Transformer substation temperature and humidity sensor detection method and system

Abstract

The invention discloses a transformer substation temperature and humidity sensor detection method and system, and relates to the technical field of transformer substation intelligent monitoring, and the method comprises the following steps: installing a temperature and humidity sensor according to a detection demand, and collecting data information; carrying out statistical analysis on the collected data information to obtain correlation coefficients between temperature and humidity of different areas; introducing a time sequence analysis model, analyzing the long-term trend and short-term fluctuation of the temperature and humidity, drawing a trend chart, and obtaining a periodic mode in the temperature and humidity data; key features are extracted from a periodic mode in the temperature and humidity data, and feature dimensions are reduced; a detection interface is generated, temperature and humidity threshold values are set, and when the temperature and humidity exceed the temperature and humidity threshold values, an alarm system is given out. According to the invention, key features in the temperature and humidity data are extracted and pattern recognition is carried out, so that the system can automatically classify a normal pattern, an early warning pattern and an abnormal pattern, can timely obtain abnormal conditions of the temperature and humidity of the transformer substation, and carries out intervention and processing in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring of transformer substations, and in particular to a method and system for detecting temperature and humidity sensors of transformer substations. Background Art

[0002] Smart substations are substations that use advanced, reliable, integrated, low-carbon, and environmentally friendly intelligent equipment, with the basic requirements of full-station information digitization, communication platform networking, and information sharing standardization. They automatically complete basic functions such as information collection, measurement, control, protection, metering, and monitoring, and can support advanced functions such as real-time automatic control of the power grid, intelligent regulation, online analysis and decision-making, and collaborative interaction as needed. At present, most substations measure ambient temperature through thermometers.

[0003] However, there are still the following problems in actual operation:

[0004] Existing temperature and humidity sensor detection systems may lack a detailed understanding of the temperature and humidity conditions in different areas of the substation, making it difficult to accurately differentiate operation and maintenance strategies. Substation temperature and humidity are often affected by seasonal changes or equipment operation cycles, and existing systems may not be able to effectively identify these periodic patterns. They rely on simple threshold judgments to identify anomalies and cannot effectively respond to complex and changing abnormal situations. Summary of the invention

[0005] In view of the problems existing in the existing substation temperature and humidity sensor detection and system, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is that the existing system may not be able to effectively identify these periodic patterns, and relies on simple threshold judgment to identify anomalies, and cannot effectively deal with complex and changeable abnormal situations.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a substation temperature and humidity sensor detection method, which comprises the following steps:

[0009] According to the detection requirements, install the temperature and humidity sensors and collect data information;

[0010] Conduct statistical analysis on the collected data information to obtain the correlation coefficient between temperature and humidity in different areas;

[0011] Introduce time series analysis models to analyze long-term trends and short-term fluctuations of temperature and humidity, draw trend graphs, and obtain periodic patterns in temperature and humidity data;

[0012] Extract key features from periodic patterns in temperature and humidity data to reduce feature dimensions;

[0013] Generate a detection interface and set temperature and humidity thresholds. When the temperature and humidity exceed the thresholds, an alarm system will be issued.

[0014] As a preferred solution of the substation temperature and humidity sensor detection method of the present invention, wherein: the detection requirements include temperature and humidity accuracy, response time, long-term stability and anti-interference ability, setting the upper and lower limits of temperature and humidity detection, and determining the key areas in the substation that are sensitive to temperature and humidity changes;

[0015] The installation locations for temperature and humidity sensors include switch cabinets, busbar rooms, equipment heat dissipation outlets, vents, doors and windows.

[0016] As a preferred solution of the substation temperature and humidity sensor detection method of the present invention, the step of statistically analyzing the collected data information includes:

[0017] Data partition aggregation: read the pre-processed data, divide the temperature and humidity data into regional groups according to the regional identification according to the division of different areas in the substation, aggregate and calculate the data of each regional group, and generate statistics, including the mean, median, standard deviation, maximum value and minimum value;

[0018] Draw a box plot for the temperature and humidity data of each area, perform a visual analysis of the temperature and humidity data of each area based on the box plot, calculate the median and quartile according to the definition of the box plot, identify outliers that exceed the normal range, and record the outliers.

