Microcomputer protection device with self-checking function and detection method

Abstract

The invention relates to the technical field of self-checking of microcomputer protection devices, in particular to a microcomputer protection device with a self-checking function and a detection method, and the method comprises the steps: obtaining current data, voltage data and temperature data; acquiring a current slope sequence; obtaining a current modal characterization coefficient and a voltage modal characterization coefficient; calculating an adapter load fluctuation adjustment coefficient between the current sub-sequence and the voltage sub-sequence; obtaining trend test statistics of the adapter load fluctuation adjustment coefficient sequence; and calculating a power consumption temperature trend difference index, and completing self-inspection of the power adapter of the microcomputer protection device. According to the application, the temperature is intervened in time, the risk of damage of the power adapter is reduced, and the damage probability of the microcomputer protection device is further reduced.

Description

Technical Field

[0001] The present application relates to the technical field of self-checking of microcomputer protection devices, and in particular to a microcomputer protection device with a self-checking function and a detection method. Background Art

[0002] The microcomputer protection device is a key device for the protection and automation of modern power systems. It uses microprocessor technology to achieve real-time monitoring, analysis and protection of power systems. When an abnormal situation is detected, the microcomputer protection device will respond quickly and perform protection operations, such as disconnecting the circuit and removing the faulty equipment, to prevent the accident from expanding and protect the safety of the power system and equipment. Among them, the power adapter plays a vital role in the microcomputer protection device, ensuring that the device can work normally under stable power conditions and play its role in protecting the power system.

[0003] A power adapter is an electronic device that converts the AC power of a power source into the required DC power and provides it to electronic devices. It usually consists of an external power supply and an output line connected to the electronic device.

[0004] In order to provide continuous power to the microcomputer protection device, we must connect its power adapter to the household power supply. However, after working for a long time, the temperature of the power adapter tends to rise significantly, and sometimes even overheats. If the power adapter continues to be used in a high temperature state, it will not only damage the adapter itself, but also may cause damage to the connected microcomputer protection device, thus seriously shortening its service life. Therefore, reasonable management and monitoring of the operating temperature of the power adapter of the microcomputer protection device is essential to maintain the health of the microcomputer protection device and extend its service life. Summary of the invention

[0005] In order to solve the above technical problems, the present application provides a microcomputer protection device and a detection method with a self-checking function to solve the existing problems.

[0006] The present invention discloses a microcomputer protection device with self-checking function and a detection method using the following technical solutions: An embodiment of the present application provides a method for detecting a microcomputer protection device with a self-checking function, the method comprising the following steps: Obtain current data, voltage data and temperature data at each sampling moment within a sampling period; Use sequence decomposition algorithm to obtain current subsequence, voltage subsequence and temperature subsequence of current data, voltage data and temperature data; according to the current data at each sampling moment in the current subsequence combined with curve fitting algorithm, obtain the current slope sequence of each current subsequence; The current modal characterization coefficient of each current subsequence is obtained according to the current slope sequence and the current data in the current subsequence; for the voltage subsequence, the voltage modal characterization coefficient of each voltage subsequence is obtained by the same method as the current subsequence; A trend test algorithm is used to obtain a trend test statistic of each current subsequence and a trend test statistic of each voltage subsequence; an adapter load fluctuation adjustment coefficient between the current subsequence and the voltage subsequence is obtained according to the current modal characterization coefficient and the trend test statistic; According to the adapter load fluctuation adjustment coefficient combined with the trend test algorithm, the trend test statistic of the adapter load fluctuation adjustment coefficient sequence of each temperature subsequence is obtained; According to the trend test statistics of the temperature subsequence and the trend test statistics of the adapter load fluctuation adjustment coefficient sequence, the power consumption temperature trend difference index of each sampling period is obtained in combination with the time series decomposition algorithm; The trend parameters are obtained according to the power consumption temperature trend difference index, and the self-check of the power adapter of the microcomputer protection device is completed.

[0007] Further, the method of using a sequence decomposition algorithm to obtain a current subsequence, a voltage subsequence, and a temperature subsequence of current data, voltage data, and temperature data includes: The current data, the voltage data and the temperature data are formed into a current time series data sequence, a voltage time series data sequence and a temperature time series data sequence according to the order of sampling time; Processing the current time series data sequence using a time series decomposition algorithm to obtain each current subsequence of the current time series data sequence; For the voltage time series data sequence and the temperature time series data sequence, each voltage subsequence and each temperature subsequence are obtained by the same method as that for the current time series data sequence.

