Temperature monitoring and early warning system for intelligent distribution box

Abstract

The invention relates to the technical field of temperature monitoring and early warning, and relates to a temperature monitoring and early warning system for an intelligent distribution box, which comprises a resistance collection unit, a risk setting unit, a state setting unit, an abnormity setting unit, a proportion updating unit and an early warning processing unit, the resistance collection unit is used for obtaining the installation position of a thermistor in the intelligent distribution box, analyzing the monitoring range according to the parameters of the thermistor, and collecting the resistance value collected by the thermistor. The risk setting unit is used for converting the resistance value into temperature data, analyzing the risk temperature of the thermistor monitoring range in combination with the working state of the intelligent distribution box, and setting a risk threshold value according to the risk temperature; according to the method, the difference between the thermistors is detected by setting the proportion abnormal threshold value, when the difference proportion exceeds the threshold value, the abnormity can be found in time, the potential risk is avoided, the actual condition of the thermistors is reflected more accurately, and the possibility of false alarm and missing alarm is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature monitoring and early warning, and in particular to a temperature monitoring and early warning system for an intelligent distribution box. Background Art

[0002] The intelligent distribution cabinet is a bridge connecting the power system and equipment. Common distribution cabinets have protection functions such as overload, short circuit, and phase loss protection. The distribution cabinet will generate heat during use, especially when some electrical components fail. The heat generated increases sharply, which can easily cause fire hazards.

[0003] At present, most of the temperature detection inside the distribution cabinet is the detection of the ambient temperature inside the distribution cabinet. When the ambient temperature reaches the set threshold, the temperature of the contact is much higher than the ambient temperature, and the circuit has failed before reaching the set threshold. This results in the failure to detect the fault in the distribution box in time, and the failure point cannot be accurately obtained. The temperature will be affected by environmental factors. Therefore, judging only by the internal monitored temperature is prone to misjudgment, which in turn affects the management process of the distribution cabinet. In order to reduce this situation, a temperature monitoring and early warning system for intelligent distribution boxes is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a temperature monitoring and early warning system for an intelligent distribution box to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, a temperature monitoring and early warning system for an intelligent distribution box is provided, comprising a resistance collection unit, a risk setting unit, a state setting unit, an abnormality setting unit, a ratio updating unit and an early warning processing unit;

[0006] The resistance collection unit is used to obtain the installation position of the thermistor in the intelligent distribution box, analyze the monitoring range according to the parameters of the thermistor, and collect the resistance value collected by the thermistor;

[0007] The risk setting unit is used to convert the resistance value into temperature data, analyze the risk temperature of the thermistor monitoring range in combination with the working state of the intelligent distribution box, and set the risk threshold according to the risk temperature;

[0008] The state setting unit is used to set a normal state and an abnormal state for the thermistor;

[0009] The abnormal setting unit is used to predict the development trend of the real-time temperature data according to the working state of the intelligent distribution box combined with the historical temperature data of the normal thermistor, and set the abnormal fluctuation amplitude threshold, and then compare the fluctuation amplitude of the real-time temperature data and the predicted temperature data. When the actual fluctuation amplitude is greater than the abnormal fluctuation threshold, the thermistor is set to an abnormal state, and an abnormal signal is sent to the ratio update unit;

[0010] The proportion updating unit is used to analyze the location where the abnormal signal is sent when an abnormal signal appears, and then extract thermistors with the same monitoring range according to the monitoring range for abnormal detection. When a new abnormal thermistor appears in the same monitoring range, the real-time temperature data of the abnormal thermistor and the temperature data of the normal thermistor are calculated for the difference ratio, and then the risk threshold and the predicted temperature data are proportionally updated according to the difference ratio, and the abnormal thermistor is restored to normal at the same time;

[0011] The early warning processing unit is used to stop predicting the development trend of the thermistor when no new abnormal thermistor appears in the same monitoring range, and perform early warning processing through real-time temperature data combined with risk thresholds.

