CN112214911A - Power supply health state prediction method - Google Patents
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Abstract
The invention discloses a method for predicting the health state of a power supply, which comprises the following steps: acquiring a monitoring parameter set sensitive to the health state of a power supply through simulation and screening; determining test parameters of the monitoring parameters in the monitoring parameter set through a decay test, collecting sample data, and establishing an influence model of the environment temperature on the mean value of the monitoring parameters; establishing a relation model of the monitoring parameter mean value and the power supply health comprehensive value; and establishing a prediction model of the power supply health state. The invention achieves the purpose of extracting the interested target and inhibiting clutter by an orthogonal projection mode. The invention monitors and evaluates the working performance change of the high-power supply in real time and ensures the task reliability of the complex electronic equipment.
Description
Technical Field
The invention relates to the field of radars, in particular to a method for predicting the health state of a power supply.
Background
The high power supply is used as the heart of complex electronic equipment and is the basis for the normal operation of the complex electronic equipment. During the task of a complex electronic device, its power supply needs to be continuously operated, and the operation often does not allow for service or can only be maintained simply. In addition, the high-power supply has many components, relatively complex failure modes and failure mechanisms, difficult establishment of an accurate failure physical model, more sensitive parameters and higher determination difficulty, which bring great difficulty to the health management of the high-power supply. The conventional parameters of the power supply can only be monitored in the current complex electronic system, the health state of the high-power supply needs to be judged by a user according to experience, and the accuracy cannot be guaranteed. At present, a complete and universal health evaluation technology for realizing real-time and accurate high-power supply does not exist.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for predicting the health status of a power supply, so as to help a user to master the health status of a high-power supply in real time, and provide support for task arrangement, maintenance according to the situation, spare part management, etc. of complex electronic equipment, the method includes the following steps:
acquiring a monitoring parameter set sensitive to the health state of a power supply through simulation and screening;
determining test parameters of the monitoring parameters in the monitoring parameter set through a decay test, collecting sample data, and establishing an influence model of the environment temperature on the mean value of the monitoring parameters;
establishing a relation model of the monitoring parameter mean value and the power supply health comprehensive value;
and establishing a prediction model of the power supply health state.
Further, the acquiring, through simulation and screening, a monitoring parameter set sensitive to the health state of the power supply specifically includes the following steps:
establishing a simulation model through simulation software according to a schematic diagram of a power supply, a BOM (bill of material) table of components and a fault mode set of the power supply;
records in a fault mode set of a power supply are simulated and injected one by modifying a circuit schematic diagram, modifying a network topology file and modifying a model definition to obtain a simulation data set;
and screening the monitoring parameter types through a neighborhood rough set algorithm to obtain a monitoring parameter set most sensitive to the health state of the power supply, wherein the monitoring parameter set comprises K monitoring parameters.
Further, the calculation steps of the neighborhood rough set algorithm are as follows:
step 1: establishing a decision information table NDT (the NDT is equal to U, A and D), wherein a condition attribute A is a power supply monitoring parameter set, a decision attribute D is a power supply failure mode set, and U is a simulation data set;
step 2: initializing a reduction set RED of expected power supply monitoring parameter varieties into an empty set;
and step 3: calculating the neighborhood relationship of each monitoring parameter in the power supply monitoring parameter set A;
and 4, step 4: calculating the importance of the monitoring parameters in the A-RED relative to a reduction set RED one by one;
and 5: selecting the monitoring parameter with the maximum importance degree, adding the monitoring parameter with the maximum importance degree into a reduction set RED, returning to the step 4 to continue calculating until the importance degree is smaller than a set threshold value;
step 6: and obtaining a final reduction set RED, wherein the final reduction result RED is the monitoring parameter set which is most sensitive to the health state of the power supply.