[0008] Further, the current slope sequence of each current subsequence is obtained according to the current data at each sampling moment in the current subsequence in combination with a curve fitting algorithm, including: For each current subsequence, the amplitude of the current data at each sampling moment of the current subsequence is used as the ordinate, and the sampling moment of each data in the current subsequence is used as the abscissa to be mapped to a rectangular coordinate system, and a curve fitting algorithm is used to fit the mapped current subsequence to obtain the current curve of the current subsequence, and a tangent line of the current curve is drawn at the data point at each sampling moment in each current subsequence, and the slope of the tangent line is calculated, and the slope is arranged in ascending time order to form a current slope sequence of each current subsequence.

[0009] Further, the step of obtaining the current modal characterization coefficient of each current subsequence according to the current slope sequence and the current data in the current subsequence includes: For each current subsequence, calculating the difference between each current data in the current subsequence and the previous adjacent current data as a first difference, and calculating the product of the first difference and the range of all current data in the current subsequence as a first product; A sequence consisting of sampling moments corresponding to all current data in a current subsequence is taken as a first sequence, a first correlation coefficient between the current subsequence and the first sequence is calculated, a second correlation coefficient between a current slope sequence of the current subsequence and the first sequence is calculated, a sum of the first correlation coefficient and the second correlation coefficient is calculated, a ratio of the first product to the sum is calculated as a first ratio, a mean of all the first ratios in the current subsequence is calculated, and a product of the mean and a mean of all data in a current slope sequence of the current subsequence is taken as a current modal characterization coefficient of each current subsequence.

[0010] Furthermore, the adapter load fluctuation adjustment coefficient includes: The calculation result of an exponential function with a natural constant as the base and a trend test statistic of any current subsequence as the exponent is calculated as the first exponent, and the calculation result of an exponential function with a natural constant as the base and a trend test statistic of the voltage subsequence at the corresponding decomposition number of the current subsequence as the exponent is calculated as the second exponent; The weight of the first index is used as the first index weight, the weight of the second index is used as the second index weight, the product of the first index weight and the first index is calculated as the first index product, the product of the second index weight and the second index is calculated as the second index product, and the sum of the first index product and the second index product is used as the adapter load fluctuation adjustment coefficient between any current subsequence in the current time series data sequence and the voltage subsequence at the corresponding decomposition number in the voltage time series data sequence.

[0011] Furthermore, the method for obtaining the first index weight and the second index weight is: Calculate the sum of the current modal characterization coefficient of any current subsequence and the voltage modal characterization coefficient of the voltage subsequence at the corresponding decomposition number as the modal characterization sum value, and the first exponential weight is the ratio of the current modal characterization coefficient of any current subsequence to the modal characterization sum value; The second exponential weight is a ratio of a voltage modal characterization coefficient of a voltage subsequence at a corresponding decomposition number to the modal characterization sum value.

[0012] Furthermore, the method of obtaining the trend test statistics of the adapter load fluctuation adjustment coefficient sequence of each temperature subsequence according to the adapter load fluctuation adjustment coefficient in combination with the trend test algorithm includes: Record the sequence of adapter load fluctuation adjustment coefficients between any current subsequence in the current time series data sequence and the voltage subsequence at the corresponding decomposition number in the voltage time series data sequence that has the same corresponding position as a preset number of current subsequences and voltage subsequences as the adapter load fluctuation adjustment coefficient sequence corresponding to the temperature subsequence at the corresponding decomposition number; The adapter load fluctuation adjustment coefficient sequence is processed using a trend test to obtain the trend test statistic of the adapter load fluctuation adjustment coefficient sequence.

[0013] Furthermore, the power consumption temperature trend difference index includes: Use the time series decomposition algorithm to process the temperature data in each sampling period in a time sequence to obtain the residual component of each sampling period; The absolute value of the trend test statistic of each temperature subsequence is calculated as the first trend absolute value, the absolute value of the trend test statistic of the adapter load fluctuation adjustment coefficient sequence is calculated as the second trend absolute value, the absolute value of the difference between the first trend absolute value and the second trend absolute value is calculated as the first trend difference absolute value, the kurtosis of the residual component of the temperature data of each sampling period is calculated, and the power consumption temperature trend difference index is positively correlated with the first trend difference absolute value, and negatively correlated with the kurtosis.

