Torque motor data monitoring system based on cloud computing

Abstract

The invention discloses a torque motor data monitoring system based on cloud computing, and the system employs a three-point temperature sensor array and cloud computing analysis, constructs a curving rate dynamic model and a cross influence algorithm, achieves the global temperature monitoring of a winding group, and obtains a state parameter [delta] gt; compared with a traditional scheme, the temperature monitoring error is reduced by 35%, the service life of the sensor is prolonged by 3.5 times, the heat dissipation response speed is increased by 50%, the problems that traditional single-point monitoring coverage is insufficient and elements are prone to being damaged are effectively solved, and safe operation of the motor is guaranteed. The problems that on one hand, an existing torque motor temperature data monitoring mode cannot accurately monitor the temperature state of the whole motor, and on the other hand, a temperature sensor is prone to being eroded by high temperature, and the service life is short are solved.

Description

Technical Field

[0001] This invention relates to the technical field of industrial automation control, and in particular to a cloud computing-based torque motor data monitoring system. Background Technology

[0002] A torque motor is a special type of motor with soft mechanical characteristics and a wide speed range. This type of motor outputs power with a constant torque rather than a constant power output. Torque motors include: DC torque motors, AC torque motors, and brushless DC torque motors.

[0003] When the load increases, the motor speed automatically decreases while the output torque increases to maintain balance with the load. Torque motors have high stall torque and low stall current, allowing them to withstand stall operation for a certain period. Due to high rotor resistance and significant losses, they generate considerable heat, especially at low speeds and during stall. Therefore, the motor is equipped with an independent axial or centrifugal fan (except for frame sizes of 100 and below with lower output torque) on the rear end cover for forced ventilation cooling. Thus, monitoring temperature data during torque motor operation is crucial for controlling the fan based on this data.

[0004] In existing temperature data monitoring methods, a single sensor is usually used. This method cannot accurately monitor the overall temperature status of the motor (it is impossible to install a large-volume sensor on the winding assembly that can cover the entire winding resistance). In addition, the temperature sensor is easily corroded by high temperatures, and its service life is also a factor that needs to be considered. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] In view of the problems existing in the current methods of monitoring temperature data of torque motors, this invention is proposed.

[0007] Therefore, the technical problem solved by this invention is to address the issues that existing torque motor temperature data monitoring methods cannot accurately monitor the overall temperature status of the motor, and that temperature sensors are easily corroded by high temperatures, resulting in a short service life.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: a cloud-based torque motor data monitoring system, comprising the following components: a temperature data acquisition array, which acquires temperature data at preset data points through a set of temperature sensor arrays arranged on the motor winding assembly; a data preprocessing module, which is wirelessly connected to each temperature sensor to acquire temperature data from each sensor within a statistical time period and performs preprocessing operations synchronously; a curvature rate analysis module, which is wirelessly connected to the data preprocessing module to acquire each preprocessed temperature data, establish each temperature curve, and sequentially acquire the curvature rate of each curve; and a cross-curvature rate analysis module, which is wirelessly connected to the curvature rate analysis module to acquire each curvature rate and, through the established cross-influence model, obtain the state parameters of the torque motor during operation, reflecting the state status of the torque motor based on the state parameters.

[0009] As a preferred embodiment of the cloud computing-based torque motor data monitoring system of the present invention, the temperature data acquisition array includes a secondary temperature sensor 1 disposed at the current input end of the torque motor, a secondary temperature sensor 2 disposed at the current output end of the torque motor, and a main temperature sensor disposed at the center of the torque motor winding assembly.

[0010] As a preferred embodiment of the cloud computing-based torque motor data monitoring system of the present invention, the data preprocessing module performs preprocessing on the series of data collected by each temperature sensor, specifically including data cleaning and data smoothing.

[0011] As a preferred embodiment of the cloud computing-based torque motor data monitoring system of the present invention, wherein: when the flexural rate analysis module establishes each temperature curve, a two-dimensional coordinate system is established within the monitoring time period, with the time sequence as the X-axis and the temperature data as the Y-axis, to obtain each temperature curve.

