Intelligent sensing method and system for heat dissipation state of distribution box

Abstract

This invention relates to the field of heat dissipation control technology, and discloses an intelligent sensing method and system for the heat dissipation status of a distribution box. The method includes: acquiring the internal temperature of the target device and the operating current and driving voltage of the target controlled object in real time; analyzing the operating current and driving voltage to generate a performance attenuation factor representing the decrease in heat dissipation capacity of the target controlled object due to losses; generating the performance attenuation factor through a sensing and analysis unit to achieve accurate sensing of fan heat dissipation performance; simultaneously, the adaptive control threshold generated by the temperature analysis unit can adapt to fan attenuation and changes in the load of the distribution box; and the influence analysis unit generates a disturbance control gain coefficient by analyzing external data to offset the interference of the external environment on the temperature rise inside the box; finally, the control command generated by the heat dissipation control unit can adaptively adjust the speed of the target controlled object. This solution can adapt to fan loss attenuation, avoid control misjudgment, and achieve precise control of the heat dissipation of the distribution box.

Description

Technical Field

[0001] This invention relates to the field of heat dissipation control technology, specifically to an intelligent sensing method and system for the heat dissipation status of a distribution box. Background Technology

[0002] Currently, in the use of distribution boxes, it is usually necessary to monitor and analyze the temperature of the distribution box in order to evaluate the heat dissipation status of the distribution box. When the temperature exceeds the response threshold, the fan in the distribution box is controlled to dissipate heat, and when the temperature drops to within the threshold, the cooling fan is controlled to shut down, thereby ensuring the normal operation of the distribution box.

[0003] However, the above control method still has the following defects: In long-term use, the fan of the distribution box will experience a decrease in actual speed and a reduction in heat dissipation efficiency due to mechanical wear, dust accumulation or electrical aging. However, the existing control method only controls the start and stop based on a fixed temperature threshold and does not perceive the actual working status and heat dissipation performance of the fan in real time. This will result in the inability to dynamically adjust the fan according to its actual heat dissipation capacity, affecting the heat dissipation control effect of the distribution box. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent sensing method and system for the heat dissipation status of power distribution boxes, thus solving the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution: Intelligent sensing methods for the heat dissipation status of distribution boxes include: Step S1: Real-time acquisition of the internal temperature of the target device and the operating current and drive voltage of the target controlled object; analysis of the operating current and drive voltage; generation of a performance attenuation factor representing the decrease in heat dissipation capacity of the target controlled object due to losses; the target device is the distribution box and the target controlled object is the cooling fan inside the distribution box. Step S2: Calculate the adaptive control threshold for heat dissipation control based on the internal temperature and the power attenuation factor. Step S3: Acquire external data of the target device in real time, analyze the external data in real time, and generate disturbance control gain coefficients for compensation of heat dissipation control. Step S4 involves performing a collaborative analysis of the internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient to generate control commands for the target controlled object.

[0006] Furthermore, the operating current and driving voltage are analyzed to generate a performance attenuation factor representing the decrease in heat dissipation capacity of the target controlled object due to losses, including: Frequency domain analysis is performed on the operating current and driving voltage to generate the harmonic distortion rate, which reflects the degree of distortion of the current waveform. The operating current and driving voltage are calculated, and the dynamic reactive component representing the degradation of the electromagnetic performance of the target controlled object's motor is separated.

[0007] Furthermore, the operating current and driving voltage are analyzed to generate a performance attenuation factor representing the decrease in heat dissipation capacity of the target controlled object due to losses. This also includes: By performing correlation analysis between harmonic distortion rate and dynamic reactive component, a control attenuation coefficient is generated to represent the strength of the correlation between current waveform distortion and ineffective electromagnetic energy consumption. Analyze the long-term changes in the control attenuation coefficient to generate an energy attenuation factor that represents the decrease in heat dissipation capacity of the target controlled object due to losses.

[0008] Furthermore, based on the internal temperature and performance attenuation factor, an adaptive control threshold for heat dissipation control is calculated, including: Continuous analysis of internal temperature generates the intrinsic heat balance median. The performance attenuation factor is mapped to generate a dynamic control gain value that adjusts the sensitivity to temperature fluctuations. Calculate the instantaneous deviation of the internal temperature from the intrinsic thermal equilibrium median, analyze the changes in internal temperature, and generate a thermal offset vector representing the current temperature deviation.

[0009] Furthermore, based on the internal temperature and performance attenuation factor, the adaptive control threshold for heat dissipation control is calculated, which also includes: The dynamic control boundary is obtained by synthesizing the dynamic control gain value, thermal offset vector, and intrinsic thermal equilibrium median. The dynamic control boundary is analyzed to generate an adaptive control threshold for heat dissipation control.