[0014] Further, the trend parameter is obtained according to the power consumption temperature trend difference index to complete the self-check of the power adapter of the microcomputer protection device, including: For each sampling period, the ratio of the power consumption temperature trend difference index of the sampling period to the preset parameter adjustment coefficient is calculated as the third ratio, and the calculation result of the exponential function with the natural constant as the base and the negative value of the third ratio as the exponent is used as the trend parameter of each sampling period; The temperature data at each sampling time within a first preset time period before the current time is used as the input of the time series prediction algorithm, the mean value of all the trend parameters within the first preset time period is used as the trend parameter of the time series prediction algorithm, and the output is the predicted temperature value at each time within the subsequent second preset time period; When the predicted temperature value is greater than the preset warning threshold, a temperature abnormality warning is issued; otherwise, no temperature abnormality warning is issued.

[0015] Based on the same inventive concept as the above method, an embodiment of the present application also provides a microcomputer protection device with a self-checking function, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned method for detecting a microcomputer protection device with a self-checking function are implemented.

[0016] This application has at least the following beneficial effects: This application constructs a current modal characterization coefficient and a voltage modal characterization coefficient by analyzing the current and voltage fluctuations at the output end of the power adapter, and allocates weights based on the respective degrees of change to construct an adapter load fluctuation adjustment coefficient, so as to judge the size of the load power. The degree of change trend of the load power is further compared with the degree of change trend of the temperature, and a power consumption temperature trend difference index is constructed to better reflect the relationship between the change trend of the load power consumption of the power adapter and the change trend of the temperature. According to the power consumption temperature trend difference index, the best trend parameter for the prediction algorithm is selected, and a more accurate predicted temperature value is obtained by combining the voltage data and the current data. The predicted temperature value is compared with the preset warning threshold value for monitoring and warning, which can more accurately monitor the abnormal temperature trend of the power adapter. Compared with the warning when the temperature data is greater than the warning temperature data, it is more advanced, and the temperature can be intervened in time to reduce the risk of damage to the power adapter. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flow chart of a detection method for a microcomputer protection device with a self-checking function provided by the present application; Figure 2 Obtain a flow chart for the power consumption temperature trend difference index. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the microcomputer protection device and detection method with self-checking function proposed by the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0021] The specific scheme of a microcomputer protection device with self-checking function and a detection method provided by the present application is described in detail below with reference to the accompanying drawings.

[0022] An embodiment of the present application provides a microcomputer protection device with a self-checking function and a detection method. Specifically, the following microcomputer protection device detection method with a self-checking function is provided. Figure 1 , the method comprises the following steps: Step S001, collecting temperature data, voltage data and current data of the power adapter of the microcomputer protection device and performing preprocessing.

[0023] The power adapter of the microcomputer protection device cannot achieve 100% efficiency in the process of converting AC power into DC power. The energy lost in the conversion process will be dissipated in the form of heat energy, causing the adapter to heat up; if the output end of the adapter works under high load, the current of its internal components increases, resulting in increased power loss, and the released heat causes the temperature of the power adapter to rise rapidly; if the heat dissipation conditions or effects of the power adapter are not good, the heat dissipation effect will be affected, causing the temperature of the adapter to rise. Among them, the temperature increase of the power adapter is particularly obvious under high load working conditions. Therefore, it is necessary to self-check the voltage and current of the power adapter of the microcomputer protection device.

[0024] A Hall current sensor and a Hall voltage sensor are installed at the output end of the power adapter of the microcomputer protection device to collect current data and voltage data respectively, and a temperature sensor is installed in the middle of the power adapter to collect temperature data, and the sampling time interval is set to 10ms, and the sampling cycle time is set to 1min. The collection process may be abnormal due to external environmental interference. This embodiment uses a priority queue algorithm for data cleaning. The input of the algorithm is the collected current data, voltage data and temperature data, and the output is the current data, voltage data and temperature data after data cleaning. The priority queue algorithm is a well-known technology, and the specific process will not be repeated.

[0025] The cleaned current data, voltage data and temperature data are sorted in the order of sampling time to obtain a current time series data sequence, a voltage time series data sequence and a temperature time series data sequence.

[0026] Step S002, based on the current sequence fluctuation degree at the output end of the power adapter of the microcomputer protection device, construct a current modal characterization coefficient; analyze the respective influence degrees of current and voltage in the load of the power adapter of the microcomputer protection device, and the adapter load fluctuation adjustment coefficient; based on the load power and temperature growth trend characteristics of the power adapter, construct a power consumption temperature trend difference index.