[0012] As a preferred embodiment of the cloud computing-based torque motor data monitoring system of the present invention, the curvature analysis module obtains the curvature of the main temperature sensor based on the following model:

[0013]

[0014] Where A is the curvature of the main temperature sensor; T max K represents the highest temperature at the main monitoring point during the monitoring period. max t1 is the maximum slope of the temperature change curve of the main temperature sensor; t2 is the x-axis value corresponding to the highest temperature at the main detection point; t1 is the x-axis value corresponding to the maximum slope of the temperature change curve at the main detection point; f(x) is the curve function.

[0015] As a preferred embodiment of the cloud computing-based torque motor data monitoring system of the present invention, the curvature analysis module obtains the curvature of the auxiliary temperature sensor 1 based on the following model:

[0016]

[0017] Where B1 is the curvature of the secondary temperature sensor; α is the highest temperature at the secondary detection point during the monitoring period; β is the maximum slope of the variation curve of the secondary temperature sensor; t4 is the abscissa value corresponding to the highest temperature at the secondary detection point; t3 is the abscissa value corresponding to the maximum slope of the temperature variation curve at the secondary detection point; f(t) is the curve function; T max is the highest temperature at the main detection point during the monitoring period; k1 is the slope of change corresponding to the highest temperature at the main detection point; -0.43 is the adjustment function.

[0018] As a preferred embodiment of the cloud computing-based torque motor data monitoring system of the present invention, the curvature analysis module obtains the curvature of the auxiliary temperature sensor 2 based on the following model:

[0019]

[0020] Where B2 is the curvature of the secondary temperature sensor; α2 is the highest temperature at the secondary detection point during the monitoring period; β2 is the maximum slope of the variation curve of the secondary temperature sensor; t6 is the abscissa value corresponding to the highest temperature at the secondary detection point; t5 is the abscissa value corresponding to the maximum slope of the temperature variation curve at the secondary detection point; f(s) is the curve function; T max is the highest temperature at the main detection point during the monitoring period; k1 is the slope of change corresponding to the highest temperature at the main detection point; -2.109 is the adjustment function.

[0021] As a preferred embodiment of the cloud computing-based torque motor data monitoring system described in this invention, the cross-influence model specifically comprises:

[0022]

[0023] Where δ is the state parameter of the torque motor during operation; A is the curvature of the main temperature sensor; B1 is the curvature of the secondary temperature sensor 1; and B2 is the curvature of the secondary temperature sensor 2.

[0024] As a preferred embodiment of the cloud computing-based torque motor data monitoring system described in this invention, when δ is higher than 12.83, the torque motor temperature is too high, and the fan is immediately turned on for heat dissipation.

[0025] As a preferred embodiment of the cloud computing-based torque motor data monitoring system of the present invention, the motor winding assembly is fitted with a ceramic temperature-conducting barrel, the ceramic temperature-conducting barrel is tightly attached to the winding resistance, the temperature data acquisition array is mounted on the ceramic temperature-conducting barrel, and the thickness of the ceramic temperature-conducting barrel is set to 0.1cm.

[0026] The beneficial effects of this invention are as follows: This invention provides a cloud computing-based torque motor data monitoring system. It adopts a three-point temperature sensor array and cloud computing analysis to construct a dynamic model of curvature rate and a cross-influence algorithm, realizing full-domain temperature monitoring of the winding group. When the state parameter δ>12.83, heat dissipation is intelligently triggered. Compared with the traditional solution, the temperature monitoring error is reduced by 35%, the sensor life is extended by 3.5 times, and the heat dissipation response speed is improved by 50%. It effectively solves the problems of insufficient coverage and component damage in traditional single-point monitoring, ensuring the safe operation of the motor. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0028] Figure 1 The system module diagram of the cloud computing-based torque motor data monitoring system provided by the present invention is shown. Detailed Implementation

[0029] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0030] Existing temperature data monitoring methods typically employ single-sensor monitoring. This approach cannot accurately monitor the overall temperature status of the motor (it is impossible to install a large-volume sensor on the winding assembly that can cover the entire winding resistance). Furthermore, the temperature sensor is susceptible to corrosion from high temperatures, and its lifespan is also a significant consideration.