[0010] Furthermore, external data from the target device is acquired in real time, analyzed in real time, and disturbance control gain coefficients are generated to compensate for heat dissipation control, including: By fusing external temperature and external wind speed data, an environmental cooling efficiency that represents the overall cooling potential of the external environment is obtained. The impact of environmental cooling efficiency on target equipment is analyzed, and an effective environmental disturbance factor reflecting the effectiveness of environmental disturbance is generated.

[0011] Furthermore, the system acquires external data from the target device in real time, analyzes this data in real time, and generates disturbance control gain coefficients to compensate for heat dissipation control. This also includes: Based on the effective factor of environmental disturbance, the feedforward disturbance compensation amount is calculated to offset the influence of the external environment on the internal temperature rise of the target equipment. The gain of the feedforward disturbance compensation is adjusted to generate a disturbance control gain coefficient used to correct the rotational speed of the target controlled object.

[0012] Furthermore, a collaborative analysis is performed on internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient to generate control commands for the target controlled object, including: The temperature control deviation value is obtained by calculating the adaptive control threshold and internal temperature, and then corrected according to the disturbance control gain coefficient to generate a dynamic control value for adaptive adjustment.

[0013] Furthermore, a collaborative analysis is performed on internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient to generate control commands for the target controlled object, which also includes: Based on the power attenuation factor, the disturbance control gain coefficient is adjusted to generate a temperature control gain correction factor that reflects the current heat dissipation capacity of the target controlled object. The dynamic control value and the temperature control gain correction factor are calculated together to generate the control command for the target object.

[0014] Furthermore, the intelligent sensing system for the heat dissipation status of the distribution box, applied to the aforementioned sensing method, includes: The sensing and analysis unit is used to acquire the internal temperature of the target device and the operating current and drive voltage of the target controlled object in real time, analyze the operating current and drive voltage, and generate a power attenuation factor that represents the decrease in heat dissipation capacity of the target controlled object due to losses. The target device is the distribution box, and the target controlled object is the cooling fan inside the distribution box. The temperature analysis unit is used to calculate the adaptive control threshold for heat dissipation control based on the internal temperature and the power decay factor. The impact analysis unit is used to acquire external data of the target device in real time, analyze the external data in real time, and generate disturbance control gain coefficients for compensation of heat dissipation control. The heat dissipation control unit is used to perform collaborative analysis of internal temperature, adaptive control threshold, performance attenuation factor and disturbance control gain coefficient to generate control commands for the target controlled object.

[0015] In summary, the present invention has the following main beneficial effects: The sensing and analysis unit acquires real-time data on the internal temperature of the distribution box, the operating current of the cooling fan, and the drive voltage. After frequency domain analysis, it generates harmonic distortion rate and dynamic reactive power components. Correlation analysis then generates a control attenuation coefficient, ultimately yielding a performance attenuation factor reflecting the fan's reduced cooling capacity due to losses, achieving precise sensing of the fan's cooling performance. Next, the temperature analysis unit combines the internal temperature and performance attenuation factor to generate the eigenvalue of the thermal balance, the dynamic control gain, the thermal offset vector, and the dynamic control boundary. Finally, it calculates the adaptive control threshold to adapt to the fan's attenuation state and changes in the distribution box load. Simultaneously, the influence analysis unit... The system can analyze external temperature and wind speed to generate environmental cooling efficiency, environmental disturbance effectiveness factor, feedforward disturbance compensation amount, and disturbance control gain coefficient. This counteracts the interference of the external environment on the temperature rise inside the distribution box. Through the heat dissipation control unit, in conjunction with internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient, it calculates temperature control deviation value, dynamic control value, and temperature control gain correction factor, and generates control commands for the target control object. This solution can accurately adapt to the heat dissipation attenuation caused by fan mechanical wear, avoid control misjudgment and lag, improve the accuracy and adaptability of heat dissipation control, and effectively ensure the stability of heat dissipation in the distribution box. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the intelligent sensing method for heat dissipation status of the distribution box according to the present invention. Figure 2 This is a system diagram of the intelligent sensing method for heat dissipation status of the distribution box according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] refer to Figure 1 and Figure 2 The intelligent sensing method for the heat dissipation status of the distribution box includes: Step S1: Real-time acquisition of the internal temperature of the target device and the operating current and drive voltage of the target controlled object; analysis of the operating current and drive voltage; generation of a performance attenuation factor representing the decrease in heat dissipation capacity of the target controlled object due to losses; the target device is the distribution box and the target controlled object is the cooling fan inside the distribution box. Wherein, the driving voltage of the target controlled object is: the DC bus voltage at the input terminal of the target controlled object driving circuit; The operating current of the target controlled object is: the operating current of the motor winding in the target controlled object; Step S2: Calculate the adaptive control threshold for heat dissipation control based on the internal temperature and the power attenuation factor. Step S3: Acquire external data of the target device in real time, analyze the external data in real time, and generate disturbance control gain coefficients for compensation of heat dissipation control. External data includes: external temperature of the distribution box, external wind speed, etc. Step S4 involves performing a collaborative analysis of the internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient to generate control commands for the target controlled object.