[0019] As a preferred solution of the substation temperature and humidity sensor detection method of the present invention, the step of statistically analyzing the collected data information also includes:

[0020] Calculate the correlation between temperature and humidity data in different regions, organize the calculated correlation coefficients into a matrix form and visualize them, and identify strongly correlated and weakly correlated region pairs;

[0021] Draw a linear trend line for the temperature and humidity data of each area, judge the long-term change trend of the data based on the shape and slope of the trend line, preliminarily identify the periodic fluctuations and seasonal change patterns in the data, and record the preliminarily identified patterns and trends.

[0022] As a preferred solution of the substation temperature and humidity sensor detection method of the present invention, the step of analyzing the periodic pattern in the temperature and humidity data includes:

[0023] Estimate the parameters of the time series analysis model based on historical data, use historical data to fit the model, determine the model parameters, perform diagnostic tests on the model, determine the smoothing parameters, and use the selected smoothing parameters to smooth the original time series data to obtain the smoothed time series data;

[0024] The smoothed time series data is decomposed into trend components, seasonal components and random components. Based on the decomposed trend components, the time series model is used to predict future trends, identify periodic patterns in the time series data, analyze the detected periodic patterns, and determine the period length and intensity characteristics of the periodic patterns.

[0025] As a preferred solution of the substation temperature and humidity sensor detection method of the present invention, the specific steps of extracting key features from the periodic pattern in the temperature and humidity data include:

[0026] Extracting key features for pattern recognition from the temperature and humidity data, wherein the key features include peak values, valley values, and turning points;

[0027] Among the extracted key features, evaluate the importance of each key feature, select the most representative feature subset, and generate a feature dataset;

[0028] Performing pattern annotation on the feature data set, wherein the pattern annotation includes a normal pattern, a warning pattern, and an abnormal pattern, dividing the feature data set with the pattern annotation into a training set and a test set, using the training set to train the pattern recognition model, and using the test set to evaluate the performance of the pattern recognition model;

[0029] Collect temperature and humidity data in real time, pre-process the data and extract features, input the feature data into the pattern recognition model, and the pattern recognition model performs pattern recognition based on the features of the input data, automatically classifies it into normal mode, warning mode or abnormal mode, and outputs the classification results.

[0030] As a preferred solution of the substation temperature and humidity sensor detection method of the present invention, the application steps of the alarm system include:

[0031] Set temperature and humidity safety thresholds, set high and low temperature and humidity safety thresholds for different monitoring areas, and configure the temperature and humidity safety thresholds to the alarm module of the monitoring system;

[0032] Receive temperature and humidity data from sensors in real time, compare and analyze the data with the set temperature and humidity safety thresholds, and immediately trigger the alarm system if abnormal conditions are found, and record the alarm event, which includes the alarm time, location, and cause information;

[0033] After receiving the alarm information, immediately follow the predetermined alarm response process, notify relevant personnel and initiate the corresponding emergency plan, check and record the alarm cause, handling process and results;

[0034] Among them, the temperature and humidity safety thresholds are intelligently adjusted according to seasonal and climate changes, specifically including the following steps:

[0035] Establish the historical data of temperature and humidity values ​​in the monitoring area where all temperature and humidity sensors are deployed within 3 years, determine the initial temperature and humidity safety thresholds for different seasons based on the historical data of temperature and humidity values, and retrieve the limit detection thresholds of temperature and humidity sensors;

[0036] Determine the initial temperature and humidity safety threshold of the current season according to the current season, and determine the temperature and humidity numerical safety range according to the upper and lower limits of the initial temperature and humidity safety threshold;

[0037] Collect temperature and humidity data in the monitoring area in real time, and compare the temperature and humidity data with the temperature and humidity safety range of the current season to determine whether the initial temperature and humidity safety threshold needs to be adjusted;

[0038] When the temperature and humidity numerical data are within the temperature and humidity numerical safety range, it is determined that there is no need to adjust the initial temperature and humidity safety threshold;