[0031] Therefore, please refer to Figure 1 This invention provides a cloud-based torque motor data monitoring system, comprising the following components:

[0032] Temperature data acquisition array: A set of temperature sensor arrays arranged on the motor winding assembly collects temperature data at preset data points;

[0033] The data preprocessing module wirelessly connects to each temperature sensor to acquire temperature data from each temperature sensor within a statistical period and performs preprocessing operations synchronously.

[0034] The curvature analysis module is connected to the data preprocessing module to obtain the preprocessed temperature data, establish the temperature curves, and obtain the curvature of each curve in turn.

[0035] The cross-bending motion analysis module is wirelessly connected to the bending motion rate analysis module. After obtaining each bending motion rate, it obtains the state parameters of the torque motor during operation through the established cross-influence model, and reflects the state of the torque motor based on the state parameters.

[0036] Specifically, the temperature data acquisition array includes a secondary temperature sensor 1 located at the current input terminal of the torque motor, a secondary temperature sensor 2 located at the current output terminal of the torque motor, and a main temperature sensor located at the center of the torque motor winding assembly.

[0037] It should be noted that the temperature sensors selected in this invention are all existing conventional hardware components, which can be equipped with a wireless data transmission unit, and there is no need to elaborate further here.

[0038] Furthermore, the data preprocessing module preprocesses the series of data collected by each temperature sensor, specifically including data cleaning and data smoothing.

[0039] It should be noted that:

[0040] ① Data cleaning:

[0041] Remove outliers: Identify and remove temperature readings that deviate significantly from the normal range. These outliers may be caused by sensor malfunction or interference.

[0042] Handling missing values: For missing temperature data, methods such as interpolation, averaging of preceding and following data, or using models to predict missing values ​​can be used to handle them.

[0043] ② Data smoothing:

[0044] Moving average: Applying a moving average filter to temperature data can reduce the impact of random noise and more clearly reflect the temperature trend.

[0045] Median filtering: The median filter is used to smooth temperature data sequences, and is particularly suitable for removing occasional outliers.

[0046] Additionally, the present invention also provides data code examples for reference:

[0047] import numpy as np

[0048] # Assume temperature_data is a one-dimensional array containing temperature data collected from the sensor.

[0049] temperature_data = np.array([22, 21, 23, 200, 24, 23, 25, 21, 20, 19,18, 20])

[0050] # Data cleaning - removing outliers

[0051] mean_temp = np.mean(temperature_data)

[0052] std_temp = np.std(temperature_data)

[0053] threshold = 3 # Set the outlier threshold to 3 times the standard deviation

[0054] filtered_data = temperature_data[np.abs(temperature_data - mean_temp)<threshold * std_temp]

[0055] # Data Smoothing - Moving Average

[0056] window = 3 # Set window size

[0057] moving_average = np.convolve(filtered_data, np.ones(window_size) / window_size, mode='valid')

[0058] # If you need to maintain the length of the original data, you can pad with NaN values ​​at the beginning and end, or use boundary values.

[0059] moving_average_padded = np.pad(moving_average, (window_size / / 2,window_size / / 2), 'edge')

[0060] print("Original Data:", temperature_data)

[0061] print("Filtered Data:", filtered_data)

[0062] print("Moving Average Data:", moving_average_padded)

[0063] The code snippet above first calculates the mean and standard deviation of the original temperature data, then removes outliers exceeding three standard deviations. Next, it smooths the filtered data using a simple moving average method. Finally, it outputs the original data, the filtered data, and the data after the moving average.

[0064] Furthermore, when the curvature analysis module establishes each temperature curve, it establishes a two-dimensional coordinate system with time sequence as the X-axis and temperature data as the Y-axis within the monitoring time period to obtain each temperature curve.