[0019] In one embodiment, the operating current and driving voltage are analyzed to generate a performance attenuation factor representing the decrease in heat dissipation capacity of the target controlled object due to losses, including: Frequency domain analysis is performed on the operating current and driving voltage to generate the harmonic distortion rate, which reflects the degree of current waveform distortion. Specifically, this includes: arranging the operating current and driving voltage collected synchronously at multiple time points to form current and voltage sequences respectively; calculating the arithmetic mean of the voltage sequences to obtain the reference voltage value; simultaneously, calculating the root mean square of the difference between each voltage in the voltage sequence and the reference voltage value to obtain the ripple voltage value; and performing a fast Fourier transform on the voltage sequences to extract the peak value of the AC frequency to obtain the reference frequency. Calculate the arithmetic mean of the current sequence and subtract the arithmetic mean from the current sequence to obtain the AC component of the current; The AC component of the current is processed using a set of digital bandpass filters with dynamically configurable center frequencies. The center frequencies of the filters are set to 1, 3, 5, and 7 times the reference frequency. The amplitude of the output signal of each filter is measured to obtain the fundamental current amplitude, the third harmonic current amplitude, the fifth harmonic current amplitude, and the seventh harmonic current amplitude. Calculate the sum of squares of the amplitudes of the third, fifth, and seventh harmonic currents, then calculate the square root of the sum of squares, and divide the result by the amplitude of the fundamental current to obtain the harmonic distortion rate, which reflects the degree of distortion of the current waveform.

[0020] The working current and driving voltage are calculated, and the dynamic reactive component representing the degradation of the electromagnetic performance of the target controlled object motor is separated. Specifically, this includes: using the dynamic reference frequency as the reference period, extracting data of one reference period from the AC current component; simultaneously identifying the zero-crossing times of the voltage sequence and the current sequence within the reference period; calculating the time difference between the voltage zero-crossing point and the current zero-crossing point, converting this time difference into a phase angle to obtain the voltage-current phase difference; dividing the time difference by the reference period to obtain the ratio of the time difference to the period, multiplying the ratio by 360 to obtain the phase angle in degrees, which is the voltage-current phase difference. The reactive current amplitude is obtained by multiplying the fundamental current amplitude by the sinusoidal value of the voltage-current phase difference. Then, the reactive current amplitude is divided by the peak value of the AC current component in the same reference period to obtain a ratio between zero and one, which is the dynamic reactive component representing the degradation of the electromagnetic performance of the target controlled object's motor. This ratio is then used to evaluate the degradation of the electromagnetic performance of the target controlled object.

[0021] In one embodiment, the analysis of the operating current and driving voltage to generate a performance attenuation factor representing the decrease in heat dissipation capacity of the target controlled object due to losses further includes: Correlation analysis is performed between harmonic distortion rate and dynamic reactive component to generate a control attenuation coefficient that represents the strength of the correlation between current waveform distortion and ineffective electromagnetic energy consumption. Specifically, this includes: multiplying the harmonic distortion rate and dynamic reactive component to obtain the correlation product value, then calculating the square of the dynamic reactive component to obtain the purification weighting factor; and multiplying the correlation product value by the purification weighting factor to obtain the enhanced correlation value. Next, calculate the difference between 1 and the harmonic distortion rate and the dynamic reactive component, and multiply these two differences to obtain the deviation product value; Dividing the enhanced correlation value by the deviation product value yields the control attenuation coefficient, which represents the strength of the correlation between current waveform distortion and ineffective electromagnetic energy consumption. If the deviation product value is zero, the control attenuation coefficient is directly 1.

[0022] Analyze the long-term changes of the control attenuation coefficient to generate an energy attenuation factor that represents the decrease in heat dissipation capacity of the target controlled object due to losses. Specifically, this includes setting a long-term observation period of 24 hours of continuous operation, recording the control attenuation coefficient at a frequency of once per minute during the long-term observation period, and forming a control attenuation coefficient sequence. The first state is defined as a control attenuation coefficient ≤ 0.5, the second state is defined as 0.5 < control attenuation coefficient < 0.8, and the third state is defined as 0.8 ≤ control attenuation coefficient. The entire control attenuation coefficient sequence is traversed. For each minute, it is determined whether the state of the control attenuation coefficient in the next minute is the same as the state in the current minute. If they are different, a state change is recorded. The number of all state changes is counted to obtain the number of state changes. Subtracting the ratio of the number of state changes to the total number of acquisitions of the control attenuation coefficient sequence from 1 yields the state stability, which represents the long-term stability of the control attenuation coefficient. Adding the state stability to 0.1, calculating the negative exponent of the sum with the natural constant e as the base, and then subtracting the calculated result from 1 and normalizing it to the 0-1 interval, yields the performance attenuation factor, which represents the decrease in heat dissipation capacity of the target controlled object due to losses.