Further, the regression test sets ntThe test is carried out at each ambient temperature, and the time for carrying out the test at each ambient temperature is tshortThe unit is second, the sampling frequency W of the monitoring parameter is Hz, and the specific calculation formula is;
wherein, TmaxMaximum operating temperature of the power supply in degrees Celsius, TminIs the lowest working temperature of the power supply in degrees centigrade, ncIs a conversion system and has the unit of centigrade; l isdDesign life of power supply in units of seconds, msIs a reduced coefficient and has no dimension; []Represents rounding down;
said acquisition is toIs ntSample data of K monitoring parameters at corresponding sampling moments at the same ambient temperature, wherein the monitoring parameter X at the same ambient temperature(k)The sample data set of
The k-th monitored parameter is represented,
sample data representing kth monitoring parameter at nth time, K being 1,2, …, K, N being 1,2, …, N being S tshort，N≥K+2。
Further, the establishing of the model of the influence of the ambient temperature on the monitoring parameter specifically includes:
carrying out mean value processing on the monitoring parameters at each environmental temperature according to the following formula to obtain the mean value of the monitoring parameters at each environmental temperature:
wherein the content of the first and second substances,
is the ith ambient temperature TiThe k < th > monitoring parameter X(k)The mean value of (a);
from ntSelecting one of the ambient temperatures TiIs a reference temperature TsEstablishing a model of the environment temperature and the mean value of the monitoring parameters, wherein the calculation formula is as follows:
wherein the content of the first and second substances,
for a selected reference temperature TsMonitoring parameter X of the kth(k)The average value of (a) of (b),
is the ith ambient temperature TiMonitoring parameter X of the kth(k)The mean value of (a);
tiis the ith ambient temperature TiNormalized numerical values, i.e.
f(ti) Monitoring parameter X for ambient temperature versus k(k)The calculation formula of the influence model is as follows:
according to ntCalculating the average value of the monitoring data and the corresponding monitoring temperature under the environment temperature by substituting the average value and the corresponding monitoring temperature into the formulas (2), (3) and (4)(k)Coefficient of influence of
And (5) solving an influence model.
Further, the establishing of the relation model of the monitoring parameter mean value and the power health comprehensive value has the formula:
wherein the content of the first and second substances,
is a reference temperature TsMean value of the following 1 st to K monitoring parameters, YsIs a reference temperature TsLower power state of health value, beta0，β1，…，βKAnd epsilon is a coefficient to be solved of the relational model;
from ntSelecting any temperature T from the ambient temperatureiAt an ambient temperature TiIs as followsSelecting K +2 moments from the N moments, and acquiring sample data of monitoring parameters at the K +2 moments, wherein the sample data of the monitoring parameters have K +2 groups in total, and each group of sample data has the same temperature TiObtaining K +2 comprehensive values y of the health state of the power supply according to sample data and empirical data, wherein one comprehensive value y of the health state of the power supply corresponds to one group of sample data;
converting K +2 groups of sample data into K +2 groups of reference temperatures T through an influence model of the environment temperature on the mean value of the monitoring parameterssConverting the comprehensive value y of the power supply health state of each group of sample data into a reference temperature T according to the average value of the monitoring parameterssComprehensive value Y of state of health of power supplysWherein Y ═ YsNamely, the comprehensive value of the health state of the power supply keeps unchanged at the same temperature and the same time; reference temperature T of K +2 groupsMean value of monitoring parameters and comprehensive value Y of power state corresponding to mean valuesSubstituting the formula (5) to form an equation set consisting of K +2 formulas, and solving the coefficient beta to be solved0，β1，…，βK，ε。
Further, establishing a prediction model of the power supply health state specifically comprises:
wherein the content of the first and second substances,
is an initial prediction model established by an adaptive prediction algorithm of an autoregressive moving average model, p is the order of the initial prediction model, p +1 is less than or equal to N,
reference temperature T for time TsA power supply health state comprehensive value;
equal time intervals for a set of power health composite valuesA sequence of equal time intervals of said set of power health composite values passing through any one of TiCalculating sample data of p +1 groups of monitoring parameters at the temperature according to the formula (5), wherein the sample data of each group of monitoring parameters is obtained from the same temperature TISample data composition of 1 to K monitoring parameters at the same time, wherein p +1 represents the number of times with equal time intervals,
q is a coefficient to be solved; expanding an equal time interval sequence of at least p groups of power supply health comprehensive values through an interpolation method, wherein the equal time interval sequence of each group of power supply health comprehensive values comprises p +1 equal time interval power supply health comprehensive values; respectively substituting the equal time interval sequences of p +1 groups of power health comprehensive values in the equal time interval sequences of the original and expanded power health comprehensive values of each group into a formula (6) to obtain an equation set consisting of p +1 formulas, and solving the coefficient to be solved
Compared with the prior art, the invention has the following beneficial effects:
1. and optimizing power health monitoring parameters, and acquiring a parameter set most sensitive to health state change.