[0012] As a further improvement of the technical solution, the resistance collection unit establishes a data transmission channel between the intelligent distribution box and the thermistor, thereby extracting the installation position of each thermistor in the intelligent distribution box, the parameters of each thermistor, and the resistance value collected by each thermistor;

[0013] The monitoring range analysis is performed based on the thermistor parameters and installation location to obtain the monitoring range of each thermistor in the intelligent distribution box.

[0014] As a further improvement of the technical solution, the risk setting unit includes a signal conversion module and a risk threshold setting module;

[0015] The signal conversion module is used to convert the resistance value collected by the thermistor into a digital signal using an analog-to-digital converter, and then perform temperature data conversion on the digital signal, so as to obtain the temperature data of the monitoring range of each thermistor;

[0016] The risk threshold setting module is used to obtain the working status of the intelligent distribution box, and then divide it according to the monitoring range of the thermistor to obtain the working status of the electronic components within the monitoring range, and then perform risk temperature analysis based on the working status of the electronic components to obtain the risk temperature corresponding to the monitoring range of the thermistor, and at the same time set the risk temperature corresponding to the monitoring range of the thermistor as the risk threshold.

[0017] As a further improvement of the technical solution, the state setting unit sets a normal state and an abnormal state for the thermistor;

[0018] The real-time temperature data of the thermistor does not exceed the risk threshold, and the actual fluctuation range of the real-time temperature data and the predicted temperature data is less than the abnormal fluctuation threshold. If both conditions are met, it is considered to be in a normal state.

[0019] The real-time temperature data of the thermistor exceeds the risk threshold, and the actual fluctuation range of the real-time temperature data and the predicted temperature data is greater than the abnormal fluctuation threshold. If one of the conditions is met, it is considered to be an abnormal state;

[0020] When the thermistor is in an abnormal state, the intelligent distribution box sends the location of the abnormal thermistor combined with a warning signal to the operator, reminding the operator to go to the intelligent distribution cabinet for maintenance.

[0021] As a further improvement of the technical solution, the abnormal setting unit includes a historical data storage module, a temperature prediction module and an amplitude comparison module:

[0022] The historical data storage module is used to collect the historical temperature data recorded by each thermistor in a normal state, and store them in a database;

[0023] The temperature prediction module is used to extract the same working state of the working state of the electronic components within the monitoring range of the thermistor from the historical temperature data, and then predict the development trend based on the historical temperature data of the same working state to obtain predicted temperature data;

[0024] The amplitude comparison module is used for the operator to set the abnormal fluctuation amplitude threshold, and then compare the fluctuation amplitude of the real-time temperature data of the thermistor with the predicted temperature data. When the actual fluctuation amplitude is greater than the abnormal fluctuation threshold, the thermistor is set to an abnormal state and sent to the proportion update unit. Conversely, when the actual fluctuation amplitude is less than the abnormal fluctuation threshold, monitoring is continued.

[0025] As a further improvement of the technical solution, the ratio update unit includes a range analysis module and an abnormality recovery module;

[0026] The range analysis module is used to receive the abnormal signal sent by the amplitude comparison module, and then perform position analysis according to the abnormal signal to obtain the installation position of the abnormal thermistor, and then extract the monitoring range of the abnormal thermistor, and perform the same range analysis on the monitoring range of the abnormal thermistor and the monitoring range of other thermistors in the distribution box to obtain other thermistors with the same monitoring range as the abnormal thermistor;

[0027] The abnormal recovery module is used to perform abnormal detection on other thermistors obtained by the range analysis module when the abnormal thermistor has not returned to a normal state. When an abnormal thermistor appears in other thermistors, a difference ratio is calculated for the abnormal thermistors with the same monitoring range based on the real-time temperature data and the temperature data in a normal state, and the risk threshold and the predicted temperature data are proportionally updated according to the difference ratio, and the abnormal thermistor is restored to normal at the same time.

