extracts the temperature and humidity fluctuation range, trend, and heating device status characteristics based on the real-time collected environmental data inside the terminal box.

[0143] Step 4, K-means clustering: The background management system clusters the terminal boxes based on the real-time collected environmental data inside the terminal boxes, and generates patrol strategies according to family classification.

[0144] During the K-means clustering process, data preparation is as follows:

[0145] Extract features from the GRU model or directly use historical data to construct the feature vector of each terminal box:

[0146] V={v 1 ,v 2 ,...,v n}

[0147] Where V is the set of feature vectors; v i is the characteristic vector of the i-th terminal box, which includes the temperature fluctuation range, the number of times the heating device starts and stops, and the mean value of temperature and humidity; n is the number of terminal boxes;

[0148] Temperature and humidity fluctuation range:

[0149] In the formula, is the fluctuation range of temperature and humidity; max(T) is the highest temperature measured in time period t; min(T) is the lowest temperature measured in time period t; is the humidity fluctuation range; max(H) is the highest humidity measured in the time period t; min(H) is the lowest humidity measured in the time period t;

[0150] The average temperature and humidity is

[0151] The number of starts and stops of the heating and dehumidification device is S count .

[0152] The K-means clustering process includes the following steps:

[0153] Step 4.1, Initialize cluster centers: Randomly select K points as the initial cluster centers {μ 1 ,μ 2 ,...,μ k};

[0154] In the formula, μ 1 is the center of the first cluster; μ k is the center of the kth cluster;

[0155] Step 4.2, calculate cluster assignment: assign each sample to the nearest cluster based on the Euclidean distance:

[0156]

[0157] In the formula, C i is the sample set belonging to the i-th cluster; v j is the jth sample; μ i is the cluster center of the i-th cluster; ||v j -μ i || 2 For sample v j To cluster center μ i The square of the Euclidean distance; Represents the index of other clusters in all clusters;

[0158] Step 4.3, calculate the new center of each cluster as the mean of the samples in the cluster:

[0159]

[0160] In the formula, μ i is the new center of the i-th cluster; |C i | is the number of samples in the i-th cluster; Represents all samples v belonging to the i-th cluster j Perform summation;

[0161] Step 4.4, iterative optimization: repeat steps 4.2 and 4.3 until the cluster centers converge or the maximum number of iterations is reached.

[0162] The optimization goal of the K-means clustering process is to minimize the intra-cluster sum of squares WCSS;

[0163]

[0164] Where WCSS is the minimum intra-cluster sum of squares; K represents the total number of clusters in the cluster; i is the index of the cluster; v j is the jth sample; C i is the sample set belonging to the i-th cluster; μ i is the cluster center of the ith cluster.

[0165] Step 5, strategy formulation: The background management system formulates high-frequency patrols for high-volatility clusters and low-frequency patrols for stable clusters.

[0166] The patrol strategy is based on the strategy classification conditions, which include:

[0167] High-frequency inspection: temperature and humidity fluctuation range R T , R H High, heating and dehumidification device operating frequency S count high;

[0168] Regular inspections: Temperature and humidity trends show moderate fluctuations and normal operating frequency;

[0169] Low-frequency inspection: low temperature and humidity fluctuation range, low operating frequency, and no obvious abnormalities;

[0170] Generate strategies through logical conditions:

[0171]

[0172] At the same time, it should be pointed out that the above description is only a typical embodiment of the present invention and does not limit the present invention in any form. Any changes made to the above embodiments based on the ideas and technical essence of the present invention are within the protection scope of the present invention.

Claims

1. A terminal box mechanism box internal environment monitoring and fault display device, characterized in that: It includes a temperature sensor, a humidity sensor, and a terminal box temperature and humidity display device; the temperature sensor and humidity sensor are installed at key positions inside the terminal box, and the temperature sensor and humidity sensor have built-in microprocessors and data transmission modules; the terminal box temperature and humidity display device is detachably installed on the outside of the terminal box through a wall-mounted bracket, and is internally integrated with a data receiving module, a display screen, and a communication interface that matches the existing video remote patrol system. It is connected to the temperature sensor and humidity sensor data transmission through the data receiving module, and the terminal box temperature and humidity display device is also connected to a heating and dehumidification device installed inside the terminal box.