2. The working performance change of the high-power supply is monitored and evaluated in real time, and the task reliability of the complex electronic equipment is ensured.
3. The health change trend of the high-power supply is predicted, and a decision basis is provided for task scheduling and optional maintenance of the complex electronic equipment.
4. The technical level of power supply guarantee is improved, and the conversion from 'reactive' maintenance to 'preventive' maintenance is promoted.
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FIG. 1 is a flow chart of a power health prediction method.
FIG. 2 is a schematic diagram of a model of the effect of ambient temperature on the mean value of the monitored parameters.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
Example 1:
in order to realize real-time and effective evaluation of the health state of a high-power supply of complex electronic equipment, the invention provides a power supply health prediction method, which mainly comprises the following steps (as shown in figure 1):
1) acquiring a monitoring parameter set sensitive to the health state of a power supply through simulation and screening;
establishing a simulation model through simulation software according to a schematic diagram of a power supply, a BOM (bill of material) table of components and a fault mode set of the power supply;
records in a fault mode set of a power supply are simulated and injected one by modifying a circuit schematic diagram, modifying a network topology file and modifying a model definition to obtain a simulation data set;
because monitoring parameters are more and partial monitoring parameters have stronger correlation, the monitoring parameter type is screened by a neighborhood rough set algorithm to obtain a monitoring parameter set which is most sensitive to the health state of a power supply, and the monitoring parameter set is set to comprise K monitoring parameters.
The calculation steps of the neighborhood rough set algorithm are as follows:
step 1: establishing a decision information table NDT (the NDT is equal to U, A and D), wherein a condition attribute A is a power supply monitoring parameter set, a decision attribute D is a power supply failure mode set, and U is a simulation data set;
step 2: initializing a reduction set RED of expected power supply monitoring parameter varieties into an empty set;
and step 3: calculating the neighborhood relationship of each monitoring parameter in the power supply monitoring parameter set A;
and 4, step 4: calculating the importance of the monitoring parameters in the A-RED relative to a reduction set RED one by one;
and 5: selecting the monitoring parameter with the maximum importance degree, adding the monitoring parameter with the maximum importance degree into a reduction set RED, returning to the step 4 to continue calculating until the importance degree is smaller than a set threshold value;
step 6: and obtaining a final reduction set RED, wherein the final reduction result RED is the monitoring parameter set which is most sensitive to the health state of the power supply.
2) Establishing an influence model of the environment temperature on the average value of the monitoring parameters;
when the high-power supply is in the same health state, the monitoring parameters of the high-power supply are different in value under different environmental temperatures, and in actual use, the environmental temperature of the power supply is changed continuously, which is not beneficial to monitoring the health state of the power supply. In order to determine the influence of the ambient temperature on the values of the monitoring parameters, the invention adopts a decay test mode to establish a model of the influence of the ambient temperature on the monitoring parameters, namely a relation curve between the temperature and the mean value of the monitoring parameters, as shown in fig. 2.
(1) Determining test parameters of the monitoring parameters by regression test, and collecting sample data
In order to obtain the variation characteristics of the monitoring parameters at different temperatures, n is set in a decay testtThe test was performed at constant ambient temperature. n istDetermined by the following equation:
wherein T ismaxMaximum operating temperature of the power supply in degrees Celsius, TminIs the lowest working temperature of the power supply in degrees centigrade, ncFor a reduced system, in degrees Celsius, for controlling the number of tests at different temperatures, ncThe value range is 5-15 ℃; []Indicating a rounding down.
The time for conducting the test at each ambient temperature is unified as tshort(unit ofIn seconds), t)shortDetermined by the following equation:
wherein L isdThe service life of the high-power supply is designed in units of seconds and msFor conversion factor, dimensionless, for controlling test duration, msGenerally 1000 to 2000.