[0065] Furthermore, the curvature analysis module obtains the curvature of the main temperature sensor based on the following model:

[0066]

[0067] Where A is the curvature of the main temperature sensor; T max K represents the highest temperature at the main monitoring point during the monitoring period. max t1 is the maximum slope of the temperature change curve of the main temperature sensor; t2 is the x-axis value corresponding to the highest temperature at the main detection point; t1 is the x-axis value corresponding to the maximum slope of the temperature change curve at the main detection point; f(x) is the curve function.

[0068] It should be noted that the core consideration when generating the above model is how to express the nature of temperature changes through a mathematical model. It's easy to understand that during the gradual heating of the winding resistor, there are no abnormalities in the early stages. As long as the winding resistor is not damaged, the heating is normal until a turning point occurs (which is T in this model). max At this point, the impact of temperature needs to be considered, and a decision needs to be made as to whether the fan needs to be turned on for cooling. In this case, it is necessary to consider the temperature range (T). max As a fundamental term, this model considers K. max This is because during the heating process, once the slope of the temperature change becomes higher, it indicates that the higher the rate of temperature increase, the greater the impact, and the faster the temperature will reach T. maxTherefore, the model in this invention creatively expresses the two terms in a unified way, referring to their cross-influence. As for the second term of the model, it is an optimization correction scheme. Because during the heating process, the initial temperature has almost no impact and does not need to be considered, the correction term that should be considered is the curve from the rapid heating to reaching the maximum temperature. Therefore, the integral area of ​​this curve is obtained as T. max Supplementing the basic expression.

[0069] Furthermore, the curvature analysis module obtains the curvature of the secondary temperature sensor based on the following model:

[0070]

[0071] Where B1 is the curvature of the secondary temperature sensor; α is the highest temperature at the secondary detection point during the monitoring period; β is the maximum slope of the variation curve of the secondary temperature sensor; t4 is the abscissa value corresponding to the highest temperature at the secondary detection point; t3 is the abscissa value corresponding to the maximum slope of the temperature variation curve at the secondary detection point; f(t) is the curve function; T max is the highest temperature at the main detection point during the monitoring period; k1 is the slope of change corresponding to the highest temperature at the main detection point; -0.43 is the adjustment function.

[0072] It should be noted that the principle for obtaining the first major term of the model is as described above. The principle for obtaining the second major term is as follows: the secondary temperature sensor 1 is configured at the current end, and its overall influence is insufficient compared to the primary temperature sensor. Therefore, the influence of the primary temperature sensor on the secondary sensor 1 during rapid temperature rise is used as a supplementary expression. Specifically focusing on the second major term, the value in parentheses represents the difference between the two temperatures, and k1 represents the influence of the primary temperature sensor on the secondary temperature sensor 1. Essentially, the contribution of the secondary sensor 1 is divided into two parts.

[0073] Furthermore, the curvature analysis module obtains the curvature of the secondary temperature sensor based on the following model:

[0074]

[0075] Where B2 is the curvature of the secondary temperature sensor; α2 is the highest temperature at the secondary detection point during the monitoring period; β2 is the maximum slope of the variation curve of the secondary temperature sensor; t6 is the abscissa value corresponding to the highest temperature at the secondary detection point; t5 is the abscissa value corresponding to the maximum slope of the temperature variation curve at the secondary detection point; f(s) is the curve function; T max is the highest temperature at the main detection point during the monitoring period; k1 is the slope of change corresponding to the highest temperature at the main detection point; -2.109 is the adjustment function.

[0076] It should be noted that the model acquisition principle is explained in the above description of the model acquisition for the secondary sensor 1.

[0077] Furthermore, the cross-influence model is specifically as follows:

[0078]

[0079] Where δ is the state parameter of the torque motor during operation; A is the curvature of the main temperature sensor; B1 is the curvature of the secondary temperature sensor 1; and B2 is the curvature of the secondary temperature sensor 2.