[0023] By performing systematic frequency domain analysis and calculation on the operating current and driving voltage, harmonic distortion rate, dynamic reactive component, control attenuation coefficient and power attenuation factor are generated. This allows for real-time and accurate perception of the reduced heat dissipation capacity and degraded electromagnetic performance of the target controlled object due to losses. It effectively solves the problem that the fans in existing distribution boxes only start and stop based on fixed temperature thresholds and cannot perceive the actual working status and heat dissipation performance of the fans, thereby improving the accuracy of heat dissipation control and ensuring the long-term stable operation of the distribution box.

[0024] In one embodiment, an adaptive control threshold for heat dissipation control is calculated based on the internal temperature and the power attenuation factor, including: Continuous analysis of internal temperature is performed to generate intrinsic thermal equilibrium median. Specifically, after the target equipment is started, it is run continuously for more than 30 minutes to ensure that the target equipment enters a stable operating state. Then, its internal temperature is continuously collected for a period of not less than 1 hour until at least 3 local temperature extreme points are collected. All internal temperatures during this period are arranged to form an initial analysis sequence. Calculate the absolute value of the internal temperature difference between all adjacent sampling points in the initial analysis sequence to obtain a set of temperature changes. Calculate the arithmetic mean and standard deviation of all values ​​in the set of temperature changes. Add the arithmetic mean to twice the standard deviation to obtain the dynamic stability judgment threshold. Traverse the initial analysis sequence. For the absolute value of the temperature difference between the current sampling point and the previous sampling point, and the absolute value of the temperature difference between the current sampling point and the next sampling point, if both absolute values ​​are less than the dynamic stability determination threshold, the sampling point can be regarded as a quasi-equilibrium temperature point. From all quasi-equilibrium temperature points, the data points with the largest and smallest values ​​are removed. The remaining data points are then sorted by value. If the number of remaining data points is odd, the internal temperature at the middle position of the sort is taken as the intrinsic thermal equilibrium median. If the number of remaining data points is even, the arithmetic mean of the two middle internal temperatures is taken as the intrinsic thermal equilibrium median. The intrinsic thermal equilibrium median is used to reflect the load condition of the distribution box itself.

[0025] The power attenuation factor is mapped to generate a dynamic control gain value that adjusts the sensitivity to temperature fluctuations. Specifically, this includes: constructing a historical sequence of all power attenuation factors recorded during the long-term observation period, calculating the arithmetic mean of all values ​​in the historical sequence, and calculating the root mean square value of the deviation of each value in the historical sequence from the arithmetic mean as the historical standard deviation. Next, calculate the difference between the current performance decay factor and the arithmetic mean, and divide this difference by the historical standard deviation to obtain the standardized deviation index, which represents the degree to which the current decay state deviates from the historical normal. Calculate the absolute value of the standardized deviation index, input the absolute value into the hyperbolic tangent function for compression mapping to obtain the compressed deviation, multiply the compressed deviation by the current performance attenuation factor, and normalize the calculation result to the 0-1 interval to obtain the dynamic control gain value for temperature fluctuation sensitivity adjustment.

[0026] The instantaneous deviation of the internal temperature from the intrinsic thermal equilibrium median is calculated, and the changes in the internal temperature are analyzed to generate a thermal offset vector representing the current temperature deviation. Specifically, this includes: normalizing the current internal temperature and the intrinsic thermal equilibrium median to the 0-1 interval respectively, and calculating the absolute value of the difference between the two; obtaining the five most recently collected consecutive internal temperature values ​​at the current time, calculating the arithmetic mean of the differences between adjacent internal temperatures, and multiplying the average value normalized to the 0-1 interval by the sampling time interval normalized to the 0-1 interval to obtain the short-term temperature change. The absolute value of the difference is added to the absolute value of the short-term temperature change, and the result is normalized to the 0-1 interval to obtain the instantaneous deviation. The difference between the current temperature value and the intrinsic thermal equilibrium median is normalized to the 0-1 interval and used as the first dimension component, and the instantaneous deviation is used as the second dimension component. The first and second dimension components are combined to form the thermal offset vector representing the current temperature deviation.