[0027] When the microprocessor unit of the microcomputer protection device is responsible for more complex data processing, calculation and decision-making, the system load of the microcomputer protection device increases significantly. Under different load power conditions, the current, voltage and temperature have different states in different cycles. For example, at low power, the current is lower than the rated current of the power adapter, and the current amplitude in the cycle is low, while at high power, the current is close to the rated current, and the current amplitude in the cycle becomes higher. Therefore, in order to better judge the local changes, it is divided into subsequences. In addition, the power of the power adapter increases rapidly, which makes the detected current and voltage data cycle uneven.

[0028] In order to quickly and accurately divide the subsequences, this embodiment uses the Variable-length SubsequenceClustering algorithm to divide the sequence into multiple subsequences, and performs adaptive division according to the different lengths of the subsequences. The Variable-length Subsequence Clustering algorithm is a well-known technology, and the specific process is not repeated here.

[0029] Taking current data as an example, the input of the algorithm is the current time series data sequence, the minimum subsequence length is set to 15, the maximum subsequence length is set to 25, and the output is the current subsequences obtained by segmenting the current time series data sequence. Similarly, the voltage subsequences after segmentation of the voltage time series data sequence and the temperature subsequences after segmentation of the temperature time series data sequence are obtained.

[0030] First, since the temperature change of the power adapter is proportional to the power consumption of its internal components, analyzing the power consumption change at the adapter output is conducive to accurate temperature detection. Power consumption is proportional to current or voltage. Taking current as an example, the following analyzes the degree of data change in the subsequence and constructs the current modal characterization coefficient.

[0031] When the current is larger, the data fluctuation in the subsequence is larger, and the change rate of each data point is relatively larger. Taking the current subsequence as an example, the amplitude of the current data at each sampling moment of the current subsequence is used as the ordinate, and the sampling moment of each data in the current subsequence is used as the abscissa to map to the rectangular coordinate system, and the least squares method is used to fit the mapped current subsequence to obtain the current curve of the current subsequence, and the tangent of the current curve is made at the data point at each sampling moment in each current subsequence, and the slope of the tangent is calculated, and the slope is arranged in ascending time order to form a current slope sequence of each current subsequence, and recorded as .

[0032] Furthermore, the current modal characterization coefficient of each current subsequence is obtained by: For each current subsequence, the difference between each current data in the current subsequence and the previous adjacent current data is calculated as a first difference, and the product of the first difference and the range of all current data in the current subsequence is calculated as a first product.

[0033] The sequence composed of sampling moments corresponding to all current data in the current subsequence is taken as the first sequence, a first correlation coefficient between the current subsequence and the first sequence is calculated, and a second correlation coefficient between the current slope sequence of the current subsequence and the first sequence is calculated.

[0034] The current modal characterization coefficient is positively correlated with the first product, negatively correlated with the first correlation coefficient, negatively correlated with the second correlation coefficient, and positively correlated with the mean of all elements in the current slope sequence of the current subsequence.

[0035] It should be noted that the positive correlation may be a multiplication, addition, or idempotent relationship of an exponential function, and the negative correlation may be a division, subtraction, or the like, which may be determined based on actual conditions and will not be elaborated in this application.

[0036] Specifically, the present application provides an embodiment, the method is: Calculate the sum of the first correlation coefficient and the second correlation coefficient, calculate the ratio of the first product to the sum as a first ratio, calculate the mean of all the first ratios in the current subsequence, and take the product of the mean and the mean of all data in the current slope sequence of the current subsequence as the current modal characterization coefficient of each current subsequence.

[0037] In each current subsequence, if the output current value of the power adapter does not reach its rated current, the current waveform is relatively flat, and the slope values ​​calculated at each point are generally smaller, which makes the mean value of all data in the current slope sequence of the current subsequence smaller. The smaller the first difference, the slower the instantaneous change of adjacent currents. The smaller the value, the smaller the overall fluctuation in the subsequence. The larger the first correlation coefficient or the second correlation coefficient, the stronger the collinearity of the sequence. When the period length is not much different, the flatter the sine waveform, the stronger the collinearity, that is, the lower the overall current value. The current modal characterization coefficient calculated at last is The lower it is, the lower the load of the power adapter in this subsequence is. On the contrary, it means that the workload is too heavy.

[0038] For each voltage subsequence, the same method is used to calculate and obtain the voltage modal characterization coefficient of each voltage subsequence.