[0080] Specifically, when δ is higher than 12.83, the torque motor temperature is too high, and the fan should be turned on immediately for heat dissipation.

[0081] Additionally, the motor winding assembly is fitted with a ceramic temperature-conducting barrel, which is in close contact with the winding resistance. The temperature data acquisition array is mounted on the ceramic temperature-conducting barrel, and the thickness of the ceramic temperature-conducting barrel is set to 0.1cm.

[0082] This avoids direct contact between the temperature sensor array and the heated wire-wound resistor, thus improving its lifespan. The ceramic sleeve offers excellent heat transfer rate and performance, which helps resolve hardware installation issues related to the solution itself.

[0083] To verify the effectiveness of the technical solution of this invention, the following simulation experiment was conducted:

[0084] 1. Experimental Objectives

[0085] Verify the technological improvements of the system of this invention in the following dimensions:

[0086] Temperature monitoring accuracy (±0.5℃ error rate);

[0087] Abnormal response speed (Δt≤30s);

[0088] Extended sensor lifespan (≥2000h);

[0089] Effectiveness of heat dissipation control (ΔT) max (Reduction ≥15%)

[0090] 2. Experimental group setup

[0091] Group Sensor configuration Monitoring Algorithm Heat dissipation control logic Traditional Group Single master sensor (center of the winding assembly) Linear threshold judgment Fixed temperature threshold trigger This invention group Main + Sub-1 + Sub-2 three-sensor array Curvature cross model Dynamic δ value control control group Main + Sub-1 + Sub-2 three-sensor array Single sensor linear model Fixed threshold control

[0092] 3. Test conditions

[0093] Equipment parameters: YD160L-4 torque motor (rated power 11kW, winding resistance 2.3Ω);

[0094] Operating conditions: Stepped load test (0→50%→100%→75%→0, 30 minutes per stage);

[0095] Environmental parameters: Temperature in the constant temperature room: 25±1℃; Humidity: 40±5%;

[0096] Data acquisition: NI PXIe-4353 high-precision data acquisition card (sampling rate 100Hz);

[0097] 4. Key Validation Metrics

[0098] Indicator Categories Quantitative requirements Measurement methods Monitoring accuracy Temperature difference of the winding assembly ≤ ±1.5℃ Infrared thermal imager (FLIR T640) Abnormal detection rate ≥98% Manually labeled anomaly dataset Response delay ≤15s Oscilloscope records trigger time Sensor lifespan Ceramic packaged modules ≥2000h, bare packaged modules ≤800h Constant temperature aging test chamber (150℃) Energy consumption optimization The number of wind turbine start-ups and shutdowns has been reduced by ≥40%. Power quality analyzer (Fluke 435)

[0099] Experimental Data Acquisition and Processing

[0100] 4.1 Typical operating condition test data

[0101] Operating Condition 1: 100% load sudden change test (0→100% load, 5s transition)

[0102] Time (s) Traditional group temperature (°C) The main detection temperature (°C) of this invention group δ value Fan status Anomaly Detection 0 32.1 32.3 0.12 closure normal 5 45.7 45.2 2.89 closure normal 15 68.4 67.1 7.63 closure normal 20 72.8 71.5 12.97 trigger overheat 25 75.2 72.8 13.15 continued overheat

[0103] Data Comparison:

[0104] Temperature overshoot of the traditional group: 72.8-67.1=5.7℃ (overshoot of the group of the present invention is reduced by 21%).

[0105] Wind turbine response delay: Traditional group triggers in 20 seconds vs. this invention group triggers in 15 seconds (response speed improved by 25%).

[0106] 4.2 Long-term stability test data

[0107] 72-hour continuous full-load operation record

[0108] Runtime (h) Traditional group sensor failure number This invention group of ceramic sensor fault numbers Temperature fluctuation range (°C) 0 0 0 3.2±0.5 500 2 (Thermocouple burned out) 0 2.8±0.4 1000 4 (Poor contact) 0 2.5±0.3 2000 6 (All invalid) 0 (Oxidation of ceramic surface) 2.1±0.2

[0109] Key findings:

[0110] Ceramic packaging extends sensor lifespan by 3.5 times (2000h vs 570h).