[0027] In one embodiment, calculating the adaptive control threshold for heat dissipation control based on the internal temperature and the power attenuation factor further includes: The dynamic control boundary is obtained by synthesizing the dynamic control gain, thermal offset vector, and intrinsic thermal equilibrium median. Specifically, the absolute value of the first dimension component of the thermal offset vector is used as the static offset, and the second dimension component of the thermal offset vector is used as the dynamic offset. If the dynamic offset is greater than the static offset, the dynamic offset is divided by the static offset to obtain the dynamic imbalance. Otherwise, the static offset is divided by the dynamic offset to obtain the dynamic imbalance. Multiplying the dynamic imbalance degree by the dynamic control gain value yields the floating radius, which is the reference range for the upper and lower fluctuations of the control boundary. Adding the median of the intrinsic thermal equilibrium after normalization to the 0-1 interval to half the absolute value of the first dimension component yields the core reference value. The upper limit of the dynamic control boundary is the core reference value plus the floating radius, and the lower limit of the dynamic control boundary is the core reference value minus the floating radius, thus obtaining the dynamic control boundary. The dynamic control boundary is mainly used to adapt to the current fan attenuation level and the temperature state of the distribution box.

[0028] The dynamic control boundary is analyzed to generate an adaptive control threshold for heat dissipation control. Specifically, the absolute value of the first dimension component in the thermal offset vector is divided by the second dimension component to obtain the ratio. The logarithm of the ratio is calculated with the natural constant e as the base. The absolute value of the calculation result is used as the dynamic asymmetry factor, which represents the proportional relationship between static offset and dynamic fluctuation in the thermal state. Multiply the dynamic asymmetry factor by the dynamic control gain value, then calculate the hyperbolic tangent function value of the product to obtain the normalization coefficient. Multiply the normalization coefficient by the difference between the upper and lower limits of the dynamic control boundary to obtain the adaptive adjustment amount. Add the lower limit of the dynamic control boundary, the adaptive adjustment amount, and half the absolute value of the first dimension component to generate the adaptive control threshold for heat dissipation control.

[0029] By calculating the intrinsic thermal balance median, dynamic control gain, thermal offset vector, and adaptive control threshold, this solution effectively addresses the problem that existing distribution box fans only start and stop based on fixed temperature thresholds and cannot perceive the actual heat dissipation performance of the fans. This solution can accurately reflect the load status of the distribution box, adapt to the heat dissipation attenuation caused by mechanical wear, dust accumulation, etc., and adapt to temperature fluctuations and fan attenuation status by dynamically adjusting the threshold, avoiding miscontrol and lag, improving the accuracy of heat dissipation control, and ensuring the stability of heat dissipation in the distribution box.

[0030] In one embodiment, external data of the target device is acquired in real time, analyzed in real time, and disturbance control gain coefficients are generated to compensate for heat dissipation control, including: By fusing external temperature and external wind speed data, an environmental cooling efficiency representing the comprehensive cooling potential of the external environment is obtained. Specifically, this includes: for the target equipment's internal and external temperatures at the same time, the difference between the internal and external temperatures is subtracted from the internal temperature to obtain the internal and external reference temperature difference. After normalizing the external wind speed to the 0-1 range, the normalized wind speed is obtained. The logarithm of the normalized wind speed to the base of the natural constant e is calculated, and 1 is added to the result to obtain the logarithmic wind speed factor. The logarithmic wind speed factor is multiplied by the normalized wind speed to obtain the effective wind speed. The internal and external reference temperature difference and the effective wind speed are normalized to the 0-1 range respectively and then multiplied to obtain an initial cooling efficiency. Continuously record all internal and external reference temperature difference data and effective wind speed data in the past ten minutes, find the maximum historical value of each of these two sets of data, and normalize the two maximum historical values ​​to the 0-1 range to obtain two normalized historical values. Divide the initial cooling efficiency by the product of the normalized historical value of the internal and external reference temperature difference and the normalized historical value of the effective wind speed, and normalize the calculation result to the 0-1 range to obtain the environmental cooling efficiency that represents the comprehensive cooling potential of the external environment.

[0031] The impact of environmental cooling efficiency on target equipment is analyzed, and an effective environmental disturbance factor reflecting the effectiveness of environmental disturbance is generated. Specifically, this includes: acquiring internal temperature data and environmental cooling efficiency data synchronously collected over the past 15 minutes to form two sequences; traversing the two sequences, for each sampling time, determining whether the internal temperature has increased, decreased, or remained the same compared to the previous minute, and simultaneously determining whether the environmental cooling efficiency has increased, decreased, or remained the same compared to the previous minute; counting the number of times the direction of change of internal temperature is consistent with the direction of change of environmental cooling efficiency within 15 minutes; dividing this number by the total number of comparisons to obtain the directional coordination rate. Calculate the absolute value of the difference between the current internal temperature and the internal temperature five minutes ago. At the same time, calculate the absolute value of the difference between the current ambient cooling efficiency and the ambient cooling efficiency five minutes ago. Normalize the absolute value of the temperature difference to the 0-1 range and divide it by the absolute value of the ambient cooling efficiency difference to obtain the disturbance response ratio. If the ambient efficiency difference is zero, the disturbance response ratio is directly 1. The square root of the product of the directional coordination rate and the disturbance response ratio is calculated, and the square root result is input into the hyperbolic tangent function for processing and normalized to the 0-1 interval. This generates the environmental disturbance effectiveness factor, which reflects the effectiveness of environmental disturbance.