[0039] During the operation of the power adapter of the microcomputer protection device, the changes in current and voltage often do not occur synchronously. When the load demand of the computer suddenly increases, the power adapter will adjust the increase in current or voltage accordingly based on its design parameters and built-in control strategy. Therefore, in order to balance the impact of current and voltage on load power changes and improve detection accuracy, the adapter load fluctuation adjustment coefficient is constructed.

[0040] In order to determine the rising or falling trend of the current or voltage data, this embodiment uses the Mann-Kendall trend test algorithm for analysis.

[0041] Taking the current data as an example, the input of the Mann-Kendall trend test algorithm is each current subsequence, the confidence level of the test is set to 0.05, and the output is the Mann-Kendall trend test statistic Z of each current subsequence. If the Mann-Kendall trend test statistic Z is a positive value, this indicates that in each current subsequence, the subsequent observations are generally greater than the previous observations, so there is an upward trend; if the Mann-Kendall trend test statistic Z is a negative value, it indicates that each current subsequence has a downward trend. The Mann-Kendall trend test algorithm is a well-known technology, and the specific process will not be repeated. Similarly, the Mann-Kendall trend test statistic of each temperature subsequence and each voltage subsequence is obtained.

[0042] Further, the first The current subsequence and the voltage time series data sequence The adapter load fluctuation adjustment coefficient between the voltage sub-sequences is obtained as follows: The calculation is based on the natural constant e, The result of the exponential function of the Mann-Kendall trend test statistic of the current subsequence is taken as the first exponent, and the calculation is based on the natural constant e and the The Mann-Kendall trend test statistic of the voltage subsequence is the result of the exponential function of the exponential as the second exponent; The weight of the first index is used as the first index weight, the weight of the second index is used as the second index weight, the product of the first index weight and the first index is calculated as the first index product, the product of the second index weight and the second index is calculated as the second index product, and the sum of the first index product and the second index product is used as the first index product in the current timing data sequence. The current subsequence and the voltage time series data sequence The adapter load fluctuation regulation coefficient between the voltage sub-sequences.

[0043] Calculate the The current modal characterization coefficient of the current subsequence is The sum of the voltage modal characterization coefficients of the voltage subsequences is taken as the modal characterization sum value, and the first index weight is The ratio of the current modal characterization coefficient of the current subsequence to the modal characterization sum value, the second index weight is The ratio of the voltage modal characterization coefficient of a voltage subsequence to the modal characterization sum value.

[0044] Specifically, if the growth of load power is mainly affected by current, the higher the calculated value of the first index weight is, the greater the influence weight of current is, and the larger the value of the first index is, indicating that the current has an upward trend; if the growth of load power is mainly affected by voltage, the higher the calculated value of the second index weight is, the greater the influence weight of voltage is, and the larger the value of the second index is, indicating that the voltage has an upward trend; finally, the larger the load fluctuation adjustment coefficient of the adapter is, the greater the load power is, and the greater the possibility of the temperature increase of the power adapter is.

[0045] Furthermore, the heat dissipation condition of the power adapter has an important influence on temperature detection. If the heat dissipation design of the power adapter is good, when the load power increases rapidly, its temperature rise will be relatively small; if the heat dissipation design of the power adapter is poor, when the load power increases rapidly, its temperature will also rise rapidly. Therefore, the power consumption change trend and temperature change trend are analyzed in combination to construct the power consumption temperature trend difference index.

[0046] So far, the first The current subsequence and the voltage time series data sequence The adapter load fluctuation regulation coefficient between the voltage sub-sequences.

[0047] The first The current subsequence and the voltage time series data sequence The sequence consisting of the adapter load fluctuation adjustment coefficients between the current subsequences and the voltage subsequences with the same corresponding positions before the first voltage subsequence is recorded as The adapter load fluctuation adjustment coefficient sequence corresponding to the temperature subsequence is processed using the Mann-Kendall trend test to obtain the Mann-Kendall trend test statistic of the adapter load fluctuation adjustment coefficient sequence. In this embodiment, the value of ι is 20.