2. The terminal box mechanism box internal environment monitoring and fault display device according to claim 1 is characterized in that: The temperature sensor and humidity sensor collect environmental data inside the terminal box in real time; the terminal box temperature and humidity display device collects and displays the temperature and humidity values ​​inside the terminal box and the working status of the heating and dehumidification device, and uses the Internet of Things to transmit the collected temperature and humidity data and heating and dehumidification device status data to the central database.

3. The terminal box mechanism box internal environment monitoring and fault display device according to claim 1 is characterized in that: The terminal box temperature and humidity display device is also provided with a preprocessing module, which performs abnormal data cleaning, data alignment, and data standardization on the temperature and humidity numerical data inside the terminal box through the preprocessing module.

4. A terminal box mechanism box internal environment monitoring method, the terminal box mechanism box internal environment monitoring method is based on the terminal box mechanism box internal environment monitoring and fault display device according to any one of claims 1-3, characterized in that: The following steps are involved: Step 1, data collection and preprocessing: the background management system collects and standardizes the temperature and humidity data of the terminal box; Step 2, GRU time series prediction: The background management system trains the GRU model and predicts the future temperature and humidity trends based on the real-time collected environmental data inside the terminal box; Step 3, feature extraction: The background management system extracts the temperature and humidity fluctuation range, trend, and heating device status characteristics based on the real-time collected terminal box environment data; Step 4, K-means clustering: The backend management system clusters the terminal boxes based on the real-time collected environmental data inside the terminal boxes, and generates patrol strategies based on the family classification; Step 5, strategy formulation: The background management system formulates high-frequency patrols for high-volatility clusters and low-frequency patrols for stable clusters.

5. The method for monitoring the internal environment of a terminal box mechanism box according to claim 4, characterized in that: During data collection and preprocessing, the terminal box temperature and humidity display device collects sensor data using the following formula: Temperature and humidity sampling data: x t ={T t ,H t } In the formula, x t represents the state vector at time t; T t is the temperature value at time t; H t is the humidity value at time t; Status S of the heating dehumidification device t , recorded as a binary signal: The preprocessing module of the terminal box temperature and humidity display device performs data standardization on the temperature and humidity data of each time step: In the formula, represents the standardized temperature and humidity data at time t; x t represents the state vector at time t; μ x is the mean of temperature and humidity data; σ x is the standard deviation of temperature and humidity data; The pre-processing module of the terminal box temperature and humidity display device performs preliminary abnormal value filtering by setting the upper and lower limits of temperature and humidity: In the formula, Represents the temperature and humidity data after preliminary outlier filtering at time t; T min , T max is the effective range of temperature; T t Represents the original temperature and humidity data vector at time t; H min , H max is the effective range of humidity; H t represents the humidity value at time t; The preprocessing module of the terminal box temperature and humidity display device synchronizes the temperature and humidity data with the timestamp of the equipment working status to perform data alignment.