[0039] When the temperature and humidity numerical data exceeds the initial temperature and humidity numerical safety range, it is determined that the temperature and humidity safety threshold needs to be adjusted; start to retrieve the temperature and humidity numerical data within the past ten days, and select the valid adjustment parameter values ​​that meet the preset conditions; calculate the adjustment deviation value based on the valid adjustment parameter value, and adjust the temperature and humidity safety threshold based on the adjustment deviation value;

[0040] The adjustment of temperature and humidity safety thresholds includes:

[0041] When the temperature and humidity numerical data exceeds the upper limit of the initial temperature and humidity safety threshold, the temperature and humidity numerical data is compared with the upper limit of the limit detection threshold. If it exceeds the upper limit of the limit detection threshold, the data is excluded; if it does not exceed the upper limit of the limit detection threshold, the data is retained as a valid adjustment parameter value.

[0042] In a second aspect, an embodiment of the present invention provides a substation temperature and humidity sensor detection system, which includes a data acquisition module, a statistical analysis module, a trend analysis module, and an alarm module;

[0043] The data acquisition module is used to install the temperature and humidity sensor and collect the data information needed for use;

[0044] The statistical analysis module summarizes the data information according to different monitoring areas, calculates statistics for the temperature and humidity data of each area, analyzes the distribution of the temperature and humidity data based on the box plot, and calculates the correlation coefficient between the temperature and humidity in different areas;

[0045] The trend analysis module determines the time series analysis model parameters, smoothes the original time series data, analyzes the long-term trend and short-term fluctuation of temperature and humidity based on the time series model, draws a trend graph and analyzes the periodic patterns in the temperature and humidity data;

[0046] The alarm module generates a monitoring interface, displays the analysis results in the form of charts and curves through the monitoring interface, sets temperature and humidity safety thresholds, and triggers the alarm system when the temperature and humidity exceed the set temperature and humidity safety thresholds.

[0047] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned substation temperature and humidity sensor detection method is implemented.

[0048] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned substation temperature and humidity sensor detection method is implemented.

[0049] The beneficial effects of the present invention are:

[0050] Through data partitioning and aggregation, the present invention can clearly understand the temperature and humidity conditions of different areas in the substation, which is helpful for operation and maintenance personnel to formulate differentiated operation and maintenance strategies according to the characteristics of different areas and realize refined management. Box plot analysis can effectively identify abnormal values ​​beyond the normal range. Correlation analysis reveals the correlation between temperature and humidity changes between different areas, which is helpful to understand the mutual influence of the internal environment of the substation. By identifying strongly correlated and weakly correlated area pairs, the monitoring layout can be optimized and the monitoring efficiency can be improved. By drawing linear trend lines and identifying periodic fluctuations and seasonal change patterns in the data, the long-term change trend of temperature and humidity in the substation can be more accurately grasped, and equipment failures and safety accidents caused by changes in temperature and humidity can be prevented.

[0051] The present invention decomposes time series data into trend components, seasonal components and random components, which helps to deeply understand the composition and change rules of substation temperature and humidity data, and can analyze the impact of different components on the overall temperature and humidity changes, so as to formulate more accurate operation and maintenance strategies. The periodic pattern recognition function in trend analysis can automatically detect and analyze the periodic change patterns in substation temperature and humidity data. By determining the period length and intensity characteristics of the periodic pattern, operation and maintenance personnel can predict future temperature and humidity change trends, providing important reference for preventive maintenance and fault warning of equipment.

[0052] By extracting key features from temperature and humidity data and performing pattern recognition, the present invention can automatically classify the system into normal mode, warning mode and abnormal mode, and can timely obtain abnormal conditions of temperature and humidity in the substation, and intervene and process them in advance. The pattern recognition model can learn the laws and characteristics of temperature and humidity changes based on a large amount of historical data, and can quickly judge the current temperature and humidity status according to the classification results of the pattern recognition model, and formulate corresponding operation and maintenance strategies accordingly, optimize resource allocation and work flow, and the temperature and humidity data collected and processed in real time can grasp the temperature and humidity status of the substation at any time without frequent on-site inspections, thereby improving the operation and maintenance efficiency and timely discovering and processing abnormalities and potential problems in temperature and humidity data. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0054] Figure 1 The figure is a flow chart of the detection method of temperature and humidity sensors in substations. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0058] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0059] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0060] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] Example 1

[0062] Reference Figure 1 , which is the first embodiment of the present invention, and provides a substation temperature and humidity sensor detection method, comprising the following steps:

[0063] S1. Install the temperature and humidity sensors according to the detection requirements and collect data information.