[0028] As a further improvement of the technical solution, the formula of the abnormal recovery module is as follows:

[0029]

[0030] Among them, D j is the temperature difference ratio between abnormal thermistors, T r,j is the real-time temperature data of thermistor j, T n,j is the temperature data of thermistor j in normal state;

[0031] V new =V old ×(1+α·D j )

[0032] T p,new =T p,old ×(1+β·D j )

[0033] Where V is the risk threshold, T p is the predicted temperature data, V new The updated risk threshold, T p,new is the updated predicted temperature data, V old is the current risk threshold, T p,old is the current predicted temperature data, and α and β are adjustment coefficients.

[0034] As a further improvement of the technical solution, the ratio updating unit further includes a ratio anomaly detection module:

[0035] The proportional anomaly detection module is used for the operator to set the proportional anomaly threshold, and then detect the difference ratio obtained between the abnormal thermistors in the abnormal recovery module in combination with the proportional anomaly threshold. When the ratio between the difference ratios is greater than the difference ratio abnormal threshold, the risk threshold update and the predicted temperature data update are not performed, and the thermistor is kept in an abnormal state.

[0036] As a further improvement of the technical solution, the formula of the ratio anomaly detection module is as follows:

[0037]

[0038] Where R1 and R2 are thermistor readings, D represents the relative difference between the two thermistor readings, and T is the ratio threshold.

[0039] If D>T, it is considered that there is an abnormality, and if D≤T, normal operation can continue.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] In the temperature monitoring and early warning system for intelligent distribution boxes, the difference between thermistors is detected by setting a ratio abnormality threshold. When the difference ratio exceeds the threshold, the abnormality can be discovered in time to avoid potential risks, more accurately reflect the actual situation of the thermistor, and reduce the possibility of false alarms and missed alarms.

[0042] By stopping the development trend prediction of the thermistor when an abnormality is detected, erroneous trend prediction results can be avoided under abnormal conditions. By not updating the risk threshold and predicted temperature data, the system can be ensured to remain stable under abnormal conditions and avoid erroneous operations caused by prediction errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is the overall structural principle diagram of the present invention.

[0044] The meaning of each number in the figure is:

[0045] 10. Resistance collection unit; 20. Risk setting unit; 30. State setting unit; 40. Abnormality setting unit; 50. Ratio update unit; 60. Early warning processing unit. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0047] See also Figure 1 As shown, the purpose of this embodiment is to provide a temperature monitoring and early warning system for an intelligent distribution box, including a resistance collection unit 10, a risk setting unit 20, a state setting unit 30, an abnormality setting unit 40, a ratio updating unit 50 and an early warning processing unit 60;

[0048] The resistance collection unit 10 is used to obtain the installation position of the thermistor in the intelligent distribution box, analyze the monitoring range according to the parameters of the thermistor, and collect the resistance value collected by the thermistor;

[0049] The resistance collection unit 10 establishes a data transmission channel between the intelligent distribution box and the thermistor, thereby extracting the installation position of each thermistor in the intelligent distribution box, the parameters of each thermistor, and the resistance value collected by each thermistor;

[0050] Determine the model and parameters of each thermistor, such as B value, resistance value, etc., and then install the thermistors at different locations of the intelligent distribution box according to the design requirements. Each thermistor is connected to the intelligent distribution box to read the resistance value;

[0051] According to the parameters of the thermistor and the installation location, the monitoring range is analyzed to obtain the monitoring range of each thermistor in the intelligent distribution box. The temperature distribution in the distribution box is modeled using the heat transfer equation. The formula is as follows:

[0052]

[0053] Where T(x,y,z) is the temperature at point x,y,z, and T ambient is the ambient temperature, Q is the heat source power, k is the thermal conductivity, and r is the distance from the heat source.

[0054] Determine the x coordinates of the installation location of each thermistor i ,y i ,z i , and calculate the temperature of the area where it is located. According to the temperature corresponding to the resistance value and the heat distribution model inside the distribution box, the monitoring range of each thermistor is determined. The heat transfer equation can be used to simulate the heat diffusion range:

[0055]

[0056] Where A is the monitoring range and ΔT is the measured temperature difference.