The sampling frequency S (in Hz) of the monitored parameter is determined by the following formula:
thus obtaining sample data, specifically ntSample data of K monitoring parameters at constant environmental temperature, and recording the sampling time of each sample data and the monitoring parameter X at the same environmental temperature(K)The sample data set of
Wherein, X(K)The k-th monitored parameter is represented,
sample data representing the kth monitoring parameter at the nth time instant, K being 1,2, …, K, N being 1,2, …, N being S tshort，N≥K+2。
(2) Establishing an influence model of environment temperature on a monitoring parameter mean value
And carrying out mean value processing on the monitored parameters at the same temperature. With a first monitoring parameter X(1)For example, according to the experimental design described above, at each temperature point Ti(i＝1,2,…,nt) Will all obtain N ═ S × tshortSample data
For which the mean value is calculated according to the following formula:
from TiAny test temperature is selected as a reference temperature TsEstablishing a model of the mean value of the environmental temperature and the monitoring parameter, and calculating according to the following formula
Wherein
Is a selected reference temperature TsFirst monitoring parameter X(1)The average value of (a) of (b),
is TiMonitoring parameter X at temperature(1)Is measured. t is tiIs TiNormalized numerical values, i.e.
f(ti) For the influence model of the ambient temperature on the mean value of the first monitoring parameter, the calculation formula is as follows:
according to ntThe average value of the monitoring data and the corresponding monitoring temperature under the environment temperature are substituted into the formulas (5), (6) and (7) to calculate the environment temperature to the monitoring parameter X(1)Coefficient of influence of
According to the method, the ambient temperature T can be obtained one by oneiTo mean value of monitoring parameter
The influence model of (a) is determined,
indicates the reference temperature Ts(s＝1,2,…,nt) Monitoring parameter X of the kth(k)Is measured.
3) Establishing a prediction model of the health status of a power supply
(1) Establishing a relation model of the mean value of the monitoring parameter and the comprehensive value of the power health
Because the sensitivity of the health state of the power supply to each monitoring parameter is different, the invention adopts the multiple linear regression to analyze the preprocessed monitoring parameters and the comprehensive value of the health of the power supply and establish a relation model.
The model of the relationship between the mean value of the monitoring parameter and the comprehensive value of the power health can be written as:
wherein the content of the first and second substances,
for conversion to a reference temperature TsMean value of the following 1 st to K monitoring parameters, YsIs a reference temperature TsLower power state of health value, beta0，β1，…，βKAnd ε is the model to be solved coefficient.
From ntSelecting any temperature T from the ambient temperatureiAt a temperature TiSelecting K +2 moments from the next N moments, and acquiring sample data of monitoring parameters at the K +2 moments, wherein the sample data of the monitoring parameters have K +2 groups in total, and each group of sample data has the same temperature TiObtaining K +2 comprehensive values y of the health state of the power supply according to sample data and empirical data, wherein one comprehensive value y of the health state of the power supply corresponds to one group of sample data;
model for monitoring parameter mean value through influence of ambient temperatureConverting K +2 groups of sample data into K +2 groups of reference temperatures TsConverting the comprehensive value y of the power supply health state of each group of sample data into a reference temperature T according to the average value of the monitoring parameterssComprehensive value Y of state of health of power supplys(Y is Y in the actual conversion process)sThat is, the comprehensive value of the health state of the power supply is kept unchanged at the same temperature and the same time); reference temperature T of K +2 groupsMean value of monitoring parameters and comprehensive value Y of power state corresponding to mean valuesSubstituting the formula (8) into a formula (8) to form an equation set consisting of K +2 formulas, and solving a coefficient beta to be solved0，β1，…，βK，ε。
(2) Establishing a prediction model of the health status of a power supply
The invention adopts an adaptive prediction algorithm (ARMA) of an Autoregressive Moving Average model to establish an initial prediction model. The general form is as follows:
wherein p is the order of the initial prediction model and p +1 is less than or equal to N,
reference temperature T for time TsA power supply health state comprehensive value;
is a group of equal time interval sequence of power supply health comprehensive values, and the equal time interval sequence of the group of power supply health comprehensive values passes through any TiCalculating sample data of p +1 groups of monitoring parameters at the temperature by using a formula (8) (the sample data of each group of monitoring parameters is at the same temperature T)iSample data composition of 1 to K monitoring parameters at the same time), p +1 represents the number of times at equal time intervals,
q is a coefficient to be solved; re-expanding at least p groups by interpolationThe equal time interval sequence of the power supply health comprehensive values, wherein the equal time interval sequence of each group of power supply health comprehensive values comprises p +1 power supply health comprehensive values with equal time intervals; respectively substituting the equal time interval sequences of p +1 groups of power health comprehensive values in the equal time interval sequences of the original and expanded power health comprehensive values in each group into a formula (10) to obtain an equation set consisting of p +1 formulas, and solving the coefficient to be solved
The one-step predictor based on the ARMA model is given by:
the method for constructing the ARMA model is a method commonly used in the field.