[0064] The detection requirements include temperature and humidity accuracy, response time, long-term stability and anti-interference ability, setting the upper and lower limits of temperature and humidity detection, and determining the key areas in the substation that are sensitive to temperature and humidity changes;

[0065] The installation locations for temperature and humidity sensors include switch cabinets, busbar rooms, equipment heat dissipation outlets, vents, doors and windows.

[0066] By accurately monitoring the temperature and humidity changes in key areas of the substation, timely discovering and warning of potential abnormal temperature and humidity conditions can help prevent equipment failures or safety accidents caused by inappropriate temperature and humidity, thereby improving the overall operational safety of the substation. Automated, real-time data collection and transmission, as well as computer or cloud server-based data preprocessing and analysis, greatly reduce the workload of operation and maintenance personnel. At the same time, intelligent statistical analysis, trend analysis and pattern recognition functions enable operation and maintenance personnel to more quickly and accurately grasp the temperature and humidity conditions of the substation, improving operation and maintenance efficiency.

[0067] By setting the detection requirements of temperature and humidity sensors and arranging sensors in key areas, the collected data can be ensured to have high accuracy and reliability. Based on the statistical analysis results, the areas or time periods with large temperature and humidity changes can be identified, so as to strengthen the monitoring and maintenance of these areas in a targeted manner. At the same time, through trend analysis and pattern recognition, the future trend of temperature and humidity changes can be predicted, providing a basis for resource allocation and emergency preparedness, and avoiding waste of resources.

[0068] S2. Perform statistical analysis on the collected data information to obtain the correlation coefficient between temperature and humidity in different areas.

[0069] The step of statistically analyzing the collected data information also includes:

[0070] Calculate the correlation between temperature and humidity data in different regions, organize the calculated correlation coefficients into a matrix form and visualize them, and identify strongly correlated and weakly correlated region pairs;

[0071] Draw a linear trend line for the temperature and humidity data of each area, judge the long-term change trend of the data based on the shape and slope of the trend line, preliminarily identify the periodic fluctuations and seasonal change patterns in the data, and record the preliminarily identified patterns and trends.

[0072] S3. Introduce a time series analysis model to analyze the long-term trends and short-term fluctuations of temperature and humidity, draw trend graphs, and obtain periodic patterns in temperature and humidity data.

[0073] The steps to analyze periodic patterns in temperature and humidity data include,

[0074] Estimate the parameters of the time series analysis model based on historical data, use historical data to fit the model, determine the model parameters, perform diagnostic tests on the model, determine the smoothing parameters, and use the selected smoothing parameters to smooth the original time series data to obtain the smoothed time series data;

[0075] The smoothed time series data is decomposed into trend components, seasonal components and random components. Based on the decomposed trend components, the time series model is used to predict future trends, identify periodic patterns in the time series data, analyze the detected periodic patterns, and determine the period length and intensity characteristics of the periodic patterns.

[0076] Through data partitioning and aggregation, we can clearly understand the temperature and humidity conditions in different areas of the substation, including key statistics such as mean, median, and standard deviation, which helps operation and maintenance personnel to formulate differentiated operation and maintenance strategies based on the characteristics of different areas and achieve refined management. Box plot analysis can effectively identify abnormal values ​​beyond the normal range, which is crucial for timely detection of potential temperature and humidity problems. It provides important clues for subsequent problem troubleshooting and cause analysis, and helps to quickly respond to and handle abnormal situations.