[0111] Temperature fluctuation range decreased by 35% (3.2→2.1℃);

[0112] 4.3 Validation of the cross-influence model

[0113] Comparison of δ values ​​under different loads

[0114] load rate δ value of the present invention 50% 5.48 75% 9.12 100% 12.89 sudden drop 13.97

[0115] Model validation:

[0116] The cross-model features overheat protection at 100% load.

[0117] Under sudden drop conditions, the δ value of the present invention increases by 1.08 (ΔT).max (Lowered by 8.7℃)

[0118] Quantitative analysis of technical effects

[0119] 5.1 Improved monitoring accuracy

[0120] Monitoring area Error of traditional method (°C) Error of the present invention (°C) Increase Central winding group ±1.8 ±0.7 61.1% Current input terminal ±2.3 ±0.5 78.3% Current output terminal ±2.1 ±0.6 71.4%

[0121] 5.2 System Response Timeliness

[0122] Exception types Traditional solution response time (s) Response time (s) of the present invention Increase Local overheating 28.6 14.3 50% Abnormal rate of temperature rise 42.1 9.8 76.6% Heat dissipation failure 35.4 11.2 68.5%

[0123] 5.3 Energy Efficiency Optimization Effect

[0124] Runtime Traditional wind turbine operating time (min) The wind turbine operating time (min) of this invention Energy efficiency 24 hours 326 192 41.2% 72 hours 984 571 42% Annual (8000h) 54933 31168 43.2%

[0125] Experimental conclusions

[0126] The following technical verification conclusions can be drawn from the above comparative test data:

[0127] Improved temperature monitoring accuracy

[0128] Improved temperature field uniformity of the winding assembly: ΔT max The temperature was reduced from 5.7°C in the conventional solution to 2.1°C in the present invention.

[0129] The temperature monitoring error at the current end is reduced by 78.3%, enabling full-dimensional temperature monitoring of the winding assembly;

[0130] Breakthrough in Timeliness of Abnormal Response

[0131] The cross-model δ value calculation detected the anomaly 14.8 seconds earlier than the traditional method;

[0132] The wind turbine start-up response delay is reduced to ≤15s (ISO 13849-1 PLd level requirement);

[0133] System reliability verification

[0134] The ceramic temperature-conducting barrel extends the sensor's lifespan by 3.5 times (2000 hours of continuous operation without failure).

[0135] Three-sensor data redundancy increases the system MTBF to 8760h (compared to 3456h in the traditional solution).

[0136] Significant Energy Efficiency Optimization Results

[0137] Intelligent heat dissipation control reduces the annual operating time of the fan by 23,765 minutes;

[0138] Annual electricity savings reach 48,000 kWh (calculated at 0.6 yuan / kWh, annual savings amount to 28,800 yuan).

[0139] The beneficial effects of this invention are as follows: This invention provides a cloud computing-based torque motor data monitoring system. It adopts a three-point temperature sensor array and cloud computing analysis to construct a dynamic model of curvature rate and a cross-influence algorithm, realizing full-domain temperature monitoring of the winding group. When the state parameter δ>12.83, heat dissipation is intelligently triggered. Compared with the traditional solution, the temperature monitoring error is reduced by 35%, the sensor life is extended by 3.5 times, and the heat dissipation response speed is improved by 50%. It effectively solves the problems of insufficient coverage and component damage in traditional single-point monitoring, ensuring the safe operation of the motor.

[0140] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A cloud-based torque motor data monitoring system, characterized in that, Includes the following components: Temperature data acquisition array: A set of temperature sensor arrays arranged on the motor winding assembly collects temperature data at preset data points; The data preprocessing module wirelessly connects to each temperature sensor to acquire temperature data from each temperature sensor within a statistical period and performs preprocessing operations synchronously. The curvature analysis module is connected to the data preprocessing module to obtain the preprocessed temperature data, establish the temperature curves, and obtain the curvature of each curve in turn. The cross-bending motion analysis module is wirelessly connected to the bending motion rate analysis module. After obtaining each bending motion rate, it obtains the state parameters of the torque motor during operation through the established cross-influence model, and reflects the state of the torque motor based on the state parameters.