[0057] The risk setting unit 20 is used to convert the resistance value into temperature data, and analyze the risk temperature of the thermistor monitoring range in combination with the working state of the intelligent distribution box, and set the risk threshold according to the risk temperature;

[0058] The risk setting unit 20 includes a signal conversion module and a risk threshold setting module;

[0059] The signal conversion module is used to convert the resistance value collected by the thermistor into a digital signal using an analog-to-digital converter, and then convert the digital signal into temperature data to obtain the temperature data of each thermistor monitoring range. The working steps are as follows:

[0060] Connect the thermistor to a voltage divider circuit to measure its resistance value, as shown in the figure:

[0061]

[0062] Among them, V in is the input voltage, R sensor is the resistance of the thermistor, R ref is a known reference resistor, V out is the output voltage.

[0063] Analog signal V out The analog-to-digital converter ADC is used to convert the analog voltage value into a digital value. The digital value of the ADC can be calculated by the following formula:

[0064]

[0065] Where D is the digital output value of the ADC, V ref is the reference voltage of the ADC, and N is the number of bits of resolution of the ADC.

[0066] The ADC digital value D calculates the analog voltage value V out :

[0067]

[0068] According to the voltage division formula, the analog voltage value is converted into the resistance value R of the thermistor. sensor :

[0069]

[0070] Use the following equation to convert the resistor value R sensor Convert to temperature T Kelvin:

[0071]

[0072] Where A, B, and C are the thermistor's Steinhart-Hart constants and T is the temperature in Kelvin.

[0073] The risk threshold setting module is used to obtain the working status of the intelligent distribution box, and then divide it according to the monitoring range of the thermistor to obtain the working status of the electronic components within the monitoring range, and then perform risk temperature analysis based on the working status of the electronic components to obtain the risk temperature corresponding to the monitoring range of the thermistor. At the same time, the risk temperature corresponding to the monitoring range of the thermistor is set as the risk threshold. The formula is as follows:

[0074] Get the working status of the smart distribution box: Get the real-time data of the smart distribution box, including current, voltage and temperature;

[0075] Determination of monitoring range: The monitoring range of the thermistor is calculated by the resistance collection unit 10;

[0076] Electronic component status monitoring: The real-time data obtained by the intelligent distribution box is screened according to the electronic components to obtain the real-time working status data of the electronic components in the distribution box, such as temperature and working current;

[0077] Risky temperature analysis: Use data analysis tools to identify whether the temperature of each component exceeds its safe operating range;

[0078] Set risk threshold: Set the risk temperature within the monitoring range of the thermistor as the risk threshold. According to the risk analysis results, adjust the threshold to a temperature point that can effectively warn.

[0079] The state setting unit 30 is used to set a normal state and an abnormal state for the thermistor;

[0080] The state setting unit 30 sets a normal state and an abnormal state for the thermistor;

[0081] The real-time temperature data of the thermistor does not exceed the risk threshold, and the actual fluctuation range of the real-time temperature data and the predicted temperature data is less than the abnormal fluctuation threshold. If both conditions are met, it is considered to be in a normal state.

[0082] The real-time temperature data of the thermistor exceeds the risk threshold, and the actual fluctuation range of the real-time temperature data and the predicted temperature data is greater than the abnormal fluctuation threshold. If one of the conditions is met, it is considered to be an abnormal state;

[0083] When the thermistor is in an abnormal state, the intelligent distribution box sends the location of the abnormal thermistor combined with a warning signal to the operator, reminding the operator to go to the intelligent distribution cabinet for maintenance.