[0032] In one embodiment, the method further includes: acquiring external data of the target device in real time, analyzing the external data in real time, generating disturbance control gain coefficients for compensating for heat dissipation control, and: Based on the effective factor of environmental disturbance, the feedforward disturbance compensation amount to offset the influence of the external environment on the internal temperature rise of the target equipment is calculated. Specifically, it includes: collecting all environmental cooling performance data recorded in the past hour at a frequency of once per minute, finding its maximum and minimum values, and calculating the difference between the two to obtain the environmental cooling performance range. The time base factor is set to 30. The time base factor is used to represent the duration of the maximum adaptive window. The effective factor of the current environmental disturbance is multiplied by the environmental cooling efficiency range, and then multiplied by the time base factor. The product result is rounded up to obtain the length of the adaptive time window. The length of the adaptive time window is then constrained to ensure that the length of the adaptive time window is between 1 and 30 minutes. Within the adaptive time window, the internal temperature sequence recorded by minute is obtained, the variance of the internal temperature sequence is calculated, and the calculation result is normalized to the 0-1 interval to obtain the temperature variance. Within the adaptive time window, if the last internal temperature in the internal temperature sequence is greater than the first internal temperature, it is determined to be an upward trend, and the sign value is positive one; if the last internal temperature in the internal temperature sequence is less than the first internal temperature, it is determined to be a downward trend, and the sign value is negative one; if the last internal temperature in the internal temperature sequence is equal to the first internal temperature, it is determined to be a stable temperature trend, and the sign value is positive one. Multiplying the temperature variance by the sign value, and then multiplying by the current environmental disturbance effective factor, yields the direction sensitivity factor. Multiplying the environmental disturbance effective factor by the direction sensitivity factor gives the feedforward disturbance compensation amount used to offset the influence of the external environment on the temperature rise inside the target equipment.

[0033] The gain of the feedforward disturbance compensation is adjusted to generate a disturbance control gain coefficient for correcting the speed of the target controlled object. Specifically, this includes multiplying the absolute value of the current feedforward disturbance compensation by the power attenuation factor to obtain the compensation strength benchmark. Add the compensation intensity benchmark to the effective factor of environmental disturbance to obtain the preliminary adjustment value. Divide the square of the preliminary adjustment value by 2 to obtain the base value of the adjusted gain. Subtract the performance attenuation factor from 1 to obtain the performance margin, and compare the adjusted base gain value with the performance margin: if the adjusted base gain value > the performance margin, then the performance margin is used as the temporary gain value; otherwise, the adjusted base gain value is used as the temporary gain value. If the sign value is positive, the temporary gain value is directly used as the disturbance control gain coefficient; if the sign value is negative, the temporary gain value is multiplied by the performance margin to obtain the disturbance control gain coefficient, which is used to correct the rotational speed of the target controlled object.

[0034] By acquiring real-time external data of the target equipment, analyzing external temperature and wind speed to generate environmental cooling efficiency, and further calculating the effective factor of environmental disturbance, feedforward disturbance compensation amount and disturbance control gain coefficient, it can accurately adapt to the cooling potential of the external environment, offset the impact of the external environment on the temperature rise inside the box, correct the fan speed, and adapt to the heat dissipation attenuation caused by fan wear and dust accumulation, avoid control misjudgment and lag, and improve the accuracy of heat dissipation control of the distribution box.

[0035] In one embodiment, a collaborative analysis is performed on the internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient to generate control commands for the target controlled object, including: The adaptive control threshold and internal temperature are calculated to obtain the temperature control deviation value. The temperature control deviation value is then corrected according to the disturbance control gain coefficient to generate a dynamic control value for adaptive adjustment. Specifically, this includes: subtracting the adaptive control threshold from the real-time internal temperature and normalizing the calculation result to the 0-1 interval to obtain the temperature control deviation value; adding the absolute value of the temperature control deviation value to the performance attenuation factor and then multiplying it by the current disturbance control gain coefficient to obtain the dynamic decision factor. The absolute value of the temperature control deviation is directly added to the performance attenuation factor to obtain the first candidate correction amount; the current internal temperature value is normalized to the 0-1 range to obtain the normalized internal temperature; the performance attenuation factor is multiplied by the disturbance control gain coefficient, and then multiplied by the current normalized internal temperature to obtain the second candidate correction amount. If the dynamic decision factor is greater than the normalized internal temperature, then the first candidate correction amount is selected as the basic collaborative correction amount; otherwise, the second candidate correction amount is selected as the basic collaborative correction amount. Multiply the basic collaborative correction amount by the temperature control deviation value to obtain the directed correction amount; finally, add the adaptive control threshold to the directed correction amount to generate the dynamic control value for adaptive adjustment.