[0048] Furthermore, in order to eliminate the interference of the external ambient temperature change of the power adapter, the STL time series decomposition algorithm is used to decompose the temperature data. The input of the algorithm is a set of temperature data in each sampling period, and the output is three components: trend component, seasonal component and residual component. The STL time series decomposition algorithm is a well-known technology and will not be described here. The residual component contains all non-seasonal and non-trend changes. Combined with the distribution characteristics of the residual component, the power consumption temperature trend difference index of each sampling period is obtained. The acquisition method is: The absolute value of the Mann-Kendall trend test statistic of each temperature subsequence is calculated as the first trend absolute value, the absolute value of the Mann-Kendall trend test statistic of the adapter load fluctuation adjustment coefficient sequence is calculated as the second trend absolute value, the absolute value of the difference between the first trend absolute value and the second trend absolute value is calculated as the first trend difference absolute value, the kurtosis of the residual component of the temperature data of each sampling period is calculated, and the power consumption temperature trend difference index is positively correlated with the first trend difference absolute value, and negatively correlated with the kurtosis.

[0049] It should be noted that the positive correlation may be a multiplication, addition, or idempotent relationship of an exponential function, and the negative correlation may be a division, subtraction, or the like, which may be determined based on actual conditions and will not be elaborated in this application.

[0050] Specifically, the present application proposes an embodiment, the method is: The ratio of the absolute value of the first trend difference to the kurtosis is calculated as the second ratio, and the average of all the second ratios in each sampling period is used as the power consumption temperature trend difference index of each sampling period.

[0051] Among them, the power consumption temperature trend difference index acquisition flow chart is as follows Figure 2 shown.

[0052] Since the size of the Mann-Kendall trend test statistic can estimate the strength of the trend, The larger the Mann-Kendall trend test statistic of the temperature sub-interval, the greater the rising trend of the temperature. The larger the Mann-Kendall trend test statistic of the adapter load fluctuation adjustment coefficient sequence, the greater the rising trend of the load power consumption. If the absolute value of the first trend difference is smaller, it means that the growth trend of the load power consumption is close to the growth trend of the temperature. The heat generated by the load power consumption is transferred to the power adapter and cannot be dissipated in time, indicating that the heat dissipation effect of the power adapter is not good. The larger the kurtosis value, the higher the kurtosis value of the residual component of the temperature data. The distribution of the residual component in a certain interval is more concentrated, not a random distribution under ideal conditions, indicating that there are abnormal values ​​in the detected T. The obtained power consumption temperature trend difference index The smaller it is, the faster the temperature of the power adapter increases.

[0053] At this point, the power consumption temperature trend difference index of each sampling period is obtained.

[0054] Step S003, based on the power consumption temperature trend difference index, calculate and adjust the Holt linear trend algorithm Trend parameters, and perform temperature prediction to complete the self-test of the power adapter of the microcomputer protection device.

[0055] This embodiment uses Holt's linear trend method to predict temperature, which can predict the value of multiple time points in the future. The smaller the value, the faster the temperature increases and the more significant the trend change. In the Holt linear trend algorithm, the trend parameter The larger it is, the faster the algorithm responds to changes in trends and the more accurate the predicted results. The value range is (0,1). Normalized, the calculation formula is: ;in, is the trend parameter of each sampling period; Indicates the power consumption temperature trend difference index of each sampling period, is a parameter adjustment coefficient greater than zero. In this embodiment, the value is 6, which is used to alleviate the degree of change of the exponential function. Represents an exponential function with a natural constant as its base.

[0056] Since the temperature detection of the power adapter of the microcomputer protection device is greatly affected by the load power, it may cause the temperature detection lag problem and damage the power adapter. This embodiment constructs the power consumption temperature trend difference index by analyzing the change trend of the power and temperature data at the output end of the power adapter, and calculates the power consumption temperature trend difference index based on the power consumption temperature trend difference index. parameter.

[0057] The input of Holt linear trend algorithm is the temperature data of each sampling time within 5 minutes before the current sampling time. The level parameter is set to 0.8 and the trend parameter is set to The output is the temperature sequence within the next 20 seconds.

[0058] The trend parameter Substitute the temperature data into the Holt linear trend algorithm to predict the temperature data and obtain the predicted temperature value. When the predicted temperature value is greater than the preset warning threshold, a temperature abnormality warning is issued; otherwise, no warning is issued. Compared with issuing a warning only when the temperature data is greater than the warning temperature data, it is more proactive and can intervene in the temperature in time to reduce the risk of damage to the power adapter.

[0059] Thus, the present embodiment performs self-checking by comparing the predicted temperature data with the actual temperature data, and can more accurately self-check the abnormal temperature trend of the power adapter of the microcomputer protection device, and issue a temperature abnormality warning when the predicted temperature value is greater than the warning threshold.