[0084] The abnormal setting unit 40 is used to predict the development trend of the real-time temperature data according to the working state of the intelligent distribution box combined with the historical temperature data of the normal thermistor, and set the abnormal fluctuation amplitude threshold, and then compare the fluctuation amplitude of the real-time temperature data and the predicted temperature data. When the actual fluctuation amplitude is greater than the abnormal fluctuation threshold, the thermistor is set to an abnormal state, and an abnormal signal is sent to the ratio update unit 50;

[0085] The abnormality setting unit 40 includes a historical data storage module, a temperature prediction module and an amplitude comparison module:

[0086] The historical data storage module is used to collect the historical temperature data recorded by each thermistor in normal state and store it in a database. The working steps are as follows:

[0087] Real-time monitoring system: Set the system to collect thermistor temperature data regularly, such as every minute, every hour, etc.

[0088] Data recording: Each time data is collected, check whether the data meets the conditions of normal status. If it does, record the temperature data and its timestamp;

[0089] Data structure: Design a data structure to store the collected database, including fields such as thermistor ID, collection time, and temperature value.

[0090] The temperature prediction module is used to extract the same working state of the electronic components within the monitoring range of the thermistor from the historical temperature data, and then predict the development trend based on the historical temperature data of the same working state to obtain the predicted temperature data. The working steps are as follows:

[0091] Thermistor data collection: obtain real-time temperature data of the monitoring equipment and related electronic component working status data, which usually includes the temperature value measured by the temperature sensor, timestamp and the status of the electronic components such as working, standby, fault, etc.

[0092] Data cleaning: remove outliers and noise from the data, fill in missing values, and ensure data consistency and integrity;

[0093] Extraction of the same working state: Assign labels to different working states to ensure accurate identification of different working states of electronic components. Then group the historical temperature data by working state and extract time series data with the same working state. These data will be used for subsequent analysis and prediction.

[0094] Feature engineering: Extract key features from temperature data, such as mean, variance, trend, etc. You can also extract time series features, such as seasonality, periodicity, etc., select features that have a greater impact on prediction, reduce redundant features, and optimize model performance;

[0095] Model training and verification: The extracted data set is divided into a training set and a test set. The training set is used to train the model, and the test set is used to verify the model performance. According to the nature of the data and the prediction target, a suitable prediction model is selected, which can be a time series model, a machine learning model, or a deep learning model.

[0096] Model validation: Use the training set data to train the selected model, adjust the model parameters to optimize the prediction effect, verify the performance of the model on the test set, and evaluate the accuracy and stability of the prediction. Common evaluation indicators include mean square error, root mean square error, mean absolute error, etc.

[0097] Trend prediction: Use the trained model to predict future temperature data. Generate temperature prediction results for a period of time in the future based on historical data and current status, and then use the temperature prediction results as predicted temperature data.

[0098] The amplitude comparison module is used for the operator to set the abnormal fluctuation amplitude threshold. The operator sets the abnormal fluctuation amplitude threshold to determine what degree of fluctuation is considered abnormal, and then compares the fluctuation amplitude of the real-time temperature data and the predicted temperature data of the thermistor, and calculates the absolute difference between the two. When the actual fluctuation amplitude is greater than the abnormal fluctuation threshold, the thermistor is set to an abnormal state and sent to the ratio update unit 50. Conversely, when the actual fluctuation amplitude is less than the abnormal fluctuation threshold, monitoring is continued.

[0099] The proportion updating unit 50 is used to analyze the position where the abnormal signal is sent when an abnormal signal appears, and then extract the thermistors with the same monitoring range according to the monitoring range for abnormal detection. When a new abnormal thermistor appears in the same monitoring range, the difference ratio of the real-time temperature data of the abnormal thermistor and the temperature data of the normal thermistor is calculated, and then the risk threshold and the predicted temperature data are proportionally updated according to the difference ratio, and the abnormal thermistor is restored to normal at the same time;

[0100] The ratio updating unit 50 includes a range analysis module and an abnormality recovery module;

[0101] The range analysis module is used to receive the abnormal signal sent by the amplitude comparison module, and then perform position analysis based on the abnormal signal to obtain the installation position of the abnormal thermistor, and then extract the monitoring range of the abnormal thermistor, and perform the same range analysis on the monitoring range of the abnormal thermistor and the monitoring range of other thermistors in the distribution box to obtain other thermistors with the same monitoring range as the abnormal thermistor. The working steps are as follows:

[0102] Receiving abnormal signals: receiving abnormal signals from the amplitude comparison module, and identifying the identifier or number of the abnormal thermistor;

[0103] Position analysis: Get the specific installation location of the thermistor based on the abnormal signal, which may be obtained through a database or positioning system;

[0104] Extract monitoring range: Extract the monitoring range information of abnormal thermistors from the system database, which usually includes specific parameters of the monitoring area, such as temperature range;

[0105] Get the monitoring range of other thermistors in the distribution box: Collect the monitoring range data of all thermistors in the distribution box;

[0106] Same range analysis: Compare the monitoring range of the abnormal thermistor with the monitoring range of other thermistors to find thermistors with the same monitoring range.

[0107] The abnormal recovery module is used to detect abnormalities in other thermistors obtained by the range analysis module when the abnormal thermistor has not returned to normal. When an abnormal thermistor appears in other thermistors, the difference ratio of the abnormal thermistor with the same monitoring range is calculated based on the real-time temperature data and the temperature data in the normal state, and the risk threshold and the predicted temperature data are proportionally updated according to the difference ratio. At the same time, the abnormal thermistor is restored to normal. The working steps are as follows:

[0108] Detect abnormal thermistors: Before the abnormal thermistor A recovers, continue to detect other thermistors B1, B2, ..., B obtained by the range analysis module. n Perform anomaly detection.

[0109] Record and analyze abnormalities: For each abnormal thermistor B detected j , record its real-time temperature data T r,j And the temperature data T under normal conditions n,j .

[0110] Calculate the difference ratio: Calculate the temperature difference ratio of the abnormal thermistor, the formula is as follows:

[0111]

[0112] Among them, D j is the temperature difference ratio between abnormal thermistors, T r,j is the real-time temperature data of thermistor j, T n,j It is the temperature data of thermistor j in normal state.

[0113] Update risk threshold and predicted temperature data: according to the difference ratio D j For risk threshold V and predicted temperature data T p Perform proportional updates;

[0114] V new =V old ×(1+α·D j )

[0115] T p,new =T p,old ×(1+β·D j )

[0116] Where V is the risk threshold, T p is the predicted temperature data, V new The updated risk threshold, T p,new is the updated predicted temperature data, V old is the current risk threshold, T p,oldis the current predicted temperature data, and α and β are adjustment coefficients used to control the sensitivity of the update.

[0117] Restoring the abnormal thermistor: performing a restoration operation on the abnormal thermistor A, marking the abnormal thermistor as a normal state, and stopping abnormal detection of the abnormal thermistor;

[0118] Re-Monitor: Continue to monitor all thermistors to ensure stable system operation, especially the impact of updated risk thresholds and predicted temperature data.

[0119] The ratio updating unit 50 further includes a ratio anomaly detection module:

[0120] The ratio anomaly detection module is used for the operator to set the ratio anomaly threshold, and then detect the difference ratio obtained between the abnormal thermistors in the abnormal recovery module in combination with the ratio anomaly threshold. When the ratio between the difference ratios is greater than the difference ratio anomaly threshold, the risk threshold update and the predicted temperature data update are not performed, and the thermistor is kept in an abnormal state. The working steps are as follows:

[0121] Setting the ratio abnormality threshold: The operator sets a ratio threshold T according to the system requirements to determine whether the difference between thermistors exceeds the normal range;

[0122] Difference ratio calculation: Measure the readings of two thermistors, R1 and R2, and calculate the difference ratio D between them. It can be calculated using the following formula:

[0123]

[0124] Where R1 and R2 are thermistor readings respectively, and D represents the relative difference between the two thermistor readings;

[0125] Compare the calculated difference ratio D with the set ratio abnormality threshold T. If D>T, it is considered that there is an abnormality. If D≤T, normal operation can continue.