The invention realizes the intelligent screening of monitoring points of a high-power supply and provides an intelligent screening method of monitoring parameters of a power supply circuit; a test design method in the modeling process of environmental temperature influence is provided; a calculation method of an influence model of the environment temperature on the monitoring parameter mean value is provided; providing a whole set of general health prediction method for the high-power supply, which comprises monitoring point screening, test design, data analysis and model establishment; according to the invention, the health state prediction of the high-power supply of the complex electronic equipment in real time and in a future period can be provided as the basis for task scheduling and maintenance according to the situation of the user. According to the prediction model, a user can prejudge the development trend of the health state of the power supply, obtain decision support of task scheduling and optional maintenance, reduce unnecessary shutdown or maintenance of the high-power supply and further improve the economic benefit of operation of complex electronic equipment.
Claims (7)
1. A method for predicting the health status of a power supply is characterized by comprising the following steps:
acquiring a monitoring parameter set sensitive to the health state of a power supply through simulation and screening;
determining test parameters of the monitoring parameters in the monitoring parameter set through a decay test, collecting sample data, and establishing an influence model of the environment temperature on the mean value of the monitoring parameters;
establishing a relation model of the monitoring parameter mean value and the power supply health comprehensive value;
and establishing a prediction model of the power supply health state.
2. The method for predicting the health status of the power supply according to claim 1, wherein the step of obtaining the monitoring parameter set sensitive to the health status of the power supply through simulation and screening specifically comprises the steps of:
establishing a simulation model through simulation software according to a schematic diagram of a power supply, a BOM (bill of material) table of components and a fault mode set of the power supply;
records in a fault mode set of a power supply are simulated and injected one by modifying a circuit schematic diagram, modifying a network topology file and modifying a model definition to obtain a simulation data set;
and screening the monitoring parameter types through a neighborhood rough set algorithm to obtain a monitoring parameter set most sensitive to the health state of the power supply, wherein the monitoring parameter set comprises K monitoring parameters.
3. The method of claim 2, wherein the neighborhood rough set algorithm is calculated by:
step 1: establishing a decision information table NDT (the NDT is equal to U, A and D), wherein a condition attribute A is a power supply monitoring parameter set, a decision attribute D is a power supply failure mode set, and U is a simulation data set;
step 2: initializing a reduction set RED of expected power supply monitoring parameter varieties into an empty set;
and step 3: calculating the neighborhood relationship of each monitoring parameter in the power supply monitoring parameter set A;
and 4, step 4: calculating the importance of the monitoring parameters in the A-RED relative to a reduction set RED one by one;
and 5: selecting the monitoring parameter with the maximum importance degree, adding the monitoring parameter with the maximum importance degree into a reduction set RED, returning to the step 4 to continue calculating until the importance degree is smaller than a set threshold value;
step 6: and obtaining a final reduction set RED, wherein the final reduction result RED is the monitoring parameter set which is most sensitive to the health state of the power supply.