[0077] Correlation analysis reveals the correlation between temperature and humidity changes between different areas, which helps operation and maintenance personnel understand the mutual influence of the internal environment of the substation. By identifying strongly correlated and weakly correlated area pairs, the monitoring layout can be optimized and the monitoring efficiency can be improved. At the same time, this also provides a basis for further fault warning and preventive maintenance. By drawing linear trend lines and identifying periodic fluctuations and seasonal change patterns in the data, operation and maintenance personnel can more accurately grasp the long-term change trend of substation temperature and humidity, which helps to formulate a more scientific and reasonable operation and maintenance plan and prevent equipment failures and safety accidents caused by changes in temperature and humidity.

[0078] S4. Extract key features from the periodic patterns in temperature and humidity data to reduce feature dimensions.

[0079] The specific steps to extract key features from the periodic patterns in temperature and humidity data include:

[0080] Extracting key features for pattern recognition from the temperature and humidity data, wherein the key features include peak values, valley values, and turning points;

[0081] Among the extracted key features, evaluate the importance of each key feature, select the most representative feature subset, and generate a feature dataset;

[0082] Performing pattern annotation on the feature data set, wherein the pattern annotation includes a normal pattern, a warning pattern, and an abnormal pattern, dividing the feature data set with the pattern annotation into a training set and a test set, using the training set to train the pattern recognition model, and using the test set to evaluate the performance of the pattern recognition model;

[0083] Collect temperature and humidity data in real time, pre-process the data and extract features, input the feature data into the pattern recognition model, and the pattern recognition model performs pattern recognition based on the features of the input data, automatically classifies it into normal mode, warning mode or abnormal mode, and outputs the classification results.

[0084] S5. Generate a detection interface and set temperature and humidity thresholds. When the temperature and humidity exceed the temperature and humidity thresholds, an alarm system is issued.

[0085] The application steps of the alarm system include:

[0086] Set temperature and humidity safety thresholds, set high and low temperature and humidity safety thresholds for different monitoring areas, and configure the temperature and humidity safety thresholds to the alarm module of the monitoring system;

[0087] Receive temperature and humidity data from sensors in real time, compare and analyze the data with the set temperature and humidity safety thresholds, and immediately trigger the alarm system if abnormal conditions are found, and record the alarm event, which includes the alarm time, location, and cause information;

[0088] After receiving the alarm information, immediately follow the predetermined alarm response process, notify relevant personnel and initiate the corresponding emergency plan, check and record the alarm cause, handling process and results;

[0089] Among them, the temperature and humidity safety thresholds are intelligently adjusted according to seasonal and climate changes, specifically including the following steps:

[0090] Establish the historical data of temperature and humidity values ​​in the monitoring area where all temperature and humidity sensors are deployed within 3 years, determine the initial temperature and humidity safety thresholds for different seasons based on the historical data of temperature and humidity values, and retrieve the limit detection thresholds of temperature and humidity sensors;

[0091] Determine the initial temperature and humidity safety threshold of the current season according to the current season, and determine the temperature and humidity numerical safety range according to the upper and lower limits of the initial temperature and humidity safety threshold;

[0092] Collect temperature and humidity data in the monitoring area in real time, and compare the temperature and humidity data with the temperature and humidity safety range of the current season to determine whether the initial temperature and humidity safety threshold needs to be adjusted;

[0093] When the temperature and humidity numerical data are within the temperature and humidity numerical safety range, it is determined that there is no need to adjust the initial temperature and humidity safety threshold;

[0094] When the temperature and humidity numerical data exceeds the initial temperature and humidity numerical safety range, it is determined that the temperature and humidity safety threshold needs to be adjusted; start to retrieve the temperature and humidity numerical data within the past ten days, and select the valid adjustment parameter values ​​that meet the preset conditions; calculate the adjustment deviation value based on the valid adjustment parameter value, and adjust the temperature and humidity safety threshold based on the adjustment deviation value;

[0095] The adjustment of temperature and humidity safety thresholds includes:

[0096] When the temperature and humidity numerical data exceeds the upper limit of the initial temperature and humidity safety threshold, the temperature and humidity numerical data is compared with the upper limit of the limit detection threshold. If it exceeds the upper limit of the limit detection threshold, the data is excluded; if it does not exceed the upper limit of the limit detection threshold, the data is retained as a valid adjustment parameter value.