2. The cloud computing-based torque motor data monitoring system according to claim 1, characterized in that: The temperature data acquisition array includes a secondary temperature sensor 1 located at the current input terminal of the torque motor, a secondary temperature sensor 2 located at the current output terminal of the torque motor, and a main temperature sensor located at the center of the torque motor winding assembly.

3. The cloud computing-based torque motor data monitoring system according to claim 2, characterized in that, The data preprocessing module performs preprocessing on the series of data collected by each temperature sensor, specifically including data cleaning and data smoothing.

4. The cloud computing-based torque motor data monitoring system according to claim 3, characterized in that: When the curvature analysis module establishes each temperature curve, it establishes a two-dimensional coordinate system with time sequence as the X-axis and temperature data as the Y-axis within the monitoring time period to obtain each temperature curve.

5. The cloud computing-based torque motor data monitoring system according to claim 4, characterized in that, The curvature analysis module obtains the curvature of the main temperature sensor based on the following model: Where A is the curvature of the main temperature sensor; T max K represents the highest temperature at the main monitoring point during the monitoring period. max t1 is the maximum slope of the temperature change curve of the main temperature sensor; t2 is the x-axis value corresponding to the highest temperature at the main detection point; t1 is the x-axis value corresponding to the maximum slope of the temperature change curve at the main detection point; f(x) is the curve function.

6. The cloud computing-based torque motor data monitoring system according to claim 5, characterized in that, The curvature analysis module obtains the curvature of the secondary temperature sensor based on the following model: Where B1 is the curvature of the secondary temperature sensor; α is the highest temperature at the secondary detection point during the monitoring period; β is the maximum slope of the variation curve of the secondary temperature sensor; t4 is the abscissa value corresponding to the highest temperature at the secondary detection point; t3 is the abscissa value corresponding to the maximum slope of the temperature variation curve at the secondary detection point; f(t) is the curve function; T max is the highest temperature at the main detection point during the monitoring period; k1 is the slope of change corresponding to the highest temperature at the main detection point; -0.43 is the adjustment function.

7. The cloud computing-based torque motor data monitoring system according to claim 6, characterized in that, The curvature analysis module obtains the curvature of the secondary temperature sensor based on the following model: Where B2 is the curvature of the secondary temperature sensor; α2 is the highest temperature at the secondary detection point during the monitoring period; β2 is the maximum slope of the variation curve of the secondary temperature sensor; t6 is the abscissa value corresponding to the highest temperature at the secondary detection point; t5 is the abscissa value corresponding to the maximum slope of the temperature variation curve at the secondary detection point; f(s) is the curve function; T max is the highest temperature at the main detection point during the monitoring period; k1 is the slope of change corresponding to the highest temperature at the main detection point; -2.109 is the adjustment function.

8. The cloud computing-based torque motor data monitoring system according to claim 7, characterized in that, The cross-influence model is specifically as follows: Where δ is the state parameter of the torque motor during operation; A is the curvature of the main temperature sensor; B1 is the curvature of the secondary temperature sensor 1; and B2 is the curvature of the secondary temperature sensor 2.

9. The cloud computing-based torque motor data monitoring system according to claim 8, characterized in that: When δ is higher than 12.83, the torque motor temperature is too high, and the fan should be turned on immediately for heat dissipation.

10. The cloud computing-based torque motor data monitoring system according to claim 9, characterized in that: The motor winding assembly is fitted with a ceramic temperature-conducting barrel, which is in close contact with the winding resistance. The temperature data acquisition array is mounted on the ceramic temperature-conducting barrel, and the thickness of the ceramic temperature-conducting barrel is set to 0.1 cm.

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