6. The method for monitoring the internal environment of a terminal box mechanism box according to claim 4, characterized in that: The process of training the GRU model in the background management system is as follows: Step 2.1, data preparation: Assume that the historical time series of temperature and humidity is: X={x1,x2,...,x t } Where X represents the historical time series of temperature and humidity; t is the temperature and humidity feature vector of the tth time step, including the temperature T t and humidity H t ; Predict the temperature and humidity values ​​for the next k time steps using label data: And={and t+1 ,and t+2 ,...,and t+k ,} Where Y represents the predicted temperature and humidity value sequence; t+1 Represents the predicted temperature and humidity values ​​at the t+1th time step; y t+2 Represents the predicted temperature and humidity values ​​at the t+2th time step; y t+k Represents the predicted temperature and humidity values ​​at the t+kth time step; Step 2.2, GRU unit structure: The update formula of GRU is as follows: z t =σ(W z ·[h t-1 ,x t ]+b z ) In the formula, z t is the update gate, which controls the degree of retention of historical information in the current state; W z is the weight matrix; h t-1 is the hidden state of the previous moment; x t Input data of the current time step; b z is the bias; σ is the Sigmoid activation function, ranging from 0 to 1; Reset Gate t : r t =σ(W r ·[h t-1 ,x t ]+b r ) In the formula, r t To reset the gate, control how to combine the historical state and the current input; W r is the weight matrix; h t-1 is the hidden state of the previous moment; x t Input data of the current time step; b r is the bias; σ is the Sigmoid activation function, ranging from 0 to 1; Candidate hidden state h t : h t =tanh(W h ·[r t ⊙h t-1 ,x t ]+b h ) In the formula, h t is the candidate hidden state of the current time step; r t is the reset gate; W h is the weight matrix; b h is the bias; ⊙ is the element-by-element multiplication; tanh is the hyperbolic tangent function, ranging from -1 to 1; Hide status updateh t : h t =z t ⊙h t-1 +(1-z t )⊙h t In the formula, h t is the hidden state of the current time step, which is a combination of historical information and current input; t For updating the door. Step 2.3, output layer: pass the fully connected layer to transform the hidden state h t Converted into predicted value y t : y t =W o ·h t +b o In the formula, y t is the predicted value of the current time step; h t The hidden state at the current time step; W o is the output layer weight; b o is the bias for the output layer.

7. The method for monitoring the internal environment of a terminal box mechanism box according to claim 4, characterized in that: During the K-means clustering process, data preparation is as follows: Extract features from the GRU model or directly use historical data to construct the feature vector of each terminal box: V={v1,v2,...,v n } In the formula, v i is the characteristic vector of the i-th terminal box, which includes the temperature fluctuation range, the number of times the heating device starts and stops, and the mean value of temperature and humidity; n is the number of terminal boxes; Temperature and humidity fluctuation range: The average temperature and humidity is The number of starts and stops of the heating and dehumidification device is S count .

8. The method for monitoring the internal environment of a terminal box mechanism box according to claim 4, characterized in that: The K-means clustering process includes the following steps: Step 4.1, initialize cluster centers: randomly select K points as the initial cluster centers {μ1,μ2,...,μ k }; Where μ1 is the center of the first cluster; μ k is the center of the kth cluster; Step 4.2, calculate cluster assignment: assign each sample to the nearest cluster based on the Euclidean distance: In the formula, C i is the sample set belonging to the i-th cluster; v j is the jth sample; μ i is the cluster center of the i-th cluster; ||v j -μ i || 2 For sample v j To cluster center μ i The square of the Euclidean distance; Represents the index of other clusters in all clusters; Step 4.3, calculate the new center of each cluster as the mean of the samples in the cluster: In the formula, μ i is the new center of the i-th cluster; |C i | is the number of samples in the i-th cluster; Represents all samples v belonging to the i-th cluster j Perform summation; Step 4.4, iterative optimization: repeat steps 4.2 and 4.3 until the cluster centers converge or the maximum number of iterations is reached.

9. The method for monitoring the internal environment of a terminal box mechanism box according to claim 4, characterized in that: The optimization goal of the K-means clustering process is to minimize the intra-cluster sum of squares WCSS; Where WCSS is the minimum intra-cluster sum of squares; K represents the total number of clusters in the cluster; i is the index of the cluster; v j is the jth sample; C i is the sample set belonging to the i-th cluster; μ i is the cluster center of the ith cluster.

10. The method for monitoring the internal environment of a terminal box mechanism box according to claim 4, characterized in that: The patrol strategy is based on strategy classification conditions, which include: High-frequency inspection: temperature and humidity fluctuation range R T , R H High, heating and dehumidification device operating frequency S count high; Regular inspections: Temperature and humidity trends show moderate fluctuations and normal operating frequency; Low-frequency inspection: low temperature and humidity fluctuation range, low operating frequency, and no obvious abnormalities; Generate strategies through logical conditions:

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