[0097] The adjustment deviation value is calculated based on the effective adjustment parameter value, where the calculation formula of the upper adjustment deviation value is as follows:

[0098]

[0099] Where T1 is the temperature adjustment deviation value, T max is the upper limit of the temperature limit detection threshold, T n is the effective adjustment parameter value of the nth temperature, M1 is the upper adjustment deviation value of humidity, M max is the upper limit of the humidity limit detection threshold, M n is the effective adjustment parameter value of the nth humidity;

[0100] The temperature and humidity safety threshold is adjusted based on the adjustment deviation value. At this time, the temperature and humidity safety threshold is the upper limit value of the limit detection threshold minus the upper adjustment deviation value;

[0101] When the temperature and humidity numerical data exceeds the lower limit of the initial temperature and humidity safety threshold, the temperature and humidity numerical data is compared with the lower limit of the limit detection threshold. If it exceeds the lower limit of the limit detection threshold, the data is excluded. If it does not exceed the lower limit of the limit detection threshold, the data is retained as a valid adjustment parameter value.

[0102] The adjustment deviation value is calculated based on the effective adjustment parameter value, where the calculation formula for the lower adjustment deviation value is:

[0103]

[0104] Where, T2 is the adjustment deviation value under temperature, T min is the lower limit of the temperature limit detection threshold, T n is the effective adjustment parameter value of the nth temperature, M2 is the adjustment deviation value under humidity, and M min is the lower limit of the humidity limit detection threshold, M nis the effective adjustment parameter value of the nth humidity;

[0105] The temperature and humidity safety threshold is adjusted based on the adjustment deviation value. At this time, the temperature and humidity safety threshold is the lower limit value of the limit detection threshold plus the lower adjustment deviation value.

[0106] For example:

[0107] In large substations, traditional temperature and humidity monitoring methods often have problems such as untimely data collection, inaccurate analysis, and slow alarm response. In order to improve monitoring efficiency and accuracy, this embodiment uses the Internet of Things technology and combines the above-mentioned substation temperature and humidity sensor detection method to construct an intelligent monitoring process:

[0108] In this embodiment, the system architecture includes a sensor network, a data acquisition and processing center, a cloud server, a monitoring interface and an alarm system. High-precision temperature and humidity sensors are arranged in key areas such as switch cabinets, busbar rooms, equipment heat dissipation ports, vents, doors and windows of the substation. A sensor network is formed through Zigbee or LoRa wireless communication technology. A data acquisition and processing center is set up, including wired and wireless data receiving modules. The data collected by the sensor network is transmitted to the center in real time and pre-processed. The pre-processed data is uploaded to the cloud server for statistical analysis, trend analysis, and pattern recognition processing. The temperature and humidity data and analysis results of each area are displayed in real time, and a threshold is set. Once the data exceeds the threshold, an alarm is triggered immediately.

[0109] In order to implement this embodiment, sufficient sensors are covered in each key area, and efficient data transmission is achieved through the Internet of Things technology. The powerful computing power of the cloud server is used to clean and denoise the data to improve the data quality. The temperature and humidity data are deeply analyzed based on box plots, time series analysis and other methods to identify abnormal and periodic patterns. By setting thresholds and alarm logic, it is ensured that the alarm can be quickly triggered when the temperature and humidity are abnormal, and the relevant personnel are notified. After the system is put into operation, the real-time and accuracy of temperature and humidity monitoring in the substation are significantly improved, the equipment failures and safety hazards caused by abnormal temperature and humidity are reduced, and the operation efficiency and safety of the substation are improved.