[0126] Abnormal processing: If it is found that the difference ratio D is greater than the threshold value T, the risk threshold is not updated, the predicted temperature data is not updated, and the state of the thermistor is maintained as abnormal.

[0127] The early warning processing unit 60 is used to stop predicting the development trend of the thermistor when no new abnormal thermistor appears in the same monitoring range, and to perform early warning processing based on real-time temperature data combined with risk thresholds.

[0128] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A temperature monitoring and early warning system for an intelligent distribution box, characterized in that: It comprises a resistance collection unit (10), a risk setting unit (20), a state setting unit (30), an abnormality setting unit (40), a ratio updating unit (50) and an early warning processing unit (60); The resistance collection unit (10) is used to obtain the installation position of the thermistor in the intelligent distribution box, analyze the monitoring range according to the parameters of the thermistor, and collect the resistance value collected by the thermistor; The risk setting unit (20) is used to convert the resistance value into temperature data, analyze the risk temperature of the thermistor monitoring range in combination with the working state of the intelligent distribution box, and set the risk threshold according to the risk temperature; The state setting unit (30) is used to set a normal state and an abnormal state for the thermistor; The abnormal setting unit (40) is used to predict the development trend of the real-time temperature data according to the working state of the intelligent distribution box combined with the historical temperature data of the normal thermistor, and set the abnormal fluctuation amplitude threshold, and then compare the fluctuation amplitude of the real-time temperature data and the predicted temperature data. When the actual fluctuation amplitude is greater than the abnormal fluctuation threshold, the thermistor is set to an abnormal state, and an abnormal signal is sent to the ratio update unit (50); The ratio updating unit (50) is used to analyze the location where the abnormal signal is sent when an abnormal signal appears, and then extract thermistors with the same monitoring range according to the monitoring range for abnormal detection; when a new abnormal thermistor appears in the same monitoring range, the difference ratio of the real-time temperature data of the abnormal thermistor and the temperature data of the normal thermistor is calculated, and then the risk threshold and the predicted temperature data are proportionally updated according to the difference ratio, and the abnormal thermistor is restored to normal at the same time; The early warning processing unit (60) is used to stop predicting the development trend of the thermistor when no new abnormal thermistor appears in the same monitoring range, and to perform early warning processing based on real-time temperature data combined with a risk threshold.

2. A temperature monitoring and early warning system for an intelligent distribution box according to claim 1, characterized in that: The resistance collection unit (10) establishes a data transmission channel between the intelligent distribution box and the thermistor, thereby extracting the installation position of each thermistor in the intelligent distribution box, the parameters of each thermistor, and the resistance value collected by each thermistor; The monitoring range analysis is performed based on the thermistor parameters and installation location to obtain the monitoring range of each thermistor in the intelligent distribution box.

3. The temperature monitoring and early warning system for an intelligent distribution box according to claim 1 is characterized in that: The risk setting unit (20) comprises a signal conversion module and a risk threshold setting module; The signal conversion module is used to convert the resistance value collected by the thermistor into a digital signal using an analog-to-digital converter, and then perform temperature data conversion on the digital signal, so as to obtain the temperature data of the monitoring range of each thermistor; The risk threshold setting module is used to obtain the working status of the intelligent distribution box, and then divide it according to the monitoring range of the thermistor to obtain the working status of the electronic components within the monitoring range, and then perform risk temperature analysis based on the working status of the electronic components to obtain the risk temperature corresponding to the monitoring range of the thermistor, and at the same time set the risk temperature corresponding to the monitoring range of the thermistor as the risk threshold.