4. The method of claim 3, wherein the degradation test setting n is settThe test is carried out at each ambient temperature, and the time for carrying out the test at each ambient temperature is tshortThe unit is second, the sampling frequency W of the monitoring parameter is Hz, and the specific calculation formula is;
wherein, TmaxMaximum operating temperature of the power supply in degrees Celsius, TminIs the lowest working temperature of the power supply in degrees centigrade, ncIs a conversion system and has the unit of centigrade; l isdDesign life of power supply in units of seconds, msIs a reduced coefficient and has no dimension; []Represents rounding down;
the collected sample data is ntSample data of K monitoring parameters at corresponding sampling moments at the same ambient temperature, wherein the monitoring parameter X at the same ambient temperature(k)The sample data set of
X(k)The k-th monitored parameter is represented,
sample data representing kth monitoring parameter at nth time, K being 1,2, …, K, N being 1,2, …, N being S tshort，N≥K+2。
5. The method according to claim 4, wherein the modeling of the effect of the ambient temperature on the monitored parameter specifically comprises:
carrying out mean value processing on the monitoring parameters at each environmental temperature according to the following formula to obtain the mean value of the monitoring parameters at each environmental temperature:
wherein the content of the first and second substances,
is the ith ambient temperature TiThe k < th > monitoring parameter X(k)The mean value of (a);
from ntSelecting one of the ambient temperatures TiIs a reference temperature TsEstablishing a model of the environment temperature and the mean value of the monitoring parameters, wherein the calculation formula is as follows:
wherein the content of the first and second substances,
for a selected reference temperature TsMonitoring parameter X of the kth(k)The average value of (a) of (b),
is the ith ambient temperature TiMonitoring parameter X of the kth(k)The mean value of (a);
tiis the ith ambient temperature TiNormalized numerical values, i.e.
f(ti) Is the ambient temperature tok monitoring parameters X(k)The calculation formula of the influence model is as follows:
according to ntCalculating the average value of the monitoring data and the corresponding monitoring temperature under the environment temperature by substituting the average value and the corresponding monitoring temperature into the formulas (2), (3) and (4)(k)Coefficient of influence of
And (5) solving an influence model.
6. The method according to claim 5, wherein the establishing of the relationship model between the average value of the monitoring parameter and the health comprehensive value of the power supply is as follows:
wherein the content of the first and second substances,
is a reference temperature TsMean value of the following 1 st to K monitoring parameters, YsIs a reference temperature TsLower power state of health value, beta0，β1，…，βKAnd epsilon is a coefficient to be solved of the relational model;
from ntSelecting any temperature T from the ambient temperatureiAt an ambient temperature TiSelecting K +2 moments from the next N moments, and acquiring sample data of monitoring parameters at the K +2 moments, wherein the sample data of the monitoring parameters have K +2 groups in total, and each group of sample data has the same temperature TiObtaining sample data composition of 1 to K monitoring parameters at the same time, and obtaining K +2 comprehensive values y of the health state of the power supply according to the sample data and empirical data, wherein one comprehensive value y of the health state of the power supply corresponds to a group of the comprehensive valuesSample data;
converting K +2 groups of sample data into K +2 groups of reference temperatures T through an influence model of the environment temperature on the mean value of the monitoring parameterssConverting the comprehensive value y of the power supply health state of each group of sample data into a reference temperature T according to the average value of the monitoring parameterssComprehensive value Y of state of health of power supplysWherein Y ═ YsNamely, the comprehensive value of the health state of the power supply keeps unchanged at the same temperature and the same time; reference temperature T of K +2 groupsMean value of monitoring parameters and comprehensive value Y of power state corresponding to mean valuesSubstituting the formula (5) to form an equation set consisting of K +2 formulas, and solving the coefficient beta to be solved0，β1，…，βK，ε。
7. The method for predicting the health status of a power supply according to claim 6, wherein the establishing of the prediction model of the health status of the power supply specifically comprises:
wherein the content of the first and second substances,
is an initial prediction model established by an adaptive prediction algorithm of an autoregressive moving average model, p is the order of the initial prediction model, p +1 is less than or equal to N,
reference temperature T for time TsA power supply health state comprehensive value;
is a group of equal time interval sequence of power supply health comprehensive values, and the equal time interval sequence of the group of power supply health comprehensive values passes through any TiCalculating sample data of p +1 groups of monitoring parameters at the temperature and a formula (5), wherein the sample data of each group of monitoring parametersFrom the same temperature TiSample data composition of 1 to K monitoring parameters at the same time, wherein p +1 represents the number of times with equal time intervals,
q is a coefficient to be solved; expanding an equal time interval sequence of at least p groups of power supply health comprehensive values through an interpolation method, wherein the equal time interval sequence of each group of power supply health comprehensive values comprises p +1 equal time interval power supply health comprehensive values; respectively substituting the equal time interval sequences of p +1 groups of power health comprehensive values in the equal time interval sequences of the original and expanded power health comprehensive values of each group into a formula (6) to obtain an equation set consisting of p +1 formulas, and solving the coefficient to be solved
Q。
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