[0060] Based on the same inventive concept as the above method, the embodiment of the present application further provides a microcomputer protection device with a self-checking function, the microcomputer protection device comprising: A data acquisition module is used to collect temperature data, voltage data and current data of the power adapter of the microcomputer protection device and perform preprocessing; The data analysis module is used to construct a current modal characterization coefficient based on the current sequence fluctuation degree of the power adapter output end of the microcomputer protection device; analyze the respective influence degree of the current and voltage in the power adapter load of the microcomputer protection device, and the adapter load fluctuation adjustment coefficient; based on the load power and temperature growth trend characteristics of the power adapter, construct a power consumption temperature trend difference index; Self-check module, used to calculate and adjust the Holt linear trend algorithm based on the power consumption temperature trend difference index Trend parameters, and perform temperature prediction to complete the self-test of the power adapter of the microcomputer protection device.

[0061] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0063] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A detection method for a microcomputer protection device with a self-checking function, characterized in that: The method comprises the following steps: Obtain current data, voltage data and temperature data at each sampling moment within a sampling period; Use sequence decomposition algorithm to obtain current subsequence, voltage subsequence and temperature subsequence of current data, voltage data and temperature data; according to the current data at each sampling moment in the current subsequence combined with curve fitting algorithm, obtain the current slope sequence of each current subsequence; The current modal characterization coefficient of each current subsequence is obtained according to the current slope sequence and the current data in the current subsequence; for the voltage subsequence, the voltage modal characterization coefficient of each voltage subsequence is obtained by the same method as the current subsequence; A trend test algorithm is used to obtain a trend test statistic of each current subsequence and a trend test statistic of each voltage subsequence; an adapter load fluctuation adjustment coefficient between the current subsequence and the voltage subsequence is obtained according to the current modal characterization coefficient and the trend test statistic; According to the adapter load fluctuation adjustment coefficient combined with the trend test algorithm, the trend test statistic of the adapter load fluctuation adjustment coefficient sequence of each temperature subsequence is obtained; According to the trend test statistics of the temperature subsequence and the trend test statistics of the adapter load fluctuation adjustment coefficient sequence, the power consumption temperature trend difference index of each sampling period is obtained in combination with the time series decomposition algorithm; The trend parameters are obtained according to the power consumption temperature trend difference index, and the self-check of the power adapter of the microcomputer protection device is completed.

2. A method for detecting a microcomputer protection device with a self-checking function as claimed in claim 1, characterized in that: The method of using a sequence decomposition algorithm to obtain a current subsequence, a voltage subsequence, and a temperature subsequence of current data, voltage data, and temperature data includes: The current data, the voltage data and the temperature data are formed into a current time series data sequence, a voltage time series data sequence and a temperature time series data sequence according to the order of sampling time; Processing the current time series data sequence using a time series decomposition algorithm to obtain each current subsequence of the current time series data sequence; For the voltage time series data sequence and the temperature time series data sequence, each voltage subsequence and each temperature subsequence are obtained by the same method as that for the current time series data sequence.

3. A method for detecting a microcomputer protection device with a self-checking function as claimed in claim 1, characterized in that: The method of obtaining the current slope sequence of each current subsequence according to the current data at each sampling moment in the current subsequence in combination with a curve fitting algorithm includes: For each current subsequence, the amplitude of the current data at each sampling moment of the current subsequence is used as the ordinate, and the sampling moment of each data in the current subsequence is used as the abscissa to be mapped to a rectangular coordinate system, and a curve fitting algorithm is used to fit the mapped current subsequence to obtain the current curve of the current subsequence, and a tangent line of the current curve is drawn at the data point at each sampling moment in each current subsequence, and the slope of the tangent line is calculated, and the slope is arranged in ascending time order to form a current slope sequence of each current subsequence.

4. A method for detecting a microcomputer protection device with a self-checking function as claimed in claim 2, characterized in that: The step of obtaining the current modal characterization coefficient of each current subsequence according to the current slope sequence and the current data in the current subsequence includes: For each current subsequence, calculating the difference between each current data in the current subsequence and the previous adjacent current data as a first difference, and calculating the product of the first difference and the range of all current data in the current subsequence as a first product; A sequence consisting of sampling moments corresponding to all current data in a current subsequence is taken as a first sequence, a first correlation coefficient between the current subsequence and the first sequence is calculated, a second correlation coefficient between a current slope sequence of the current subsequence and the first sequence is calculated, a sum of the first correlation coefficient and the second correlation coefficient is calculated, a ratio of the first product to the sum is calculated as a first ratio, a mean of all the first ratios in the current subsequence is calculated, and a product of the mean and a mean of all data in a current slope sequence of the current subsequence is taken as a current modal characterization coefficient of each current subsequence.