4. The temperature monitoring and early warning system for an intelligent distribution box according to claim 1 is characterized in that: The state setting unit (30) sets a normal state and an abnormal state for the thermistor; The real-time temperature data of the thermistor does not exceed the risk threshold, and the actual fluctuation range of the real-time temperature data and the predicted temperature data is less than the abnormal fluctuation threshold. If both conditions are met, it is considered to be in a normal state. The real-time temperature data of the thermistor exceeds the risk threshold, and the actual fluctuation range of the real-time temperature data and the predicted temperature data is greater than the abnormal fluctuation threshold. If one of the conditions is met, it is considered to be an abnormal state; When the thermistor is in an abnormal state, the intelligent distribution box sends the location of the abnormal thermistor combined with a warning signal to the operator, reminding the operator to go to the intelligent distribution cabinet for maintenance.

5. The temperature monitoring and early warning system for an intelligent distribution box according to claim 1 is characterized in that: The abnormality setting unit (40) comprises a historical data storage module, a temperature prediction module and an amplitude comparison module: The historical data storage module is used to collect the historical temperature data recorded by each thermistor in a normal state, and store them in a database; The temperature prediction module is used to extract the same working state of the working state of the electronic components within the monitoring range of the thermistor from the historical temperature data, and then predict the development trend based on the historical temperature data of the same working state to obtain predicted temperature data; The amplitude comparison module is used for the operator to set the abnormal fluctuation amplitude threshold, and then compare the fluctuation amplitude of the real-time temperature data of the thermistor with the predicted temperature data. When the actual fluctuation amplitude is greater than the abnormal fluctuation threshold, the thermistor is set to an abnormal state and sent to the ratio update unit (50). On the contrary, when the actual fluctuation amplitude is less than the abnormal fluctuation threshold, the monitoring is continued.

6. The temperature monitoring and early warning system for an intelligent distribution box according to claim 1 is characterized in that: The ratio updating unit (50) comprises a range analysis module and an abnormality recovery module; The range analysis module is used to receive the abnormal signal sent by the amplitude comparison module, and then perform position analysis according to the abnormal signal to obtain the installation position of the abnormal thermistor, and then extract the monitoring range of the abnormal thermistor, and perform the same range analysis on the monitoring range of the abnormal thermistor and the monitoring range of other thermistors in the distribution box to obtain other thermistors with the same monitoring range as the abnormal thermistor; The abnormal recovery module is used to perform abnormal detection on other thermistors obtained by the range analysis module when the abnormal thermistor has not returned to a normal state. When an abnormal thermistor appears in other thermistors, a difference ratio is calculated for the abnormal thermistors with the same monitoring range based on the real-time temperature data and the temperature data in a normal state, and the risk threshold and the predicted temperature data are proportionally updated according to the difference ratio, and the abnormal thermistor is restored to normal at the same time.

7. A temperature monitoring and early warning system for an intelligent distribution box according to claim 6, characterized in that: The formula of the abnormal recovery module is as follows: Among them, D j is the temperature difference ratio between abnormal thermistors, T r,j is the real-time temperature data of thermistor j, T n,j is the temperature data of thermistor j in normal state; V new =V old ×(1+α·D j ) T p,new =T p,old ×(1+β·D j ) Where V is the risk threshold, T p is the predicted temperature data, V new The updated risk threshold, T p,new is the updated predicted temperature data, V old is the current risk threshold, T p,old is the current predicted temperature data, and α and β are adjustment coefficients.

8. The temperature monitoring and early warning system for an intelligent distribution box according to claim 1 is characterized in that: The ratio updating unit (50) further comprises a ratio abnormality detection module: The proportional anomaly detection module is used for the operator to set the proportional anomaly threshold, and then detect the difference ratio obtained between the abnormal thermistors in the abnormal recovery module in combination with the proportional anomaly threshold. When the ratio between the difference ratios is greater than the difference ratio abnormal threshold, the risk threshold update and the predicted temperature data update are not performed, and the thermistor is kept in an abnormal state.

9. A temperature monitoring and early warning system for an intelligent distribution box according to claim 8, characterized in that: The formula of the proportional anomaly detection module is as follows: Where R1 and R2 are thermistor readings, D represents the relative difference between the two thermistor readings, and T is the ratio threshold. If D>T, it is considered that there is an abnormality, and if D≤T, normal operation can continue.

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