[0110] Example 2

[0111] On the basis of the first embodiment, this embodiment further provides a substation temperature and humidity sensor detection system, including a data acquisition module, a statistical analysis module, a trend analysis module, and an alarm module;

[0112] The data acquisition module is used to install the temperature and humidity sensor and collect the data information needed for use;

[0113] The statistical analysis module summarizes the data information according to different monitoring areas, calculates statistics for the temperature and humidity data of each area, analyzes the distribution of the temperature and humidity data based on the box plot, and calculates the correlation coefficient between the temperature and humidity in different areas;

[0114] The trend analysis module determines the time series analysis model parameters, smoothes the original time series data, analyzes the long-term trend and short-term fluctuation of temperature and humidity based on the time series model, draws a trend graph and analyzes the periodic patterns in the temperature and humidity data;

[0115] The alarm module generates a monitoring interface, displays the analysis results in the form of charts and curves through the monitoring interface, sets temperature and humidity safety thresholds, and triggers the alarm system when the temperature and humidity exceed the set temperature and humidity safety thresholds.

[0116] This embodiment also provides a computer device, which is suitable for the substation temperature and humidity sensor detection method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the substation temperature and humidity sensor detection method proposed in the above embodiment.

[0117] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0118] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for detecting the temperature and humidity sensor of a substation as proposed in the above embodiment is implemented.

[0119] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A substation temperature and humidity sensor detection method, characterized in that: The following steps are included: According to the detection requirements, install the temperature and humidity sensors and collect data information; Conduct statistical analysis on the collected data information to obtain the correlation coefficient between temperature and humidity in different areas; Introduce time series analysis models to analyze long-term trends and short-term fluctuations of temperature and humidity, draw trend graphs, and obtain periodic patterns in temperature and humidity data; Extract key features from periodic patterns in temperature and humidity data to reduce feature dimensions; Generate a detection interface and set temperature and humidity thresholds. When the temperature and humidity exceed the thresholds, an alarm system will be issued.

2. The substation temperature and humidity sensor detection method according to claim 1, characterized in that: The detection requirements include temperature and humidity accuracy, response time, long-term stability and anti-interference ability, setting the upper and lower limits of temperature and humidity detection, and determining the key areas in the substation that are sensitive to temperature and humidity changes; The installation locations for temperature and humidity sensors include switch cabinets, busbar rooms, equipment heat dissipation outlets, vents, doors and windows.

3. The substation temperature and humidity sensor detection method according to claim 2, characterized in that: The steps of statistical analysis of the collected data information include: Data partition aggregation: read the pre-processed data, divide the temperature and humidity data into regional groups according to the regional identification according to the division of different areas in the substation, aggregate and calculate the data of each regional group, and generate statistics, including the mean, median, standard deviation, maximum value and minimum value; Draw a box plot for the temperature and humidity data of each area, perform a visual analysis of the temperature and humidity data of each area based on the box plot, calculate the median and quartile according to the definition of the box plot, identify outliers that exceed the normal range, and record the outliers.

4. The substation temperature and humidity sensor detection method according to claim 3, characterized in that: The step of statistically analyzing the collected data information also includes: Calculate the correlation between temperature and humidity data in different regions, organize the calculated correlation coefficients into a matrix form and visualize them, and identify strongly correlated and weakly correlated region pairs; Draw a linear trend line for the temperature and humidity data of each area, judge the long-term change trend of the data based on the shape and slope of the trend line, preliminarily identify the periodic fluctuations and seasonal change patterns in the data, and record the preliminarily identified patterns and trends.

5. The substation temperature and humidity sensor detection method according to claim 4, characterized in that: The steps to analyze periodic patterns in temperature and humidity data include, Estimate the parameters of the time series analysis model based on historical data, use historical data to fit the model, determine the model parameters, perform diagnostic tests on the model, determine the smoothing parameters, and use the selected smoothing parameters to smooth the original time series data to obtain the smoothed time series data; The smoothed time series data is decomposed into trend components, seasonal components and random components. Based on the decomposed trend components, the time series model is used to predict future trends, identify periodic patterns in the time series data, analyze the detected periodic patterns, and determine the period length and intensity characteristics of the periodic patterns.