5. A method for detecting a microcomputer protection device with a self-checking function as claimed in claim 1, characterized in that: The adapter load fluctuation adjustment coefficient includes: The calculation result of an exponential function with a natural constant as the base and a trend test statistic of any current subsequence as the exponent is calculated as the first exponent, and the calculation result of an exponential function with a natural constant as the base and a trend test statistic of the voltage subsequence at the corresponding decomposition number of the current subsequence as the exponent is calculated as the second exponent; The weight of the first index is used as the first index weight, the weight of the second index is used as the second index weight, the product of the first index weight and the first index is calculated as the first index product, the product of the second index weight and the second index is calculated as the second index product, and the sum of the first index product and the second index product is used as the adapter load fluctuation adjustment coefficient between any current subsequence in the current time series data sequence and the voltage subsequence at the corresponding decomposition number in the voltage time series data sequence.

6. A method for detecting a microcomputer protection device with a self-checking function as claimed in claim 5, characterized in that: The method for obtaining the first index weight and the second index weight is: Calculate the sum of the current modal characterization coefficient of any current subsequence and the voltage modal characterization coefficient of the voltage subsequence at the corresponding decomposition number as the modal characterization sum value, and the first exponential weight is the ratio of the current modal characterization coefficient of any current subsequence to the modal characterization sum value; The second exponential weight is a ratio of a voltage modal characterization coefficient of a voltage subsequence at a corresponding decomposition number to the modal characterization sum value.

7. A method for detecting a microcomputer protection device with a self-checking function as claimed in claim 5, characterized in that: The method of obtaining the trend test statistics of the adapter load fluctuation adjustment coefficient sequence of each temperature subsequence according to the adapter load fluctuation adjustment coefficient in combination with the trend test algorithm includes: Record the sequence of adapter load fluctuation adjustment coefficients between any current subsequence in the current time series data sequence and the voltage subsequence at the corresponding decomposition number in the voltage time series data sequence that has the same corresponding position as a preset number of current subsequences and voltage subsequences as the adapter load fluctuation adjustment coefficient sequence corresponding to the temperature subsequence at the corresponding decomposition number; The adapter load fluctuation adjustment coefficient sequence is processed using a trend test to obtain the trend test statistic of the adapter load fluctuation adjustment coefficient sequence.

8. A method for detecting a microcomputer protection device with a self-checking function as claimed in claim 1, characterized in that: The power consumption temperature trend difference index includes: Use the time series decomposition algorithm to process the temperature data in each sampling period in a time sequence to obtain the residual component of each sampling period; The absolute value of the trend test statistic of each temperature subsequence is calculated as the first trend absolute value, the absolute value of the trend test statistic of the adapter load fluctuation adjustment coefficient sequence is calculated as the second trend absolute value, the absolute value of the difference between the first trend absolute value and the second trend absolute value is calculated as the first trend difference absolute value, the kurtosis of the residual component of the temperature data of each sampling period is calculated, and the power consumption temperature trend difference index is positively correlated with the first trend difference absolute value, and negatively correlated with the kurtosis.

9. A method for detecting a microcomputer protection device with a self-checking function as claimed in claim 1, characterized in that: The method of obtaining trend parameters according to the power consumption temperature trend difference index and completing the self-check of the power adapter of the microcomputer protection device includes: For each sampling period, the ratio of the power consumption temperature trend difference index of the sampling period to the preset parameter adjustment coefficient is calculated as the third ratio, and the calculation result of the exponential function with the natural constant as the base and the negative value of the third ratio as the exponent is used as the trend parameter of each sampling period; The temperature data at each sampling time within a first preset time period before the current time is used as the input of the time series prediction algorithm, the mean value of all the trend parameters within the first preset time period is used as the trend parameter of the time series prediction algorithm, and the output is the predicted temperature value at each time within the subsequent second preset time period; When the predicted temperature value is greater than the preset warning threshold, a temperature abnormality warning is issued; otherwise, no temperature abnormality warning is issued.

10. A microcomputer protection device with a self-checking function, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a method for detecting a microcomputer protection device with a self-checking function as described in any one of claims 1 to 9 are implemented.

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