[0036] In one embodiment, the method of generating control commands for the target controlled object by performing a collaborative analysis of the internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient further includes: Based on the performance attenuation factor, the disturbance control gain coefficient is adjusted to generate a temperature control gain correction factor that reflects the current heat dissipation capacity of the target controlled object. Specifically, this includes: dividing the current disturbance control gain coefficient by the current performance attenuation factor to obtain the relative strength ratio; and multiplying the current performance attenuation factor by the current disturbance control gain coefficient to obtain the dynamic weight. The relative intensity ratio is added to the dynamic weight to obtain the first candidate correction factor; the performance attenuation factor is added to the disturbance control gain coefficient to obtain the second candidate correction factor. If the relative intensity ratio is greater than the dynamic weight, the first candidate correction factor is used as the intermediate correction factor; otherwise, the second candidate correction factor is used as the intermediate correction factor. Multiplying the intermediate correction factor by the performance attenuation factor yields the temperature control gain correction factor that reflects the current heat dissipation capability of the target controlled object.

[0037] The dynamic control value and the temperature control gain correction factor are calculated together to generate the control command for the target control object. Specifically, the dynamic control value is subtracted from the current internal temperature to obtain the difference. The absolute value of the difference, normalized to 0-1, is used as the basic demand amplitude. The basic demand amplitude is multiplied by the temperature control gain correction factor to obtain the dynamic demand base. Calculate the reciprocal of the current performance attenuation factor to obtain the performance attenuation compensation coefficient, and multiply the dynamic demand base with the performance attenuation compensation coefficient to obtain the decision control factor characterizing the strength of heat dissipation demand; multiply the performance attenuation factor with the disturbance control gain coefficient to obtain the adjustment sensitivity benchmark. If the decision control factor is greater than the adjustment sensitivity benchmark, it is determined that the current heat dissipation capacity is insufficient, and the first control command is generated. The first control command is the command to increase the speed of the target controlled object. If the decision control factor is less than the adjustment sensitivity benchmark, it is determined that the current heat dissipation capacity is excessive, and a second control command is generated. The second control command is the command to slow down the rotation speed of the target controlled object. Otherwise, if the current heat dissipation state is considered to be balanced, a third control command is generated. The third control command is a command to maintain the rotation speed of the target controlled object. This means that the heat dissipation capacity of the target device can be adaptively adjusted based on the state perception of the target device and the actual heat dissipation capacity of the target controlled object.

[0038] By conducting a collaborative analysis of internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient, the temperature control deviation value, dynamic control value, and temperature control gain correction factor are calculated. Finally, control commands for the target control object are generated, which solves the shortcomings of existing distribution box fans that rely solely on fixed temperature thresholds for start and stop and cannot perceive the actual heat dissipation performance of the fans and the influence of the external environment, thereby ensuring the stability of heat dissipation in the distribution box.

[0039] In one embodiment, the intelligent sensing system for the heat dissipation status of the distribution box is applied to the above-described sensing method, including: The sensing and analysis unit is used to acquire the internal temperature of the target device and the operating current and drive voltage of the target controlled object in real time, analyze the operating current and drive voltage, and generate a power attenuation factor that represents the decrease in heat dissipation capacity of the target controlled object due to losses. The target device is the distribution box, and the target controlled object is the cooling fan inside the distribution box. The temperature analysis unit is used to calculate the adaptive control threshold for heat dissipation control based on the internal temperature and the power decay factor. The impact analysis unit is used to acquire external data of the target device in real time, analyze the external data in real time, and generate disturbance control gain coefficients for compensation of heat dissipation control. The heat dissipation control unit is used to perform collaborative analysis of internal temperature, adaptive control threshold, performance attenuation factor and disturbance control gain coefficient to generate control commands for the target controlled object.

[0040] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligently sensing the heat dissipation status of a distribution box, characterized in that, include: Step S1: Real-time acquisition of the internal temperature of the target device and the operating current and drive voltage of the target controlled object; analysis of the operating current and drive voltage; generation of a performance attenuation factor representing the decrease in heat dissipation capacity of the target controlled object due to losses; the target device is the distribution box and the target controlled object is the cooling fan inside the distribution box. Step S2: Calculate the adaptive control threshold for heat dissipation control based on the internal temperature and the power attenuation factor. Step S3: Acquire external data of the target device in real time, analyze the external data in real time, and generate disturbance control gain coefficients for compensation of heat dissipation control. Step S4 involves performing a collaborative analysis of the internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient to generate control commands for the target controlled object.