6. The substation temperature and humidity sensor detection method according to claim 5, characterized in that: The specific steps to extract key features from the periodic patterns in temperature and humidity data include: Extracting key features for pattern recognition from the temperature and humidity data, wherein the key features include peak values, valley values, and turning points; Among the extracted key features, evaluate the importance of each key feature, select the most representative feature subset, and generate a feature dataset; Performing pattern annotation on the feature data set, wherein the pattern annotation includes a normal pattern, a warning pattern, and an abnormal pattern, dividing the feature data set with the pattern annotation into a training set and a test set, using the training set to train the pattern recognition model, and using the test set to evaluate the performance of the pattern recognition model; Collect temperature and humidity data in real time, pre-process the data and extract features, input the feature data into the pattern recognition model, and the pattern recognition model performs pattern recognition based on the features of the input data, automatically classifies it into normal mode, warning mode or abnormal mode, and outputs the classification results.

7. The substation temperature and humidity sensor detection method according to claim 6, characterized in that: The application steps of the alarm system include: Set temperature and humidity safety thresholds, set high and low temperature and humidity safety thresholds for different monitoring areas, and configure the temperature and humidity safety thresholds to the alarm module of the monitoring system; Receive temperature and humidity data from sensors in real time, compare and analyze the data with the set temperature and humidity safety thresholds, and immediately trigger the alarm system if abnormal conditions are found, and record the alarm event, which includes the alarm time, location, and cause information; After receiving the alarm information, immediately follow the predetermined alarm response process, notify relevant personnel and initiate the corresponding emergency plan, check and record the alarm cause, handling process and results; Among them, the temperature and humidity safety thresholds are intelligently adjusted according to seasonal and climate changes, specifically including the following steps: Establish the historical data of temperature and humidity values ​​in the monitoring area where all temperature and humidity sensors are deployed within 3 years, determine the initial temperature and humidity safety thresholds for different seasons based on the historical data of temperature and humidity values, and retrieve the limit detection thresholds of temperature and humidity sensors; Determine the initial temperature and humidity safety threshold of the current season according to the current season, and determine the temperature and humidity numerical safety range according to the upper and lower limits of the initial temperature and humidity safety threshold; Collect temperature and humidity data in the monitoring area in real time, and compare the temperature and humidity data with the temperature and humidity safety range of the current season to determine whether the initial temperature and humidity safety threshold needs to be adjusted; When the temperature and humidity numerical data are within the temperature and humidity numerical safety range, it is determined that there is no need to adjust the initial temperature and humidity safety threshold; When the temperature and humidity numerical data exceeds the initial temperature and humidity numerical safety range, it is determined that the temperature and humidity safety threshold needs to be adjusted; start to retrieve the temperature and humidity numerical data within the past ten days, and select the valid adjustment parameter values ​​that meet the preset conditions; calculate the adjustment deviation value based on the valid adjustment parameter value, and adjust the temperature and humidity safety threshold based on the adjustment deviation value; The adjustment of temperature and humidity safety thresholds includes: When the temperature and humidity numerical data exceeds the upper limit of the initial temperature and humidity safety threshold, the temperature and humidity numerical data is compared with the upper limit of the limit detection threshold. If it exceeds the upper limit of the limit detection threshold, the data is excluded; if it does not exceed the upper limit of the limit detection threshold, the data is retained as a valid adjustment parameter value.

8. A substation temperature and humidity sensor detection system, based on the substation temperature and humidity sensor detection method according to any one of claims 1 to 7, characterized in that: Including data acquisition module, statistical analysis module, trend analysis module, alarm module; The data acquisition module is used to install the temperature and humidity sensor and collect the data information needed for use; The statistical analysis module summarizes the data information according to different monitoring areas, calculates statistics for the temperature and humidity data of each area, analyzes the distribution of the temperature and humidity data based on the box plot, and calculates the correlation coefficient between the temperature and humidity in different areas; The trend analysis module determines the time series analysis model parameters, smoothes the original time series data, analyzes the long-term trend and short-term fluctuation of temperature and humidity based on the time series model, draws a trend graph and analyzes the periodic patterns in the temperature and humidity data; The alarm module generates a monitoring interface, displays the analysis results in the form of charts and curves through the monitoring interface, sets temperature and humidity safety thresholds, and triggers the alarm system when the temperature and humidity exceed the set temperature and humidity safety thresholds.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the substation temperature and humidity sensor detection method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the substation temperature and humidity sensor detection method according to any one of claims 1 to 7 are implemented.

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