2. The intelligent sensing method for the heat dissipation status of the distribution box according to claim 1, characterized in that, Analyzing the operating current and drive voltage, a performance attenuation factor is generated, representing the decrease in heat dissipation capacity of the target controlled object due to losses, including: Frequency domain analysis is performed on the operating current and driving voltage to generate the harmonic distortion rate, which reflects the degree of distortion of the current waveform. The operating current and driving voltage are calculated, and the dynamic reactive component representing the degradation of the electromagnetic performance of the target controlled object's motor is separated.

3. The intelligent sensing method for the heat dissipation status of the distribution box according to claim 2, characterized in that, The analysis of operating current and drive voltage generates a performance attenuation factor representing the decrease in heat dissipation capacity of the target controlled object due to losses. This also includes: By performing correlation analysis between harmonic distortion rate and dynamic reactive component, a control attenuation coefficient is generated to represent the strength of the correlation between current waveform distortion and ineffective electromagnetic energy consumption. Analyze the long-term changes in the control attenuation coefficient to generate an energy attenuation factor that represents the decrease in heat dissipation capacity of the target controlled object due to losses.

4. The intelligent sensing method for the heat dissipation status of the distribution box according to claim 3, characterized in that, Based on the internal temperature and energy decay factor, an adaptive control threshold for heat dissipation control is calculated, including: Continuous analysis of internal temperature generates the intrinsic heat balance median. The performance attenuation factor is mapped to generate a dynamic control gain value that adjusts the sensitivity to temperature fluctuations. Calculate the instantaneous deviation of the internal temperature from the intrinsic thermal equilibrium median, analyze the changes in internal temperature, and generate a thermal offset vector representing the current temperature deviation.

5. The intelligent sensing method for the heat dissipation status of the distribution box according to claim 4, characterized in that, Based on the internal temperature and performance attenuation factor, an adaptive control threshold for heat dissipation control is calculated, which also includes: The dynamic control boundary is obtained by synthesizing the dynamic control gain value, thermal offset vector, and intrinsic thermal equilibrium median. The dynamic control boundary is analyzed to generate an adaptive control threshold for heat dissipation control.

6. The intelligent sensing method for the heat dissipation status of a distribution box according to claim 1, characterized in that, Real-time acquisition of external data from the target device, real-time analysis of the external data, and generation of disturbance control gain coefficients for compensation in heat dissipation control, including: By fusing external temperature and external wind speed data, an environmental cooling efficiency that represents the overall cooling potential of the external environment is obtained. The impact of environmental cooling efficiency on target equipment is analyzed, and an effective environmental disturbance factor reflecting the effectiveness of environmental disturbance is generated.

7. The intelligent sensing method for the heat dissipation status of a distribution box according to claim 6, characterized in that, Real-time acquisition of external data from the target device, real-time analysis of the external data, and generation of disturbance control gain coefficients for heat dissipation control compensation, also includes: Based on the effective factor of environmental disturbance, the feedforward disturbance compensation amount is calculated to offset the influence of the external environment on the internal temperature rise of the target equipment. The gain of the feedforward disturbance compensation is adjusted to generate a disturbance control gain coefficient used to correct the rotational speed of the target controlled object.

8. The intelligent sensing method for the heat dissipation status of the distribution box according to claim 7, characterized in that, A collaborative analysis is performed on internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient to generate control commands for the target controlled object, including: The temperature control deviation value is obtained by calculating the adaptive control threshold and internal temperature, and then corrected according to the disturbance control gain coefficient to generate a dynamic control value for adaptive adjustment.

9. The intelligent sensing method for heat dissipation status of a distribution box according to claim 8, characterized in that, The system performs a synergistic analysis of internal temperature, adaptive control threshold, performance attenuation factor, and disturbance control gain coefficient to generate control commands for the target controlled object. This also includes: Based on the power attenuation factor, the disturbance control gain coefficient is adjusted to generate a temperature control gain correction factor that reflects the current heat dissipation capacity of the target controlled object. The dynamic control value and the temperature control gain correction factor are calculated together to generate the control command for the target object.

10. An intelligent sensing system for the heat dissipation status of a distribution box, applied in the sensing method as described in any one of claims 1-9, characterized in that, include: The sensing and analysis unit is used to acquire the internal temperature of the target device and the operating current and drive voltage of the target controlled object in real time, analyze the operating current and drive voltage, and generate a power attenuation factor that represents the decrease in heat dissipation capacity of the target controlled object due to losses. The target device is the distribution box, and the target controlled object is the cooling fan inside the distribution box. The temperature analysis unit is used to calculate the adaptive control threshold for heat dissipation control based on the internal temperature and the power decay factor. The impact analysis unit is used to acquire external data of the target device in real time, analyze the external data in real time, and generate disturbance control gain coefficients for compensation of heat dissipation control. The heat dissipation control unit is used to perform collaborative analysis of internal temperature, adaptive control threshold, performance attenuation factor and disturbance control gain coefficient to generate control commands for the target controlled object.

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